/*
      Human library
      homepage: <https://github.com/vladmandic/human>
      author: <https://github.com/vladmandic>'
    */
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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Sy(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Sy(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=ac(c,this.backendName);L(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:I}=v;return this.makeTensorFromDataId(b,w,I)});if(a){let v=this.getTensorsForGradient(c,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:c}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=Sy(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),a&&this.addTapeNode(l,u,t,h,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=my(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(L(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Is(e[0])&&(r=e.map(o=>Qu(o)));let s=a.write(r,t,n),i=new St(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=G3(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new St(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new ad(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*ty(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ad||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*ty(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=my(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=n[d],p=nc(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Iy(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(L(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));L(r instanceof St,()=>"The result y returned by f() must be a tensor.");let s=OF(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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v_(e,t,n){let a=O(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);L(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),L(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),L(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return V.runKernel(mv,s,i)}var Bk=U({batchToSpaceND_:v_});function w_(e){let t;return e.rank===0||e.rank===1?t=le(e,[1,1,1,e.size]):e.rank===2?t=le(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=le(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function k_(e,t,n,a,r,s){s==null&&(s=.001);let i=O(e,"x","batchNorm"),o=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;r!=null&&(u=O(r,"scale","batchNorm"));let d;a!=null&&(d=O(a,"offset","batchNorm")),L(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance 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i=O(e,"x","batchNorm"),o=O(t,"mean","batchNorm"),l=O(n,"variance","batchNorm"),u;r!=null&&(u=O(r,"scale","batchNorm"));let d;return a!=null&&(d=O(a,"offset","batchNorm")),L(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),L(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),L(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&L(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&L(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Ac(i,o,l,d,u,s)}var C_=U({batchNorm4d_:E_});function M_(e,t,n){let a=O(e,"x","bincount"),r=O(t,"weights","bincount");L(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),L(n>=0,()=>`size must be non-negative, but got 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F_=U({ceil_:R_});function O_(e,t,n){let a=O(e,"x","clipByValue");L(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return V.runKernel(Av,r,s)}var D_=U({clipByValue_:O_});function __(e){return sn(e,0)}var z_=U({concat1d_:__});function P_(e,t){return sn(e,t)}var ld=U({concat2d_:P_});function L_(e,t){return sn(e,t)}var W_=U({concat3d_:L_});function B_(e,t){return sn(e,t)}var V_=U({concat4d_:B_});function U_(e,t,n,a,r="NHWC",s=[1,1],i){let o=O(e,"x","conv2d"),l=O(t,"filter","conv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=le(o,[1,o.shape[0],o.shape[1],o.shape[2]])),L(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),L(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&L(Zn(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h=r==="NHWC"?u.shape[3]:u.shape[1];L(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),L(Jr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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Got stride ${n} and dilation '${s}'`),L(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let h=le(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=le(u,[u.shape[0],1,u.shape[1],u.shape[2]]),c=bc(p,h,[1,n],a,"NHWC",[1,s],i);return d?le(c,[c.shape[2],c.shape[3]]):le(c,[c.shape[0],c.shape[2],c.shape[3]])}var H_=U({conv1d_:j_});function G_(e,t,n,a,r,s="NHWC",i){L(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=le(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),L(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),L(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),L(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let d=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];L(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),L(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&L(Zn(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let p={dy:l,filter:n},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=V.runKernel(Iv,p,c);return u?le(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Uk=U({conv2DBackpropInput_:G_});function q_(e,t,n,a,r,s){let i=O(e,"x","conv2dTranspose"),o=O(t,"filter","conv2dTranspose");return Uk(n,i,o,a,r,"NHWC",s)}var K_=U({conv2dTranspose_:q_});function X_(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=O(e,"x","conv3d"),o=O(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=le(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),L(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),L(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),L(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),L(Jr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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l={image:i,transforms:o},u={interpolation:n,fillMode:a,fillValue:r,outputShape:s};return V.runKernel(N7,l,u)}var ZB=U({transform_:XB});function YB(e,t,n){L(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),L(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=O(e,"a","bandPart");L(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=le(cd(0,s,1,"int32"),[-1,1]),l=cd(0,i,1,"int32"),u=je(o,l),d=Sc(n1(u,dt(+t,"int32")),Zk(u,dt(-n,"int32"))),h=Go([s,i],a.dtype);return le(Ii(fd(le(a,[-1,s,i])).map(p=>Ho(d,p,h))),r)}var JB=U({bandPart_:YB});function QB(e){let t;if(Array.isArray(e)){t=!1,L(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, 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V.tidy(()=>{L(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=qk(n),s=Yr(e),i=ns([[1]],[1,1]),o=Yr(i),l=n>=a?a:n;for(let u=0;u<l;++u){let d=s,h=o,p=r;[o,s,r]=V.tidy(()=>{let