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human/dist/demo-browser-index.js

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/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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r=u=>{u.startEndTensor.dispose(),u.startPoint.dispose(),u.endPoint.dispose()},a=u=>({startEndTensor:u,startPoint:Me(u,[0,0],[-1,2]),endPoint:Me(u,[0,2],[-1,2])}),s=(u,h)=>{let d=B(u.startPoint,h),p=B(u.endPoint,h),f=Xl([d,p],1);return a(f)};function i(u,h,d){let p=Me(u,[0,1],[-1,2]),f=ie(p,h),m=Me(u,[0,3],[-1,2]),A=Se(m,d),y=Se(f,d),g=Se(A,2),w=be(y,g),x=ie(y,g),_=B(w,d),b=B(x,d);return Xl([_,b],1)}function o(u,h){return j(()=>{let d=u.box?u.box:u;return s(d,h).startEndTensor.squeeze()})}var l=class{constructor(u,h){this.blazeFaceModel=u,this.width=h.face.detector.inputSize,this.height=h.face.detector.inputSize,this.anchorsData=n(h.face.detector.inputSize),this.anchors=pr(this.anchorsData),this.inputSize=tn([this.width,this.height]),this.config=h,this.scaleFaces=.8}async getBoundingBoxes(u){if(!u||u.isDisposedInternal||u.shape.length!==4||u.shape[1]<1||u.shape[2]<1)return null;let[h,d,p]=j(()=>{let w=u.resizeBilinear([this.width,this.height]),x=be(w.div(127.5),1),_=this.blazeFaceModel.predict(x),b;if(Array.isArray(_)){let C=_.sort((O,V)=>O.size-V.size),$=pt([C[0],C[2]],2),D=pt([C[1],C[3]],2);b=pt([D,$],1).squeeze(0)}else b=_.squeeze();let T=i(b,this.anchors,this.inputSize),S=Me(b,[0,0],[-1,1]),N=tr(S).squeeze();return[b,T,N]}),f=await Dt.nonMaxSuppressionAsync(d,p,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),m=f.arraySync();f.dispose();let A=m.map(w=>Me(d,[w,0],[1,-1])).map(w=>{let x=w.arraySync();return w.dispose(),x}),y=p.dataSync(),g=[];for(let w=0;w<A.length;w++){let x=m[w],_=y[x];if(_>this.config.face.detector.minConfidence){let b=a(A[w]),T=this.anchorsData[x],S=j(()=>Me(h,[x,t-1],[1,-1]).squeeze().reshape([t,-1]));g.push({box:b,landmarks:S,anchor:T,confidence:_})}}return h.dispose(),d.dispose(),p.dispose(),h.dispose(),{boxes:g,scaleFactor:[u.shape[2]/this.width,u.shape[1]/this.height]}}async estimateFaces(u){let{boxes:h,scaleFactor:d}=await this.getBoundingBoxes(u),p=[];for(let f of h){let m=f.landmarks.arraySync(),A=o(f,d),y=s.arraySync(),g=f.probability.arraySync(),w=f.anchor,[x,_]=d,b=m.map(S=>[(S[0]+w[0])*x,(S[1]+w[1])*_]),T={topLeft:y.slice(0,2),bottomRight:y.slice(2),landmarks:b,probability:g};r(f.box),f.landmarks.dispose(),f.probability.dispose(),A.dispose(),p.push(T)}return p}};async function c(u){let h=await fr(u.face.detector.modelPath,{fromTFHub:u.face.detector.modelPath.includes("tfhub.dev")}),d=new l(h,u);return Je(`load model: ${u.face.detector.modelPath.match(/\/(.*)\./)[1]}`),d}e.load=c,e.BlazeFaceModel=l,e.disposeBox=r}),Dv=ht(e=>{function t(o,l){let c=[o.startPoint[0]*l[0],o.startPoint[1]*l[1]],u=[o.endPoint[0]*l[0],o.endPoint[1]*l[1]];return{startPoint:c,endPoint:u}}e.scaleBoxCoordinates=t;function n(o){return[Math.abs(o.endPoint[0]-o.startPoint[0]),Math.abs(o.endPoint[1]-o.startPoint[1])]}e.getBoxSize=n;function 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t=Be(Dv()),n=Be(Ov()),r=Be(X2()),a=468,s=13,i=[s,r.MESH_ANNOTATIONS.midwayBetweenEyes[0]],o=3,l=2,c=[o,l],u=r.MESH_ANNOTATIONS.leftEyeLower0,h=[u[0],u[u.length-1]],d=r.MESH_ANNOTATIONS.rightEyeLower0,p=[d[0],d[d.length-1]],f=3,m=4,A=71,y=76;function g(x,_,b,T){for(let S=0;S<r.MESH_TO_IRIS_INDICES_MAP.length;S++){let{key:N,indices:C}=r.MESH_TO_IRIS_INDICES_MAP[S],$=r.MESH_ANNOTATIONS[`${b}${N}`];if(T==null||T.includes(N))for(let D=0;D<C.length;D++){let O=C[D];x[$[D]]=[_[O][0],_[O][1],(_[O][2]+x[$[D]][2])/2]}}}var w=class{constructor(x,_,b,T){this.storedBoxes=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=x,this.meshDetector=_,this.irisModel=b,this.meshWidth=T.face.mesh.inputSize,this.meshHeight=T.face.mesh.inputSize,this.irisSize=T.face.iris.inputSize,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(x,_,b,T){let S=t.getBoxSize({startPoint:_.startPoint,endPoint:_.endPoint}),N=[S[0]/this.meshWidth,S[1]/this.meshHeight],C=x.map(W=>[N[0]*(W[0]-this.meshWidth/2),N[1]*(W[1]-this.meshHeight/2),W[2]]),$=b!==0?n.buildRotationMatrix(b,[0,0]):n.IDENTITY_MATRIX,D=b!==0?C.map(W=>[...n.rotatePoint(W,$),W[2]]):C,O=b!==0?n.invertTransformMatrix(T):n.IDENTITY_MATRIX,V=[...t.getBoxCenter({startPoint:_.startPoint,endPoint:_.endPoint}),1];return D.map(W=>[W[0]+n.dot(V,O[0]),W[1]+n.dot(V,O[1]),W[2]])}getLeftToRightEyeDepthDifference(x){let _=x[h[0]][2],b=x[p[0]][2];return _-b}getEyeBox(x,_,b,T,S=!1){let N=t.squarifyBox(t.enlargeBox(this.calculateLandmarksBoundingBox([x[b],x[T]]),this.irisEnlarge)),C=t.getBoxSize(N),$=Dt.cropAndResize(_,[[N.startPoint[1]/this.meshHeight,N.startPoint[0]/this.meshWidth,N.endPoint[1]/this.meshHeight,N.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return S&&($=Dt.flipLeftRight($)),{box:N,boxSize:C,crop:$}}getEyeCoords(x,_,b,T=!1){let S=[];for(let N=0;N<y;N++){let C=x[N*3],$=x[N*3+1],D=x[N*3+2];S.push([(T?1-C/this.irisSize:C/this.irisSize)*b[0]+_.startPoint[0],$/this.irisSize*b[1]+_.startPoint[1],D])}return{rawCoords:S,iris:S.slice(A)}}getAdjustedIrisCoords(x,_,b){let T=x[r.MESH_ANNOTATIONS[`${b}EyeUpper0`][f]][2],S=x[r.MESH_ANNOTATIONS[`${b}EyeLower0`][m]][2],N=(T+S)/2;return _.map((C,$)=>{let D=N;return $===2?D=T:$===4&&(D=S),[C[0],C[1],D]})}async predict(x,_){let b=!1,T;if((this.skipped===0||this.skipped>_.face.detector.skipFrames||!_.face.mesh.enabled||!_.videoOptimized)&&(T=await this.boundingBoxDetector.getBoundingBoxes(x),this.skipped=0),_.videoOptimized&&this.skipped++,T&&T.boxes&&(!_.face.mesh.enabled||T.boxes.length!==this.detectedFaces&&this.detectedFaces!==_.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let N of T.boxes)this.storedBoxes.push({startPoint:N.box.startPoint.dataSync(),endPoint:N.box.endPoint.dataSync(),landmarks:N.landmarks,confidence:N.confidence});this.storedBoxes.length>0&&(b=!0)}if(b){if(!T||!T.boxes||T.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let N=0;N<this.storedBoxes.length;N++){let C=t.scaleBoxCoordinates({startPoint:this.storedBoxes[N].startPoint,endPoint:this.storedBoxes[N].endPoint},T.scaleFactor),$=t.enlargeBox(C),D=t.squarifyBox($),O=this.storedBoxes[N].landmarks.arraySync(),V=this.storedBoxes[N].confidence;this.storedBoxes[N]={...D,confidence:V,landmarks:O}}this.runsWithoutFaceDetector=0}T&&T.boxes&&T.boxes.forEach(N=>{N.box.startPoint.dispose(),N.box.endPoint.dispose(),N.landmarks.dispose()});let S=j(()=>this.storedBoxes.map((N,C)=>{let $,D=0,O;if(_.face.detector.rotation){let[re,ce]=N.landmarks.length>=a?i:c;D=n.computeRotation(N.landmarks[re],N.landmarks[ce]);let he=t.getBoxCenter({startPoint:N.startPoint,endPoint:N.endPoint}),me=[he[0]/x.shape[2],he[1]/x.shape[1]],ye=Dt.rotateWithOffset(x,D,0,me);O=n.buildRotationMatrix(-D,he),$=t.cutBoxFromImageAndResize({startPoint:N.startPoint,endPoint:N.endPoint},ye,[this.meshHeight,this.meshWidth]).div(255)}else{O=n.IDENTITY_MATRIX;let re=x.clone();$=t.cutBoxFromImageAndResize({startPoint:N.startPoint,endPoint:N.endPoint},re,[this.meshHeight,this.meshWidth]).div(255)}if(!_.face.mesh.enabled)return{coords:null,box:N,faceConfidence:null,confidence:N.confidence,image:$};let[,V,W]=this.meshDetector.predict($),Z=V.dataSync()[0];if(Z<_.face.detector.minConfidence)return null;let K=X(W,[-1,3]).arraySync();if(_.face.iris.enabled){let{box:re,boxSize:ce,crop:he}=this.getEyeBox(K,$,h[0],h[1],!0),{box:me,boxSize:ye,crop:ge}=this.getEyeBox(K,$,p[0],p[1]),Ee=this.irisModel.predict(pt([he,ge])).dataSync(),Re=Ee.slice(0,y*3),{rawCoords:Oe,iris:Ge}=this.getEyeCoords(Re,re,ce,!0),Ve=Ee.slice(y*3),{rawCoords:et,iris:it}=this.getEyeCoords(Ve,me,ye),je=this.getLeftToRightEyeDepthDifference(K);Math.abs(je)<30?(g(K,Oe,"left"),g(K,et,"right")):je<1?g(K,Oe,"left",["EyeUpper0","EyeLower0"]):g(K,et,"right",["EyeUpper0","EyeLower0"]);let lt=this.getAdjustedIrisCoords(K,Ge,"left"),ut=this.getAdjustedIrisCoords(K,it,"right");K=K.concat(lt).concat(ut)}let te=this.transformRawCoords(K,N,D,O),J=t.enlargeBox(this.calculateLandmarksBoundingBox(te)),se=t.squarifyBox(J),Q=pr(te),le={coords:Q,box:J,faceConfidence:Z,confidence:N.confidence,image:$};return 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m=l.face.mesh.returnRawData&&h.box?{topLeft:h.box.startPoint,bottomRight:h.box.endPoint}:null,A=h.box?[Math.max(0,h.box.startPoint[0]),Math.max(0,h.box.startPoint[1]),Math.min(o.shape[2],h.box.endPoint[0])-h.box.startPoint[0],Math.min(o.shape[1],h.box.endPoint[1])-h.box.startPoint[1]]:0;u.push({confidence:h.confidence||0,box:A,mesh:d,boxRaw:m,meshRaw:p,annotations:f,image:h.image?Er(h.image):null}),h.coords&&h.coords.dispose(),h.image&&h.image.dispose()}return u}},s=[null,null,null];async function i(o){s=await Promise.all([!s[0]&&o.face.enabled?t.load(o):null,!s[1]&&o.face.mesh.enabled?fr(o.face.mesh.modelPath,{fromTFHub:o.face.mesh.modelPath.includes("tfhub.dev")}):null,!s[2]&&o.face.iris.enabled?fr(o.face.iris.modelPath,{fromTFHub:o.face.iris.modelPath.includes("tfhub.dev")}):null]);let l=new a(s[0],s[1],s[2],o);return o.face.mesh.enabled&&Je(`load model: ${o.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),o.face.iris.enabled&&Je(`load model: 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n.age?a<l.face.age.skipFrames&&l.videoOptimized&&r.age&&r.age>0?(a++,r):(l.videoOptimized?a=0:a=Number.MAX_SAFE_INTEGER,new Promise(async c=>{let u=Dt.resizeBilinear(o,[l.face.age.inputSize,l.face.age.inputSize],!1),h=B(u,[255]);$e(u);let d,p={};if(!l.profile)l.face.age.enabled&&(d=await n.age.predict(h));else{let f=l.face.age.enabled?await Zl(()=>n.age.predict(h)):{};d=f.result.clone(),f.result.dispose(),t.run("age",f)}if(h.dispose(),d){let f=d.dataSync();p.age=Math.trunc(10*f[0])/10}d.dispose(),r=p,c(p)})):null}e.predict=i,e.load=s}),Wv=ht(e=>{var t=Be(Kl()),n={},r={gender:""},a=Number.MAX_SAFE_INTEGER,s=!1,i=[.2989,.587,.114];async function o(c){return n.gender||(n.gender=await fr(c.face.gender.modelPath),s=n.gender.inputs[0].shape[3]===1,Je(`load model: ${c.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),n.gender}async function l(c,u){return n.gender?a<u.face.gender.skipFrames&&u.videoOptimized&&r.gender!==""?(a++,r):(u.videoOptimized?a=0:a=Number.MAX_SAFE_INTEGER,new Promise(async 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t=Be(Xv()),n=Be(Yl()),r=Be(Y2());e.load=t.load,e.PoseNet=t.PoseNet,e.partChannels=n.partChannels,e.partIds=n.partIds,e.partNames=n.partNames,e.poseChain=n.poseChain,e.getAdjacentKeyPoints=r.getAdjacentKeyPoints,e.getBoundingBox=r.getBoundingBox,e.getBoundingBoxPoints=r.getBoundingBoxPoints,e.scaleAndFlipPoses=r.scaleAndFlipPoses,e.scalePose=r.scalePose}),Yv=ht(e=>{var t=class{constructor(n,r,a){this.model=n,this.anchors=a.map(s=>[s.x_center,s.y_center]),this.anchorsTensor=pr(this.anchors),this.inputSizeTensor=tn([r,r]),this.doubleInputSizeTensor=tn([r*2,r*2])}normalizeBoxes(n){return j(()=>{let r=Me(n,[0,0],[-1,2]),a=Me(n,[0,2],[-1,2]),s=ie(Se(r,this.inputSizeTensor),this.anchorsTensor),i=Se(a,this.doubleInputSizeTensor),o=B(be(s,i),this.inputSizeTensor),l=B(ie(s,i),this.inputSizeTensor);return Xl([o,l],1)})}normalizeLandmarks(n,r){return j(()=>{let a=ie(Se(n.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[r]);return B(a,this.inputSizeTensor)})}async getBoxes(n,r){let a=this.model.predict(n),s=a.squeeze();a.dispose();let i=j(()=>tr(Me(s,[0,0],[-1,1])).squeeze()),o=i.dataSync(),l=Me(s,[0,1],[-1,4]),c=this.normalizeBoxes(l);l.dispose();let u=await Dt.nonMaxSuppressionAsync(c,o,r.hand.maxHands,r.hand.iouThreshold,r.hand.scoreThreshold),h=u.arraySync();i.dispose(),u.dispose();let d=[];for(let p of h)if(o[p]>=r.hand.minConfidence){let f=Me(c,[p,0],[1,-1]),m=Me(s,[p,5],[1,14]),A=j(()=>this.normalizeLandmarks(m,p).reshape([-1,2]));m.dispose(),d.push({box:f,palmLandmarks:A,confidence:o[p]})}return s.dispose(),c.dispose(),d}async estimateHandBounds(n,r){let a=n.shape[1],s=n.shape[2],i=j(()=>n.resizeBilinear([r.hand.inputSize,r.hand.inputSize]).div(127.5).sub(1)),o=await this.getBoxes(i,r);i.dispose();let l=[];if(!o||o.length===0)return l;for(let c of o){let u=c.box.dataSync(),h=u.slice(0,2),d=u.slice(2,4),p=c.palmLandmarks.arraySync();c.box.dispose(),c.palmLandmarks.dispose(),l.push(Zv({startPoint:h,endPoint:d,palmLandmarks:p,confidence:c.confidence},[s/r.hand.inputSize,a/r.hand.inputSize]))}return l}};e.HandDetector=t}),t4=ht(e=>{var t=5,n=1.65,r=[0,5,9,13,17,1,2],a=0,s=2,i=class{constructor(o,l,c){this.handDetector=o,this.landmarkDetector=l,this.inputSize=c,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(o,l){let c=o.map(h=>Q2([...h,1],l)),u=this.calculateLandmarksBoundingBox(c);return pf(ff(u),t)}getBoxForHandLandmarks(o){let l=this.calculateLandmarksBoundingBox(o),c=pf(ff(l),n);c.palmLandmarks=[];for(let u=0;u<r.length;u++)c.palmLandmarks.push(o[r[u]].slice(0,2));return c}transformRawCoords(o,l,c,u){let h=df(l),d=[h[0]/this.inputSize,h[1]/this.inputSize,(h[0]+h[1])/this.inputSize/2],p=o.map(w=>[d[0]*(w[0]-this.inputSize/2),d[1]*(w[1]-this.inputSize/2),d[2]*w[2]]),f=J2(c,[0,0]),m=p.map(w=>[...Q2(w,f),w[2]]),A=e4(u),y=[...hh(l),1],g=[Ka(y,A[0]),Ka(y,A[1])];return m.map(w=>[w[0]+g[0],w[1]+g[1],w[2]])}async estimateHands(o,l){let c=!1,u;(this.skipped===0||this.skipped>l.hand.skipFrames||!l.hand.landmarks||!l.videoOptimized)&&(u=await this.handDetector.estimateHandBounds(o,l),this.skipped=0),l.videoOptimized&&this.skipped++,u&&u.length>0&&(u.length!==this.detectedHands&&this.detectedHands!==l.hand.maxHands||!l.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...u],this.storedBoxes.length>0&&(c=!0));let h=[];for(let d=0;d<this.storedBoxes.length;d++){let p=this.storedBoxes[d];if(p)if(l.hand.landmarks){let f=l.hand.rotation?Qv(p.palmLandmarks[a],p.palmLandmarks[s]):0,m=hh(p),A=[m[0]/o.shape[2],m[1]/o.shape[1]],y=l.hand.rotation?Dt.rotateWithOffset(o,f,0,A):o.clone(),g=J2(-f,m),w=c?this.getBoxForPalmLandmarks(p.palmLandmarks,g):p,x=Jv(w,y,[this.inputSize,this.inputSize]),_=x.div(255);x.dispose(),y.dispose();let[b,T]=await this.landmarkDetector.predict(_);_.dispose();let S=b.dataSync()[0];if(b.dispose(),S>=l.hand.minConfidence){let N=X(T,[-1,3]),C=N.arraySync();T.dispose(),N.dispose();let $=this.transformRawCoords(C,w,f,g),D=this.getBoxForHandLandmarks($);this.storedBoxes[d]=D;let O={landmarks:$,confidence:S,box:{topLeft:D.startPoint,bottomRight:D.endPoint}};h.push(O)}else this.storedBoxes[d]=null;T.dispose()}else{let f=pf(ff(p),n),m={confidence:p.confidence,box:{topLeft:f.startPoint,bottomRight:f.endPoint}};h.push(m)}}return this.storedBoxes=this.storedBoxes.filter(d=>d!==null),this.detectedHands=h.length,h}calculateLandmarksBoundingBox(o){let 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Promise.all([o.hand.enabled?fr(o.hand.detector.modelPath,{fromTFHub:o.hand.detector.modelPath.includes("tfhub.dev")}):null,o.hand.landmarks?fr(o.hand.skeleton.modelPath,{fromTFHub:o.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),u=new t.HandDetector(l,o.hand.inputSize,r.anchors),h=new n.HandPipeline(u,c,o.hand.inputSize),d=new s(h);return o.hand.enabled&&Je(`load model: ${o.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),o.hand.landmarks&&Je(`load model: ${o.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),d}e.load=i}),a4=ht(e=>{e.body=t=>{if(!t)return[];let n=[];for(let r=0;r<t.length;r++){let a=t[r].keypoints.find(c=>c.part==="leftWrist"),s=t[r].keypoints.find(c=>c.part==="rightWrist"),i=t[r].keypoints.find(c=>c.part==="nose");i&&a&&s&&a.position.y<i.position.y&&s.position.y<i.position.y?n.push({body:r,gesture:"i give up"}):i&&a&&a.position.y<i.position.y?n.push({body:r,gesture:"raise left hand"}):i&&s&&s.position.y<i.position.y&&n.push({body:r,gesture:"raise right hand"});let o=t[r].keypoints.find(c=>c.part==="leftShoulder"),l=t[r].keypoints.find(c=>c.part==="rightShoulder");o&&l&&n.push({body:r,gesture:`leaning ${o.position.y>l.position.y?"left":"right"}`})}return n},e.face=t=>{if(!t)return[];let n=[];for(let r=0;r<t.length;r++)if(t[r].mesh&&t[r].mesh.length>0){let a=t[r].mesh[35][2]-t[r].mesh[263][2];Math.abs(a)<10?n.push({face:r,gesture:"facing camera"}):n.push({face:r,gesture:`facing ${a<0?"right":"left"}`}),Math.abs(t[r].mesh[374][1]-t[r].mesh[386][1])/Math.abs(t[r].mesh[443][1]-t[r].mesh[450][1])<.2&&n.push({face:r,gesture:"blink left eye"}),Math.abs(t[r].mesh[145][1]-t[r].mesh[159][1])/Math.abs(t[r].mesh[223][1]-t[r].mesh[230][1])<.2&&n.push({face:r,gesture:"blink right eye"});let s=Math.min(100,500*Math.abs(t[r].mesh[13][1]-t[r].mesh[14][1])/Math.abs(t[r].mesh[10][1]-t[r].mesh[152][1]));s>10&&n.push({face:r,gesture:`mouth ${Math.trunc(s)}% open`});let i=t[r].mesh[152][2];Math.abs(i)>10&&n.push({face:r,gesture:`head ${i<0?"up":"down"}`})}return 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Buffer shape=${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 Or().makeTensor(this.values,this.shape,this.dtype)}},Or=null,Jo=null,xk=null;function wk(e){Or=e}function _k(e){Jo=e}function bk(e){xk=e}var H=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Pt(e),this.strides=Ko(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 Jo.buffer(this.shape,this.dtype,e)}bufferSync(){return Jo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Zo(this.shape,e)}arraySync(){return Zo(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Or().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Rd(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=Or().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Rd(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 Or().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Or().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Jo.print(this,e)}clone(){return this.throwIfDisposed(),Jo.clone(this)}toString(e=!1){let t=this.dataSync();return gk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Jo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Or().makeVariable(this,e,t,n)}};Object.defineProperty(H,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});var gu=class extends H{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(!na(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Or().disposeTensor(this),this.dataId=e.dataId,Or().incRef(this,null)}dispose(){Or().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(gu,Symbol.hasInstance,{value:e=>e instanceof H&&e.assign!=null&&e.assign instanceof Function});var mr={};Pe(mr,{assertTypesMatch:()=>H0,getTensorsInContainer:()=>fm,isTensorInList:()=>vk,makeTypesMatch:()=>Nt});var gf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(gf||(gf={}));var mm;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(mm||(mm={}));var Am;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Am||(Am={}));var ym;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(ym||(ym={}));var gm;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(gm||(gm={}));var kk={float32:ym,int32:mm,bool:Am,complex64:gm};function nr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return kk[e][t]}function jh(e){return nr(e,"int32")}function Nt(e,t){if(e.dtype===t.dtype)return[e,t];let n=nr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function H0(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function vk(e,t){return t.some(n=>n.id===e.id)}function fm(e){let t=[],n=new Set;return G0(e,t,n),t}function G0(e,t,n){if(e==null)return;if(e instanceof H){t.push(e);return}if(!Ik(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),G0(s,t,n))}}function Ik(e){return Array.isArray(e)||typeof e=="object"}var q0=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Xu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new q0}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 fk(this.backendInstance),!0}setupRegisteredKernels(){yu(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){yu(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 Ql)&&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);r.disposeData(t),n.backend=e,e.move(t,a,n.shape,n.dtype),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 Xu.nextTensorId++}nextVariableId(){return Xu.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return L.runKernelFunc(c=>c.cast(s,i),o,null,es,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n,r,a){let s=null,i=null;return this.runKernelFunc(s,t,i,e,n,r,a)}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,t,n,r,a,s,i){let o,l=[],c=this.isTapeOn();r==null&&(r=this.state.activeScope!=null?this.state.activeScope.name:"");let u=this.state.numBytes,h=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let d;this.backendName==null&&this.backend;let p=Af(r,this.backendName),f;if(p!=null)d=()=>{let A=this.backend.numDataIds();f=p.kernelFunc({inputs:t,attrs:a,backend:this.backend});let y=Array.isArray(f)?f:[f];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,A,y);let g=y.map(w=>{if(w.rank!=null)return w;let{dataId:x,shape:_,dtype:b}=w;return this.makeTensorFromDataId(x,_,b)});if(c){let w=this.getTensorsForGradient(r,t,g);if(w==null){i==null&&(i=[]);let x=g.filter((_,b)=>i[b]);w=(s||[]).slice().concat(x)}l=this.saveTensorsForBackwardMode(w)}return g};else{if(e==null)throw new Error(`Error running ${r}: Neither modular kernel nor forward func passed`);let A=y=>{!c||(l=y.map(g=>this.keep(this.clone(g))))};d=()=>{let y=this.backend.numDataIds();f=this.tidy(()=>e(this.backend,A));let g=Array.isArray(f)?f:[f];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(r,y,g),g}}let m;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?o=d():(m=this.profiler.profileKernel(r,t,()=>d()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(m),o=m.outputs)}),c&&this.addTapeNode(r,t,o,n,l,a),this.state.profiling&&this.state.activeProfile.kernels.push({name:r,bytesAdded:this.state.numBytes-u,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-h,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:o.map(A=>A.shape),kernelTimeMs:m.timeMs,extraInfo:m.extraInfo}),Array.isArray(f)?o:o[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=yf(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 null}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"&&ka(e[0])&&(a=e.map(o=>ju(o)));let s=r.write(a,t,n),i=new H(t,n,s,this.nextTensorId());if(this.incRef(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=O0(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new H(t,n,e,this.nextTensorId());return this.incRef(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 gu(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}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*D0(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof gu||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}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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s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(M(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(a instanceof H,()=>"The result y returned by f() must be a tensor.");let s=mk(this.state.activeTape,t,a);if(!r&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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n=R(e,"labels","sigmoidCrossEntropyWithLogits"),r=R(t,"logits","sigmoidCrossEntropyWithLogits");st(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=Mr(r),s=B(r,n),i=td(Bn(kt(zt(r))));return ie(be(a,s),i)}function DE(e,t,n,r=0,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"multiClassLabels","sigmoidCrossEntropy"),i=R(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","sigmoidCrossEntropy")),st(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=Te(r),u=Te(1),h=Te(.5);s=ie(B(s,be(u,c)),B(h,c))}let l=$E(s,i);return aa(l,o,a)}var OE=P({sigmoidCrossEntropy_:DE});function zE(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Cr((r,a,s)=>{let i=Wf(a,[n],!0),o=be(xe(a,"float32"),i);s([r,o]);let l=kt(B(o,r));return{value:Ce(l,[n]),gradFunc:(c,u)=>{let[h,d]=u,p=ti(c.shape,[n]);return[B(X(c,p),be(xe(h,"float32"),Bn(d))),B(X(c,p),be(Bn(d),xe(h,"float32")))]}}})(e,t)}function PE(e,t,n,r=0,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),st(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=Te(r),u=Te(1),h=Te(s.shape[1]);s=ie(B(s,be(u,c)),Se(c,h))}let l=zE(s,i);return aa(l,o,a)}var LE=P({softmaxCrossEntropy_:PE}),U4={fft:Ou,ifft:Ho,rfft:zu,irfft:md},j4={hammingWindow:PT,hannWindow:L5,frame:W5,stft:VT},Dt={flipLeftRight:GT,resizeNearestNeighbor:G5,resizeBilinear:H5,rotateWithOffset:XT,cropAndResize:jT,nonMaxSuppression:ZT,nonMaxSuppressionAsync:aE,nonMaxSuppressionWithScore:iE,nonMaxSuppressionWithScoreAsync:lE,nonMaxSuppressionPadded:cE,nonMaxSuppressionPaddedAsync:dE},t0={bandPart:AE,gramSchmidt:gE,qr:wE},H4={absoluteDifference:vE,computeWeightedLoss:aa,cosineDistance:IE,hingeLoss:SE,huberLoss:EE,logLoss:RE,meanSquaredError:ME,sigmoidCrossEntropy:OE,softmaxCrossEntropy:LE},ea=class extends I5{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 $e(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Sg(e,t)}dispose(){this.iterations_!=null&&$e(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Te(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ea,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var xd=class extends ea{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=L.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:j(()=>qe(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:j(()=>qe(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;j(()=>{let l=ie(B(i,this.rho),B(dt(s),1-this.rho)),c=B(Se(Yt(ie(o,this.epsilon)),Yt(ie(i,this.epsilon))),s),u=ie(B(o,this.rho),B(dt(c),1-this.rho));i.assign(l),o.assign(u);let h=ie(B(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&($e(this.accumulatedGrads.map(e=>e.variable)),$e(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};xd.className="Adadelta";Ta(xd);var wd=class extends ea{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=L.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:j(()=>Nu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;j(()=>{let i=ie(s,dt(a));s.assign(i);let o=ie(B(Se(a,Yt(ie(i,L.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&$e(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};wd.className="Adagrad";Ta(wd);var _d=class extends ea{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Te(t).variable(),this.accBeta2=Te(n).variable()}),r==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=be(1,this.accBeta1),r=be(1,this.accBeta2);t.forEach((a,s)=>{let i=L.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:j(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:j(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=ie(B(c,this.beta1),B(l,1-this.beta1)),d=ie(B(u,this.beta2),B(dt(l),1-this.beta2)),p=Se(h,n),f=Se(d,r);c.assign(h),u.assign(d);let m=ie(B(Se(p,ie(Yt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&$e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&$e(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(Fr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};_d.className="Adam";Ta(_d);var bd=class extends ea{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Te(0).variable(),this.accBeta1=Te(t).variable()}),r==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=be(1,this.accBeta1),r=Se(-this.learningRate,ie(B(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=L.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,h=ie(B(c,this.beta1),B(l,1-this.beta1)),d=B(u,this.beta2),p=zt(l),f=yr(d,p);c.assign(h),u.assign(f);let m=ie(B(Se(r,n),Se(h,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&$e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&$e(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)}};bd.className="Adamax";Ta(bd);var Lu=class extends ea{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=L.registeredVariables[t];j(()=>{let s=ie(B(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(Te(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};vd.className="Momentum";Ta(vd);var kd=class extends ea{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=L.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:j(()=>qe(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:j(()=>qe(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:j(()=>qe(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;j(()=>{let l=ie(B(i,this.decay),B(dt(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=ie(B(c,this.decay),B(s,1-this.decay)),h=Se(B(s,this.learningRate),Yt(be(l,ie(dt(u),this.epsilon)))),d=ie(B(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(d);let p=be(r,d);r.assign(p)}else{let c=ie(B(i,this.decay),B(dt(s),1-this.decay)),u=ie(B(o,this.momentum),Se(B(s,this.learningRate),Yt(ie(c,this.epsilon))));i.assign(c),o.assign(u);let h=be(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&$e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&$e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&$e(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};kd.className="RMSProp";Ta(kd);var ni=class{static sgd(e){return new Lu(e)}static momentum(e,t,n=!1){return new vd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new kd(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new _d(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new xd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new bd(e,t,n,r,a)}static 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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.