c=Ze(s,[u,u],[n-u,1]),m=u1(c),f=Ze(s,[u,u],[1,1]),g=Ho(kc(f,0),ns([[-1]]),ns([[1]])),y=je(f,fe(g,m)),A=Qe(c,y);A.shape[0]===1?o=Yr(i):o=sn([i,Ze(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=Ms(Qe(yt(g,y),m)),v=Ze(s,[u,0],[n-u,a]),b=fe(x,o),w=fc(o);if(u===0)s=je(v,yt(b,yt(w,v)));else{let C=je(v,yt(b,yt(w,v)));s=sn([Ze(s,[0,0],[u,a]),C],0)}let I=fc(b),T=Ze(r,[0,u],[n,r.shape[1]-u]);if(u===0)r=je(T,yt(yt(T,o),I));else{let C=je(T,yt(yt(T,o),I));r=sn([Ze(r,[0,0],[n,u]),C],1)}return[o,s,r]}),Ve([d,h,p])}return!t&&n>a&&(r=Ze(r,[0,0],[n,a]),s=Ze(s,[0,0],[a,a])),[r,s]})}var nV=U({qr_:tV}),_n;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(_n||(_n={}));function aV(e,t,n=_n.SUM_BY_NONZERO_WEIGHTS){let a=O(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=O(t,"weights","computeWeightedLoss"));let s=r==null?a:fe(a,r);if(n===_n.NONE)return s;if(n===_n.SUM)return $t(s);if(n===_n.MEAN){if(r==null)return Nc(s);{let i=a.size/r.size,o=Qe($t(s),$t(r));return i>1?Qe(o,dt(i)):o}}if(n===_n.SUM_BY_NONZERO_WEIGHTS){if(r==null)return Qe($t(s),dt(a.size));{let i=fe(r,wi(a.shape)),o=zt($t(l6(i,dt(0))),"float32");return Qe($t(s),o)}}throw Error(`Unknown reduction: ${n}`)}var as=U({computeWeightedLoss_:aV});function rV(e,t,n,a=_n.SUM_BY_NONZERO_WEIGHTS){let r=O(e,"labels","absoluteDifference"),s=O(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=O(n,"weights","absoluteDifference")),On(r.shape,s.shape,"Error in absoluteDifference: ");let o=Sa(je(r,s));return as(o,i,a)}var sV=U({absoluteDifference_:rV});function iV(e,t,n,a,r=_n.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","cosineDistance"),i=O(t,"predictions","cosineDistance"),o=null;a!=null&&(o=O(a,"weights","cosineDistance")),On(s.shape,i.shape,"Error in cosineDistance: ");let l=dt(1),u=je(l,$t(fe(s,i),n,!0));return as(u,o,r)}var oV=U({cosineDistance_:iV});function lV(e,t,n,a=_n.SUM_BY_NONZERO_WEIGHTS){let r=O(e,"labels","hingeLoss"),s=O(t,"predictions","hingeLoss"),i=null;n!=null&&(i=O(n,"weights","hingeLoss")),On(r.shape,s.shape,"Error in hingeLoss: ");let o=dt(1);r=je(fe(dt(2),r),o);let l=Ec(je(o,fe(r,s)));return as(l,i,a)}var uV=U({hingeLoss_:lV});function dV(e,t,n,a=1,r=_n.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","huberLoss"),i=O(t,"predictions","huberLoss"),o=null;n!=null&&(o=O(n,"weights","huberLoss")),On(s.shape,i.shape,"Error in huberLoss: ");let l=dt(a),u=Sa(je(i,s)),d=o6(u,l),h=je(u,d),p=De(fe(dt(.5),tr(d)),fe(l,h));return as(p,o,r)}var hV=U({huberLoss_:dV});function pV(e,t,n,a=1e-7,r=_n.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"labels","logLoss"),i=O(t,"predictions","logLoss"),o=null;n!=null&&(o=O(n,"weights","logLoss")),On(s.shape,i.shape,"Error in logLoss: ");let l=dt(1),u=dt(a),d=Ms(fe(s,ud(De(i,u)))),h=fe(je(l,s),ud(De(je(l,i),u))),p=je(d,h);return as(p,o,r)}var cV=U({logLoss_:pV});function fV(e,t,n,a=_n.SUM_BY_NONZERO_WEIGHTS){let r=O(e,"labels","meanSquaredError"),s=O(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=O(n,"weights","meanSquaredError")),On(r.shape,s.shape,"Error in meanSquaredError: ");let o=m6(r,s);return as(o,i,a)}var mV=U({meanSquaredError_:fV});function gV(e,t){let n=O(e,"labels","sigmoidCrossEntropyWithLogits"),a=O(t,"logits","sigmoidCrossEntropyWithLogits");On(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ec(a),s=fe(a,n),i=Jk(vi(Ms(Sa(a))));return De(je(r,s),i)}function yV(e,t,n,a=0,r=_n.SUM_BY_NONZERO_WEIGHTS){let s=O(e,"multiClassLabels","sigmoidCrossEntropy"),i=O(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=O(n,"weights","sigmoidCrossEntropy")),On(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=dt(a),d=dt(1),h=dt(.5);s=De(fe(s,je(d,u)),fe(h,u))}let l=gV(s,i);return as(l,o,r)}var AV=U({sigmoidCrossEntropy_:yV});function xV(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. 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s={skipEmpty:n},i={input:a,delimiter:r},o=V.runKernel(b7,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var FV=U({stringSplit_:RV});function OV(e,t){let n=O(e,"input","stringToHashBucketFast","string"),a={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return V.runKernel(v7,r,a)}var DV=U({stringToHashBucketFast_:OV}),_V={fft:o1,ifft:Cc,rfft:l1,irfft:f6},zV={hammingWindow:cB,hannWindow:w6,frame:k6,stft:yB},Ye={flipLeftRight:vB,resizeNearestNeighbor:HB,resizeBilinear:UB,rotateWithOffset:kB,cropAndResize:xB,nonMaxSuppression:SB,nonMaxSuppressionAsync:FB,nonMaxSuppressionWithScore:DB,nonMaxSuppressionWithScoreAsync:zB,nonMaxSuppressionPadded:LB,nonMaxSuppressionPaddedAsync:BB,threshold:KB,transform:ZB},PV={bandPart:JB,gramSchmidt:eV,qr:nV},LV={absoluteDifference:sV,computeWeightedLoss:as,cosineDistance:oV,hingeLoss:uV,huberLoss:hV,logLoss:cV,meanSquaredError:mV,sigmoidCrossEntropy:AV,softmaxCrossEntropy:vV},WV={sparseFillEmptyRows:kV,sparseReshape:SV,sparseSegmentMean:TV,sparseSegmentSum:CV},BV={stringNGrams:$V,stringSplit:FV,stringToHashBucketFast:DV},Rs=class extends Mk{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Ve(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Qk(e,t)}dispose(){this.iterations_!=null&&Ve(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:dt(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Rs,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Oc=class extends Rs{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=V.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Ue(()=>Na(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Ue(()=>Na(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;Ue(()=>{let l=De(fe(i,this.rho),fe(tr(s),1-this.rho)),u=fe(Qe(ts(De(o,this.epsilon)),ts(De(i,this.epsilon))),s),d=De(fe(o,this.rho),fe(tr(u),1-this.rho));i.assign(l),o.assign(d);let h=De(fe(u,-this.learningRate),a);a.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ve(this.accumulatedGrads.map(e=>e.variable)),Ve(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Oc.className="Adadelta";Cs(Oc);var Dc=class extends Rs{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=V.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Ue(()=>wc(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;Ue(()=>{let i=De(s,tr(r));s.assign(i);let o=De(fe(Qe(r,ts(De(i,V.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ve(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)}};Dc.className="Adagrad";Cs(Dc);var _c=class extends Rs{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ue(()=>{this.accBeta1=dt(t).variable(),this.accBeta2=dt(n).variable()}),a==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Ue(()=>{let n=je(1,this.accBeta1),a=je(1,this.accBeta2);t.forEach((r,s)=>{let i=V.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ue(()=>Na(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:Ue(()=>Na(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=De(fe(u,this.beta1),fe(l,1-this.beta1)),p=De(fe(d,this.beta2),fe(tr(l),1-this.beta2)),c=Qe(h,n),m=Qe(p,a);u.assign(h),d.assign(p);let f=De(fe(Qe(c,De(ts(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(fe(this.accBeta1,this.beta1)),this.accBeta2.assign(fe(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ve(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ve(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),Ue(()=>{this.accBeta1.assign(pd(this.beta1,this.iterations_+1)),this.accBeta2.assign(pd(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};_c.className="Adam";Cs(_c);var zc=class extends Rs{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ue(()=>{this.iteration=dt(0).variable(),this.accBeta1=dt(t).variable()}),a==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Ue(()=>{let n=je(1,this.accBeta1),a=Qe(-this.learningRate,De(fe(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};zc.className="Adamax";Cs(zc);var md=class extends Rs{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 a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=V.registeredVariables[t];Ue(()=>{let s=De(fe(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dk(dt(-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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a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return xh.nextTensorId++}nextVariableId(){return xh.nextVariableId++}clone(e){let t=j.runKernel(pl,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return j.runKernel(el,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(sA(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=mA(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(mA(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=sA(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:I}=v;return this.makeTensorFromDataId(b,w,I)});if(a){let v=this.getTensorsForGradient(c,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:c}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=mA(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),a&&this.addTapeNode(l,u,t,h,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=G6(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Vc(e[0])&&(r=e.map(o=>cf(o)));let s=a.write(r,t,n),i=new Tt(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=L6(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Tt(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new gf(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*m1(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gf||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*m1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=G6(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=n[d],p=Gc(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=fA(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(r instanceof Tt,()=>"The result y returned by f() must be a tensor.");let s=mj(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?Nj(r.shape):n,gj(i,s,l=>this.tidy(l),Tj);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return P(jc(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(i=>i instanceof Tt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),P(n.value instanceof Tt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(jc(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];P(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),P(u.every(h=>h instanceof Tt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let d={};return u.forEach((h,p)=>{d[p]=()=>h}),d};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=mh(),n=await this.backend.time(e);return n.wallMs=mh()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new r4;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};xh.nextTensorId=0;xh.nextVariableId=0;function Nj(e){let t=g1(on(e),"float32");return j.makeTensor(t,e,"float32")}function s4(){let e=H6();if(e._tfengine==null){let t=new XU(e);e._tfengine=new xh(t)}return QU(e._tfengine.ENV),bj(()=>e._tfengine),e._tfengine}var j=s4();function Tj(e,t){let n={a:e,b:t};return j.runKernel(Os,n)}var yf={};$e(yf,{isBrowser:()=>i4,isMobile:()=>Cj});function Ej(){return typeof navigator!="undefined"&&navigator!=null}function Cj(e){if(e||Ej()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function i4(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var rr=se();rr.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. 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i=F(e,"x","batchNorm"),o=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let d;return a!=null&&(d=F(a,"offset","batchNorm")),P(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),P(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),P(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&P(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&P(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Xl(i,o,l,d,u,s)}var MG=B({batchNorm4d_:CG});function $G(e,t,n){let a=F(e,"x","bincount"),r=F(t,"weights","bincount");P(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),P(n>=0,()=>`size must be non-negative, but got 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t8=B({ceil_:FG});function OG(e,t,n){let a=F(e,"x","clipByValue");P(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return j.runKernel(Ti,r,s)}var ua=B({clipByValue_:OG});function DG(e){return en(e,0)}var _G=B({concat1d_:DG});function zG(e,t){return en(e,t)}var PG=B({concat2d_:zG});function LG(e,t){return en(e,t)}var WG=B({concat3d_:LG});function BG(e,t){return en(e,t)}var VG=B({concat4d_:BG});function UG(e,t,n,a,r="NHWC",s=[1,1],i){let o=F(e,"x","conv2d"),l=F(t,"filter","conv2d"),u=o,d=!1;o.rank===3&&(d=!0,u=Y(o,[1,o.shape[0],o.shape[1],o.shape[2]])),P(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),P(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&P(mn(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h=r==="NHWC"?u.shape[3]:u.shape[1];P(h===l.shape[2],()=>`Error in conv2d: depth 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Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.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={},a={},r={},s={},i=[],o=(y,A,x,v,b,w)=>{(v==null||b==null||w==null)&&(v=y.sourceLayer,b=y.nodeIndex,w=y.tensorIndex);let I=v.inboundNodes[b];if(x.indexOf(I)!==-1)throw new ur(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(A.indexOf(I)!==-1)return;this.containerNodes.add(Dr.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(I)===-1&&x.push(I);let T=I.inboundLayers.length;for(let C=0;C<T;C++){let z=I.inputTensors[C],$=I.inboundLayers[C],S=I.nodeIndices[C],D=I.tensorIndices[C];o(z,A,x,$,S,D)}for(A.push(I);x.indexOf(I)>=0;)x.splice(x.indexOf(I),1);i.push(I)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];A=Math.max(A,x),a[y.outboundLayer.id]=A,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let v=0;v<y.inboundLayers.length;v++){let b=y.inboundLayers[v],w=y.nodeIndices[v],I=b.inboundNodes[w],T=t[I.id]==null?0:t[I.id];t[I.id]=Math.max(A+1,T),n[I.id]=I}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(n[y])}let p={};for(let y in a){let A=a[y];A in p||(p[A]=[]),p[A].push(r[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort(jf);this.layers=[];for(let y of c){let A=p[y];A.sort((x,v)=>{let b=s[x.id],w=s[v.id];return b<w?