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ix=Mt((e,t)=>e+t),TF=Zm((e,t,n,r)=>({real:e+n,imag:t+r})),Ju=qt(fa,ix,TF),EF={kernelName:fa,backendName:"cpu",kernelFunc:Ju};function Hm(e,t,n,r,a){let s=k.sizeFromShape(r),i=k.makeZerosTypedArray(a,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function ox(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=Ue([a,n],t.dtype);for(let o=0;o<a;o++)for(let l=0;l<s;l++){let c=e.get(o,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(r?i.set(1,o,c):t.size>0?i.set(i.get(o,c)+t.get(o,l),o,c):i.set(i.get(o,c)+1,o,c))}return i}function sl(e){return(t,n,r)=>{let a=k.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function ct(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(Ie(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let 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dM={kernelName:ks,backendName:"cpu",kernelFunc:Mx},$x=ct(Is,e=>Math.max(0,e)),pM={kernelName:Is,backendName:"cpu",kernelFunc:$x},Dx=ct(Ss,e=>Math.min(Math.max(0,e),6)),fM={kernelName:Ss,backendName:"cpu",kernelFunc:Dx};function Qm(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return $x({inputs:{x:t},backend:e});if(n==="elu")return Rx({inputs:{x:t},backend:e});if(n==="relu6")return Dx({inputs:{x:t},backend:e});if(n==="prelu")return Mx({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Fx({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function wt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=k.sizeFromShape(a.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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it=0;for(let je=Ee;je<Ge;je++){let lt=Math.min(me,A-1)*Z,ut=Math.min(me,y-1)*Q,zn=D[lt+Ve*K+je*te],tt=O[je*J+et*se+ut];it+=zn*tt}ce[me*le+(Ve*C+et)]+=it}}return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(T),n.makeTensorInfo(w,re.dtype,re.values)}var AM={kernelName:Qa,backendName:"cpu",kernelFunc:Ox};function yM(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,d,p,f,m=[];d=Ox({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(p=Ju({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),u&&(f=Qm(n,d,u,o,h),m.push(d),d=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return d}var gM={kernelName:Ws,backendName:"cpu",kernelFunc:yM},xM=ct(Oi,e=>Math.acos(e)),wM={kernelName:Oi,backendName:"cpu",kernelFunc:xM},_M=ct(zi,e=>Math.acosh(e)),bM={kernelName:zi,backendName:"cpu",kernelFunc:_M};function vM(e){let{inputs:t,backend:n}=e,r=t;Ie(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=Ue(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 kM={kernelName:Za,backendName:"cpu",kernelFunc:vM};function IM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Ie(a,"all");let o=k.parseAxisParam(s,a.shape),l=o,c=F.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=ir({inputs:{x:a},backend:n,attrs:{perm:c}}),l=F.getInnerMostAxes(l.length,a.shape.length)),F.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=F.computeOutAndReduceShapes(u.shape,l),p=k.sizeFromShape(d),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let x=0;x<p;++x){let _=m[g+x];w=w&&_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=F.expandShapeToKeepDim(h,o),g=wt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var NM={kernelName:ph,backendName:"cpu",kernelFunc:IM};function SM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Ie(a,"any");let o=k.parseAxisParam(s,a.shape),l=o,c=F.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=ir({inputs:{x:a},backend:n,attrs:{perm:c}}),l=F.getInnerMostAxes(l.length,a.shape.length)),F.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=F.computeOutAndReduceShapes(u.shape,l),p=k.sizeFromShape(d),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let x=0;x<p;++x){let _=m[g+x];w=w||_}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=F.expandShapeToKeepDim(h,o),g=wt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var TM={kernelName:fh,backendName:"cpu",kernelFunc:SM};function EM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;Ie(a,"argMax");let i=k.parseAxisParam(s,a.shape),o=F.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=ir({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=F.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),p=k.makeZerosTypedArray(d,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let x=0;x<f;++x){let _=m[y+x];_>g&&(g=_,w=x)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var CM={kernelName:Ya,backendName:"cpu",kernelFunc:EM};function RM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;Ie(a,"argMin");let i=k.parseAxisParam(s,a.shape),o=F.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=ir({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=F.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),p=k.makeZerosTypedArray(d,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let x=0;x<f;++x){let _=m[y+x];_<g&&(g=_,w=x)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var FM={kernelName:eu,backendName:"cpu",kernelFunc:RM},MM=ct(Pi,e=>Math.asin(e)),$M={kernelName:Pi,backendName:"cpu",kernelFunc:MM},DM=ct(Li,e=>Math.asinh(e)),OM={kernelName:Li,backendName:"cpu",kernelFunc:DM},zM=ct(Wi,e=>Math.atan(e)),PM={kernelName:Wi,backendName:"cpu",kernelFunc:zM},LM=Mt((e,t)=>Math.atan2(e,t)),WM=qt(Vi,LM),BM={kernelName:Vi,backendName:"cpu",kernelFunc:WM},VM=ct(Bi,e=>Math.atanh(e)),UM={kernelName:Bi,backendName:"cpu",kernelFunc:VM};function eA(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ue(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],w=a.outShape[3];for(let x=0;x<a.batchSize;++x){let _=x*y,b=x*r[0];for(let T=0;T<a.inChannels;++T)for(let S=0;S<a.outHeight;++S){let 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W=V-T,Z=m.get(A,D,V,y);Z>C&&(C=Z,a?$=s?((A*r.inHeight+D)*r.inWidth+V)*r.inChannels+y:(D*r.inWidth+V)*r.inChannels+y:$=O*d+W)}}i.set($,A,g,b,y)}}return i}function Px(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=Ue(a.outShape,n),x=w.values,_=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],T=a.outShape[3]*a.outShape[4],S=a.outShape[4];for(let N=0;N<a.batchSize;++N){let C=N*_,$=N*r[0];for(let D=0;D<a.inChannels;++D)for(let O=0;O<a.outDepth;++O){let V=O*i-m,W=V;for(;W<0;)W+=c;let Z=Math.min(a.inDepth,d+V),K=C+O*b;for(let te=0;te<a.outHeight;++te){let J=te*o-A,se=J;for(;se<0;)se+=u;let Q=Math.min(a.inHeight,p+J),le=K+te*T;for(let re=0;re<a.outWidth;++re){let ce=re*l-y,he=ce;for(;he<0;)he+=h;let me=Math.min(a.inWidth,f+ce),ye=le+re*S,ge=g,Ee=0,Re=0;for(let Ge=W;Ge<Z;Ge+=c){let Ve=$+Ge*r[1];for(let et=se;et<Q;et+=u){let it=Ve+et*r[2];for(let je=he;je<me;je+=h){let lt=it+je*r[3],ut=e[lt+D];if(s==="max"&&ut>ge?ge=ut:s==="avg"&&(Ee+=ut,Re++),isNaN(ge))break}if(isNaN(ge))break}if(isNaN(ge))break}let Oe=ye+D;x[Oe]=s==="avg"?Ee/Re:ge}}}}return w}function jM(e,t){let n=Ue(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,w=g;for(;w<0;)w+=i;let x=Math.min(t.inDepth,c+g);for(let _=0;_<t.outHeight;++_){let b=_*a-p,T=b;for(;T<0;)T+=o;let S=Math.min(t.inHeight,u+b);for(let N=0;N<t.outWidth;++N){let C=N*s-f,$=C;for(;$<0;)$+=l;let D=Math.min(t.inWidth,h+C),O=Number.NEGATIVE_INFINITY,V=-1;for(let W=w;W<x;W+=i){let Z=W-g;for(let K=T;K<S;K+=o){let te=K-b;for(let J=$;J<D;J+=l){let se=J-C,Q=e.get(m,W,K,J,A);Q>=O&&(O=Q,V=Z*u*h+te*u+se)}}}n.set(V,m,y,_,N,A)}}}return n}function HM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Ie(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(F.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=F.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=k.computeStrides(a.shape),f=eA(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var GM={kernelName:Ja,backendName:"cpu",kernelFunc:HM};function qM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c,dilations:u}=r;Ie(a,"avgPool3d");let h=u;h==null&&(h=[1,1,1]);let d=F.computePool3DInfo(a.shape,s,i,h,o,l,c),p=n.data.get(a.dataId).values,f=Px(p,a.shape,a.dtype,k.computeStrides(a.shape),d,"avg");return n.makeTensorInfo(f.shape,"float32",f.values)}var XM={kernelName:tu,backendName:"cpu",kernelFunc:qM};function KM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dilations:c,dimRoundingMode:u}=r;Ie([a,s],"avgPool3DGrad");let h=F.computePool3DInfo(s.shape,i,o,c,l,u),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=h.dilationDepth,w=h.dilationHeight,x=h.dilationWidth,_=h.effectiveFilterDepth,b=h.effectiveFilterHeight,T=h.effectiveFilterWidth,S=_-1-h.padInfo.front,N=T-1-h.padInfo.left,C=b-1-h.padInfo.top,$=Ue(s.shape,"float32"),D=1/(m*A*y),O=n.bufferSync(a);for(let V=0;V<h.batchSize;++V)for(let W=0;W<h.inChannels;++W)for(let Z=0;Z<h.inDepth;++Z)for(let K=0;K<h.inHeight;++K)for(let te=0;te<h.inWidth;++te){let J=Z-S,se=K-C,Q=te-N,le=0;for(let re=0;re<_;re+=g){let ce=(J+re)/d;if(!(ce<0||ce>=h.outDepth||Math.floor(ce)!==ce))for(let he=0;he<b;he+=w){let me=(se+he)/p;if(!(me<0||me>=h.outHeight||Math.floor(me)!==me))for(let ye=0;ye<T;ye+=x){let ge=(Q+ye)/f;ge<0||ge>=h.outWidth||Math.floor(ge)!==ge||(le+=O.get(V,ce,me,ge,W))}}}$.set(le*D,V,Z,K,te,W)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var ZM={kernelName:Ah,backendName:"cpu",kernelFunc:KM};function YM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Ie([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=F.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,w=g-1-u.padInfo.left,x=y-1-u.padInfo.top,_=Ue(i.shape,"float32"),b=1/(p*f),T=n.data.get(a.dataId).values,S=Ue(a.shape,"float32",T);for(let N=0;N<u.batchSize;++N)for(let C=0;C<u.inChannels;++C)for(let $=0;$<u.inHeight;++$)for(let D=0;D<u.inWidth;++D){let O=$-x,V=D-w,W=0;for(let Z=0;Z<y;Z+=m){let K=(O+Z)/h;if(!(K<0||K>=u.outHeight||Math.floor(K)!==K))for(let te=0;te<g;te+=A){let J=(V+te)/d;J<0||J>=u.outWidth||Math.floor(J)!==J||(W+=S.get(N,K,J,C))}}_.set(W*b,N,$,D,C)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var JM={kernelName:mh,backendName:"cpu",kernelFunc:YM};function QM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ie([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),A=f.length,y=p.length,g=d.length,w=h.length,x=0,_=0,b=0,T=0;for(let S=0;S<u.length;++S)m[S]=f[x++]+(u[S]-h[_++])*p[b++]/Math.sqrt(d[T++]+c),x>=A&&(x=0),_>=w&&(_=0),b>=y&&(b=0),T>=g&&(T=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var e$={kernelName:cs,backendName:"cpu",kernelFunc:QM};function t$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;Ie([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=F.getReshaped(a.shape,s,o),c=F.getPermuted(l.length,s.length),u=F.getReshapedPermuted(a.shape,s,o),h=F.getSliceBeginCoords(i,s.length),d=F.getSliceSize(u,i,s.length),p=wt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=ir({inputs:{x:p},backend:n,attrs:{perm:c}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var n$={kernelName:nu,backendName:"cpu",kernelFunc:t$};function r$(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=Hm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var a$={kernelName:yh,backendName:"cpu",kernelFunc:r$},s$=ct(ma,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),i$={kernelName:ma,backendName:"cpu",kernelFunc:s$},o$=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(k.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")},l$={kernelName:ru,backendName:"cpu",kernelFunc:o$};function ol(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 u$={kernelName:Rh,backendName:"cpu",kernelFunc:ol};function ll(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=F.computeOutShape(t.map(m=>m.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>k.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(F.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(x=>ri({inputs:{input:x},backend:n})),A=o.map(x=>ol({inputs:{input:x},backend:n})),y=ll({inputs:m,backend:n,attrs:{axis:s}}),g=ll({inputs:A,backend:n,attrs:{axis:s}}),w=En({inputs:{real:y,imag:g},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),A.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),w}let c=o.map(m=>{let A=k.sizeFromShape(m.shape.slice(s));return wt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=F.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=Gm(u,i,t[0].dtype,h),p=F.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var c$={kernelName:ji,backendName:"cpu",kernelFunc:ll};function Lx(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;Ie([a,s],"conv2d");let h=F.convertConv2DDataFormat(l),d=F.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,w=d.dataFormat==="channelsLast",x=new Ot(d.outShape,a.dtype),_=k.computeStrides(a.shape),b=k.computeStrides(s.shape),T=_[0],S=w?_[1]:_[2],N=w?_[2]:1,C=w?1:_[1],$=x.strides[0],D=w?x.strides[1]:x.strides[2],O=w?x.strides[2]:1,V=w?1:x.strides[1],W=n.data.get(a.dataId).values,Z=n.data.get(s.dataId).values,K=x.values;for(let te=0;te<d.batchSize;++te){let J=te*T,se=te*$;for(let Q=0;Q<d.outHeight;++Q){let le=se+Q*D,re=Q*d.strideHeight-g;for(let ce=0;ce<p;++ce){let he=re+ce*m;if(he<0||he>=d.inHeight)continue;let me=ce*b[0],ye=J+he*S;for(let ge=0;ge<d.outWidth;++ge){let Ee=le+ge*O,Re=ge*d.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let Ge=Re+Oe*A;if(Ge<0||Ge>=d.inWidth)continue;let Ve=me+Oe*b[1],et=ye+Ge*N,it=Ve;for(let je=0;je<d.inChannels;++je){let lt=W[et+je*C];for(let ut=0;ut<d.outChannels;++ut)K[Ee+ut*V]+=lt*Z[it+ut];it+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,K)}var h$={kernelName:ts,backendName:"cpu",kernelFunc:Lx};function d$(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;Ie([a,s],"conv2dBackpropFilter");let h=F.convertConv2DDataFormat(l),d=F.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new Ot(d.filterShape,"float32"),w=d.padInfo.left,x=d.padInfo.top,_=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,T=new Ot(a.shape,a.dtype,_),S=new Ot(s.shape,s.dtype,b);for(let N=0;N<m;++N){let C=Math.max(0,Math.ceil((x-N)/p)),$=Math.min(d.outHeight,(d.inHeight+x-N)/p);for(let D=0;D<A;++D){let O=Math.max(0,Math.ceil((w-D)/f)),V=Math.min(d.outWidth,(d.inWidth+w-D)/f);for(let W=0;W<d.inChannels;++W)for(let Z=0;Z<d.outChannels;++Z){let K=0;for(let te=0;te<d.batchSize;++te)for(let J=C;J<$;++J){let se=N+J*p-x;for(let Q=O;Q<V;++Q){let le=D+Q*f-w;y?K+=T.get(te,se,le,W)*S.get(te,J,Q,Z):K+=T.get(te,W,se,le)*S.get(te,Z,J,Q)}}g.set(K,N,D,W,Z)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var p$={kernelName:xh,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;Ie([a,s],"conv2dBackpropInput");let h=k.computeStrides(s.shape),d=k.computeStrides(a.shape),p=F.convertConv2DDataFormat(c),f=F.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new Ot(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[w,x,_]=h,{batchSize:b,filterHeight:T,filterWidth:S,inChannels:N,inHeight:C,inWidth:$,outChannels:D,outHeight:O,outWidth:V,strideHeight:W,strideWidth:Z}=f;p=f.dataFormat;let K=T-1-f.padInfo.top,te=S-1-f.padInfo.left,J=p==="channelsLast",se=m.strides[0],Q=J?m.strides[1]:m.strides[2],le=J?m.strides[2]:1,re=J?1:m.strides[1],ce=d[0],he=J?d[1]:d[2],me=J?d[2]:1,ye=J?1:d[1];for(let ge=0;ge<b;++ge)for(let Ee=0;Ee<N;++Ee)for(let Re=0;Re<C;++Re){let Oe=Re-K,Ge=Math.max(0,Math.ceil(Oe/W)),Ve=Math.min(O,(T+Oe)/W);for(let et=0;et<$;++et){let it=et-te,je=Math.max(0,Math.ceil(it/Z)),lt=Math.min(V,(S+it)/Z),ut=0;for(let tt=Ge;tt<Ve;++tt){let wn=tt*W-Oe;for(let Zt=je;Zt<lt;++Zt){let _n=Zt*Z-it,Zn=ce*ge+he*tt+me*Zt,dn=w*(T-1-wn)+x*(S-1-_n)+_*Ee;for(let rn=0;rn<D;++rn){let Yn=y[Zn+ye*rn],Sr=g[dn+rn];ut+=Yn*Sr}}}let zn=se*ge+Q*Re+le*et+re*Ee;A[zn]=ut}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var m$={kernelName:ns,backendName:"cpu",kernelFunc:f$};function A$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;Ie([a,s],"conv3d");let c=F.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,w=A.top,x=new Ot(c.outShape,a.dtype),_=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,T=x.values,S=k.computeStrides(a.shape),N=k.computeStrides(s.shape);for(let C=0;C<c.batchSize;++C){let $=C*S[0],D=C*x.strides[0];for(let O=0;O<c.outDepth;++O){let V=D+O*x.strides[1],W=O*c.strideDepth-y;for(let Z=0;Z<u;++Z){let K=W+Z*p;if(K<0||K>=c.inDepth)continue;let te=Z*N[0],J=$+K*S[1];for(let se=0;se<c.outHeight;++se){let Q=V+se*x.strides[2],le=se*c.strideHeight-w;for(let re=0;re<h;++re){let ce=le+re*f;if(ce<0||ce>=c.inHeight)continue;let he=te+re*N[1],me=J+ce*S[2];for(let ye=0;ye<c.outWidth;++ye){let ge=Q+ye*c.outChannels,Ee=ye*c.strideWidth-g;for(let Re=0;Re<d;++Re){let Oe=Ee+Re*m;if(Oe<0||Oe>=c.inWidth)continue;let Ge=he+Re*N[2],Ve=me+Oe*c.inChannels,et=Ge;for(let it=0;it<c.inChannels;++it){let je=_[Ve+it];for(let lt=0;lt<c.outChannels;++lt)T[ge+lt]+=je*b[et+lt];et+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var y$={kernelName:au,backendName:"cpu",kernelFunc:A$};function g$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;Ie([a,s],"conv3dBackpropFilterV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=F.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new Ot(h.filterShape,"float32"),w=g.values,[x,_,b,T]=g.strides,S=n.data.get(s.dataId).values,[N,C,$,D]=u,O=n.data.get(a.dataId).values,[V,W,Z,K]=c,te=h.padInfo.front,J=h.padInfo.left,se=h.padInfo.top;for(let Q=0;Q<m;++Q){let le=Math.max(0,Math.ceil((te-Q)/d)),re=Math.min(h.outDepth,(h.inDepth+te-Q)/d),ce=Q*x;for(let he=0;he<A;++he){let me=Math.max(0,Math.ceil((se-he)/p)),ye=Math.min(h.outHeight,(h.inHeight+se-he)/p),ge=he*_+ce;for(let Ee=0;Ee<y;++Ee){let Re=Math.max(0,Math.ceil((J-Ee)/f)),Oe=Math.min(h.outWidth,(h.inWidth+J-Ee)/f),Ge=Ee*b+ge;for(let Ve=0;Ve<h.inChannels;++Ve){let et=Ve*T+Ge;for(let it=0;it<h.outChannels;++it){let je=0;for(let lt=0;lt<h.batchSize;++lt){let ut=lt*V,zn=lt*N;for(let tt=le;tt<re;++tt){let wn=(Q+tt*d-te)*W+ut,Zt=tt*C+zn;for(let _n=me;_n<ye;++_n){let Zn=(he+_n*p-se)*Z+wn,dn=_n*$+Zt;for(let rn=Re;rn<Oe;++rn){let Yn=(Ee+rn*f-J)*K+Zn,Sr=rn*D+dn;je+=O[Yn+Ve]*S[Sr+it]}}}}w[et+it]=je}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var x$={kernelName:wh,backendName:"cpu",kernelFunc:g$};function w$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;Ie([a],"conv3dBackpropInputV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=F.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[w,x,_,b]=c,T=n.data.get(s.dataId).values,[S,N,C,$]=u,{batchSize:D,filterDepth:O,filterHeight:V,filterWidth:W,inChannels:Z,inDepth:K,inHeight:te,inWidth:J,outChannels:se,outDepth:Q,outHeight:le,outWidth:re,strideDepth:ce,strideHeight:he,strideWidth:me}=h,ye=O-1-h.padInfo.front,ge=V-1-h.padInfo.top,Ee=W-1-h.padInfo.left;for(let Re=0;Re<D;++Re)for(let Oe=0;Oe<Z;++Oe)for(let Ge=0;Ge<K;++Ge){let Ve=Ge-ye,et=Math.max(0,Math.ceil(Ve/ce)),it=Math.min(Q,(O+Ve)/ce);for(let je=0;je<te;++je){let lt=je-ge,ut=Math.max(0,Math.ceil(lt/he)),zn=Math.min(le,(V+lt)/he);for(let tt=0;tt<J;++tt){let wn=tt-Ee,Zt=Math.max(0,Math.ceil(wn/me)),_n=Math.min(re,(W+wn)/me),Zn=0;for(let dn=et;dn<it;++dn){let rn=dn*ce-Ve;for(let Yn=ut;Yn<zn;++Yn){let Sr=Yn*he-lt;for(let bn=Zt;bn<_n;++bn){let bi=bn*me-wn,El=w*Re+x*dn+_*Yn+b*bn,hr=S*(O-1-rn)+N*(V-1-Sr)+C*(W-1-bi)+$*Oe;for(let Jn=0;Jn<se;++Jn){let dr=g[El+Jn],vi=T[hr+Jn];Zn+=dr*vi}}}}p[f*Re+m*Ge+A*je+y*tt+Oe]=Zn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var _$={kernelName:_h,backendName:"cpu",kernelFunc:w$},b$=ct(rs,e=>Math.cos(e)),v$={kernelName:rs,backendName:"cpu",kernelFunc:b$},k$=ct(Hi,e=>Math.cosh(e)),I$={kernelName:Hi,backendName:"cpu",kernelFunc:k$};function N$(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=Ue([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,x=n.data.get(a.dataId).values,_=k.computeStrides(a.shape),b=k.computeStrides(y.shape);for(let T=0;T<f;T++){let S=T*4,N=g[S],C=g[S+1],$=g[S+2],D=g[S+3],O=w[T];if(O>=u)continue;let V=m>1?($-N)*(h-1)/(m-1):0,W=A>1?(D-C)*(d-1)/(A-1):0;for(let Z=0;Z<m;Z++){let K=m>1?N*(h-1)+Z*V:.5*(N+$)*(h-1);if(K<0||K>h-1){for(let te=0;te<A;te++)for(let J=0;J<p;J++){let se=J+te*b[2]+Z*b[1]+T*b[0];y.values[se]=c}continue}if(l==="bilinear"){let te=Math.floor(K),J=Math.ceil(K),se=K-te;for(let Q=0;Q<A;Q++){let le=A>1?C*(d-1)+Q*W:.5*(C+D)*(d-1);if(le<0||le>d-1){for(let me=0;me<p;me++){let ye=me+Q*b[2]+Z*b[1]+T*b[0];y.values[ye]=c}continue}let re=Math.floor(le),ce=Math.ceil(le),he=le-re;for(let me=0;me<p;me++){let ye=me+re*_[2]+te*_[1]+O*_[0],ge=x[ye];ye=me+ce*_[2]+te*_[1]+O*_[0];let Ee=x[ye];ye=me+re*_[2]+J*_[1]+O*_[0];let Re=x[ye];ye=me+ce*_[2]+J*_[1]+O*_[0];let Oe=x[ye],Ge=ge+(Ee-ge)*he,Ve=Re+(Oe-Re)*he;ye=me+Q*b[2]+Z*b[1]+T*b[0],y.values[ye]=Ge+(Ve-Ge)*se}}}else for(let te=0;te<A;++te){let J=A>1?C*(d-1)+te*W:.5*(C+D)*(d-1);if(J<0||J>d-1){for(let le=0;le<p;le++){let re=le+te*b[2]+Z*b[1]+T*b[0];y.values[re]=c}continue}let se=Math.round(J),Q=Math.round(K);for(let le=0;le<p;le++){let re=le+se*_[2]+Q*_[1]+O*_[0],ce=le+te*b[2]+Z*b[1]+T*b[0];y.values[ce]=x[re]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var S$={kernelName:Gi,backendName:"cpu",kernelFunc:N$};function T$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;Ie(a,"cumsum");let l=F.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=ir({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=F.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=nr(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=f)for(let g=0;g<f;g++){let w=m(y,g);if(g===0)d[w]=i?0:p[w];else{let x=m(y,g-1);d[w]=i?p[x]+d[x]:p[w]+d[x]}}let A=n.makeTensorInfo(c.shape,h,d);if(l!=null){let y=F.getUndoAxesPermutation(l),g=ir({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var E$={kernelName:as,backendName:"cpu",kernelFunc:T$};function C$(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=Hm(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=ox(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${a.shape.length}.`)}var R$={kernelName:bh,backendName:"cpu",kernelFunc:C$};function F$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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r=k.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(sD(r)){let o=rA(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",k.createScalarValue(r,"float32")),d=Lr({inputs:{x:h},backend:n}),p=nA.kernelFunc({inputs:{a:c,b:h},backend:n}),f=nA.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,A=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:A}}return o}else{let o=F.mergeRealAndImagArrays(s,i),l=iD(o,r,t);return F.splitRealAndImagArrays(l)}}function sD(e){return(e&e-1)==0}function rA(e,t,n,r,a){if(n===1)return{real:e,imag:t};let 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d=Ue([c,u],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<c;m++){let A=[],y=0;for(let g=0;g<o;g++){let w=p[m*o+g];y+=w*h[g],A.push(w)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<u;g++)d.values[m*u+g]=f[y*u+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var wD={kernelName:to,backendName:"cpu",kernelFunc:xD};function _D(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;Ie([a,s],"gatherV2");let l=o;o==null&&(l=0);let c=k.sizeFromShape(s.shape),u=k.parseAxisParam(i,a.shape)[0],h=F.segment_util.collectGatherOpShapeInfo(a,s,u,l),d=wt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=wt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],m=n.bufferSync(p),A=n.bufferSync(d),y=dx(A,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var bD={kernelName:eo,backendName:"cpu",kernelFunc:_D},vD=Mt((e,t)=>e>=t?1:0),kD=qt(hs,vD,null,"bool"),ID={kernelName:hs,backendName:"cpu",kernelFunc:kD};function ND(e){let{inputs:t,backend:n}=e,{input:r}=t,a=k.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=wt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Vx(o,!0,n),c=wt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var SD={kernelName:Ch,backendName:"cpu",kernelFunc:ND},TD=ct(ao,e=>Number.isFinite(e)?1:0,"bool"),ED={kernelName:ao,backendName:"cpu",kernelFunc:TD},CD=ct(so,e=>Math.abs(e)===Infinity?1:0,"bool"),RD={kernelName:so,backendName:"cpu",kernelFunc:CD},FD=ct(io,e=>Number.isNaN(e)?1:0,"bool"),MD={kernelName:io,backendName:"cpu",kernelFunc:FD},$D=Mt((e,t)=>e<=t?1:0),DD=qt(lo,$D,null,"bool"),OD={kernelName:lo,backendName:"cpu",kernelFunc:DD};function zD(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=mx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var PD={kernelName:Fh,backendName:"cpu",kernelFunc:zD},LD=ct(uo,e=>Math.log1p(e)),WD={kernelName:uo,backendName:"cpu",kernelFunc:LD},BD=Mt((e,t)=>e&&t),VD=qt(co,BD,null,"bool"),UD={kernelName:co,backendName:"cpu",kernelFunc:VD},jD=ct(ou,e=>e?0:1,"bool"),HD={kernelName:ou,backendName:"cpu",kernelFunc:jD},GD=Mt((e,t)=>e||t),qD=qt(lu,GD,null,"bool"),XD={kernelName:lu,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;Ie(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,d=k.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%c,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,u),w=0;for(;y<=g;y++){let x=h[y];w+=x*x}return w}for(let m=0;m<d;m++){let A=f(m),y=h[m]*Math.pow(i+o*A,-l);p[m]=y}return n.makeTensorInfo(a.shape,a.dtype,p)}var ZD={kernelName:uu,backendName:"cpu",kernelFunc:KD};function YD(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;Ie(i,"LRNGrad");let h=k.sizeFromShape(i.shape),d=i.shape[3],p=n.data.get(i.dataId).values,f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let w=g%d,x=g-w+Math.max(0,w-o),_=g-w+Math.min(d,w+o+1),b=0;for(let T=x;T<_;T++)b+=Math.pow(f[T],2);b=c*b+l;for(let T=x;T<_;T++){let S=-2*c*u*f[T]*m[g]/b;g===T&&(S+=Math.pow(b,-u)),S*=p[g],A[T]+=S}}return n.makeTensorInfo(i.shape,a.dtype,A)}var JD={kernelName:Mh,backendName:"cpu",kernelFunc:YD};function Ux(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,c=l.length,u=k.parseAxisParam(s,l),h=u,d=F.getAxesPermutation(h,c),p=o.data.get(a.dataId).values;if(d!=null){let x=new Array(c);for(let _=0;_<x.length;_++)x[_]=l[d[_]];p=Xm(p,l,a.dtype,d,x),h=F.getInnerMostAxes(h.length,c),l=x}Ie(a,"max"),F.assertAxesAreInnerMostDims("max",h,c);let[f,m]=F.computeOutAndReduceShapes(l,h),A=k.sizeFromShape(m),y=yx(p,A,f,a.dtype),g=o.write(y,f,a.dtype),w=f;return i&&(w=F.expandShapeToKeepDim(f,u)),{dataId:g,shape:w,dtype:a.dtype}}var QD={kernelName:fs,backendName:"cpu",kernelFunc:Ux};function eO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Ie(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(F.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. 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h=F.computePool3DInfo(s.shape,i,o,c,l,u),d=n.bufferSync(s),p=jM(d,h),f=h.strideDepth,m=h.strideHeight,A=h.strideWidth,y=h.dilationDepth,g=h.dilationHeight,w=h.dilationWidth,x=h.effectiveFilterDepth,_=h.effectiveFilterHeight,b=h.effectiveFilterWidth,T=x-1-h.padInfo.front,S=b-1-h.padInfo.left,N=_-1-h.padInfo.top,C=Ue(s.shape,"float32"),$=n.bufferSync(a);for(let D=0;D<h.batchSize;++D)for(let O=0;O<h.inChannels;++O)for(let V=0;V<h.inDepth;++V)for(let W=0;W<h.inHeight;++W)for(let Z=0;Z<h.inWidth;++Z){let K=V-T,te=W-N,J=Z-S,se=0;for(let Q=0;Q<x;Q+=y){let le=(K+Q)/f;if(!(le<0||le>=h.outDepth||Math.floor(le)!==le))for(let re=0;re<_;re+=g){let ce=(te+re)/m;if(!(ce<0||ce>=h.outHeight||Math.floor(ce)!==ce))for(let he=0;he<b;he+=w){let me=(J+he)/A;if(me<0||me>=h.outWidth||Math.floor(me)!==me)continue;let ye=x*_*b-1-p.get(D,le,ce,me,O),ge=Q*_*b+re*b+he,Ee=ye===ge?1:0;Ee!==0&&(se+=$.get(D,le,ce,me,O)*Ee)}}}C.set(se,D,V,W,Z,O)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var sO={kernelName:Dh,backendName:"cpu",kernelFunc:aO};function iO(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=F.computePool2DInfo(o.shape,l,c,1,u,h),p=n.data.get(o.dataId).values,f=Ue(d.outShape,o.dtype,zx(p,o.shape,o.dtype,d).values),m=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,w=d.effectiveFilterHeight,x=d.effectiveFilterWidth,_=x-1-d.padInfo.left,b=w-1-d.padInfo.top,T=Ue(o.shape,"float32"),S=n.data.get(a.dataId).values,N=Ue(a.shape,"float32",S);for(let C=0;C<d.batchSize;++C)for(let $=0;$<d.inChannels;++$)for(let D=0;D<d.inHeight;++D)for(let O=0;O<d.inWidth;++O){let V=D-b,W=O-_,Z=0;for(let K=0;K<w;K+=y){let te=(V+K)/m;if(!(te<0||te>=d.outHeight||Math.floor(te)!==te))for(let J=0;J<x;J+=g){let se=(W+J)/A;if(se<0||se>=d.outWidth||Math.floor(se)!==se)continue;let Q=w*x-1-f.get(C,te,se,$),le=K*x+J,re=Q===le?1:0;re!==0&&(Z+=N.get(C,te,se,$)*re)}}T.set(Z,C,D,O,$)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var oO={kernelName:$h,backendName:"cpu",kernelFunc:iO};function lO(e,t,n,r,a){let s=k.computeStrides(t),i=eA(e,t,n,s,a,"max"),o=zx(e,t,n,a,!0,r);return[i.values,o.values]}var uO={kernelName:Oh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;Ie(r,"MaxPoolWithArgmax");let c=l.data.get(r.dataId).values,u=F.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=lO(c,r.shape,r.dtype,o,u),p=l.write(h,u.outShape,r.dtype),f=l.write(d,u.outShape,r.dtype);return[{dataId:p,shape:u.outShape,dtype:r.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function Hd(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Ie(a,"sum");let o;a.dtype==="bool"?o=Ca({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Lr({inputs:{x:a},backend:n});let l=o.shape.length,c=k.parseAxisParam(s,o.shape),u=F.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=ir({inputs:{x:o},backend:n,attrs:{perm:u}}),h=F.getInnerMostAxes(h.length,l)),F.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=F.computeOutAndReduceShapes(d.shape,h),m=F.upcastType(d.dtype,"int32"),A=Ud(n,p,m),y=k.sizeFromShape(f),g=n.data.get(A.dataId).values,w=n.data.get(d.dataId).values;for(let x=0;x<g.length;++x){let _=x*y,b=0;for(let T=0;T<y;++T)b+=w[_+T];g[x]=b}if(i){let x=F.expandShapeToKeepDim(A.shape,c),_=A;A=wt({inputs:{x:A},backend:n,attrs:{shape:x}}),n.disposeIntermediateTensorInfo(_)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),A}var cO={kernelName:$s,backendName:"cpu",kernelFunc:Hd};function hO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=k.parseAxisParam(s,a.shape),l=F.computeOutAndReduceShapes(a.shape,o)[1],c=k.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let d=Ca({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});u.push(d);let p=tA({inputs:{a:d,b:h},backend:n});u.push(p);let f=Hd({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var dO={kernelName:ys,backendName:"cpu",kernelFunc:hO};function pO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;Ie(a,"min");let o=k.parseAxisParam(s,a.shape),l=o,c=F.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=ir({inputs:{x:a},backend:n,attrs:{perm:c}}),l=F.getInnerMostAxes(l.length,a.shape.length)),F.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,d]=F.computeOutAndReduceShapes(u.shape,l),p=k.sizeFromShape(d),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let x=0;x<p;++x){let _=m[g+x];_<w&&(w=_)}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=F.expandShapeToKeepDim(h,o),g=wt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var fO={kernelName:gs,backendName:"cpu",kernelFunc:pO};function mO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;Ie(a,"mirrorPad");let o=s.map((g,w)=>g[0]+a.shape[w]+g[1]),l=s.map(g=>g[0]),c=s.map((g,w)=>g[0]+a.shape[w]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=k.computeStrides(a.shape),f=k.sizeFromShape(o),m=o.length,A=k.computeStrides(o),y=k.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let w=k.indexToLoc(g,m,A);for(let _=0;_<m;_++)w[_]<l[_]?w[_]=l[_]*2-w[_]-u:w[_]>=c[_]&&(w[_]=(c[_]-1)*2-w[_]+u);w=w.map((_,b)=>_-l[b]);let x=k.locToIndex(w,d,p);y[g]=h[x]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var AO={kernelName:hu,backendName:"cpu",kernelFunc:mO},yO=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),gO=qt(ho,yO),xO={kernelName:ho,backendName:"cpu",kernelFunc:gO},wO=Xo(I8());function jx(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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l=o?a:jx({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],h=n.data.get(l.dataId).values,d=[c,s],p=k.makeZerosTypedArray(k.sizeFromShape(d),"int32");for(let f=0;f<c;++f){let m=f*u,A=new Float32Array(u-1);A[0]=h[m];for(let w=1;w<A.length;++w)A[w]=A[w-1]+h[m+w];let y=wO.alea(i.toString()),g=f*s;for(let w=0;w<s;++w){let x=y();p[g+w]=A.length;for(let _=0;_<A.length;_++)if(x<A[_]){p[g+w]=_;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var vO={kernelName:zh,backendName:"cpu",kernelFunc:bO},kO=$r.nonMaxSuppressionV3Impl;function IO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;Ie(a,"NonMaxSuppression");let c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=kO(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var NO={kernelName:mo,backendName:"cpu",kernelFunc:IO},SO=$r.nonMaxSuppressionV4Impl;function TO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;Ie(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=SO(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var EO={kernelName:Ao,backendName:"cpu",kernelFunc:TO},CO=$r.nonMaxSuppressionV5Impl;function RO(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;Ie(a,"NonMaxSuppressionWithScore");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=CO(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var FO={kernelName:yo,backendName:"cpu",kernelFunc:RO};function MO(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;Ie(a,"oneHot");let l=k.sizeFromShape(a.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(a.dataId).values;for(let h=0;h<l;++h)u[h]>=0&&u[h]<s&&(c[h*s+u[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",c)}var $O={kernelName:_s,backendName:"cpu",kernelFunc:MO};function Gd(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let a=ri({inputs:{input:r},backend:n}),s=Gd({inputs:{x:a},backend:n}),i=ol({inputs:{input:r},backend:n}),o=Gd({inputs:{x:i},backend:n}),l=En({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return aA({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var DO={kernelName:Do,backendName:"cpu",kernelFunc:Gd};function 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}
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ivec3 outCoordsFromFlatIndex(int index) {
${li(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}},fP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=nc.DENSE;let t=ac(e),n=ln();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${li(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}},mP=class{constructor(e){this.variableNames=["A"],this.outTexUsage=qn.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
${ww}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},AP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=qn.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
${ww}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},yP=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=ln(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${hA(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
vec4 values = ${r.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${r.output} = vec4(${i}, 0., 0., 0.);
}
`}},gP=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=ln(),[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};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
values = ${r.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${hA(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${r.output} = ${o};
}
`}},s0={};Pe(s0,{bindVertexProgramAttributeStreams:()=>Ew,createBufferFromOutputTexture:()=>Fw,createFloat16MatrixTexture:()=>Iw,createFloat16PackedMatrixTexture:()=>Tw,createFloat32MatrixTexture:()=>kw,createIndexBuffer:()=>vw,createPackedMatrixTexture:()=>Sw,createUnsignedBytesMatrixTexture:()=>Nw,createVertexBuffer:()=>bw,createVertexShader:()=>_w,downloadByteEncodedFloatMatrixFromOutputTexture:()=>$w,downloadFloat32MatrixFromBuffer:()=>Mw,downloadMatrixFromPackedOutputTexture:()=>Ow,downloadPackedMatrixFromBuffer:()=>Dw,getInternalFormatForFloat16MatrixTexture:()=>pA,getInternalFormatForFloat16PackedMatrixTexture:()=>AA,getInternalFormatForFloat32MatrixTexture:()=>dA,getInternalFormatForPackedMatrixTexture:()=>mA,getInternalFormatForUnsignedBytesMatrixTexture:()=>fA,uploadDenseMatrixToTexture:()=>Cw,uploadPixelDataToTexture:()=>Rw});function _w(e){let t=ln(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return Jx(e,n)}function bw(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 nw(e,t)}function vw(e){let t=new Uint16Array([0,1,2,2,1,3]);return rw(e,t)}function sc(e,t,n,r,a,s){sw(t,n);let i=aw(e),o=e.TEXTURE_2D;return ve(e,()=>e.bindTexture(o,i)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ve(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),ve(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function dA(e){return e.internalFormatFloat}function kw(e,t,n,r){let[a,s]=rc(t,n);return sc(e,a,s,dA(r),r.textureFormatFloat,e.FLOAT)}function pA(e){return e.internalFormatHalfFloat}function Iw(e,t,n,r){let[a,s]=rc(t,n);return sc(e,a,s,pA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function fA(e){return e.downloadTextureFormat}function Nw(e,t,n,r){let[a,s]=rc(t,n);return sc(e,a,s,fA(r),e.RGBA,e.UNSIGNED_BYTE)}function mA(e){return e.internalFormatPackedFloat}function Sw(e,t,n,r){let[a,s]=cl(t,n);return sc(e,a,s,mA(r),e.RGBA,e.FLOAT)}function AA(e){return e.internalFormatPackedHalfFloat}function Tw(e,t,n,r){let[a,s]=cl(t,n);return sc(e,a,s,AA(r),e.RGBA,r.textureTypeHalfFloat)}function Ew(e,t,n){let r=0,a=3*4,s=3*4+2*4;return ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),sA(e,t,"clipSpacePos",n,3,s,r)&&sA(e,t,"uv",n,2,s,a)}function Cw(e,t,n,r,a,s){ve(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),ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Rw(e,t,n){ve(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ve(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ve(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Fw(e,t,n,r){let a=e.createBuffer();ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return ve(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ve(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Mw(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 $w(e,t,n,r){let[a,s]=rc(t,n),i=4,o=new Uint8Array(sP(t*n,i));return ve(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Dw(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(iP(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 Ow(e,t,n){let r=new Float32Array(t*n*4);return ve(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var rm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,nm(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Qu(this.gl,a),Gn(this.gl,s))this.textureHalfFloatExtension=Qu(this.gl,s);else if(ee().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),Gn(this.gl,r))this.colorBufferHalfFloatExtension=Qu(this.gl,r);else if(ee().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",Gn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Gn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=bw(this.gl),this.indexBuffer=vw(this.gl),this.framebuffer=iw(this.gl),this.textureConfig=uA(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().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;ve(e,()=>e.finish()),ve(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ve(e,()=>e.deleteFramebuffer(this.framebuffer)),ve(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ve(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ve(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),kw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Iw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Nw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Rw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Cw(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Tw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Sw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(iA(this.gl,this.framebuffer),this.outputTexture=null),ve(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$w(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Dw(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Mw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Fw(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(ee().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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ow(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Qx(t,e),r=_w(t),a=ew(t);return ve(t,()=>t.attachShader(a,r)),ve(t,()=>t.attachShader(a,n)),tw(t,a),this.debug&&qd(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Ew(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ve(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&qd(this.gl,this.program),ve(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?lw(this.gl,e,t):uw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ve(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(),cw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=cl(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&&qd(this.gl,this.program),ec(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ve(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ve(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Qu(this.gl,ee().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(ee().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(ee().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().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=xP(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Xd(this.gl,e,this.framebuffer),this.debug&&ec(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Xd(this.gl,this.outputTexture,this.framebuffer),this.debug&&ec(this.gl)):iA(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;Xd(r,e,this.framebuffer),this.debug&&ec(r),this.outputTexture=e,ve(r,()=>r.viewport(0,0,t,n)),ve(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ve(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 xP(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:zw}=F;function TP(e,t,n,r){let a=[];e.forEach(p=>{let f=k.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=>wP(p,t,r)).join(`
`),o=t.texShape,l=ln(),c=vP(l),u,h,d=NP(l);return t.isPacked?(u=_P(t.logicalShape,o),h=IP(l)):(u=bP(t.logicalShape,o),h=kP(l)),r&&(d+=SP),[d,c,h,s,u,i,n].join(`
`)}function hl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return EP(e);case 1:return CP(e);case 2:return RP(e);case 3:return FP(e);case 4:return MP(e);case 5:return $P(e);case 6:return DP(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Pw(e){switch(e.shapeInfo.logicalShape.length){case 0:return OP(e);case 1:return zP(e);case 2:return PP(e);case 3:return LP(e);default:return WP(e)}}function wP(e,t,n=!1){let r="";n?r+=Pw(e):r+=hl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=BP(e,t):r+=VP(e,t)),r}function _P(e,t){switch(e.length){case 0:return Lw();case 1:return UP(e,t);case 2:return GP(e,t);case 3:return jP(e,t);default:return HP(e,t)}}function bP(e,t){switch(e.length){case 0:return Lw();case 1:return qP(e,t);case 2:return JP(e,t);case 3:return XP(e,t);case 4:return KP(e,t);case 5:return ZP(e,t);case 6:return YP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function vP(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function kP(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function IP(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function NP(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${QP}
${eL}
${tL}
`}var QP=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,eL=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,tL=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,SP=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function Lw(){return`
int getOutputCoords() {
return 0;
}
`}function UP(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);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function qP(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function jP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function XP(e,t){let n=li(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function HP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${o});
}
`}function KP(e,t){let n=li(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function ZP(e,t){let n=li(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function YP(e,t){let n=li(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function GP(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let r=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function JP(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function ui(e){return`offset${e}`}function OP(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=ln();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function EP(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=ui(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function zP(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=ln();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function CP(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${dl(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=ui(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${t}, uv);
}
`:a===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function PP(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=ln();if(a!=null&&k.arraysEqual(t,a))return`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function RP(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&&k.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}=k.squeezeShape(t),o=s;if(o.length<t.length){let h=pl(e,o),d=["row","col"];return`
${hl(h)}
float ${r}(int row, int col) {
return ${r}(${fl(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${dl(e)}
}
`;let l=a[0],c=a[1],u=ui(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) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function LP(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=pl(e,h),f=["b","row","col"];return`
${Pw(p)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${fl(f,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=ln();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function FP(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}=k.squeezeShape(t),l=i;if(l.length<t.length){let f=pl(e,l),m=["row","col","depth"];return`
${hl(f)}
float ${r}(int row, int col, int depth) {
return ${r}(${fl(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)));
${dl(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let p=ui(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 WP(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=ln();return`
vec4 ${a}(${h}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${p.texture2D}(${r}, uv);
}
`}function MP(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}=k.squeezeShape(t);if(o.length<t.length){let f=pl(e,o),m=["row","col","depth","depth2"];return`
${hl(f)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${fl(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)));
${dl(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(d===a&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let p=ui(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} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
return sampleTexture(${n}, uv);
}
`}function $P(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}=k.squeezeShape(t);if(l.length<t.length){let m=pl(e,l),A=["row","col","depth","depth2","depth3"];return`
${hl(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${fl(A,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${dl(e)}
}
`;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(p===a&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let f=ui(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} +
depth2 * ${a} + depth3 + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function DP(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=k.squeezeShape(t);if(a.length<t.length){let A=pl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${hl(A)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${fl(y,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 ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${dl(e)}
}
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(f===i&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let m=ui(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.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${p}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function dl(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function BP(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=zw(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${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=k.sizeFromShape(e.shapeInfo.logicalShape)===1,m=k.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`
vec4 ${a}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${r}(${d});
${p}
}
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float ${a}() {
return sampleTexture(${n}, resultUV);
}
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${zL(t)}
${hA(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
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`}};function zL(e){return`
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${t}
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void main() {
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float y = unaryOperation(x);
setOutput(y);
}
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return (x < 0.0) ? 0.0 : min(6.0, x);
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vec4 result;
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result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
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vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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`,KL=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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result.b = isNaN.b ? x.b : result.b;
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`,ml=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
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`}},ZL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=un("rc",t),r=ft(t),a=RL(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
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vec4 packedInput = getA(${a});
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Please use tf.complex(real, imag).");let r={};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:qn.UPLOAD,refCount:1,complexParentRefCount:0}),r}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--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,r){if(ee().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:qn.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}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 ml(i,Qd):h=new Ra(i,Qd);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=k.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=F.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.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 ml(r,Qd):p=new Ra(r,Qd);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(!ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ee().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"&&ee().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=F.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=k.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),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Zx(n))throw ee().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=k.sizeFromShape(t);if(ee().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...ac(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=ee().getBool("WEBGL_PACK")&&r===!0,i=s?Kd(t):t,o=s?new AP(i):new mP(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}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 ee().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Wn().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=tW){let n=this.getCPUBackend();return!ee().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&&k.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){F.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return YL(e.shape,t)}packedUnaryOp(e,t,n){let r=new ml(e.shape,t);return this.compileAndRun(r,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=Vw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(ee().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Xw,e.dtype);let t=new Ra(e.shape,Xw);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let a=n.map(s=>k.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 Wn().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new ZL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new DL(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[si(e.shape),...ii(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[si(t),...ii(t)],s=new jw(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=Kd(r),i;n?i=new fP(s):i=new pP(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===nc.DENSE){let f=ac(e.outputShape);i.texShape=f.map(m=>m*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let m=this.texData.get(f.dataId);if(m.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=ee().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:m.values};e.packedInputs&&(m.isPacked=!0,m.shape=f.shape)}else if(!!m.isPacked!=!!e.packedInputs)f=m.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),m=this.texData.get(f.dataId);else if(m.isPacked&&!tc(m.shape,f.shape)){let A=f,y=f.shape;f.shape=m.shape,f=this.packedReshape(f,y),o.push(f),m=this.texData.get(f.dataId),A.shape=y}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:m,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=aL(e,l,c),h=this.getAndSaveBinary(u,()=>nL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;if(d&&(p=this.startTimer()),rL(this.gpgpu,h,l,c,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)})),!ee().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){n=n||t[0].dtype;let s=this.runWebGLProgram(e,t,n,r,a);return Wn().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ee().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=j(()=>{if(!ee().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ee().getBool("DEBUG");ee().set("DEBUG",!1);let t=this.abs(Te(1e-8)).dataSync()[0];if(ee().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?JL:QL}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=k.now());let u=t.texShape;if(u==null&&(u=dw(n,o),t.texShape=u),a!=null){let h=Kd(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=cl(u[0],u[1]),d=new gP(h,[f,p],m)):d=new yP(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=qn.PIXELS:this.texData.get(A.dataId).usage=qn.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+=k.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=aW(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]*k.bytesPerElement(t)}};function aW(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 i0="2.8.5";function o0(){ee().set("WEBGL_FORCE_F16_TEXTURES",!0)}Hh.isBrowser()&&xu("webgl",()=>new am,2);var G4={forceHalfFloat:o0},Kw=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Al=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
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`}},ep=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,ic=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=F.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||k.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
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${ft(a)} coords = getOutputCoords();
`,a===1)s+=`
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bool nextRowOutOfBounds =
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bool nextColOutOfBounds =
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vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
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vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function Cn(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 sW={kernelName:ro,backendName:"webgl",kernelFunc:Cn};function Fa(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=Cn({inputs:{x:r},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Cn({inputs:{x:a},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var iW={kernelName:gh,backendName:"webgl",kernelFunc:Fa},Zw="return (a < 0.) ? b * a : a;",Yw=`
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vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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if (isnan(a)) return a;
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result.r = isNaN.r > 0. ? NAN : result.r;
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result.a = isNaN.a > 0. ? NAN : result.a;
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}`: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=`
${m}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${g};
int batchB = ${w};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${p[0]} * ${f[0]});
result += (${p[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${A}
setOutput(result);
}
`}},n_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},r_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},a_="return a * b;";function s_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=F.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new r_(n_.REAL,r.shape,a.shape),u=new r_(n_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Fa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=wL(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new ic(a_,r.shape,a.shape):i=new Al(a_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var pW={kernelName:ws,backendName:"webgl",kernelFunc:s_};function fW(e,t,n){let r=[si(e.shape),...ii(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[si(t),...ii(t)],i=new jw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function we(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=k.sizeFromShape(a.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${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&&!tc(a.shape,l)&&!(u.texture!==null&&tc(u.shape,l))?fW(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var mW={kernelName:bo,backendName:"webgl",kernelFunc:we},i_=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 * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},AW=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
}
`,d="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${u===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${u===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function yW(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=F.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ci(e,t,n,r){let a=yW(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 i_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new i_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new AW({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 xW=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=ft(this.rank),a=gW(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 wW=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 ${this.rank} is not yet supported.`);let r=ft(this.rank),a=Uw("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${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.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function np(e,t,n){let r=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wW(e.shape,t):new xW(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function _W(e,t,n,r){let a=t,s=e.shape.length,i=k.parseAxisParam(a,e.shape),o=i,l=F.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=np(e,l,r),o=F.getInnerMostAxes(o.length,s)),F.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=F.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=F.expandShapeToKeepDim(h,i));let f=k.sizeFromShape(d),m=k.sizeFromShape(e.shape)/f,A=we({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=jh(e.dtype),g=ci(A,y,"sum",r),w=we({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function xA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return _W(a,s,i,n)}var bW={kernelName:$s,backendName:"webgl",kernelFunc:xA};function An(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=yA(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=np(a,s,i);return c}var vW={kernelName:Ls,backendName:"webgl",kernelFunc:An},o_=1e3;function rp({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=k.sizeFromShape(m),g=k.sizeFromShape(A),w=y===g||y===1||g===1;k.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 x=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);k.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=${r} must match.`);let _=n?[y,h,p]:[y,p,h],b=r?[g,f,d]:[g,d,f],T=we({inputs:{x:e},backend:a,attrs:{shape:_}}),S=we({inputs:{x:t},backend:a,attrs:{shape:b}}),N=[T,S],C=Math.max(y,g),$=n?T.shape[1]:T.shape[2],D=s!=null,O=i!=null,V=l==="leakyrelu",W=l!=null?tp(l,!0):null,Z=D||O||V||W!=null,K;if((p===1||f===1)&&$>o_&&Z===!1){let J=T,se=S;n&&(J=An({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),N.push(J)),r&&(se=An({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),N.push(se));let Q=f!==1,le=f===1,re=J;Q&&(re=we({inputs:{x:J},backend:a,attrs:{shape:[C,$,1]}}),N.push(re));let ce=f===1?2:1,he=se;le&&(he=we({inputs:{x:se},backend:a,attrs:{shape:[C,1,$]}}),N.push(he));let me=s_({inputs:{a:re,b:he},backend:a});K=xA({inputs:{x:me},backend:a,attrs:{axis:ce,keepDims:!0}}),N.push(me)}else{let J=nr(e.dtype,t.dtype),se=new t_(_,b,[C,p,f],n,r,D,W,O,V),Q=[T,S];if(s!=null&&Q.push(s),O&&Q.push(i),V){let le=a.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(le),N.push(le)}K=a.runWebGLProgram(se,Q,J)}let te=we({inputs:{x:K},backend:a,attrs:{shape:x}});N.push(K);for(let J of N)a.disposeIntermediateTensorInfo(J);return te}function kW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return rp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var IW={kernelName:Ws,backendName:"webgl",kernelFunc:kW},l_="return abs(x);";function NW(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Vw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new ml(r.shape,l_):a=new Ra(r.shape,l_),n.runWebGLProgram(a,[r],r.dtype)}var SW={kernelName:Di,backendName:"webgl",kernelFunc:NW},TW=xr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,EW=Qe({opSnippet:TW}),CW={kernelName:Oi,backendName:"webgl",kernelFunc:EW},RW=xr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,FW=Qe({opSnippet:RW}),MW={kernelName:zi,backendName:"webgl",kernelFunc:FW},u_="return a + b;",$W=Qt({opSnippet:u_,packedOpSnippet:u_,supportsComplex:!0,cpuKernelImpl:sL}),DW={kernelName:fa,backendName:"webgl",kernelFunc:$W},OW=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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(`
`)}
float result = ${r};
setOutput(result);
}