-1:b>w?1:0});for(let x of A)x instanceof Dr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort(jf);let m=this.inputs.slice(),f=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let v of A.inputTensors)if(m.indexOf(v)===-1)throw new ur(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function BI(e,t){return wte(e,t,"classWeight")}async function VI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=Z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Ge(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),$n(i,"float32")}else return null}function kte(e,t){return K(e,t)}var Ite=32;function UI(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=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=jI("input",e.inputNames,n),i=jI("output",e.outputNames,a),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 jI(e,t,n){if(n instanceof Tt)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 a=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function Ste(e){if(e.length===3)throw new He("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Nte(e,t,n){let a=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(!a||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()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let c=0;c<this.inputs.length;++c)u.push({key:this.inputs[c],value:n[c]});let d=new uo(u),h=jh(this.outputs,d,{training:!0}),p;for(let c=0;c<this.lossFunctions.length;++c){let m=this.lossFunctions[c](a[c],h[c]);r[c]!=null&&(m=kte(m,r[c]));let f=Xt(m);t.push(f),c===0?p=m:p=pe(p,m)}for(let c=0;c<this.metricsTensors.length;++c){let m;if(this.outputs.length>1&&c<this.outputs.length)m=t[c];else{let f=this.metricsTensors[c][0],g=this.metricsTensors[c][1];m=Xt(f(a[g],h[g]))}Sn(m),s.push(m)}return p=Xt(p),this.calculateLosses().forEach(c=>{p=pe(p,c)}),p},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>Z(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=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:a[l]});let i=new uo(s),o=jh(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Xt(u(r[l],o[l]));l===0?n=d:n=pe(n,d),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Xt(u(r[d],o[d]));t.push(h)}return t})}async fit(e,t,n={}){return $te(this,e,t,n)}async fitDataset(e,t){return Nte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Ge(s),Qn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=CA().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-CA().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=hs(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>hs(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=hs(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)r[s]=ro(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=la.getSaveHandlers(e);if(i.length===0)throw new G(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new G(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await la.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:_te,generatedBy:`TensorFlow.js tfjs-layers v${ex}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await la.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=la.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;zI(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){zI(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ps.className="Model";ue.registerClass(ps);var ZI=class extends ps{};ZI.className="Functional";ue.registerClass(ZI);async function zte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Uh(n),r=cr(a,t);if(e.weightsManifest!=null){let s=await la.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Ge(s)}return r}async function Pte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=la.getLoadHandlers(e,t);if(n.length===0)n.push(la.browserHTTPRequest(e,t));else if(n.length>1)throw new G(`Found more than one (${n.length}) load handlers for URL 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};fx.className="ThresholdedReLU";ue.registerClass(fx);var mx=class extends rt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ox().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ke(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}};mx.className="Softmax";ue.registerClass(mx);function ou(e,t,n){if(typeof e=="number")return ao(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(e=ct(e,[0,2,1])),r==="causal")throw new He("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=DA(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=hr(o,n)),o})}function mS(e,t,n,a=[1,1],r="valid",s,i,o=null){return Z(()=>{if(s==null&&(s=lr()),Yt(s),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=gx(e,s);if(r==="causal")throw new He("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=eo.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=ct(l,[0,3,1,2])),l})}function Kte(e,t,n,a=[1,1,1],r="valid",s,i){return Z(()=>{if(s==null&&(s=lr()),Yt(s),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=fS(e,s);if(r==="causal")throw new He("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=n8(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=hr(o,n)),s==="channelsFirst"&&(o=ct(o,[0,4,1,2,3])),o})}var yx=class extends rt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",yx.verifyArgs(t),this.rank=e,An(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new He(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ou(t.kernelSize,e,"kernelSize"),this.strides=ou(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Oa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Yt(this.dataFormat),this.activation=Zs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=pn(t.biasConstraint),this.biasRegularizer=Lt(t.biasRegularizer),this.activityRegularizer=Lt(t.activityRegularizer),this.dilationRate=ou(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!N2(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Xs(this.activation),useBias:this.useBias,biasInitializer:jt(this.biasInitializer),biasRegularizer:wt(this.biasRegularizer),activityRegularizer:wt(this.activityRegularizer),biasConstraint:hn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qh=class extends yx{constructor(e,t){super(e,t);this.kernel=null,qh.verifyArgs(t),this.filters=t.filters,An(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=pn(t.kernelConstraint),this.kernelRegularizer=Lt(t.kernelRegularizer)}build(e){e=At(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return Z(()=>{e=Ke(e);let n,a=this.bias==null?null:this.bias.read(),r=rI(this.activation.getClassName());if(r!