`}},zW=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function ap(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Cn({inputs:{x:r[0]},backend:n});if(r.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=ap({inputs:r.slice(0,o),backend:n}),c=ap({inputs:r.slice(o),backend:n});return ap({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>nr(o,l)),s=r.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new zW(r[0].shape,s):new OW(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var PW={kernelName:Za,backendName:"webgl",kernelFunc:ap};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=F.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=F.getInnerMostAxes(c.length,o)),F.assertAxesAreInnerMostDims("all",c,o);let[d,p]=F.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=we({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=ci(m,m.dtype,"all",n),y;if(i){let g=F.expandShapeToKeepDim(d,l);y=we({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=we({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var WW={kernelName:ph,backendName:"webgl",kernelFunc:LW};function BW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=F.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=F.getInnerMostAxes(c.length,o)),F.assertAxesAreInnerMostDims("any",c,o);let[d,p]=F.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=we({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=ci(m,m.dtype,"any",n),y;if(i){let g=F.expandShapeToKeepDim(d,l);y=we({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=we({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var VW={kernelName:fh,backendName:"webgl",kernelFunc:BW},UW=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];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},jW=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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=ft(o),c=un("coords",o),u,h;if(s===1){h=o+1;let T=ft(h);u=`
${T} sourceLocR = ${T}(${c.join()}, 0);
++${c[o-1]};
${T} sourceLocG = ${T}(${c.join()}, 0);
++${c[o-2]};
${T} sourceLocA = ${T}(${c.join()}, 0);
--${c[o-1]};
${T} sourceLocB = ${T}(${c.join()}, 0);
--${c[o-2]};`}else h=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${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(T=>"int "+T),m=un("sourceLocR",h-1).concat("inIdx.r"),A=un("sourceLocG",h-1).concat("inIdx.g"),y=un("sourceLocB",h-1).concat("inIdx.b"),g=un("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",x=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${g.join()})));`,_=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,b=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${b}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
sourceLocB${p}, sourceLocA${p}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${_};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${_};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function c_(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=F.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new UW(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=c_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function h_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=F.computeOptimalWindowSize(s),o=new jW(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=h_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function d_(e,t,n,r){let a=[n];if(F.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=F.computeOutAndReduceShapes(t.shape,a),l=k.sizeFromShape(o),c=we({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=c_(e,c,r);s.push(u);let h=we({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return h_(e,t,r)}function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=F.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=An({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=d_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var GW={kernelName:Ya,backendName:"webgl",kernelFunc:HW};function qW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=F.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=An({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=d_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var XW={kernelName:eu,backendName:"webgl",kernelFunc:qW},KW=xr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,ZW=Qe({opSnippet:KW}),YW={kernelName:Pi,backendName:"webgl",kernelFunc:ZW},JW=xr+"return log(x + sqrt(x * x + 1.0));",QW=Qe({opSnippet:JW}),eB={kernelName:Li,backendName:"webgl",kernelFunc:QW},tB=xr+`
return atan(x);
`,nB=Qe({opSnippet:tB}),rB={kernelName:Wi,backendName:"webgl",kernelFunc:nB},aB=hW+`
return atan(a, b);
`,sB=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+dW+`
return result;
`,iB=Qt({opSnippet:aB,packedOpSnippet:sB}),oB={kernelName:Vi,backendName:"webgl",kernelFunc:iB},lB=xr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,uB=Qe({opSnippet:lB}),cB={kernelName:Bi,backendName:"webgl",kernelFunc:uB},oc=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let T=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${T} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?m:A:`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 x=Math.floor(s/4)*4,_=s%4,b=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${g}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${b}
}
int xC = xCCorner + ${x};
if (${_===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${b}
} else if (${_===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${b}
} else if (${_===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${b}
}
}
setOutput(${w});
}
`}},wA=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 N=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${h}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let b=Math.floor(s/4)*4,T=s%4,S=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
const float initializationValue = ${w};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${w});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC += 4) {
int xC = xCCorner + wC * ${h};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
);
${S}
}
int xC = xCCorner + ${b};
if (${T===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${S}
} else if (${T===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${S}
} else if (${T===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
initializationValue
);
${S}
}
}
setOutput(${_});
}
}
`}};function hB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ul(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(F.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=F.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Cn({inputs:{x:a},backend:n});let h=new oc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var dB={kernelName:Ja,backendName:"webgl",kernelFunc:hB};function pB(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=F.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new wA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var fB={kernelName:tu,backendName:"webgl",kernelFunc:pB},mB=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});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},AB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${p}, ${f}, ${m});
const float avgMultiplier = float(${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${a}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${h};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function yB(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=F.computePool3DInfo(i.shape,o,l,h,c,u),p=new AB(d);return n.runWebGLProgram(p,[a],i.dtype)}var gB={kernelName:Ah,backendName:"webgl",kernelFunc:yB};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ul([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=F.computePool2DInfo(i.shape,o,l,1,c),h=new mB(u);return n.runWebGLProgram(h,[a],i.dtype)}var wB={kernelName:mh,backendName:"webgl",kernelFunc:xB};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return rp({a,b:s,transposeA:i,transposeB:o,backend:n})}var bB={kernelName:Qa,backendName:"webgl",kernelFunc:_B},vB=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},kB=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},IB=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;k.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.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=ee().getBool("WEBGL_PACK_NORMALIZATION")?new kB(r.shape,a.shape,s.shape,u,h,l):new vB(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},NB={kernelName:cs,backendName:"webgl",kernelFunc:IB},TB=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=`uniform int start[${this.rank}];`,r=SB(this.rank),a,s=e.map((i,o)=>`sourceLoc.${_A[o]} = start[${o}] + coords.${_A[o]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${a}
setOutput(getSource(${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)}}},_A=["x","y","z","w","u","v"];function SB(e){if(e===1)return"sourceLoc";if(e<=6)return _A.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var EB=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ft(this.rank),n=un("coords",this.rank),r=un("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${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]};
result.y = ${s};
--${r[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${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 CB(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.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=sn.computeFlatOffset(t,k.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 lc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=sn.parseSliceParams(a,s,i);if(sn.assertParamsValid(a,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=IL(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=sn.isSliceContinous(a.shape,o,l);if(c||!u){let h=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new EB(l):new TB(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),CB(a,o,l,n)}var RB={kernelName:No,backendName:"webgl",kernelFunc:lc},FB=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;k.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=F.getReshaped(a.shape,s,o),c=F.getPermuted(l.length,s.length),u=F.getReshapedPermuted(a.shape,s,o),h=F.getSliceBeginCoords(i,s.length),d=F.getSliceSize(u,i,s.length),p=[],f=we({inputs:{x:a},backend:n,attrs:{shape:l}}),m=An({inputs:{x:f},backend:n,attrs:{perm:c}}),A=we({inputs:{x:m},backend:n,attrs:{shape:u}}),y=lc({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},MB={kernelName:nu,backendName:"webgl",kernelFunc:FB};function $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Bw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var DB={kernelName:yh,backendName:"webgl",kernelFunc:$B},OB="return float(a != b);",p_=Qt({opSnippet:OB,dtype:"bool"}),zB={kernelName:fo,backendName:"webgl",kernelFunc:p_};function uc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Cn({inputs:{x:a.complexTensorInfos.real},backend:n})}var PB={kernelName:Ph,backendName:"webgl",kernelFunc:uc},LB="return float(int(x));";function WB(e,t){let n=new Ra(e.shape,LB),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function bA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Cn({inputs:{x:a},backend:n});let i=Ct(a.shape),o=bA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Fa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=uc({inputs:{input:a},backend:n}),o=bA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Cn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return WB(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=p_({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 BB={kernelName:es,backendName:"webgl",kernelFunc:bA},f_="return ceil(x);",VB=Qe({opSnippet:f_,packedOpSnippet:f_,cpuKernelImpl:oL}),UB={kernelName:Ui,backendName:"webgl",kernelFunc:VB},jB=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
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)}}},HB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
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 GB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;ee().getBool("WEBGL_PACK_CLIP")?o=new HB(a.shape):o=new jB(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var qB={kernelName:ma,backendName:"webgl",kernelFunc:GB},XB=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function m_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function KB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new XB(r.shape),i=[m_(r,a.complexTensorInfos.real),m_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var ZB={kernelName:ru,backendName:"webgl",kernelFunc:KB},YB=class{constructor(e){this.outputShape=[],this.outputShape=F.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;
int yC = coords.y;
${n.join(`
`)}
}
`}},JB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=F.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ft(r),s=un("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${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}(${sp(i,l,m)}),
vec2(${sp(c,l,m)}));
}`}let d=o.length,p=o[o.length-1];h+=`
return getChannel(
getT${d}(${sp(i,l,p)}),
vec2(${sp(c,l,p)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[r-1]} = ${s[r-1]} + 1;
if (${s[r-1]} < ${n[r-1]}) {
result.g = getValue(${s});
}
${s[r-2]} = ${s[r-2]} + 1;
if (${s[r-2]} < ${n[r-2]}) {
result.a = getValue(${s});
}
${s[r-1]} = ${s[r-1]} - 1;
if (${s[r-2]} < ${n[r-2]} &&
${s[r-1]} < ${n[r-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function sp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function ip(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Cn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var QB={kernelName:Rh,backendName:"webgl",kernelFunc:ip};function yl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>uc({inputs:{input:f},backend:n})),u=e.map(f=>ip({inputs:{input:f},backend:n})),h=yl(c,t,n),d=yl(u,t,n),p=Fa({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}=A_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=lL(h,u,r,d),f=F.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=yl(e.slice(0,c),t,n),h=yl(e.slice(c),t,n),d=yl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new JB(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=A_(e,t,n),i=new YB(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=we({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function A_(e,t,n){let r=F.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>we({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function y_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=F.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Cn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return F.assertParamsConsistent(l,s),yl(o,s,n)}var eV={kernelName:ji,backendName:"webgl",kernelFunc:y_},g_=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="",x="";n&&(r?w=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?w=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:w=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${w}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${g}];
ivec2 xRCCorner =
ivec2(coords[${A}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${p}) *
getW(wR, wC, ${p}, d2);
} else {
dotProd +=
getX(batch, ${p}, xR, xC) *
getW(wR, wC, ${p}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2),
getW(wR, wC, ${p} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1),
getX(batch, xR, xC, ${p} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC),
getX(batch, ${p} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${_}
${x}
setOutput(result);
}
`}},tV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${p}) *
getW(wF, wR, wC, ${p}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1),
getX(batch, xF, xR, xC, ${p} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2),
getW(wF, wR, wC, ${p} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},nV=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=ln(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let x=0;x<=1;x++)for(let _=0;_<=1;_++)w+=`
blockIndex = rc.y + ${_};
pos = rc.x + ${x};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
d0 = offsetY + ${u} * (pos / ${f});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${a}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${x*2+_}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${x*2+_}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${w}
${m.output} = result;
}
`}};function x_({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>o_,w=l[2]%2!=0&&!!c.isPacked;if(g||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let x=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=we({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),b=we({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),T=rp({a:_,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=we({inputs:{x:T},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(b),y.push(T)}else{let x=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(tc(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let T=we({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(T);let S=rp({a:_,b:T,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=r.texData.get(S.dataId);k.assert(N.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,N.shape=n.outShape,A=Cn({inputs:{x:S},backend:r}),A.shape=n.outShape,y.push(S)}for(let x of y)r.disposeIntermediateTensorInfo(x);return A}function w_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,x=[],_=we({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=we({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});x.push(_),x.push(b);let T=new nV(y,_.shape,n),S=r.runWebGLProgram(T,[_],"float32"),N=we({inputs:{x:S},backend:r,attrs:{shape:[1,y[0],y[1]]}});x.push(S),x.push(N);let C=a!=null,$=s!=null,D=o==="leakyrelu",O=o?tp(o,!0):null,V=new t_(N.shape,b.shape,[1,A,n.outChannels],g,w,C,O,$,D),W=[N,b];if(a&&W.push(a),$&&W.push(s),D){let J=r.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));W.push(J),x.push(J)}let Z=r.runWebGLProgram(V,W,"float32"),K=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],te=we({inputs:{x:Z},backend:r,attrs:{shape:K}});x.push(Z);for(let J of x)r.disposeIntermediateTensorInfo(J);return te}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=F.convertConv2DDataFormat(l),d=F.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=x_({x:a,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=w_({x:a,filter:s,convInfo:d,backend:n});else{let m=new g_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=we({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var aV={kernelName:ts,backendName:"webgl",kernelFunc:rV},sV=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;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},iV=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}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = 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) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},oV=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;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${a};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},lV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${a}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function uV(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=F.convertConv2DDataFormat(l),d=F.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new sV(d);return n.runWebGLProgram(p,[a,s],"float32")}var cV={kernelName:xh,backendName:"webgl",kernelFunc:uV};function hV(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=F.convertConv2DDataFormat(c),d=F.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new iV(d);return n.runWebGLProgram(p,[a,s],"float32")}var dV={kernelName:ns,backendName:"webgl",kernelFunc:hV};function pV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=F.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new tV(c);return n.runWebGLProgram(u,[a,s],"float32")}var fV={kernelName:au,backendName:"webgl",kernelFunc:pV};function mV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=F.computeConv3DInfo(a.shape,l,i,1,o),u=new oV(c);return n.runWebGLProgram(u,[a,s],"float32")}var AV={kernelName:wh,backendName:"webgl",kernelFunc:mV};function yV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=F.computeConv3DInfo(l,s.shape,o,1,i),u=new lV(c);return n.runWebGLProgram(u,[a,s],"float32")}var gV={kernelName:_h,backendName:"webgl",kernelFunc:yV},xV=e_+`
return cos(x);
`,wV=Qe({opSnippet:xV}),_V={kernelName:rs,backendName:"webgl",kernelFunc:wV},bV=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,vV=Qe({opSnippet:bV}),kV={kernelName:Hi,backendName:"webgl",kernelFunc:vV},IV=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) * ${p}`],[g,w,x]=h>1?[`${(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() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${A};
float width_scale = ${w};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${p} ) {
setOutput(float(${a}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},NV=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new IV(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},SV={kernelName:Gi,backendName:"webgl",kernelFunc:NV},v_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${__(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() {
${ft(r)} coords = getOutputCoords();
int end = ${b_(r,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${b_(r,"coords")} = idx;
val += getX(${__(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 __(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 ${e} is not yet supported`)}function b_(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function TV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=F.getAxesPermutation([s],l),u=a;c!=null&&(u=An({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=F.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=a.shape[h],p=Cn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new v_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new v_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=F.getUndoAxesPermutation(c),m=An({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var EV={kernelName:as,backendName:"webgl",kernelFunc:TV};function CV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Bw(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=iL(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var RV={kernelName:bh,backendName:"webgl",kernelFunc:CV},FV=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];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
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 MV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.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 FV(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var $V={kernelName:qi,backendName:"webgl",kernelFunc:MV},k_=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) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?A=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:A=`
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=`
${A}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${m};
int q = d2 - d1 * ${m};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${g}
${y}
setOutput(result);
}
`}},I_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let x=0;x<p;x++)for(let _=0;_<f;_++)A+=`
vec4 xTexelR${x}C${_*2} = vec4(0.);
vec4 wR${x}C${_} = vec4(0.);
vec4 xR${x}C${_} = vec4(0.);`;for(let x=0;x<p;x++)for(let _=0;_<m;_++){let b=_*2;if(A+=`
xR = xRCorner + ${x*h};
xC = xCCorner + ${b*d};
`,u===1){if(b<f&&(l%2==1?A+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${x}C${b}.zw = vec2(0.);
}
} else {
xTexelR${x}C${b} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${x}C${b} = vec4(previous.zw, xTexelR${x}C${b}.xy);
} else {
xR${x}C${b} = vec4(0, 0, xTexelR${x}C${b}.xy);
}
`:A+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
xR${x}C${b} = xTexelR${x}C${b};
`,b+1<f)){let T=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
xCOffset = xC + ${l%2} + ${T};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(A+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
`),A+=`
xR${x}C${b+1} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.xy);
`):A+=`
xCOffset = xC + ${T};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
}
xR${x}C${b+1} = xTexelR${x}C${b+2};
`}}else b<f&&(A+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(A+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${x}C${b+2} = vec4(0.);
}
xR${x}C${b} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.zw);
`,b+1<f&&(A+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${x}C${b+1} = vec4(xTexelR${x}C${b+2}.xy, final.xy);
`)):(A+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b+2} = vec4(0.);
}
xR${x}C${b} = vec4(
xTexelR${x}C${b}.xy, xTexelR${x}C${b+2}.xy);
`,b+1<f&&(A+=`
xR${x}C${b+1} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.zw);
`)),A+="}");b<f&&(A+=`
vec4 wTexelR${x}C${b} = getW(${x}, ${b}, d1, q);
wR${x}C${b} = vec4(wTexelR${x}C${b}.xz, wTexelR${x}C${b}.xz);
`,b+1<f&&(A+=`
vec4 wTexelR${x}C${b+1} = getW(${x}, ${b+1}, d1, q);
wR${x}C${b+1} =
vec4(wTexelR${x}C${b+1}.xz, wTexelR${x}C${b+1}.xz);`))}for(let x=0;x<p;x++)for(let _=0;_<f;_++)A+=`dotProd += xR${x}C${_} * wR${x}C${_};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`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=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${A}
vec4 result = dotProd;
${w}
${g}
setOutput(result);
}
`}};function DV(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]),k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=F.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new I_(h):d=new k_(h),n.runWebGLProgram(d,[a,s],"float32")}var OV={kernelName:ss,backendName:"webgl",kernelFunc:DV},zV=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;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// 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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},PV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function LV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=F.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new zV(h);return n.runWebGLProgram(d,[a,s],"float32")}var WV={kernelName:vh,backendName:"webgl",kernelFunc:LV};function BV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=F.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new PV(h);return n.runWebGLProgram(d,[a,s],"float32")}var VV={kernelName:kh,backendName:"webgl",kernelFunc:BV},UV=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function jV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=k.sizeFromShape(r.shape),i=we({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new UV(s),l=n.runWebGLProgram(o,[i],i.dtype),c=we({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var HV={kernelName:Ih,backendName:"webgl",kernelFunc:jV},GV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${u}, ${h});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function qV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=F.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new GV(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=we({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var XV={kernelName:su,backendName:"webgl",kernelFunc:qV},KV="return (x >= 0.0) ? x : (exp(x) - 1.0);",ZV=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,YV=Qe({opSnippet:KV,packedOpSnippet:ZV}),JV={kernelName:Xi,backendName:"webgl",kernelFunc:YV},QV="return (b >= 1.0) ? a : a * (b + 1.0);",eU=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,tU=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ic(eU,r.shape,a.shape):new Al(QV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},nU={kernelName:Th,backendName:"webgl",kernelFunc:tU},rU=`
return vec4(equal(a, b));
`,aU="return float(a == b);",sU=Qt({opSnippet:aU,packedOpSnippet:rU,dtype:"bool"}),iU={kernelName:Zi,backendName:"webgl",kernelFunc:sU},oU=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${F.ERF_P};
float a1 = ${F.ERF_A1};
float a2 = ${F.ERF_A2};
float a3 = ${F.ERF_A3};
float a4 = ${F.ERF_A4};
float a5 = ${F.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,lU=Qe({opSnippet:oU}),uU={kernelName:Ki,backendName:"webgl",kernelFunc:lU},N_="return exp(x);",S_=Qe({opSnippet:N_,packedOpSnippet:N_,cpuKernelImpl:uL}),cU={kernelName:os,backendName:"webgl",kernelFunc:S_};function vA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(k.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),we({inputs:{x:s},backend:r,attrs:{shape:o}})}var hU={kernelName:Yi,backendName:"webgl",kernelFunc:vA},T_="return exp(x) - 1.0;",dU=Qe({opSnippet:T_,packedOpSnippet:T_,cpuKernelImpl:cL}),pU={kernelName:Ji,backendName:"webgl",kernelFunc:dU},E_=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) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function C_(e,t,n){let r=n.texData.get(e.dataId),a=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=we({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new E_("real",l,t),u=new E_("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=Fa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=we({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function fU(e){let{inputs:t,backend:n}=e,{input:r}=t;return C_(r,!1,n)}var mU={kernelName:Eh,backendName:"webgl",kernelFunc:fU},AU=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 kA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||k.inferDtype(a),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new AU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var yU={kernelName:iu,backendName:"webgl",kernelFunc:kA},gU=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];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},xU={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new gU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},R_="return floor(x);",wU=Qe({opSnippet:R_,packedOpSnippet:R_,cpuKernelImpl:hL}),_U={kernelName:ls,backendName:"webgl",kernelFunc:wU},bU=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,vU=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,kU=Qt({opSnippet:bU,packedOpSnippet:vU,dtype:"int32"}),IU={kernelName:us,backendName:"webgl",kernelFunc:kU},NU=class{constructor(e){this.variableNames=["A"];let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},SU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},EU={kernelName:Uh,backendName:"webgl",kernelFunc:TU},gl;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=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[c,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[u,c],d=[u,c,s];(o||i||l)&&(gl==null&&(gl=document.createElement("canvas").getContext("2d")),gl.canvas.width=c,gl.canvas.height=u,gl.drawImage(a,0,0,c,u),a=gl.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=qn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let f=ee().getBool("WEBGL_PACK")?new SU(d):new NU(d),m=n.runWebGLProgram(f,[p],"int32");return n.disposeData(p.dataId),m}function CU(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=F.convertConv2DDataFormat(u),A=F.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=x_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(ee().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=w_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,_=o!=null,b=p==="leakyrelu",T=p?tp(p,!1):null,S=new g_(A,x,T,_,b),N=[a,s];if(i&&N.push(i),o&&N.push(o),b){let C=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));N.push(C),g.push(C)}y=n.runWebGLProgram(S,N,"float32")}let w=we({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),w}var RU={kernelName:Bs,backendName:"webgl",kernelFunc:CU};function FU(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]),k.assert(F.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=F.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?tp(d,y):null,w=[a,s],x=i!=null,_=o!=null,b=d==="leakyrelu";if(x&&w.push(i),_&&w.push(o),b){let N=n.makeTensorInfo([],"float32",k.createScalarValue(p,"float32"));w.push(N),f.push(N)}let T;y?T=new I_(A,x,g,_,b):T=new k_(A,x,g,_,b);let S=n.runWebGLProgram(T,w,"float32");return f.forEach(N=>n.disposeIntermediateTensorInfo(N)),S}var MU={kernelName:Vs,backendName:"webgl",kernelFunc:FU},$U=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ft(t.length),a=ft(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${this.strides});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function DU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=F.prepareAndValidate(r,a),h=we({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=we({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/c,c]}}),p=new $U(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=we({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var OU={kernelName:to,backendName:"webgl",kernelFunc:DU},PU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),r=zU(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function zU(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 LU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=k.parseAxisParam(i,a.shape)[0],c=F.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=k.sizeFromShape(s.shape),h=[],d=we({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=we({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),x=dL(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let m=new PU(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=we({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var WU={kernelName:eo,backendName:"webgl",kernelFunc:LU},BU="return float(a > b);",VU=`
return vec4(greaterThan(a, b));
`,UU=Qt({opSnippet:BU,packedOpSnippet:VU,cpuKernelImpl:pL,dtype:"bool"}),jU={kernelName:no,backendName:"webgl",kernelFunc:UU},HU="return float(a >= b);",GU=`
return vec4(greaterThanEqual(a, b));
`,qU=Qt({opSnippet:HU,packedOpSnippet:GU,dtype:"bool"}),XU={kernelName:hs,backendName:"webgl",kernelFunc:qU};function KU(e){let{inputs:t,backend:n}=e,{input:r}=t;return C_(r,!0,n)}var ZU={kernelName:Ch,backendName:"webgl",kernelFunc:KU},YU="return float(!isnan(x) && !isinf(x));",JU=Qe({opSnippet:YU,dtype:"bool"}),QU={kernelName:ao,backendName:"webgl",kernelFunc:JU},ej="return float(isinf(x));",tj=Qe({opSnippet:ej,dtype:"bool"}),nj={kernelName:so,backendName:"webgl",kernelFunc:tj},rj="return float(isnan(x));",aj=Qe({opSnippet:rj,dtype:"bool"}),sj={kernelName:io,backendName:"webgl",kernelFunc:aj},ij="return float(a < b);",oj=`
return vec4(lessThan(a, b));
`,lj=Qt({opSnippet:ij,packedOpSnippet:oj,cpuKernelImpl:fL,dtype:"bool"}),uj={kernelName:oo,backendName:"webgl",kernelFunc:lj},cj="return float(a <= b);",hj=`
return vec4(lessThanEqual(a, b));
`,dj=Qt({opSnippet:cj,packedOpSnippet:hj,dtype:"bool"}),pj={kernelName:lo,backendName:"webgl",kernelFunc:dj};function fj(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=mL(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var mj={kernelName:Fh,backendName:"webgl",kernelFunc:fj},Aj=`if (x < 0.0) return NAN;
return log(x);`,yj=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,gj=Qe({opSnippet:Aj,packedOpSnippet:yj,cpuKernelImpl:AL}),xj={kernelName:ps,backendName:"webgl",kernelFunc:gj},wj="return log(1.0 + x);",_j=Qe({opSnippet:wj}),bj={kernelName:uo,backendName:"webgl",kernelFunc:_j},vj="return float(a >= 1.0 && b >= 1.0);",kj=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,Ij=Qt({opSnippet:vj,packedOpSnippet:kj,dtype:"bool"}),Nj={kernelName:co,backendName:"webgl",kernelFunc:Ij},Sj="return float(!(x >= 1.0));",Tj=Qe({opSnippet:Sj}),Ej={kernelName:ou,backendName:"webgl",kernelFunc:Tj},Cj="return float(a >= 1.0 || b >= 1.0);",Rj=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Fj=Qt({opSnippet:Cj,packedOpSnippet:Rj,dtype:"bool"}),Mj={kernelName:lu,backendName:"webgl",kernelFunc:Fj},$j=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];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},Dj=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},Oj=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new Dj(a.shape,s,i,o,l):new $j(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},zj={kernelName:uu,backendName:"webgl",kernelFunc:Oj},Pj=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];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* float(${a})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},Lj=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 Pj(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},Wj={kernelName:Mh,backendName:"webgl",kernelFunc:Lj};function Bj(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=we({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,e.dtype,"max",r),l=we({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function F_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=F.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 b=0;b<w.length;b++)w[b]=a.shape[u[b]];let x=yA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=x}else p=np(a,u,n);c=F.getInnerMostAxes(c.length,o)}F.assertAxesAreInnerMostDims("max",c,o);let[f,m]=F.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=F.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=yL(g,k.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let x=n.texData.get(y.dataId);x.values=w}else y=Bj(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var Vj={kernelName:fs,backendName:"webgl",kernelFunc:F_},Uj=Kw+`
return max(a, b);
`,jj=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+ep+`
return result;
`,Hj=Qt({opSnippet:Uj,packedOpSnippet:jj,cpuKernelImpl:gL}),Gj={kernelName:ms,backendName:"webgl",kernelFunc:Hj};function qj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ul(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(F.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=F.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Cn({inputs:{x:a},backend:n});let h=new oc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var Xj={kernelName:As,backendName:"webgl",kernelFunc:qj};function Kj(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=F.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new wA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var Zj={kernelName:cu,backendName:"webgl",kernelFunc:Kj},Yj=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() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${a};
wR += ${r}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Jj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${h}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${a}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${p} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Qj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=F.computePool3DInfo(i.shape,o,l,h,c,u),p=new wA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new Jj(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var eH={kernelName:Dh,backendName:"webgl",kernelFunc:Qj};function tH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;ul([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=F.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new oc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new Yj(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var nH={kernelName:$h,backendName:"webgl",kernelFunc:tH};function rH(e,t,n,r){let a=new oc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new oc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var aH={kernelName:Oh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];k.assert(F.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=F.computePool2DInfo(r.shape,a,s,c,i),[h,d]=rH(r,o,u,l);return[h,d]}};function sH(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=we({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=ci(i,"float32","mean",r),l=we({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var iH={kernelName:ys,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=k.parseAxisParam(s,r.shape),c=l,u=F.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,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[u[T]];let _=yA(w,r.shape,r.dtype,u,x);f=i.makeTensorInfo(x,r.dtype);let b=i.texData.get(f.dataId);b.values=_}else f=np(r,u,i);p.push(f),c=F.getInnerMostAxes(c.length,o)}F.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=F.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=F.expandShapeToKeepDim(m,l));let g=sH(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function oH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=F.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=F.getInnerMostAxes(c.length,a.shape.length)),F.assertAxesAreInnerMostDims("min",c,o);let[d,p]=F.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=we({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=ci(m,m.dtype,"min",n),y;if(i){let g=F.expandShapeToKeepDim(d,l);y=we({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=we({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var lH={kernelName:gs,backendName:"webgl",kernelFunc:oH},uH=Kw+`
return min(a, b);
`,cH=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+ep+`
return result;
`,hH=Qt({opSnippet:uH,packedOpSnippet:cH,cpuKernelImpl:xL}),dH={kernelName:xs,backendName:"webgl",kernelFunc:hH},pH=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=ft(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};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
for (int i = 0; i < ${r}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${a} coords = outC - start;
setOutput(getX(${o}));
}
`}},fH=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=ft(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=un("rc",r),l=un("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};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}else{let p=`
${a} source = rc;
${a} lt = ${a}(lessThan(source, start));
${a} gte = ${a}(greaterThanEqual(source, end));
${a} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${h}) +
gte * ((end - 1) * 2 - source + ${h});
source -= start;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {
${p}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},mH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fH(r.shape,a,s):new pH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},AH={kernelName:hu,backendName:"webgl",kernelFunc:mH},yH=`if (b == 0.0) return NAN;
return mod(a, b);`,gH=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+ep+`
return result;
`,xH=Qt({opSnippet:yH,packedOpSnippet:gH}),wH={kernelName:ho,backendName:"webgl",kernelFunc:xH},_H=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},bH=`
if (a == b) {
return 1.0;
};
return a / b;`,vH=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,M_=Qt({opSnippet:bH,packedOpSnippet:vH,checkOutOfBounds:!0}),kH={kernelName:is,backendName:"webgl",kernelFunc:M_},$_="return a - b;",D_=Qt({opSnippet:$_,packedOpSnippet:$_,supportsComplex:!0,cpuKernelImpl:SL}),IH={kernelName:zs,backendName:"webgl",kernelFunc:D_};function O_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=k.parseAxisParam([s],a.shape),o=F_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=F.expandShapeToKeepDim(o.shape,i),c=we({inputs:{x:o},backend:n,attrs:{shape:l}}),u=D_({inputs:{a,b:c},backend:n}),h=S_({inputs:{x:u},backend:n}),d=xA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=we({inputs:{x:d},backend:n,attrs:{shape:l}}),f=M_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var NH={kernelName:Ds,backendName:"webgl",kernelFunc:O_};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:O_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new _H(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var TH={kernelName:zh,backendName:"webgl",kernelFunc:SH},z_="return -x;";function EH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=_L(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new ml(r.shape,z_):a=new Ra(r.shape,z_),n.runWebGLProgram(a,[r],r.dtype)}var CH={kernelName:po,backendName:"webgl",kernelFunc:EH},RH=$r.nonMaxSuppressionV3Impl;function FH(e){F.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}=RH(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var MH={kernelName:mo,backendName:"webgl",kernelFunc:FH},$H=$r.nonMaxSuppressionV4Impl;function DH(e){F.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}=$H(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var OH={kernelName:Ao,backendName:"webgl",kernelFunc:DH},zH=$r.nonMaxSuppressionV5Impl;function PH(e){F.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}=zH(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var LH={kernelName:yo,backendName:"webgl",kernelFunc:PH},WH=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)));
}
`}},BH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=k.sizeFromShape(a.shape),c=new WH(l,s,i,o),u=we({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=we({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},VH={kernelName:_s,backendName:"webgl",kernelFunc:BH};function op(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=uc({inputs:{input:r},backend:n}),s=op({inputs:{x:a},backend:n}),i=ip({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return kA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var UH={kernelName:Do,backendName:"webgl",kernelFunc:op};function P_(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=uc({inputs:{input:r},backend:n}),s=P_({inputs:{x:a},backend:n}),i=ip({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=Fa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return kA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var jH={kernelName:go,backendName:"webgl",kernelFunc:P_};function HH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return vA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=vA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=y_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var GH={kernelName:xo,backendName:"webgl",kernelFunc:HH},qH=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=ft(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 = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},XH=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=ft(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",r),l=un("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;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
${h[f]}
if (${d}) {
result[${f}] = float(${n});
} else {
${a} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;p+=r===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},L_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XH(a.shape,s,i):new qH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},KH={kernelName:bs,backendName:"webgl",kernelFunc:L_},ZH=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,YH=`
// 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);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+ep+`
return result;
`,JH=Qt({opSnippet:ZH,packedOpSnippet:YH}),QH={kernelName:vs,backendName:"webgl",kernelFunc:JH};function eG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=k.parseAxisParam(s,a.shape),u=c,h=F.getAxesPermutation(u,o),d=a;h!=null&&(d=An({inputs:{x:a},backend:n,attrs:{perm:h}}),u=F.getInnerMostAxes(u.length,o),l.push(d)),F.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=bL(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=F.computeOutAndReduceShapes(d.shape,u),A=k.sizeFromShape(m),y=we({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=jh(a.dtype),w=ci(y,g,"prod",n);p=we({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=F.expandShapeToKeepDim(p.shape,c);p=we({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var tG={kernelName:wo,backendName:"webgl",kernelFunc:eG},W_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=vL(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},nG={kernelName:du,backendName:"webgl",kernelFunc:W_},rG="return 1.0 / x;",aG=Qe({opSnippet:rG}),sG={kernelName:_o,backendName:"webgl",kernelFunc:aG},iG=xr+`
return (x < 0.0) ? 0.0 : x;
`,oG=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,lG=Qe({opSnippet:iG,packedOpSnippet:oG}),uG={kernelName:Is,backendName:"webgl",kernelFunc:lG},cG=xr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,hG=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,dG=Qe({opSnippet:cG,packedOpSnippet:hG}),pG={kernelName:Ss,backendName:"webgl",kernelFunc:dG},fG=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]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},mG=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]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function AG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new mG(a.shape,l,c,s,i):new fG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var yG={kernelName:Ns,backendName:"webgl",kernelFunc:AG},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=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function xG(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 wG={kernelName:Wh,backendName:"webgl",kernelFunc:xG},_G=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]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function bG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new _G(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var vG={kernelName:pu,backendName:"webgl",kernelFunc:bG},kG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function IG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new kG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var NG={kernelName:Lh,backendName:"webgl",kernelFunc:IG},SG=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=ft(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},TG=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-${n} tensor is not yet supported`);this.outputShape=e;let r=un("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ft(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${a}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(r.slice())};
if(${a}){
result.g = ${l(r.slice())};
}
if(${s}) {
result.b = ${c(r.slice())};
if(${a}) {
result.a = ${u(r.slice())};
}
}
setOutput(result);
}
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function EG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=k.parseAxisParam(s,a.shape);if(i===0)return Cn({inputs:{x:a},backend:n});let l=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TG(a.shape,o):new SG(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var CG={kernelName:Ts,backendName:"webgl",kernelFunc:EG},RG=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=F.getImageCenter(r,a,s),u=l.toFixed(3),h=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
vec3 fill = vec3(${n.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - ${u}) * ${o} - (float(y) - ${h}) * ${i};
float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${h}) * ${o};
int coordX = int(round(coordXFloat + ${u}));
int coordY = int(round(coordYFloat + ${h}));
${d}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${a}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},FG={kernelName:Oo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new RG(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},MG=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,$G=Qe({opSnippet:MG}),DG={kernelName:Es,backendName:"webgl",kernelFunc:$G},OG="return inversesqrt(x);",zG=Qe({opSnippet:OG,cpuKernelImpl:kL}),PG={kernelName:Cs,backendName:"webgl",kernelFunc:zG},B_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ft(a.length),l=ft(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${p};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function LG(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}=F.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=we({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=we({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new B_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=we({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var WG={kernelName:vo,backendName:"webgl",kernelFunc:LG},BG=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=ft(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function VG(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new BG(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],nr(a.dtype,s.dtype))}var UG={kernelName:ko,backendName:"webgl",kernelFunc:VG},jG=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${F.SELU_SCALEALPHA};
float scale = ${F.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,HG=Qe({opSnippet:jG}),GG={kernelName:Io,backendName:"webgl",kernelFunc:HG},qG="return 1.0 / (1.0 + exp(-1.0 * x));",XG=Qe({opSnippet:qG}),KG={kernelName:Fs,backendName:"webgl",kernelFunc:XG},ZG=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,YG=Qe({opSnippet:ZG}),JG={kernelName:To,backendName:"webgl",kernelFunc:YG},QG=e_+`
return sin(x);
`,eq=Qe({opSnippet:QG}),tq={kernelName:Rs,backendName:"webgl",kernelFunc:eq},nq=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,rq=Qe({opSnippet:nq}),aq={kernelName:So,backendName:"webgl",kernelFunc:rq},sq=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,iq=Qe({opSnippet:sq}),oq={kernelName:Eo,backendName:"webgl",kernelFunc:iq},lq=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;k.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=L_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=F.getReshaped(u.shape,s,o,!1),d=F.getPermuted(h.length,s.length,!1),p=F.getReshapedPermuted(u.shape,s,o,!1),f=we({inputs:{x:u},backend:n,attrs:{shape:h}}),m=An({inputs:{x:f},backend:n,attrs:{perm:d}}),A=we({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},uq={kernelName:fu,backendName:"webgl",kernelFunc:lq};function cq(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}=F.calculateShapes(s,a,o),d=!1,p=new B_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=we({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var hq={kernelName:Bh,backendName:"webgl",kernelFunc:cq};function dq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=k.parseAxisParam(i,a.shape)[0],l=F.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=lc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var pq={kernelName:Co,backendName:"webgl",kernelFunc:dq},fq="return sqrt(x);",mq=Qe({opSnippet:fq}),Aq={kernelName:Ms,backendName:"webgl",kernelFunc:mq},yq="return x * x;",gq=Qe({opSnippet:yq}),xq={kernelName:mu,backendName:"webgl",kernelFunc:gq},V_="return (a - b) * (a - b);",wq=Qt({opSnippet:V_,packedOpSnippet:V_}),_q={kernelName:Os,backendName:"webgl",kernelFunc:wq};function bq({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=xr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Ra(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var vq={kernelName:ya,backendName:"webgl",kernelFunc:bq},kq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ft(n.length),s=ft(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});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function Iq(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}=sn.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=we({inputs:{x:a},backend:n,attrs:{shape:y}}),x;if(p){let b=lc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});x=we({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))x=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let b=n.texData.get(w.dataId).values,T=Ue(w.shape,w.dtype,b),S=NL(g,T,m,f);x=n.makeTensorInfo(g,w.dtype,S.values)}else{let b=new kq(f,m,g);x=n.runWebGLProgram(b,[w],w.dtype)}let _=we({inputs:{x},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(x),_}var Nq={kernelName:Ro,backendName:"webgl",kernelFunc:Iq},Sq="return tan(x);",Tq=Qe({opSnippet:Sq}),Eq={kernelName:Fo,backendName:"webgl",kernelFunc:Tq},Cq=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Rq=Qe({opSnippet:Cq}),Fq={kernelName:Ps,backendName:"webgl",kernelFunc:Rq},$q=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ft(this.rank),a=Mq(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function Mq(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function U_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>k.decodeString(u)),l=Ue(a.shape,a.dtype,o),c=TL(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new $q(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var Dq={kernelName:Aa,backendName:"webgl",kernelFunc:U_};function Oq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=EL(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var zq={kernelName:Mo,backendName:"webgl",kernelFunc:Oq};function Pq(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;ul(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=CL(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var Lq={kernelName:Vh,backendName:"webgl",kernelFunc:Pq};function Wq(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=lc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=we({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Bq={kernelName:$o,backendName:"webgl",kernelFunc:Wq},Vq=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
sumValue += dot(values, segFilter);
`,d="";a%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${p}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function Uq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=F.getAxesPermutation([c],o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=F.getInnerMostAxes(1,o)[0]);let d=F.segment_util.computeOutShape(h.shape,c,i),p=k.sizeFromShape([h.shape[c]]),f=we({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=jh(a.dtype),A=(x,_,b,T,S)=>{let N=x.shape[0],C=x.shape[1],$=F.segment_util.segOpComputeOptimalWindowSize(C,S),D={windowSize:$,inSize:C,batchSize:N,numSegments:S},O=new Vq(D,_),V=n.compileAndRun(O,[x,b],T);if(l.push(V),V.shape[1]===S)return V;let W=W_({backend:n,attrs:{start:0,stop:S,step:1,dtype:"float32"}}),Z=U_({inputs:{x:W},backend:n,attrs:{reps:[C/$]}});return l.push(W),l.push(Z),A(V,_,Z,T,S)},y=A(f,"unsortedSegmentSum",s,m,i),g=we({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(u!=null){l.push(g);let x=F.getUndoAxesPermutation(u);w=An({inputs:{x:w},backend:n,attrs:{perm:x}})}return l.forEach(x=>n.disposeIntermediateTensorInfo(x)),w}var jq={kernelName:Au,backendName:"webgl",kernelFunc:Uq},Hq=[zj,Wj,IW,SW,CW,MW,DW,PW,WW,VW,GW,XW,YW,eB,oB,rB,cB,fB,dB,gB,wB,bB,NB,MB,DB,BB,UB,qB,ZB,iW,eV,cV,dV,aV,AV,gV,fV,_V,kV,SV,EV,RV,$V,WV,VV,OV,HV,XV,JV,nU,iU,uU,cU,hU,pU,mU,yU,xU,_U,IU,EU,RU,MU,OU,WU,jU,XU,sW,ZU,QB,QU,nj,sj,lW,uj,pj,mj,bj,xj,Nj,Ej,Mj,Vj,Zj,Xj,eH,nH,aH,Gj,iH,lH,dH,AH,wH,TH,pW,CH,MH,OH,LH,zB,VH,jH,GH,KH,QH,cW,tG,nG,PB,kH,sG,pG,uG,mW,yG,wG,vG,NG,CG,FG,DG,PG,WG,UG,GG,KG,JG,tq,aq,RB,NH,oq,uq,hq,pq,Aq,xq,_q,vq,Nq,IH,bW,Eq,Fq,Dq,zq,vW,Lq,Bq,jq,UH];for(let e of Hq)zo(e);var Rn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Rn||(Rn={}));var cc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(cc||(cc={}));var j_;function Gq(e){j_=e.wasm.cwrap(Ws,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let S=n.dataIdMap.get(i.dataId);if(S.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${S.shape.length}.`);f=S.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=cc[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],w=a.shape[0],x=n.makeOutput([w,y,g],a.dtype),_=n.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(a.shape).buffer),T=new 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Got input batch dimensions of (${f}) and (${m}).`);let w=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);k.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[A,u,d]:[A,d,u],_=o?[y,p,h]:[y,h,p],b=wr({inputs:{x:a},backend:n,attrs:{shape:x}}),T=wr({inputs:{x:s},backend:n,attrs:{shape:_}}),S=n.dataIdMap.get(b.dataId).id,N=n.dataIdMap.get(T.dataId).id,C=i?b.shape[2]:b.shape[1],$=o?T.shape[1]:T.shape[2],D=Math.max(A,y),O=n.makeOutput([D,C,$],b.dtype),V=n.dataIdMap.get(O.dataId).id,W=new Uint8Array(new Int32Array(b.shape).buffer),Z=new Uint8Array(new Int32Array(T.shape).buffer);return K_(S,W,b.shape.length,N,Z,T.shape.length,i,o,V),O.shape=w,O}var mX={kernelName:Qa,backendName:"wasm",setupFunc:pX,kernelFunc:fX};function cp(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var AX={kernelName:es,backendName:"wasm",kernelFunc:cp},Z_;function yX(e){Z_=e.wasm.cwrap(ma,null,["number","number","number","number"])}function gX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(l.dataId).id;return Z_(o,s,i,c),l}var xX={kernelName:ma,backendName:"wasm",setupFunc:yX,kernelFunc:gX};function Y_(e){let{inputs:t,backend:n}=e,r=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=F.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>k.sizeFromShape(p.shape)>0);if(s.length===1)return lp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(k.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(F.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(w=>{let x=k.sizeFromShape(w.shape.slice(r));return wr({inputs:{x:w},backend:n,attrs:{shape:[-1,x]}})}),f=p.map(w=>({vals:n.readSync(w.dataId),shape:w.shape}));a=F.computeOutShape(p.map(w=>w.shape),1);let m=p[0].shape[0]===1,A=Gm(f,a,t[0].dtype,m),y=F.computeOutShape(s.map(w=>w.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=F.fromStringArrayToUint8(A),i}let l=k.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(p=>{let f=k.sizeFromShape(p.shape.slice(r));return c+=f,f}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let f=p*c;for(let m=0;m<h.length;m++){let A=u[m],y=p*A,g=h[m].subarray(y,y+A);d.set(g,f),f+=A}}return i}var wX={kernelName:ji,backendName:"wasm",kernelFunc:Y_},J_;function _X(e){J_=e.wasm.cwrap(ts,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h,dataFormat:d}=n,p=F.convertConv2DDataFormat(d),f=F.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,p),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,w=f.padInfo.bottom,x=f.padInfo.left,_=f.dilationHeight,b=f.dilationWidth,T=f.strideHeight,S=f.strideWidth,N=f.inChannels,C=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Please use 'channelsLast'.`);let $=r.makeOutput(p.outShape,"float32"),D=r.dataIdMap.get($.dataId).id;return rb(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,w,C,x,_,b,T,S,N,D),$}var LX={kernelName:ss,backendName:"wasm",setupFunc:zX,kernelFunc:PX},WX=!1,BX=cn(Zi,WX,"bool"),VX=Fn(os);function NA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),wr({inputs:{x:a},backend:r,attrs:{shape:o}})}var UX={kernelName:Yi,backendName:"wasm",kernelFunc:NA};function jX(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var HX={kernelName:iu,backendName:"wasm",kernelFunc:jX},ab;function GX(e){ab=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function qX(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return ab(s,o,l,c,u,i),a}var XX={kernelName:Qi,backendName:"wasm",kernelFunc:qX,setupFunc:GX},KX=Fn(ls),ZX=!1,YX=cn(us,ZX),sb;function JX(e){sb=e.wasm.cwrap(cs,null,["number","number","number","number","number","number","number"])}function QX(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return sb(u,h,d,p,f,a,A),m}var eK={kernelName:cs,backendName:"wasm",setupFunc:JX,kernelFunc:QX},ib;function tK(e){ib=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=F.computeConv2DInfo(a.shape,s.shape,l,u,c,d),A=cc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,w=m.outChannels,x=0;if(i!=null){let re=r.dataIdMap.get(i.dataId);if(re.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${re.shape.length}.`);if(re.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${re.shape}) does not match the number of output channels (${w})`);x=re.id}let _=m.filterHeight,b=m.filterWidth,T=m.padInfo.top,S=m.padInfo.right,N=m.padInfo.bottom,C=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,O=m.strideHeight,V=m.strideWidth,W=m.inChannels,Z=m.padInfo.type==="SAME"?1:0,K=m.batchSize,te=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),Q=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return ib(y,K,te,J,g,_,b,x,T,S,N,C,Z,$,D,O,V,W,w,A,le,f||0,Q),se}var rK={kernelName:Bs,backendName:"wasm",setupFunc:tK,kernelFunc:nK},ob;function aK(e){ob=e.wasm.cwrap(Vs,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 sK(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=F.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),A=cc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,w=m.outChannels,x=0;if(i!=null){let re=r.dataIdMap.get(i.dataId);if(re.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${re.shape.length}.`);if(re.