=null&&this.rank===2)n=mS(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=qte(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=mS(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Kte(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new He("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=At(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=fr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:jt(this.kernelInitializer),kernelRegularizer:wt(this.kernelRegularizer),kernelConstraint:hn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},gS=class extends qh{constructor(e){super(2,e);gS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!N2(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},f0=gS;f0.className="Conv2D";ue.registerClass(f0);var yS=class extends qh{constructor(e){super(3,e);yS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},m0=yS;m0.className="Conv3D";ue.registerClass(m0);var Ax=class extends f0{constructor(e){super(e);if(this.inputSpec=[new tn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new tn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{let n=Ke(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=_r(o,h,u,this.padding),m=_r(l,p,d,this.padding),f=[r,c,m,this.filters];this.dataFormat!=="channelsLast"&&(n=ct(n,[0,2,3,1]));let g=zA(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=ct(g,[0,3,1,2])),this.bias!=null&&(g=hr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=At(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=_r(t[a],o,s,this.padding),t[r]=_r(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ax.className="Conv2DTranspose";ue.registerClass(Ax);var xx=class extends m0{constructor(e){super(e);if(this.inputSpec=[new tn({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new tn({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{let n=Ke(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],d=a[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=_r(l,m,h,this.padding),A=_r(u,f,p,this.padding),x=_r(d,g,c,this.padding),v=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=ct(n,[0,2,3,4,1]));let b=ZG(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=ct(b,[0,4,1,2,3])),this.bias!==null&&(b=hr(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=At(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[a]=_r(t[a],u,i,this.padding),t[r]=_r(t[r],d,o,this.padding),t[s]=_r(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};xx.className="Conv3DTranspose";ue.registerClass(xx);var AS=class extends qh{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 G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Lt(t.depthwiseRegularizer),this.depthwiseConstraint=pn(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Lt(t.pointwiseRegularizer),this.pointwiseConstraint=pn(t.pointwiseConstraint)}build(e){if(e=At(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 tn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Z(()=>{e=Ke(e);let n;if(this.rank===1)throw new He("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=ct(e,[0,2,3,1])),n=b8(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=ct(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=jt(this.depthwiseInitializer),e.pointwiseInitializer=jt(this.pointwiseInitializer),e.depthwiseRegularizer=wt(this.depthwiseRegularizer),e.pointwiseRegularizer=wt(this.pointwiseRegularizer),e.depthwiseConstraint=hn(this.depthwiseConstraint),e.pointwiseConstraint=hn(this.pointwiseConstraint),e}};AS.className="SeparableConv";var bx=class extends AS{constructor(e){super(2,e)}};bx.className="SeparableConv2D";ue.registerClass(bx);var xS=class extends qh{constructor(e){super(1,e);xS.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"&&!N2(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},vx=xS;vx.className="Conv1D";ue.registerClass(vx);var wx=class extends rt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return Z(()=>{if(e=Ke(e),this.dataFormat==="channelsLast"){let n=Hf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Hf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Hf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Hf(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}};wx.className="Cropping2D";ue.registerClass(wx);var kx=class extends rt{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,Yt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,uee(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Z(()=>{let n=Ke(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=ct(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return ct(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};kx.className="UpSampling2D";ue.registerClass(kx);function Xte(e,t,n=[1,1],a="valid",r,s){return Z(()=>{r==null&&(r=lr()),Yt(r);let i=gx(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Nh(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=ct(i,[0,3,1,2])),i})}var Ix=class extends yx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=pn(e.depthwiseConstraint),this.depthwiseRegularizer=Lt(e.depthwiseRegularizer)}build(e){if(e=At(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Z(()=>{e=Ke(e);let n=Xte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=fr(t,this.kernelSize[0],this.padding,this.strides[0]),s=fr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=jt(this.depthwiseInitializer),e.depthwiseRegularizer=wt(this.depthwiseRegularizer),e.depthwiseConstraint=hn(this.depthwiseRegularizer),e}};Ix.className="DepthwiseConv2D";ue.registerClass(Ix);function bS(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function vS(e,t,n,a=!1,r,s,i=!1,o=!1){return Z(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(dr(2,l));if(t=ct(t,u),s!=null)throw new He("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Ea(r,-1)),r=ct(r,u)),a&&(t=Ra(t,0),r!=null&&(r=Ra(r,0)));let d=[],h,p=n,c=t.shape[0],m=or(t),f;r!=null&&(f=or(r));for(let y=0;y<c;++y){let A=m[y],x=Z(()=>e(A,p));if(r==null)h=x[0],p=x[1];else{let v=Z(()=>{let b=f[y],w=$a(b).sub(b),I=x[0].mul(b).add(p[0].mul(w)),T=p.map((C,z)=>x[1][z].mul(b).add(C.mul(w)));return{output:I,newStates:T}});h=v.output,p=v.newStates}o&&d.push(h)}let g;return o&&(g=Fa(d,1)),[h,g,p]})}var wS=class extends rt{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new A0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new tn({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 dr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){U2(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return Z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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 He("Constants support is not implemented in RNN yet.");U2(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new tn({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new He("Constants support is not implemented in RNN yet.");this.cell.build(r);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 G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new tn({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Z(()=>{if(!this.stateful)throw new ds("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>un([n,a])):this.states_=[un([n,this.cell.stateSize])];else if(e==null)Ge(this.states_),this.keptStates!