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${re.shape}) does not match the number of output channels (${w})`);x=re.id}let _=m.filterHeight,b=m.filterWidth,T=m.padInfo.top,S=m.padInfo.right,N=m.padInfo.bottom,C=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,O=m.strideHeight,V=m.strideWidth,W=m.inChannels,Z=m.padInfo.type==="SAME"?1:0,K=m.batchSize,te=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let se=r.makeOutput(m.outShape,"float32"),Q=r.dataIdMap.get(se.dataId).id,le=o==null?0:r.dataIdMap.get(o.dataId).id;return ob(y,K,te,J,g,_,b,x,T,S,N,C,Z,$,D,O,V,W,w,A,le,f||0,Q),se}var iK={kernelName:Vs,backendName:"wasm",setupFunc:aK,kernelFunc:sK},lb;function oK(e){lb=e.wasm.cwrap(to,null,["number","number","number","number","number","number","array","number"])}function lK(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=xf.prepareAndValidate(r,a),c=t.makeOutput(s,r.dtype);if(i===0)return c;let u=a.shape,h=u[u.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(c.dataId).id;return lb(d,Rn[r.dtype],p,i,h,o,f,m),c}var uK={kernelName:to,backendName:"wasm",setupFunc:oK,kernelFunc:lK},ub;function cK(e){ub=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hK(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=k.parseAxisParam(i,a.shape)[0],c=F.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=wr({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=k.sizeFromShape(s.shape),d=wr({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),p=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],f=t.makeOutput(p,a.dtype);if(k.sizeFromShape(a.shape)===0)return f;let m=u.shape.length-1,A=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(p)).buffer);return ub(A,Rn[a.dtype],w,m,y,c.batchSize,x,g),f.shape=c.outputShape,f}var dK={kernelName:eo,backendName:"wasm",setupFunc:cK,kernelFunc:hK},pK=!1,fK=cn(no,pK,"bool"),mK=!1,AK=cn(hs,mK,"bool"),cb;function yK(e){cb=e.wasm.cwrap(ds,null,["number","number","number"])}function gK(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;cb(a,n,i)}return s}var xK={kernelName:ds,backendName:"wasm",setupFunc:yK,kernelFunc:gK},wK=!1,_K=cn(oo,wK,"bool"),bK=!1,vK=cn(lo,bK,"bool"),kK=Fn(ps),IK=!1,NK=cn(co,IK,"bool"),hb;function SK(e){hb=e.wasm.cwrap(fs,null,["number, number, number"])}function TK(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=xl(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;F.assertAxesAreInnerMostDims("max",u,p);let[f,m]=F.computeOutAndReduceShapes(l.shape,u),A=k.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(k.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;hb(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=F.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var EK={kernelName:fs,backendName:"wasm",setupFunc:SK,kernelFunc:TK},CK=!1,RK=cn(ms,CK),db;function FK(e){db=e.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function MK(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=F.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.dilationHeight,g=u.dilationWidth,w=u.strideHeight,x=u.strideWidth,_=u.inChannels,b=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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ZK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=Ab(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=SA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var YK={kernelName:Ao,backendName:"wasm",setupFunc:KK,kernelFunc:ZK},yb;function JK(e){yb=e.wasm.cwrap(yo,"number",["number","number","number","number","number","number"])}function QK(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=yb(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=SA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var eZ={kernelName:yo,backendName:"wasm",setupFunc:JK,kernelFunc:QK},tZ=!1,nZ=cn(fo,tZ,"bool"),gb;function rZ(e){gb=e.wasm.cwrap(_s,null,["number","number","number","number","number"])}function aZ(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 gb(u,s,i,o,c),l}var sZ={kernelName:_s,backendName:"wasm",setupFunc:rZ,kernelFunc:aZ};function iZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var oZ={kernelName:go,backendName:"wasm",kernelFunc:iZ};function lZ(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return NA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>NA({inputs:{input:l},backend:n,attrs:{dim:a}}));return Y_({inputs:o,backend:n,attrs:{axis:a}})}var uZ={kernelName:xo,backendName:"wasm",kernelFunc:lZ},xb;function cZ(e){xb=e.wasm.cwrap(bs,null,["number","array","number","number","array","array","number","number"])}function hZ(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 xb(i,c,t.shape.length,Rn[t.dtype],d,p,a,l),o}var dZ={kernelName:bs,backendName:"wasm",kernelFunc:hZ,setupFunc:cZ},pZ=!1,fZ=cn(vs,pZ),wb;function mZ(e){wb=e.wasm.cwrap(ks,null,["number","number","number"])}function AZ(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 wb(s,i,l),o}var yZ={kernelName:ks,backendName:"wasm",setupFunc:mZ,kernelFunc:AZ},_b;function gZ(e){_b=e.wasm.cwrap(wo,null,["number","number","number","number"])}function xZ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=xl(i,a,t),f=h;if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w,f=F.getInnerMostAxes(f.length,c.shape.length))}F.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,A]=F.computeOutAndReduceShapes(c.shape,f),y=k.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(k.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;_b(l,y,Rn[g.dtype],w)}if(p&&t.disposeData(u.dataId),s){let w=F.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var wZ={kernelName:wo,backendName:"wasm",setupFunc:gZ,kernelFunc:xZ},_Z=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Km(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},bZ={kernelName:du,backendName:"wasm",kernelFunc:_Z},vZ=!0,kZ=cn(is,vZ),IZ=Fn(Is),NZ=Fn(Ss),bb;function SZ(e){bb=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number"])}function TZ(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,d,p]=a.shape,f=[u,l,c,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=cp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(k.sizeFromShape(a.shape)===0)return g;let w=t.dataIdMap.get(g.dataId).id;return bb(y,u,h,d,p,l,c,s?1:0,i?1:0,w),A!=null&&t.disposeData(A.dataId),g}var EZ={kernelName:Ns,backendName:"wasm",setupFunc:SZ,kernelFunc:TZ},vb;function CZ(e){vb=e.wasm.cwrap(Ts,null,["number","array","number","array","number","number"])}function RZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=k.parseAxisParam(s,a.shape);if(a.shape.length===0)return lp({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);return vb(l,u,i.length,h,a.shape.length,c),wr({inputs:{x:o},attrs:{shape:a.shape},backend:n})}var FZ={kernelName:Ts,backendName:"wasm",kernelFunc:RZ,setupFunc:CZ},kb;function MZ(e){kb=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function $Z(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=r,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(l.dataId).id,[h,d,p,f]=a.shape,[m,A]=F.getImageCenter(o,d,p),y=i===0,g=255,w=typeof 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VZ(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(c.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:k.sizeFromShape(a.shape.slice(1));return Nb(i,o,l,p,u),c}var UZ={kernelName:ko,backendName:"wasm",kernelFunc:VZ,setupFunc:BZ},Sb;function jZ(e){Sb=e.wasm.cwrap(Fs,null,["number","number"])}function HZ(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return k.sizeFromShape(a.shape)===0||Sb(r,s),a}var GZ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:jZ,kernelFunc:HZ},qZ=Fn(Rs);function hp(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=sn.parseSliceParams(t,n,r),o=sn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),c=a.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let f=sn.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(f,f+k.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(f,f+k.sizeFromShape(i))),c}if(t.dtype==="string"){let f=Vd(l,s,i,t.shape,t.dtype);return h.stringBytes=f,c}let d=a.typedArrayFromHeap(c),p=t.shape.length;if(p===2)XZ(l,u[0],d,s,i);else if(p===3)KZ(l,u[0],u[1],d,s,i);else if(p===4)ZZ(l,u[0],u[1],u[2],d,s,i);else{let f=Vd(l,s,i,t.shape,t.dtype);d.set(f)}return c}function XZ(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+a[1]),s),s+=a[1]}}function KZ(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],c=a[2],u=o+s[0],h=l+s[1];for(let d=o;d<u;d++)for(let p=l;p<h;p++){let f=d*t+p*n+c;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function ZZ(e,t,n,r,a,s,i){let o=0,l=s[0],c=s[1],u=s[2],h=l+i[0],d=c+i[1],p=u+i[2],f=s[3];for(let m=l;m<h;m++)for(let A=c;A<d;A++)for(let y=u;y<p;y++){let g=m*t+A*n+y*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var 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Ht=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||{}}},gr=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=r3(),s!=null&&(this.originalName=Hb(s),this.name=Gb(this.originalName)),this.rank=t.length}},zQ=0,Sp=class{constructor(e,t){this.callArgs=t,this.id=zQ++,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}}},PQ=0,Ze=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=PQ++,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=oa(n)+"_"+kp(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 br(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return yn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return yn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ia(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&r.dtype!==a.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let i=r.shape;for(let o in a.axes){let l=Number(o),c=a.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=r.shape[i];if(o!=null&&l!=null&&o!==l)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=gt(e),r=!0;for(let s of n)if(!(s instanceof gr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof gr){a=!1;break}if(r===a)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return fi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of gt(e))s.push(i.shape);this.build(yn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=gt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=yn(o),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=LQ(e),i=this.computeOutputShape(s),o,l=WQ(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,u)=>new gr(l,c,this,gt(e),t,this.name,u)):o=new gr(l,i,this,gt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ia(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ia(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new br(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Np(this.weights)}build(e){this.built=!0}getWeights(e=!1){return ey(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ma(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,w=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,x=y.tensorIndex;Br(w===0,"input layer has >1 nodes"),Br(x===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(x)}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 bl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,w,x,_,b)=>{(x==null||_==null||b==null)&&(x=y.sourceLayer,_=y.nodeIndex,b=y.tensorIndex);let T=x.inboundNodes[_];if(w.indexOf(T)!==-1)throw new br(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(g.indexOf(T)!==-1)return;this.containerNodes.add(jr.nodeKey(x,_)),x.id in s||(s[x.id]=Object.keys(s).length),w.indexOf(T)===-1&&w.push(T);let S=T.inboundLayers.length;for(let N=0;N<S;N++){let C=T.inputTensors[N],$=T.inboundLayers[N],D=T.nodeIndices[N],O=T.tensorIndices[N];o(C,g,w,$,D,O)}for(g.push(T);w.indexOf(T)>=0;)w.splice(w.indexOf(T),1);i.push(T)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],w=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,w),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let x=0;x<y.inboundLayers.length;x++){let _=y.inboundLayers[x],b=y.nodeIndices[x],T=_.inboundNodes[b],S=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(g+1,S),n[T.id]=T}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(pp);this.layers=[];for(let y of p){let g=d[y];g.sort((w,x)=>{let _=s[w.id],b=s[x.id];return _<b?-1:_>b?1:0});for(let w of g)w instanceof jr&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(pp);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let w=g.outboundLayer;if(w!=null){for(let x of g.inputTensors)if(f.indexOf(x)===-1)throw new br(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${w.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 n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)a.push([n[i],e[s]]);else if(t)throw new U(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new U(`${s.length} of ${r} weights are not set: ${s}`)}ty(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${sm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ly(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=gt(e);let n=new yi;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return kc(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=gt(e);let n;return t==null?n=hi(null,e.length):n=gt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Ip(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(pp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,w=n[g];u.push(w)}let h=c.computeOutputShape(yn(u)),d=Ip(h),p=c.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${c.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Br(o in n),a.push(n[o])}return yn(a)}runInternalGraph(e,t){t==null&&(t=hi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(pp);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,d=c.outputTensors,p=new Array;for(let f of h)f.id in n&&p.push(n[f.id]);if(p.length===h.length){let f={},m,A,y,g;if(c.callArgs!=null&&(f=c.callArgs),p.length===1){let[w,x]=p[0];f.mask==null&&(f.mask=x),y=gt(u.call(w,f)),g=gt(u.computeMask(w,x)),m=[w],A=[x]}else m=p.map(w=>w[0]),A=p.map(w=>w[1]),f.mask==null&&(f.mask=A),y=gt(u.call(m,f)),g=gt(u.computeMask(m,A));if(u.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<d.length;++w){let x=d[w],_=y[w],b=g[w];n[x.id]=[_,b]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof jr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=jr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=jr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let h=s.inboundNodes[u],d=jr.nodeKey(s,u),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],w=jr.nodeKey(A,y),x=t[w];x==null&&(x=0),f.push([A.name,x,g,p])}l.push(f)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let w of A){let x=w[0],_=w[1],b=w[2];if(g=w[3]==null?{}:w[3],!(x in a)){i(m,A);return}let T=a[x];if(T.inboundNodes.length<=_){i(m,A);return}let S=T.inboundNodes[_];y.push(S.outputTensors[b])}y.length>0&&m.apply(yn(y),g)}function l(m){let A=m.name,y=kr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!UY(s);)for(let m of u){let A=a[m.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let m of p){let A=m[0],y=m[1],g=m[2];Br(A in a);let w=a[A].inboundNodes[y].outputTensors;h.push(w[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Br(A in a);let w=a[A].inboundNodes[y].outputTensors;d.push(w[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gee(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function _3(e,t){return gee(e,t,"classWeight")}async function b3(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=j(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),tn(i,"float32")}else return null}function xee(e,t){return B(e,t)}var wee=32;function k3(e,t){let n,r,a=t;n=a.xs,r=a.ys,k.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=v3("input",e.inputNames,n),i=v3("output",e.outputNames,r),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function v3(e,t,n){if(n instanceof H)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function _ee(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function vee(e,t,n){let r=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.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}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=n.validationData!=null,s,i;if(a)if(I3(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=_ee(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=u3(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=c3(u,h,n.epochs,null,null,bee(t,n),null,a,c);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await d.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let w=await m.next();if(r&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===Cp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=T3(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=T3(t,this.feedOutputNames,a,!1,"target"),Eee(e,t,null),Cee(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. 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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 yi(c),h=kc(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=xee(f,a[p]));let m=It(f);t.push(m),p===0?d=f:d=ie(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]))}jt(f),s.push(f)}return d=It(d),this.calculateLosses().forEach(p=>{d=ie(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=>j(()=>{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 yi(s),o=kc(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=ie(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 See(this,e,t,n)}async fitDataset(e,t){return vee(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 $e(s),yn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Gh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Gh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=oa(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>oa(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=oa(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[oa($p(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>oa($p(e)));{let e={};for(let t in this.metrics)e[t]=oa($p(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=vc(e.optimizer_config),n=kr(t),r;if(typeof e.loss=="string")r=di(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>di(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=di(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>di(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=di(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=pn.getSaveHandlers(e);if(i.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new U(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await pn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Fee,generatedBy:`TensorFlow.js tfjs-layers v${sm}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await pn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=pn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;y3(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){y3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ta.className="Model";ae.registerClass(ta);var C3=class extends ta{};C3.className="Functional";ae.registerClass(C3);async function Mee(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=vc(n),a=kr(r,t);if(e.weightsManifest!=null){let s=await pn.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),$e(s)}return a}async function Dee(e,t){if(t==null&&(t={}),typeof e=="string"){let n=pn.getLoadHandlers(e,t);if(n.length===0)n.push(pn.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return $ee(e,void 0,t)}async function $ee(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=kr(vc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. 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For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=i3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new ta({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");a=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof qo))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=kr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};qo.className="Sequential";ae.registerClass(qo);function X4(e){return new ta(e)}function K4(e){return new qo(e)}function Z4(e,t){return t==null&&(t={}),Dee(e,t)}function g0(e){return i3(e)}function Y4(e,t){ur.registerCallbackConstructor(e,t)}var Mn=class extends ae.Serializable{getConfig(){return{}}},R3=class extends Mn{apply(e,t=1){return gJ(e,t)}};R3.className="elu";ae.registerClass(R3);var F3=class extends Mn{apply(e){return cd(e)}};F3.className="selu";ae.registerClass(F3);var M3=class extends Mn{apply(e){return Mr(e)}};M3.className="relu";ae.registerClass(M3);var $3=class extends Mn{apply(e){return j(()=>Gs(6,Mr(e)))}};$3.className="relu6";ae.registerClass($3);var D3=class extends Mn{apply(e){return e}};D3.className="linear";ae.registerClass(D3);var O3=class extends Mn{apply(e){return 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e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:Bt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};xy.className="PReLU";ae.registerClass(xy);var wy=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${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=Le(e);return Wo(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wy.className="ELU";ae.registerClass(wy);var _y=class extends Ze{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=Le(e);return n.mul(yc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};_y.className="ThresholdedReLU";ae.registerClass(_y);var by=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new fy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Le(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}};by.className="Softmax";ae.registerClass(by);function Il(e,t,n){if(typeof e=="number")return hi(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!fJ(a))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(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 Op(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Da([n-t,0]);else if(r==="same")e=e*t;else throw new U(`Unsupport padding mode: ${r}.`);return e}function vy(e,t){return j(()=>(Rt(t),t==="channelsFirst"?ot(e,[0,2,3,1]):e))}function G3(e,t){return j(()=>(Rt(t),t==="channelsFirst"?ot(e,[0,2,3,4,1]):e))}function Lee(e,t,n,r=1,a="valid",s,i=1){return j(()=>{if(s==null&&(s=_r()),Rt(s),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=ot(e,[0,2,1])),a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Zh(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function q3(e,t,n,r=[1,1],a="valid",s,i,o=null){return j(()=>{if(s==null&&(s=_r()),Rt(s),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=vy(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=va.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=ot(l,[0,3,1,2])),l})}function Wee(e,t,n,r=[1,1,1],a="valid",s,i){return j(()=>{if(s==null&&(s=_r()),Rt(s),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=G3(e,s);if(a==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Ff(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=ot(o,[0,4,1,2,3])),o})}var ky=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ky.verifyArgs(t),this.rank=e,Xt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Il(t.kernelSize,e,"kernelSize"),this.strides=Il(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Pa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=_t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=Il(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!MA(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:za(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Sc=class extends ky{constructor(e,t){super(e,t);this.kernel=null,Sc.verifyArgs(t),this.filters=t.filters,Xt(this.filters,"filters"),this.kernelInitializer=_t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],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 j(()=>{e=Le(e);let n,r=this.bias==null?null:this.bias.read(),a=Lb(this.activation.getClassName());if(a!=null&&this.rank===2)n=q3(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Lee(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=q3(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Wee(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=mt(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=Ir(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:St(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:Bt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Tc=class extends Sc{constructor(e){super(2,e);Tc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!MA(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Tc.className="Conv2D";ae.registerClass(Tc);var zp=class extends Sc{constructor(e){super(3,e);zp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};zp.className="Conv3D";ae.registerClass(zp);var Iy=class extends Tc{constructor(e){super(e);if(this.inputSpec=[new Ht({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Le(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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=Op(o,h,c,this.padding),f=Op(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=ot(n,[0,2,3,1]));let A=Yh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=ot(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=mt(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]=Op(t[r],o,s,this.padding),t[a]=Op(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Iy.className="Conv2DTranspose";ae.registerClass(Iy);var X3=class extends Sc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=_t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=_t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],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 Ht({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Le(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=ot(e,[0,2,3,1])),n=Hf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=ot(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=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};X3.className="SeparableConv";var Ny=class extends X3{constructor(e){super(2,e)}};Ny.className="SeparableConv2D";ae.registerClass(Ny);var Pp=class extends Sc{constructor(e){super(1,e);Pp.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"&&!MA(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Pp.className="Conv1D";ae.registerClass(Pp);var Sy=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=fp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return fp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=fp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return fp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Cropping2D";ae.registerClass(Sy);var Ty=class extends Ze{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,hJ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Le(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=ot(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 ot(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}};Ty.className="UpSampling2D";ae.registerClass(Ty);function Bee(e,t,n=[1,1],r="valid",a,s){return j(()=>{a==null&&(a=_r()),Rt(a);let i=vy(e,a);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=js(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=ot(i,[0,3,1,2])),i})}var Ey=class extends ky{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=_t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 j(()=>{e=Le(e);let n=Bee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=mt(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=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(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=At(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};Ey.className="DepthwiseConv2D";ae.registerClass(Ey);function K3(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("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 Z3(e,t,n,r=!1,a,s,i=!1,o=!1){return j(()=>{let l=t.shape.length;if(l<3)throw new U(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(vr(2,l));if(t=ot(t,c),s!=null)throw new ze("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=Vn(a,-1)),a=ot(a,c)),r&&(t=Nn(t,0),a!=null&&(a=Nn(a,0)));let u=[],h,d=n,p=t.shape[0],f=ar(t),m;a!=null&&(m=ar(a));for(let y=0;y<p;++y){let g=f[y],w=j(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let x=j(()=>{let _=m[y],b=In(_).sub(_),T=w[0].mul(_).add(d[0].mul(b)),S=d.map((N,C)=>w[1][C].mul(_).add(N.mul(b)));return{output:T,newStates:S}});h=x.output,d=x.newStates}o&&u.push(h)}let A;return o&&(A=Sn(u,1)),[h,A,d]})}var Dr=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Lp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ht({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 vr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){QA(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 j(()=>{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 ze("Constants support is not implemented in RNN yet.");QA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ht({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("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(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ht({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new ia("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)$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 U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):$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(!k.arraysEqual(a.shape,i))throw new U(`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=K3(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 Ht({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 gr){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 j(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Le(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 U(`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=Z3((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 j(()=>{let t=Ct(e.shape);return t=Ce(t,[1,2]),t=gc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?LA(t,[1,n]):t):this.cell.stateSize>1?[LA(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()===Dr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=kr(r,n);return new e(Object.assign(t,{cell:a}))}};Dr.className="RNN";ae.registerClass(Dr);var _c=class extends Ze{},Wp=class extends _c{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,Xt(this.units,"units"),this.activation=Pa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=_t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=_l([1,Da([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_l([1,Da([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=La({ones:()=>In(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=La({ones:()=>In(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(B(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=B(n,i));let o=ie(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Wp.className="SimpleRNNCell";ae.registerClass(Wp);var Cy=class extends Dr{constructor(e){e.cell=new Wp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&($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)}};Cy.className="SimpleRNN";ae.registerClass(Cy);var Bp=class extends _c{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Xt(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=_t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=_l([1,Da([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_l([1,Da([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=La({ones:()=>In(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=La({ones:()=>In(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=B(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=an(u,[2*this.units,this.units],u.rank-1),p=Vr(r,h),[f,m,A]=an(c,3,c.rank-1),[y,g]=an(p,2,p.rank-1);i=this.recurrentActivation.apply(ie(f,y)),o=this.recurrentActivation.apply(ie(m,g));let w=Vr(B(o,r),d);l=this.activation.apply(ie(A,w));let x=ie(B(i,r),B(ie(1,kt(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:za(this.activation),recurrentActivation:za(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Bp.className="GRUCell";ae.registerClass(Bp);var Ry=class extends Dr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&($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)}};Ry.className="GRU";ae.registerClass(Ry);var Ec=class extends _c{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,Xt(this.units,"units"),this.activation=Pa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Pa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=_t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=_l([1,Da([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_l([1,Da([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=mt(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 lr{apply(i,o){let l=a.apply([s]),c=new Ap().apply([s]),u=a.apply([s*2]);return Xb(Xb(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 j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=La({ones:()=>In(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=La({ones:()=>In(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,i[0])),h=ie(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=an(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=ie(B(l,a),B(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=B(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:za(this.activation),recurrentActivation:za(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Ec.className="LSTMCell";ae.registerClass(Ec);var Fy=class extends Dr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Ec(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&($e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&($e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="LSTM";ae.registerClass(Fy);var Lp=class extends _c{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){QA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{fi(`RNNCell_${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(kr(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 ey(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]])}ty(t)}};Lp.className="StackedRNNCells";ae.registerClass(Lp);function La(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>Zb(t(),n),i=()=>wc(s,t,r);return!a||a<=1?jt(i().clone()):Array(a).fill(void 0).map(i).map(o=>jt(o.clone()))}var Vee=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},Y3=class extends Dr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ht({ndim:5})]}call(e,t){return j(()=>{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 U("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 j(()=>{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){j(()=>{if(!this.stateful)throw new ia("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 U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>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 U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):$e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!k.arraysEqual(i.shape,o))throw new U(`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=Ir(l,r[0],a,s[0],i[0]),h=Ir(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};Y3.className="ConvRNN2D";var Vp=class extends Ec{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,Xt(this.filters,"filters"),this.kernelSize=Il(n,2,"kernelSize"),this.kernelSize.forEach(o=>Xt(o,"kernelSize")),this.strides=Il(r||1,2,"strides"),this.strides.forEach(o=>Xt(o,"strides")),this.padding=a||"valid",Xn(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Il(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Xt(o,"dilationRate"))}build(e){var t;e=mt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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 lr{apply(u,h){let d=l.apply([c]),p=Rr([c]),f=l.apply([c*2]);return BA([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 j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=La({ones:()=>In(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,se,Q)=>!se||!se[Q]?J:B(se[Q],J),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=La({ones:()=>In(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,x,_,b]=an(this.kernel.read(),i,g),[T,S,N,C]=this.useBias?an(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,T,this.padding),u=this.inputConv(u,x,S,this.padding),h=this.inputConv(h,_,N,this.padding),d=this.inputConv(d,b,C,this.padding);let[$,D,O,V]=an(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,$),m=this.recurrentConv(m,D),A=this.recurrentConv(A,O),y=this.recurrentConv(y,V);let W=this.recurrentActivation.apply(ie(c,f)),Z=this.recurrentActivation.apply(ie(u,m)),K=ie(B(Z,s),B(W,this.activation.apply(ie(h,A)))),te=B(this.recurrentActivation.apply(ie(d,y)),this.activation.apply(K));return[te,te,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Vee(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=Zr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return Zr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Vp.className="ConvLSTM2DCell";ae.registerClass(Vp);var My=class extends Y3{constructor(e){let t=new Vp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};My.className="ConvLSTM2D";ae.registerClass(My);var Up=class extends Ze{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 j(()=>{this.invokeCallHook(e,t);let n=Le(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return wc(()=>Zb(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()}};Up.className="Dropout";ae.registerClass(Up);var $y=class extends Up{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};$y.className="SpatialDropout1D";ae.registerClass($y);var Dy=class extends Ze{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,Xt(this.units,"units"),this.activation=Pa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=_t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=_t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(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=mt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(e),r=Lb(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:za(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Dense";ae.registerClass(Dy);var Oy=class extends Ze{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],$a(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(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 yJ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Oy.className="Flatten";ae.registerClass(Oy);var zy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.activation=Pa(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:za(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Activation";ae.registerClass(zy);var Py=class extends Ze{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Le(e),mJ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Py.className="RepeatVector";ae.registerClass(Py);var Ly=class extends Ze{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 U("Can only specifiy one unknown dimension.");else a*=l}let i=$a(e);if(s!==null){if(a===0||i%a!=0)throw new U(n);r[s]=i/a}else if(i!==a)throw new U(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 j(()=>{this.invokeCallHook(e,t);let n=Le(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}};Ly.className="Reshape";ae.registerClass(Ly);var Wy=class extends Ze{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=vr(1,e.dims.length+1);if(!k.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 Ht({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return ot(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="Permute";ae.registerClass(Wy);var By=class extends Ze{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=Le(e),r=-1;return wu(_a(n,this.maskValue),r)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(e),r=-1,a=!0,s=wu(_a(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};By.className="Masking";ae.registerClass(By);var Vy=class extends Ze{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(gt(e.inputLength))}this.inputDim=e.inputDim,Xt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Xt(this.outputDim,"outputDim"),this.embeddingsInitializer=_t(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Vt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Le(e),_a(e,qe(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=gt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new U(`"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 j(()=>{this.invokeCallHook(e,t);let n=Le(e);return n.dtype!=="int32"&&(n=yc(n,"int32")),Kb(this.embeddings.read(),n.as1D()).reshape(mt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:St(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:Bt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="Embedding";ae.registerClass(Vy);var xi=class extends Ze{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}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 U("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=[mt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ma(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ma(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Da(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=gc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat($a(c.slice(1))));d=ot(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=vr(1,l).concat([0]);n.push(ot(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=ot(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(vr(0,i-1));s=ot(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ma(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Vn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=rr(n,t[r]);return n})}},Uy=class extends xi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};Uy.className="Add";ae.registerClass(Uy);var jy=class extends xi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};jy.className="Multiply";ae.registerClass(jy);var Hy=class extends xi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return B(1/e.length,t)})}};Hy.className="Average";ae.registerClass(Hy);var Gy=class extends xi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=yr(t,e[n]);return t})}};Gy.className="Maximum";ae.registerClass(Gy);var qy=class extends xi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Gs(t,e[n]);return t})}};qy.className="Minimum";ae.registerClass(qy);var Xy=class extends xi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>BA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(In(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Vn(t[s],-1)):r.push(t[s]);let a=pt(r,this.axis);return Kh(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Concatenate";ae.registerClass(Xy);function Cc(e,t){for(;e<0;)e+=t;return e}function Uee(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 ze("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 j(()=>{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 Ky=class extends xi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 ze("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 U(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Cc(a,e[s].shape.length)):r=[Cc(this.axes,t.shape.length),Cc(this.axes,n.shape.length)],this.normalize&&(t=Tp(t,r[0]),n=Tp(n,r[1])),Uee(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cc(this.axes,e.length),Cc(this.axes,t.length)],n}computeOutputShape(e){k.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 ze("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Dot";ae.registerClass(Ky);var Zy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(e);return wc(()=>mp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Zy.className="GaussianNoise";ae.registerClass(Zy);var Yy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?wc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(mp(n.shape,1,r))},()=>n,t.training||!1):n})}};Yy.className="GaussianDropout";ae.registerClass(Yy);var Jy=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return wc(()=>{let r=Le(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Jr(jo(n),this.rate);o=yc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Le(e),t.training||!1)}return e})}};Jy.className="AlphaDropout";ae.registerClass(Jy);function Rc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=fg(e,t,n,r,a,s);else if(e.rank===3)i=mg(e,t,n,r,a,s);else if(e.rank===4)i=Ag(e,t,n,r,a,s);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function jee(e,t,n,r,a=.001){return j(()=>{let s=sd(e,r),i=s.mean,o=s.variance;return[Rc(e,i,o,n,t,a),i,o]})}function Hee(e,t,n,r,a=.001){return j(()=>{let s=sd(e,r),i=s.mean,o=s.variance,l=[];for(let p of vr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Rc(e,c,u,d,h,a),i,o]})}function Gee(e,t,n,r,a=.001){return k.arraysEqual(r.slice().sort(),vr(0,e.rank-1))?jee(e,t,n,r,a):Hee(e,t,n,r,a)}var Qy=class extends Ze{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=_t(e.betaInitializer||"zeros"),this.gammaInitializer=_t(e.gammaInitializer||"ones"),this.movingMeanInitializer=_t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=_t(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ht({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 j(()=>{let n=t.training==null?!1:t.training,r=Le(e),a=r.shape,s=a.length,i=vr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=hi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,vr(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 Rc(r,A,y,g,w,this.epsilon)}else return Rc(r,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 h();let[d,p,f]=Gee(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{j(()=>{let w=1-g,x=A.read(),_=x.sub(y).mul(w);A.write(x.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),movingMeanInitializer:St(this.movingMeanInitializer),movingVarianceInitializer:St(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:Bt(this.betaConstraint),gammaConstraint:Bt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Qy.className="BatchNormalization";ae.registerClass(Qy);var e2=class extends Ze{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=_t(e.betaInitializer||"zeros"),this.gammaInitializer=_t(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ma(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=Le(e),r=n.shape,a=r.length;return j(()=>{let s=!0,{mean:i,variance:o}=sd(n,this.axis,s),l=hi(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),Rc(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:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};e2.className="LayerNormalization";ae.registerClass(e2);function qee(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=_r()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Qr(e,r)})}var t2=class extends Ze{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_r():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){e=mt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>qee(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};t2.className="ZeroPadding2D";ae.registerClass(t2);function jp(e,t,n,r,a,s){return j(()=>{Rt(a),Vb(s),Xn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=_r()),s==null&&(s="max"),e=vy(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Cu(e,t,n,o):i=bu(e,t,n,o),a==="channelsFirst"&&(i=ot(i,[0,3,1,2])),i})}function J3(e,t,n,r,a,s){return j(()=>{Rt(a),Vb(s),Xn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=_r()),s==null&&(s="max"),e=G3(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Bf(e,t,n,o):i=Cf(e,t,n,o),a==="channelsFirst"&&(i=ot(i,[0,4,1,2,3])),i})}var Q3=class extends Ze{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 U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Xt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xn(this.padding),this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(e){e=mt(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=gc(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ba(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},n2=class extends Q3{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),jp(e,t,n,r,a,"max")}};n2.className="MaxPooling1D";ae.registerClass(n2);var r2=class extends Q3{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),jp(e,t,n,r,a,"avg")}};r2.className="AveragePooling1D";ae.registerClass(r2);var e7=class extends Ze{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 U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xn(this.padding),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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 e7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),jp(e,t,n,r,a,"max")}};a2.className="MaxPooling2D";ae.registerClass(a2);var s2=class extends e7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),jp(e,t,n,r,a,"avg")}};s2.className="AveragePooling2D";ae.registerClass(s2);var t7=class extends Ze{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 U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xn(this.padding),this.inputSpec=[new Ht({ndim:5})]}computeOutputShape(e){e=mt(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=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(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 j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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}},i2=class extends t7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),J3(e,t,n,r,a,"max")}};i2.className="MaxPooling3D";ae.registerClass(i2);var o2=class extends t7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Rt(a),Xn(r),J3(e,t,n,r,a,"avg")}};o2.className="AveragePooling3D";ae.registerClass(o2);var n7=class extends Ze{constructor(e){super(e);this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},l2=class extends n7{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Le(e);return It(n,1)})}};l2.className="GlobalAveragePooling1D";ae.registerClass(l2);var u2=class extends n7{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Le(e);return jn(n,1)})}};u2.className="GlobalMaxPooling1D";ae.registerClass(u2);var r7=class extends Ze{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},c2=class extends r7{call(e,t){return j(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};c2.className="GlobalAveragePooling2D";ae.registerClass(c2);var h2=class extends r7{call(e,t){return j(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?jn(n,[1,2]):jn(n,[2,3])})}};h2.className="GlobalMaxPooling2D";ae.registerClass(h2);var a7=class extends Ze{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 r=t.layer,a=kr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},d2=class extends a7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=mt(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 j(()=>(e=Le(e),Z3((n,r)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};d2.className="TimeDistributed";ae.registerClass(d2);function Xee(e){pi(cJ,"BidirectionalMergeMode",e)}var Kee="concat",p2=class extends a7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=kr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=kr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Kee:e.mergeMode,Xee(this.mergeMode),e.weights)throw new ze("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()):yn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=K3(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 U("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 Ht({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 ze("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof gr;for(let l of s)if(l instanceof gr!==o)throw new U("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 j(()=>{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=Nn(a,1));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=Wte(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=Lte(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=qp(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 jte=(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[Zh(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=qp(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Zr(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=E7(e,t,n);return[va.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=E7(e,t,n);return[va.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=qp(e,t,n);return[Yh(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=qp(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[js(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Ff(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[bu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Cu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=Cg(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Cf(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Bf(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[$f(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hte=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[Nu(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[Ng(r,a,s)]}case"Multinomial":{let 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r=I("boxes",e,t,n),a=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var Gte=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=N2(e,t,n),c=await Dt.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=N2(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Dt.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=N2(e,t,n);return[await Dt.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=xe(I("condition",e,t,n),"bool"),a=[await Jf(r)];return 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I("x",e,t,n).map(c=>tn(c.shape));case"Size":return[Te(I("x",e,t,n).size,"int32")];case"Rank":return[Te(I("x",e,t,n).rank,"int32")];case"NoOp":return[Te(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kte=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Te(0),this.tensorMap=new Map,jt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),j(()=>{let r=ar(t),a=n.length,s=r.length;k.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];jt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return j(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return Sn(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Zte=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new Kte(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yte=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Dt.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Dt.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[Dt.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jte=(e,t,n)=>{switch(e.op){case"Equal":return[Yr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[_a(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Un(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Jr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Tu(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[wa(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[rr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Eu(I("a",e,t,n))];case"LogicalOr":return[rd(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[mn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},ene=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Us(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[Us(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Lf(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[Du(I("x",e,t,n))];case"LogSoftmax":return[nd(I("x",e,t,n))];case"SparseToDense":return[Qf(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tne=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[jn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[It(I("x",e,t,n),i,o)]}case"Min":{let 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r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[Mf(I("x",e,t,n),r,a)]}case"BroadcastTo":return[ku(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function C7(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return j(()=>Dte(s,i,o));case"basic_math":return j(()=>Ote(s,i,o));case"control":return Ute(s,i,o);case"convolution":return j(()=>jte(s,i,o));case"creation":return j(()=>Hte(s,i,o));case"dynamic":return Gte(s,i,o);case"evaluation":return j(()=>qte(s,i,o));case"image":return j(()=>Yte(s,i,o));case"graph":return j(()=>Xte(s,i,o));case"logical":return j(()=>Jte(s,i,o));case"matrices":return j(()=>Qte(s,i,o));case"normalization":return j(()=>ene(s,i,o));case"reduction":return j(()=>tne(s,i,o));case"slice_join":return j(()=>nne(s,i,o));case"spectral":return j(()=>rne(s,i,o));case"transformation":return j(()=>ane(s,i,o));case"hash_table":return Zte(s,i,o,r);case"custom":let l=o7(s.op);if(l&&l.customExecutor)return l.customExecutor(new $te(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var R7=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function M7(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>$n(d)[0]),u=[];r!=null&&(u=r.map(d=>$n(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((F7(d)||sne(d)||ine(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 one(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>$n(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 lne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],une=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],cne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function F7(e){return lne.indexOf(e.op)>=0}function sne(e){return une.indexOf(e.op)>=0}function ine(e){return cne.indexOf(e.op)>=0}var S2=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 S2(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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=M7(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${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: [${r}]`)}return one(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[$n(u)[0]]),a=t.map(u=>$n(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return j(()=>{let u=new R7(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=$n(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=C7(m,h,u,this._resourceManager);if(k.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(f=>xn(f,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=mte(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new R7(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>xn(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[$n(g)[0]]),i=n.map(g=>$n(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=M7(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[w,x]=$n(g),_=[];_[x]=e[g],p[w]=_});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}u==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!F7(g)&&!xn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=la(u.node.name,n)),r[u.node.name]==null){let d=C7(u.node,r,n,this._resourceManager);h||([h]=la(u.node.name,n));let p=n.currentContext;k.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=la(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!xn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!xn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=$n(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);k.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&k.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=$n(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=$n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},hne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},dne="?tfjs-format=file",pne="model.json",k0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new hne}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=pn.browserHTTPRequest(e,this.loadOptions);else{let t=pn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(pn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=pn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new S2(N7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=N7.Instance.transformGraph(e.modelInitializer);this.initializer=new S2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=pn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 H)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function fr(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${pne}${dne}`);let n=new k0(e,t);return await n.load(),n}var t8="2.8.5",I0={};Pe(I0,{CSVDataset:()=>D7,Dataset:()=>Nl,FileDataSource:()=>O7,TextLineDataset:()=>$7,URLDataSource:()=>z7,array:()=>fne,csv:()=>Ane,func:()=>yne,generator:()=>gne,microphone:()=>wne,version_data:()=>_ne,webcam:()=>xne,zip:()=>mne});var bne=Xo(S0()),vne=Xo(S0());function kne(e,t){return Xp(e,t)}function Xp(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(Sl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=Xp(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 Ine(e,t=L7){return P7(e,t)}function P7(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(Sl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=P7(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 L7(e){return e===null?null:Sl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function W7(e,t){let n=new Map;Xp(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(k.isPromise(a)){let s=await a;n.set(r,s)}}return Xp(e,t,n)}function Sl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof H))}function Sne(e){return e==null||Nne(e)||Array.isArray(e)||typeof e=="object"&&e instanceof H||k.isTypedArray(e)}function Nne(e){return e===null||typeof e!="object"&&typeof e!="function"}function Ene(e){return kne(e,Tne)}function Tne(e){return e instanceof H?{value:e.clone(),recurse:!1}:Sl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var B7=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}},T2=class extends B7{constructor(){super(T2.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}};T2.INITIAL_CAPACITY=32;function V7(e){return new Cne(e)}function E2(e){return new Rne(e)}function Fne(e,t){return new U7(e,t)}function $ne(e,t=Wa.FAIL){return new Mne(e,t)}var Kt=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 Bne(this,e)}filter(e){return new Lne(this,e)}map(e){return new Wne(this,e)}mapAsync(e){return new j7(this,e)}serialMapAsync(e){return new j7(this,e).serial()}flatmap(e){return new Vne(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 Pne(this,e,t)}columnMajorBatch(e,t=!0,n=L7){return this.rowMajorBatch(e,t).map(r=>Ine(r,n))}concatenate(e,t){return new U7(V7([this,e]),t)}take(e){return e<0||e==null?this:new zne(this,e)}skip(e){return e<0||e==null?this:new One(this,e)}prefetch(e){return new H7(this,e)}shuffle(e,t){return new Une(this,e,t)}serial(){return new Dne(this)}},Cne=class extends Kt{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:Ene(e),done:!1}}},Rne=class extends Kt{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: ${e.message}`,e}}},Dne=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},One=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;$e(e.value)}return this.upstream.next()}},zne=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Pne=class extends Kt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},Lne=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;$e(e.value)}}},Wne=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Bne=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},j7=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},C2=class extends Kt{constructor(){super();this.outputQueue=new T2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Vne=class extends C2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=mr.getTensorsInContainer(e.value),n=this.transform(e.value),r=mr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return!0}},U7=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 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}},Wa;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Wa||(Wa={}));var Mne=class extends Kt{constructor(e,t=Wa.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 Kt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await W7(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Wa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Wa.SHORTEST:return{value:null,done:!0};case Wa.