=null&&(Ge(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>un([n,a])):this.states_[0]=un([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ge(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new G(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Sn(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=bS(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.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 tn({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof pr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return Z(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ke(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new G(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=vS((p,c)=>{let m=this.cell.call([p].concat(c),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return Z(()=>{let t=un(e.shape);return t=Ce(t,[1,2]),t=Lh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?O2(t,[1,n]):t):this.cell.stateSize>1?[O2(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()===wS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let a=t.cell,r=cr(a,n);return new e(Object.assign(t,{cell:r}))}},cs=wS;cs.className="RNN";ue.registerClass(cs);var Kh=class extends rt{},g0=class extends Kh{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,An(this.units,"units"),this.activation=Zs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lt(e.kernelRegularizer),this.recurrentRegularizer=Lt(e.recurrentRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.kernelConstraint=pn(e.kernelConstraint),this.recurrentConstraint=pn(e.recurrentConstraint),this.biasConstraint=pn(e.biasConstraint),this.dropout=au([1,qs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=au([1,qs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Z(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ys({ones:()=>$a(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ys({ones:()=>$a(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Fr(K(e,s),this.kernel.read()):r=Fr(e,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),i!=null&&(n=K(n,i));let o=pe(r,Fr(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:Xs(this.activation),useBias:this.useBias,kernelInitializer:jt(this.kernelInitializer),recurrentInitializer:jt(this.recurrentInitializer),biasInitializer:jt(this.biasInitializer),kernelRegularizer:wt(this.kernelRegularizer),recurrentRegularizer:wt(this.recurrentRegularizer),biasRegularizer:wt(this.biasRegularizer),activityRegularizer:wt(this.activityRegularizer),kernelConstraint:hn(this.kernelConstraint),recurrentConstraint:hn(this.recurrentConstraint),biasConstraint:hn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};g0.className="SimpleRNNCell";ue.registerClass(g0);var Sx=class extends cs{constructor(e){e.cell=new g0(e),super(e)}call(e,t){return Z(()=>{this.cell.dropoutMask!=null&&(Ge(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ge(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Sx.className="SimpleRNN";ue.registerClass(Sx);var y0=class extends Kh{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 G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,An(this.units,"units"),this.activation=Zs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Zs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Lt(e.kernelRegularizer),this.recurrentRegularizer=Lt(e.recurrentRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.kernelConstraint=pn(e.kernelConstraint),this.recurrentConstraint=pn(e.recurrentConstraint),this.biasConstraint=pn(e.biasConstraint),this.dropout=au([1,qs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=au([1,qs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Z(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ys({ones:()=>$a(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ys({ones:()=>$a(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=K(e,r[0]));let u=Fr(e,this.kernel.read());this.useBias&&(u=hr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=K(a,s[0]));let d=this.recurrentKernel.read(),[h,p]=da(d,[2*this.units,this.units],d.rank-1),c=Fr(a,h),[m,f,g]=da(u,3,u.rank-1),[y,A]=da(c,2,c.rank-1);i=this.recurrentActivation.apply(pe(m,y)),o=this.recurrentActivation.apply(pe(f,A));let x=Fr(K(o,a),p);l=this.activation.apply(pe(g,x));let v=pe(K(i,a),K(pe(1,Kt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Xs(this.activation),recurrentActivation:Xs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:jt(this.kernelInitializer),recurrentInitializer:jt(this.recurrentInitializer),biasInitializer:jt(this.biasInitializer),kernelRegularizer:wt(this.kernelRegularizer),recurrentRegularizer:wt(this.recurrentRegularizer),biasRegularizer:wt(this.biasRegularizer),activityRegularizer:wt(this.activityRegularizer),kernelConstraint:hn(this.kernelConstraint),recurrentConstraint:hn(this.recurrentConstraint),biasConstraint:hn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};y0.className="GRUCell";ue.registerClass(y0);var Nx=class extends cs{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. 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Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>un(r)):this.states_=[un(r)];else if(e==null)Ge(this.states_),this.keptStates!=null&&(Ge(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>un(r)):this.states_[0]=un(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ge(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new G(`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=>Sn(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=fr(l,a[0],r,s[0],i[0]),h=fr(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,h]:[d,h,n]]}};kS.className="ConvRNN2D";var x0=class extends Xh{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,An(this.filters,"filters"),this.kernelSize=ou(n,2,"kernelSize"),this.kernelSize.forEach(o=>An(o,"kernelSize")),this.strides=ou(a||1,2,"strides"),this.strides.forEach(o=>An(o,"strides")),this.padding=r||"valid",Oa(this.padding),this.dataFormat=s||"channelsLast",Yt(this.dataFormat),this.dilationRate=ou(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>An(o,"dilationRate"))}build(e){var t;e=At(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Xa{apply(d,h){let p=l.apply([u]),c=os([u]),m=l.apply([u*2]);return F2([p,c,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Z(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ys({ones:()=>$a(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(ee,ie,ae)=>!ie||!ie[ae]?ee:K(ie[ae],ee),u=l(a,o,0),d=l(a,o,1),h=l(a,o,2),p=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ys({ones:()=>$a(r),rate:this.recurrentDropout,training:n,count:i}));let c=this.recurrentDropoutMask,m=l(r,c,0),f=l(r,c,1),g=l(r,c,2),y=l(r,c,3),A=3,[x,v,b,w]=da(this.kernel.read(),i,A),[I,T,C,z]=this.useBias?da(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,I,this.padding),d=this.inputConv(d,v,T,this.padding),h=this.inputConv(h,b,C,this.padding),p=this.inputConv(p,w,z,this.padding);let[$,S,D,_]=da(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,D),y=this.recurrentConv(y,_);let