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},H7=class extends Kt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new B7(t)}summary(){return`${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()}},Une=class extends H7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=vne.alea(n||k.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}}},Nl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Dn(async()=>(await n.iterator()).columnMajorBatch(e,t,jne),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Dn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Dn(async()=>(await t.iterator()).filter(r=>j(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Dn(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return Dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Dn(async()=>{let r=E2(async()=>({value:await t.iterator(),done:!1}));return Fne(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Dn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=bne.alea(t||k.now().toString());return Dn(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,Dn(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()}};Nl.MAX_BUFFER_SIZE=1e4;function Dn(e,t=null){return new class extends Nl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function fne(e){return Dn(async()=>V7(e),e.length)}function mne(e){if(!Sl(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 Dn(async()=>{let n=await W7(e,r=>{if(r instanceof Nl)return{value:r.iterator(),recurse:!1};if(Sl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return $ne(n,Wa.SHORTEST)},t)}function jne(e){if(e===null)return null;let t=e[0];return Sne(t)?{value:Hne(e),recurse:!1}:{value:null,recurse:!0}}function Hne(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof H?Sn(e):Ar(e)}var $7=class extends Nl{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))}},Kp='"',Mc=Symbol("out"),G7=Symbol("field"),Zp=Symbol("quote"),R2=Symbol("quoteafterquote"),q7=Symbol("quoteinquote"),D7=class extends Nl{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 $7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.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&&k.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(k.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=Mc;for(let i=0;i<a;i++)switch(s){case Mc:switch(e.charAt(i)){case Kp:r=i+1,s=Zp;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Mc;break;default:s=G7,r=i;break}break;case G7:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Mc,r=i+1;break;default:}break;case Zp:switch(e.charAt(i)){case Kp:s=R2;break;default:}break;case R2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Mc,r=i+1;break;case Kp:s=Zp;break;default:s=q7;break}break;case q7:switch(e.charAt(i)){case Kp:s=Zp;break;default:}break;default:}if(s===R2?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}},X7=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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new X7(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(k.sizeFromShape(t));return n.set(e,n.length-e.length),Ar(n,t)}},K7=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=tn([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=pr([s,a,o,i],[1,4])}else this.cropBox=pr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ee().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 K7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=Jl.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=e.toFloat().expandDims(0),n;n=Dt.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return n.reshape(r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Z7=class{},Y7=class extends Kt{split(e){return new Gne(this,e)}},Gne=class extends Y7{constructor(e,t){super();this.upstream=e,this.impl=new qne(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qne=class extends C2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Kne=class extends Kt{decodeUTF8(){return new Xne(this)}},Xne=class extends Y7{constructor(e){super();this.upstream=e,this.impl=new Zne(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zne=class extends C2{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new 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2Q==`,rre="0.10.1",yt=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function Tl(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Tl(s,i):n[a]=i}),n),{})}var i6=class{constructor(e={}){this.tf=eg,this.version=rre,this.config=Tl(r6,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=F2,this.age=$c,this.gender=Dc,this.emotion=Oc,this.body=M2,this.hand=$2}profile(){return this.config.profile?nre.data:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let t=Wn().state.numTensors,n=this.numTensors;this.numTensors=t;let r=t-n;r!==0&&Je(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(vn.flags.IS_NODE&&!(e instanceof H))return"input must be a tensor";try{qh()}catch(t){return"backend not loaded"}return null}simmilarity(e,t){return this.config.face.embedding.enabled?zc.simmilarity(e,t):0}async load(e){this.state="load";let t=yt();e&&(this.config=Tl(this.config,e)),this.firstRun&&(Je(`version: ${this.version} TensorFlow/JS version: ${ug}`),await this.checkBackend(!0),vn.flags.IS_BROWSER&&(Je("configuration:",this.config),Je("tf flags:",vn.flags))),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?F2.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?$c.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Dc.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Oc.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?zc.load(this.config):null),this.models.posenet||(this.config.body.enabled?M2.load(this.config):null),this.models.handpose||(this.config.hand.enabled?$2.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await F2.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await $c.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Dc.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Oc.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await zc.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await M2.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await $2.load(this.config))),this.firstRun&&(Je("tf engine state:",Wn().state.numBytes,"bytes",Wn().state.numTensors,"tensors"),this.firstRun=!1);let n=Math.trunc(yt()-t);n>(this.perf.load||0)&&(this.perf.load=n)}async checkBackend(e){if(this.config.backend&&this.config.backend!==""&&e||qh()!==this.config.backend){let t=yt();this.state="backend",Je("setting backend:",this.config.backend),this.config.backend==="wasm"&&(Je("settings wasm path:",this.config.wasmPath),u0(this.config.wasmPath),await ee().getAsync("WASM_HAS_SIMD_SUPPORT")||Je("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Qne();try{await hg(this.config.backend)}catch(n){Je("error: cannot set backend:",this.config.backend,n)}if(cg(),qh()==="webgl"){this.config.deallocate&&(Je("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),vn.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),vn.set("WEBGL_FORCE_F16_TEXTURES",!0),vn.set("WEBGL_PACK_DEPTHWISECONV",!0);let n=await _f().getGPGPUContext().gl;Je(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}await dg(),this.perf.backend=Math.trunc(yt()-t)}}async detectFace(e){var t;let n,r,a,s,i,o=[];this.state="run:face",n=yt();let l=await((t=this.models.facemesh)==null?void 0:t.estimateFaces(e,this.config));this.perf.face=Math.trunc(yt()-n);for(let c of l){if(this.analyze("Get Face"),!c.image||c.image.isDisposedInternal){Je("Face object is disposed:",c.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?$c.predict(c.image,this.config):{}:(this.state="run:age",n=yt(),r=this.config.face.age.enabled?await $c.predict(c.image,this.config):{},this.perf.age=Math.trunc(yt()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?Dc.predict(c.image,this.config):{}:(this.state="run:gender",n=yt(),a=this.config.face.gender.enabled?await Dc.predict(c.image,this.config):{},this.perf.gender=Math.trunc(yt()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?Oc.predict(c.image,this.config):{}:(this.state="run:emotion",n=yt(),s=this.config.face.emotion.enabled?await Oc.predict(c.image,this.config):{},this.perf.emotion=Math.trunc(yt()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?zc.predict(c.image,this.config):{}:(this.state="run:embedding",n=yt(),i=this.config.face.embedding.enabled?await zc.predict(c.image,this.config):{},this.perf.embedding=Math.trunc(yt()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),c.image.dispose(),this.config.face.iris.enabled||(delete c.annotations.leftEyeIris,delete c.annotations.rightEyeIris);let u=c.annotations.leftEyeIris&&c.annotations.rightEyeIris?11.7*Math.max(Math.abs(c.annotations.leftEyeIris[3][0]-c.annotations.leftEyeIris[1][0]),Math.abs(c.annotations.rightEyeIris[4][1]-c.annotations.rightEyeIris[2][1])):0;o.push({confidence:c.confidence,box:c.box,mesh:c.mesh,boxRaw:c.boxRaw,meshRaw:c.meshRaw,annotations:c.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:u!==0?Math.trunc(u)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async image(e,t={}){this.state="image",this.config=Tl(this.config,t);let n=n6.process(e,this.config);return n.tensor.dispose(),n.canvas}async detect(e,t={}){return new Promise(async n=>{var r,a,s,i;this.state="config";let o;this.config=Tl(this.config,t),this.state="check";let l=this.sanity(e);l&&(Je(l,e),n({error:l}));let c,u,h,d=yt();await this.checkBackend(),await this.load(),this.config.scoped&&Wn().startScope(),this.analyze("Start Scope:"),o=yt();let p=n6.process(e,this.config);if(!p||!p.tensor){Je("could not convert input to tensor"),n({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(yt()-o),this.analyze("Get Image:"),this.config.async?(h=this.config.face.enabled?this.detectFace(p.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",o=yt(),h=this.config.face.enabled?await this.detectFace(p.tensor):[],this.perf.face=Math.trunc(yt()-o)),this.analyze("Start Body:"),this.config.async?(c=this.config.body.enabled?(r=this.models.posenet)==null?void 0:r.estimatePoses(p.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",o=yt(),c=this.config.body.enabled?await((a=this.models.posenet)==null?void 0:a.estimatePoses(p.tensor,this.config)):[],this.perf.body=Math.trunc(yt()-o)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(s=this.models.handpose)==null?void 0:s.estimateHands(p.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",o=yt(),u=this.config.hand.enabled?await((i=this.models.handpose)==null?void 0:i.estimateHands(p.tensor,this.config)):[],this.perf.hand=Math.trunc(yt()-o)),this.analyze("End Hand:"),this.config.async&&([h,c,u]=await Promise.all([h,c,u])),p.tensor.dispose(),this.config.scoped&&Wn().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(o=yt(),f=[...Yp.face(h),...Yp.body(c),...Yp.hand(u),...Yp.iris(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(yt()-o)),this.perf.total=Math.trunc(yt()-d),this.state="idle",n({face:h,body:c,hand:u,gesture:f,performance:this.perf,canvas:p.canvas})})}async warmupBitmap(){let e=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(s=>s.blob()),t,n;switch(this.config.warmup){case"face":t=await e(a6);break;case"full":t=await e(s6);break;default:t=null}if(t){let r=await createImageBitmap(t);n=await this.detect(r,D2),r.close()}return n}async warmupCanvas(){return new Promise(e=>{let t,n=0;switch(this.config.warmup){case"face":n=256,t="data:image/jpeg;base64,"+a6;break;case"full":n=1200,t="data:image/jpeg;base64,"+s6;break;default:t=null}let r=new Image(n,n);r.onload=()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(n,n):document.createElement("canvas");a.width=n,a.height=n;let s=a.getContext("2d");s.drawImage(r,0,0);let i=s.getImageData(0,0,n,n);this.detect(i,D2).then(o=>e(o))},t?r.src=t:e(null)})}async warmup(e){let t=yt();e&&(this.config=Tl(this.config,e));let n=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():r=await this.warmupCanvas(),this.config.videoOptimized=n;let a=yt();return Je("Warmup",this.config.warmup,Math.round(a-t),"ms",r),r}};async function are(e,t,n){if(!e)return;let r=t.getContext("2d");r.font=n.baseFont,r.fillStyle=n.baseLabel;let a=1;for(let s=0;s<e.length;s++){let[i,o]=Object.entries(e[s]);if(o.length>1&&o[1].length>0){let l=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${l}: ${o[1]}`;r.fillStyle="black",r.fillText(c,8,2+a*n.baseLineHeight),r.fillStyle=n.baseLabel,r.fillText(c,6,0+a*n.baseLineHeight),a+=1}}}async function sre(e,t,n,r){if(!e)return;let a=t.getContext("2d");for(let s of e){a.font=n.baseFont,a.strokeStyle=n.baseColor,a.fillStyle=n.baseColor,a.lineWidth=n.baseLineWidth,a.beginPath(),n.drawBoxes&&a.rect(s.box[0],s.box[1],s.box[2],s.box[3]);let i=[];if(s.genderConfidence&&i.push(`${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)}% ${l.emotion}`);i.push(o.join(" "))}i.length===0&&i.push("face"),a.fillStyle=n.baseLabel;for(let o=0;o<i.length;o++)a.fillStyle="black",a.fillText(i[o],s.box[0]+1,s.box[1]-(i.length-o)*n.baseLineHeight+6),a.fillStyle=n.baseLabel,a.fillText(i[o],s.box[0]+0,s.box[1]-(i.length-o)*n.baseLineHeight+5);if(a.fillStyle=n.baseColor,a.stroke(),a.lineWidth=1,s.mesh){if(n.drawPoints)for(let o of s.mesh)a.fillStyle=n.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:n.baseColor,a.beginPath(),a.arc(o[0],o[1],2,0,2*Math.PI),a.fill();if(n.drawPolygons){for(let o=0;o<r.length/3;o++){let l=[r[o*3+0],r[o*3+1],r[o*3+2]].map(u=>s.mesh[u]),c=new Path2D;c.moveTo(l[0][0],l[0][1]);for(let u of l)c.lineTo(u[0],u[1]);c.closePath(),a.strokeStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.stroke(c),n.fillPolygons&&(a.fillStyle=n.useDepth?`rgba(${127.5+2*l[0][2]}, ${127.5-2*l[0][2]}, 255, 0.3)`:n.baseColor,a.fill(c))}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,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(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.baseColor,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(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.baseColor,a.fill())}}}}}var Ba=[];async function ire(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a=0;a<e.length;a++){if(!Ba[a]&&n.buffered&&(Ba[a]={...e[a]}),r.fillStyle=n.baseColor,r.strokeStyle=n.baseColor,r.font=n.baseFont,r.lineWidth=n.baseLineWidth,n.drawPoints)for(let s=0;s<e[a].keypoints.length;s++)r.beginPath(),n.buffered?(Ba[a].keypoints[s].position.x=(Ba[a].keypoints[s].position.x+e[a].keypoints[s].position.x)/2,Ba[a].keypoints[s].position.y=(Ba[a].keypoints[s].position.y+e[a].keypoints[s].position.y)/2,r.arc(Ba[a].keypoints[s].position.x,Ba[a].keypoints[s].position.y,2,0,2*Math.PI)):r.arc(e[a].keypoints[s].position.x,e[a].keypoints[s].position.y,2,0,2*Math.PI),r.fill();if(n.drawPolygons){let s=new Path2D,i,o;i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightShoulder"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftShoulder"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftWrist"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightWrist"),o&&s.lineTo(o.position.x,o.position.y)),r.stroke(s)}}}async function ore(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a of e){if(r.font=n.baseFont,r.lineWidth=n.baseLineWidth,n.drawBoxes&&(r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.baseColor,r.fillStyle=n.baseColor,r.rect(a.box[0],a.box[1],a.box[2],a.box[3]),r.fillStyle="black",r.fillText("hand",a.box[0]+3,1+a.box[1]+n.baseLineHeight,a.box[2]),r.fillStyle=n.baseLabel,r.fillText("hand",a.box[0]+2,0+a.box[1]+n.baseLineHeight,a.box[2]),r.stroke()),n.drawPoints&&a.landmarks&&a.landmarks.length>0)for(let s of a.landmarks)r.fillStyle=n.useDepth?`rgba(${127.5+2*s[2]}, ${127.5-2*s[2]}, 255, 0.5)`:n.baseColor,r.beginPath(),r.arc(s[0],s[1],2,0,2*Math.PI),r.fill();if(n.drawPolygons){let s=i=>{if(!!i)for(let o=0;o<i.length;o++)r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.useDepth?`rgba(${127.5+2*i[o][2]}, ${127.5-2*i[o][2]}, 255, 0.5)`:n.baseColor,r.moveTo(i[o>0?o-1:0][0],i[o>0?o-1:0][1]),r.lineTo(i[o][0],i[o][1]),r.stroke()};s(a.annotations.indexFinger),s(a.annotations.middleFinger),s(a.annotations.ringFinger),s(a.annotations.pinky),s(a.annotations.thumb)}}}var Pc={face:sre,body:ire,hand:ore,gesture:are};var Lc=0,o6=!1,vt={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function lre(){if(o6)return;let e=`
:root { --rounded: 0.2rem; }
.menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;
box-shadow: 0 0 8px dimgrey; background: ${vt.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }
.menu:hover { box-shadow: 0 0 8px ${vt.hover}; }
.menu-container { display: block; max-height: 100vh; }
.menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }
.menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }
.menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }
.menu-title { cursor: pointer; }
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
.menu-label { padding: 0; font-weight: 800; }
.menu-list { margin-right: 0.8rem; }
select:focus { outline: none; }
.menu-list-item { background: ${vt.itemBackground}; color: ${vt.itemColor}; border: none; padding: 0.2rem; font-family: inherit;
font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }
.menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}
.menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }
.menu-button { border: 0; background: ${vt.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;
border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }
.menu-button:hover { background: ${vt.buttonHover}; box-shadow: 4px 4px 4px 0 black; }
.menu-button:focus { outline: none; }
.menu-checkbox { width: 2.8rem; height: 1rem; background: ${vt.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }
.menu-checkbox:after { content: 'OFF'; color: ${vt.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
.menu-checkbox:before { content: 'ON'; color: ${vt.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
.menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${vt.checkboxOff};
border-radius: var(--rounded); transition: left 0.6s ease; }
input[type=checkbox] { visibility: hidden; }
input[type=checkbox]:checked + label { left: 1.4rem; background: ${vt.checkboxOn}; }
.menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${vt.rangeBackground}; }
.menu-range:before { color: ${vt.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }
input[type=range] { -webkit-appearance: none; }
input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${vt.itemBackground}; border-radius: var(--rounded); border: 1px; }
input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${vt.itemBackground}; border-radius: var(--rounded); border: 1px; }
input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${vt.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${vt.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
.svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }
.svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }
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menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`<div class="menu-checkbox"><input class="menu-checkbox" type="checkbox" id="${this.newID}" ${n[r]?"checked":""}/><label class="menu-checkbox-label" for="${this.ID}"></label></div>${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=`<option value="${o}" ${o===r?"selected":""}>${o}</option>`;return s.innerHTML=`<div class="menu-list"><select name="${this.ID}" class="menu-list-item">${i}</select><label 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#gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }
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#gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }
#gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }
#gl-bench rect { fill: black; }
#gl-bench .opacity { stroke: black; }
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<div class="gl-box">
<svg viewBox="0 0 55 60">
<text x="27" y="56" class="gl-fps">00 FPS</text>
<text x="30" y="8" class="gl-mem"></text>
<rect x="0" y="14" rx="4" ry="4" width="55" height="32"></rect>
<polyline class="gl-chart"></polyline>
</svg>
<svg viewBox="0 0 14 60" class="gl-cpu-svg">
<line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
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<svg viewBox="0 0 14 60" class="gl-gpu-svg">
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<line x1="7" y1="38" x2="7" y2="11" class="gl-gpu" stroke-dasharray="0 27"/>
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`,u6=class{constructor(t,n={}){this.css=ure,this.svg=cre,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(u,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-u;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(u,h,d)=>{let p=h.now();u.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},c="drawElements";t[c]?t[c]=l(t[c],this,t):console.log("bench: cannot attach to webgl 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video: ${oe.camera.name} | facing: ${oe.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${oe.camera.width} x ${oe.camera.height} ${o}<br>
backend: ${pe.tf.getBackend()} | ${i}<br>
performance: ${hre(t.performance)}ms FPS process:${l} refresh:${c}<br>
${u}<br>
`,oe.framesDraw++,oe.lastFrame=performance.now(),oe.buffered?oe.drawThread=requestAnimationFrame(()=>e1(e,n)):!oe.buffered&&oe.drawThread&&(On("stopping buffered refresh"),cancelAnimationFrame(oe.drawThread),oe.drawThread=null)}async function t1(){var c;if(oe.busy)return null;oe.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(Kn("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=`
${a}`,On(a),Kn(a),oe.busy=!1,a;let s,i={audio:!1,video:{facingMode:oe.facing?"user":"environment",resizeMode:oe.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(u){return u.name==="PermissionDeniedError"||u.name==="NotAllowedError"?a="camera permission denied":u.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${u.message||u}`,n.innerText+=`
${a}`,Kn(a),On("camera error:",u),oe.busy=!1,a}if(s)e.srcObject=s;else return oe.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return oe.camera={name:(c=o.label)==null?void 0:c.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(u=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",oe.menuWidth.input.setAttribute("value",e.width),oe.menuHeight.input.setAttribute("value",e.height);let h=Math.trunc(window.devicePixelRatio*(8+4*t.width/window.innerWidth));oe.baseFont=oe.baseFontProto.replace(/{size}/,`${h}px`),oe.baseLineHeight=h+2,r&&e.play(),r&&!oe.detectThread&&Bc(e,t),oe.busy=!1,Kn(""),u()}})}function d6(){if(!wi){let e=null;wi=new c6(e,{trackGPU:!1,chartHz:20,chartLen:20}),wi.begin()}}function pre(e,t,n,r){Jp||(On("creating worker thread"),Jp=new Worker(oe.worker,{type:"module"}),Jp.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&oe.detectFPS.push(1e3/a.data.result.performance.total),oe.detectFPS.length>oe.maxFPSframes&&oe.detectFPS.shift(),oe.bench&&(wi||d6(),wi.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=oe.bench?"block":"none"),Qp=a.data.result,oe.framesDetect++,oe.drawThread||e1(e),oe.detectThread=requestAnimationFrame(s=>Bc(e,n,s))})),Jp.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:Hr},[t.data.buffer])}function Bc(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){oe.drawThread&&cancelAnimationFrame(oe.drawThread),oe.detectThread&&cancelAnimationFrame(oe.detectThread),oe.drawThread=null,oe.detectThread=null,e.paused?On("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>Bc(e,t),500):On(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(oe.drawThread),oe.drawThread=null,On("frame statistics: process:",oe.framesDetect,"refresh:",oe.framesDraw),On("memory",pe.tf.engine().memory());return}if(Kn(""),oe.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);pre(e,o,t,Hr,n)}else pe.detect(e,Hr).then(s=>{s.performance&&s.performance.total&&oe.detectFPS.push(1e3/s.performance.total),oe.detectFPS.length>oe.maxFPSframes&&oe.detectFPS.shift(),oe.bench&&(wi||d6(),wi.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=oe.bench?"block":"none"),s.error?(On(s.error),document.getElementById("log").innerText+=`
Human error: ${s.error}`):(Qp=s,oe.drawThread||e1(e),oe.framesDetect++,oe.detectThread=requestAnimationFrame(i=>Bc(e,t,i)))})}async function fre(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{On("Processing image:",n.src);let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=pe.config.filter.width&&pe.config.filter.width>0?pe.config.filter.width:n.naturalWidth,r.height=pe.config.filter.height&&pe.config.filter.height>0?pe.config.filter.height:n.naturalHeight,Qp=await pe.detect(n,Hr),await e1(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(oe.columns+.1),s.height=r.height/(window.innerWidth/s.width),s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function p6(){Hr.videoOptimized=!0,document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start<br>video",Kn("paused"),e.pause();else{let n=await t1();if(n)Kn(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(_e))r.hide();Kn(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause<br>video",await e.play(),oe.detectThread||Bc(e,t)}}}async function mre(){document.getElementById("play").style.display="none",Hr.videoOptimized=!1;let e=Math.trunc(window.devicePixelRatio*(8+4*oe.columns));oe.baseFont=oe.baseFontProto.replace(/{size}/,`${e}px`),oe.baseLineHeight=e+2,document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",On("Running detection of sample images"),Kn("processing images"),document.getElementById("samples-container").innerHTML="";for(let t of oe.samples)await fre(t);Kn("")}function Are(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],_e.display=new Wc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),_e.display.addBool("perf monitor",oe,"bench",t=>oe.bench=t),_e.display.addBool("buffered output",oe,"buffered",t=>oe.buffered=t),_e.display.addBool("crop & scale",oe,"crop",()=>t1()),_e.display.addBool("camera facing",oe,"facing",()=>t1()),_e.display.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.display.addBool("use 3D depth",oe,"useDepth"),_e.display.addBool("draw boxes",oe,"drawBoxes"),_e.display.addBool("draw polygons",oe,"drawPolygons"),_e.display.addBool("Fill Polygons",oe,"fillPolygons"),_e.display.addBool("draw points",oe,"drawPoints"),_e.image=new Wc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),_e.image.addBool("enabled",pe.config.filter,"enabled"),oe.menuWidth=_e.image.addRange("image width",pe.config.filter,"width",0,3840,10,t=>pe.config.filter.width=parseInt(t)),oe.menuHeight=_e.image.addRange("image height",pe.config.filter,"height",0,2160,10,t=>pe.config.filter.height=parseInt(t)),_e.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.image.addRange("brightness",pe.config.filter,"brightness",-1,1,.05,t=>pe.config.filter.brightness=parseFloat(t)),_e.image.addRange("contrast",pe.config.filter,"contrast",-1,1,.05,t=>pe.config.filter.contrast=parseFloat(t)),_e.image.addRange("sharpness",pe.config.filter,"sharpness",0,1,.05,t=>pe.config.filter.sharpness=parseFloat(t)),_e.image.addRange("blur",pe.config.filter,"blur",0,20,1,t=>pe.config.filter.blur=parseInt(t)),_e.image.addRange("saturation",pe.config.filter,"saturation",-1,1,.05,t=>pe.config.filter.saturation=parseFloat(t)),_e.image.addRange("hue",pe.config.filter,"hue",0,360,5,t=>pe.config.filter.hue=parseInt(t)),_e.image.addRange("pixelate",pe.config.filter,"pixelate",0,32,1,t=>pe.config.filter.pixelate=parseInt(t)),_e.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.image.addBool("negative",pe.config.filter,"negative"),_e.image.addBool("sepia",pe.config.filter,"sepia"),_e.image.addBool("vintage",pe.config.filter,"vintage"),_e.image.addBool("kodachrome",pe.config.filter,"kodachrome"),_e.image.addBool("technicolor",pe.config.filter,"technicolor"),_e.image.addBool("polaroid",pe.config.filter,"polaroid"),_e.process=new Wc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),_e.process.addList("backend",["cpu","webgl","wasm","humangl"],pe.config.backend,t=>pe.config.backend=t),_e.process.addBool("async operations",pe.config,"async",t=>pe.config.async=t),_e.process.addBool("enable profiler",pe.config,"profile",t=>pe.config.profile=t),_e.process.addBool("memory shield",pe.config,"deallocate",t=>pe.config.deallocate=t),_e.process.addBool("use web worker",oe,"useWorker"),_e.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.process.addLabel("model parameters"),_e.process.addRange("max objects",pe.config.face.detector,"maxFaces",1,50,1,t=>{pe.config.face.detector.maxFaces=parseInt(t),pe.config.body.maxDetections=parseInt(t),pe.config.hand.maxHands=parseInt(t)}),_e.process.addRange("skip frames",pe.config.face.detector,"skipFrames",0,50,1,t=>{pe.config.face.detector.skipFrames=parseInt(t),pe.config.face.emotion.skipFrames=parseInt(t),pe.config.face.age.skipFrames=parseInt(t),pe.config.hand.skipFrames=parseInt(t)}),_e.process.addRange("min confidence",pe.config.face.detector,"minConfidence",0,1,.05,t=>{pe.config.face.detector.minConfidence=parseFloat(t),pe.config.face.gender.minConfidence=parseFloat(t),pe.config.face.emotion.minConfidence=parseFloat(t),pe.config.hand.minConfidence=parseFloat(t)}),_e.process.addRange("score threshold",pe.config.face.detector,"scoreThreshold",.1,1,.05,t=>{pe.config.face.detector.scoreThreshold=parseFloat(t),pe.config.hand.scoreThreshold=parseFloat(t),pe.config.body.scoreThreshold=parseFloat(t)}),_e.process.addRange("overlap",pe.config.face.detector,"iouThreshold",.1,1,.05,t=>{pe.config.face.detector.iouThreshold=parseFloat(t),pe.config.hand.iouThreshold=parseFloat(t)}),_e.process.addBool("detection rotation",pe.config.face.detector,"rotation",t=>{pe.config.face.detector.rotation=t,pe.config.hand.rotation=t}),_e.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.process.addButton("process sample images","process images",()=>mre()),_e.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.process.addChart("FPS","FPS"),_e.models=new Wc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),_e.models.addBool("face detect",pe.config.face,"enabled"),_e.models.addBool("face mesh",pe.config.face.mesh,"enabled"),_e.models.addBool("face iris",pe.config.face.iris,"enabled"),_e.models.addBool("face age",pe.config.face.age,"enabled"),_e.models.addBool("face gender",pe.config.face.gender,"enabled"),_e.models.addBool("face emotion",pe.config.face.emotion,"enabled"),_e.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.models.addBool("body pose",pe.config.body,"enabled"),_e.models.addBool("hand pose",pe.config.hand,"enabled"),_e.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.models.addBool("gestures",pe.config.gesture,"enabled"),_e.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),_e.models.addBool("face compare",pe.config.face.embedding,"enabled",t=>{_i=null,pe.config.face.embedding.enabled=t}),document.getElementById("btnDisplay").addEventListener("click",t=>_e.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>_e.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>_e.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>_e.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>p6()),document.getElementById("play").addEventListener("click",()=>p6())}async function yre(){On("Demo starting ..."),On("Browser:",navigator==null?void 0:navigator.userAgent),Are(),document.getElementById("log").innerText=`Human: version ${pe.version}`,oe.modelsPreload&&!oe.useWorker&&(Kn("loading"),await pe.load(Hr)),oe.useWorker||(Kn("initializing"),await pe.warmup(Hr)),Kn("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",On("Demo ready...")}window.onload=yre;window.onresize=t1;
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=demo-browser-index.js.map
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