W=this.recurrentActivation.apply(pe(u,m)),X=this.recurrentActivation.apply(pe(d,f)),q=pe(K(X,s),K(W,this.activation.apply(pe(h,g)))),Q=K(this.recurrentActivation.apply(pe(p,y)),this.activation.apply(q));return[Q,Q,q]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,a){let r=Ws(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hr(r,n,this.dataFormat):r}recurrentConv(e,t){return Ws(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};x0.className="ConvLSTM2DCell";ue.registerClass(x0);var Ex=class extends kS{constructor(e){let t=new x0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};Ex.className="ConvLSTM2D";ue.registerClass(Ex);var b0=class extends rt{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Bh(()=>mI(n,this.rate,r,this.seed),()=>n,a)}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()}};b0.className="Dropout";ue.registerClass(b0);var Cx=class extends b0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Cx.className="SpatialDropout1D";ue.registerClass(Cx);var Mx=class extends rt{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,An(this.units,"units"),this.activation=Zs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=pn(e.kernelConstraint),this.biasConstraint=pn(e.biasConstraint),this.kernelRegularizer=Lt(e.kernelRegularizer),this.biasRegularizer=Lt(e.biasRegularizer),this.activityRegularizer=Lt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=At(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=At(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e),a=rI(this.activation.getClassName()),r;return a!=null?r=Fr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Fr(n,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Xs(this.activation),useBias:this.useBias,kernelInitializer:jt(this.kernelInitializer),biasInitializer:jt(this.biasInitializer),kernelRegularizer:wt(this.kernelRegularizer),biasRegularizer:wt(this.biasRegularizer),activityRegularizer:wt(this.activityRegularizer),kernelConstraint:hn(this.kernelConstraint),biasConstraint:hn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="Dense";ue.registerClass(Mx);var $x=class extends rt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=At(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Z(()=>(e=Ke(e),cee(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="RepeatVector";ue.registerClass(Fx);var Ox=class extends rt{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new G("Can only specifiy one unknown dimension.");else r*=l}let i=Gs(e);if(s!==null){if(r===0||i%r!=0)throw new G(n);a[s]=i/r}else if(i!==r)throw new G(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Ox.className="Reshape";ue.registerClass(Ox);var Dx=class extends rt{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=dr(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 tn({ndim:this.dims.length+1})]}computeOutputShape(e){e=At(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return 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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,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var jx=class extends po{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 He("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new G(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Hx=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return Bh(()=>Gf(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Hx.className="GaussianNoise";ue.registerClass(Hx);var Gx=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return Z(()=>{this.invokeCallHook(e,t);let n=Ke(e);return this.rate>0&&this.rate<1?Bh(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Gf(n.shape,1,a))},()=>n,t.training||!1):n})}};Gx.className="GaussianDropout";ue.registerClass(Gx);var qx=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:jt(this.betaInitializer),gammaInitializer:jt(this.gammaInitializer),betaRegularizer:wt(this.betaRegularizer),gammaRegularizer:wt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="LayerNormalization";ue.registerClass(Xx);function ene(e,t,n){return Z(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=lr()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. 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s==="max"?i=Mf(e,t,n,o):i=Sf(e,t,n,o),r==="channelsFirst"&&(i=ct(i,[0,3,1,2])),i})}function IS(e,t,n,a,r,s){return Z(()=>{Yt(r),lI(s),Oa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=lr()),s==null&&(s="max"),e=fS(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=g8(e,t,n,o):i=Q4(e,t,n,o),r==="channelsFirst"&&(i=ct(i,[0,4,1,2,3])),i})}var SS=class extends rt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(An(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);An(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Oa(this.padding),this.inputSpec=[new tn({ndim:3})]}computeOutputShape(e){e=At(e);let t=fr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Z(()=>{this.invokeCallHook(e,t),e=Lh(Ke(e),2);let n=this.poolingFunction(Ke(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Jl(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Yx=class extends SS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Yt(r),Oa(a),v0(e,t,n,a,r,"max")}};Yx.className="MaxPooling1D";ue.registerClass(Yx);var Jx=class extends SS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return 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t=fr(t,this.poolSize[0],this.padding,this.strides[0]),n=fr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(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}},Qx=class extends NS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Yt(r),Oa(a),v0(e,t,n,a,r,"max")}};Qx.className="MaxPooling2D";ue.registerClass(Qx);var e5=class extends NS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Yt(r),Oa(a),v0(e,t,n,a,r,"avg")}};e5.className="AveragePooling2D";ue.registerClass(e5);var TS=class extends rt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];An(this.poolSize,"poolSize"),An(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Yt(this.dataFormat),Oa(this.padding),this.inputSpec=[new tn({ndim:5})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=fr(t,this.poolSize[0],this.padding,this.strides[0]),n=fr(n,this.poolSize[1],this.padding,this.strides[1]),a=fr(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return Z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(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}},t5=class extends TS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Yt(r),Oa(a),IS(e,t,n,a,r,"max")}};t5.className="MaxPooling3D";ue.registerClass(t5);var n5=class extends TS{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Yt(r),Oa(a),IS(e,t,n,a,r,"avg")}};n5.className="AveragePooling3D";ue.registerClass(n5);var ES=class extends rt{constructor(e){super(e);this.inputSpec=[new tn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new He}},a5=class extends ES{constructor(e){super(e||{})}call(e,t){return Z(()=>{let n=Ke(e);return Xt(n,1)})}};a5.className="GlobalAveragePooling1D";ue.registerClass(a5);var r5=class extends ES{constructor(e){super(e||{})}call(e,t){return Z(()=>{let n=Ke(e);return sr(n,1)})}};r5.className="GlobalMaxPooling1D";ue.registerClass(r5);var CS=class extends rt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Yt(this.dataFormat),this.inputSpec=[new tn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new He}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},s5=class extends CS{call(e,t){return Z(()=>{let n=Ke(e);return this.dataFormat==="channelsLast"?Xt(n,[1,2]):Xt(n,[2,3])})}};s5.className="GlobalAveragePooling2D";ue.registerClass(s5);var i5=class extends CS{call(e,t){return Z(()=>{let n=Ke(e);return this.dataFormat==="channelsLast"?sr(n,[1,2]):sr(n,[2,3])})}};i5.className="GlobalMaxPooling2D";ue.registerClass(i5);var MS=class extends rt{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 a=t.layer,r=cr(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},o5=class extends MS{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=At(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=At(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return Z(()=>(e=Ke(e),vS((n,a)=>[Ke(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};o5.className="TimeDistributed";ue.registerClass(o5);function tne(e){so(lee,"BidirectionalMergeMode",e)}var nne="concat",l5=class extends MS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=cr(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=cr(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?nne:e.mergeMode,tne(this.mergeMode),e.weights)throw new He("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Qn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=bS(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(d=>new 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i;return this.mergeMode==="concat"?i=F2([a,r]):this.mergeMode==="sum"?i=pe(a,r):this.mergeMode==="ave"?i=K(.5,pe(a,r)):this.mergeMode==="mul"?i=K(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){io(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),io(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=cr(t.layer);if(delete t.layer,t.numConstants!=null)throw new He("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};l5.className="Bidirectional";ue.registerClass(l5);function ane(e){return new ru(e)}function rne(e){return new cx(e)}function sne(e){return new dx(e)}function ine(e){return new hx(e)}function one(e){return new px(e)}function lne(e){return new mx(e)}function une(e){return new fx(e)}function dne(e){return new vx(e)}function hne(e){return new f0(e)}function pne(e){return new 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Ere(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>ha(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&a.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Cre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Mre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],$re=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function y9(e){return Cre.indexOf(e.op)>=0}function Rre(e){return Mre.indexOf(e.op)>=0}function Fre(e){return $re.indexOf(e.op)>=0}var N5=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new N5(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return 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u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=fs(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Ln(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Ln(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=ha(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=ha(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ha(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ore=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Dre="?tfjs-format=file",_re="model.json",A9=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ore}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=la.browserHTTPRequest(e,this.loadOptions);else{let t=la.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(la.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=la.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new N5(l9.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=l9.Instance.transformGraph(e.modelInitializer);this.initializer=new N5(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=la.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Tt)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Et(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${_re}${Dre}`);let n=new A9(e,t);return await n.load(),n}var zre="3.7.0",x9={};$e(x9,{CSVDataset:()=>F9,Dataset:()=>uu,FileDataSource:()=>W9,TextLineDataset:()=>M9,URLDataSource:()=>B9,array:()=>ise,csv:()=>yse,func:()=>Ase,generator:()=>xse,microphone:()=>vse,version_data:()=>wse,webcam:()=>bse,zip:()=>ose});var Pre=qr(P3()),Lre=qr(P3());function Wre(e,t){return S0(e,t)}function S0(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(lu(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=S0(o,t,n,a);s[i]=l}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},O9=class extends xn{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(se().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new O9(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Cr(n,t)}},D9=class extends xn{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=$n([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ql([s,r,o,i],[1,4])}else this.cropBox=Ql([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(se().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 D9(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=k4.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Z(()=>{let t=Ea(we(e,"float32"),0),n;n=to.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return Y(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},_9=class{},z9=class extends xn{split(e){return new dse(this,e)}},dse=class extends z9{constructor(e,t){super();this.upstream=e,this.impl=new hse(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},hse=class extends E5{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},pse=class extends xn{decodeUTF8(){return new cse(this)}},cse=class extends z9{constructor(e){super();this.upstream=e,this.impl=new fse(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},fse=class extends E5{constructor(e){super();if(this.upstream=e,se().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=wR();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return se().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},P9=class extends pse{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(se().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function mse(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=gse(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new P9(s,t)}else throw new Error(r.statusText)}var gse=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function L9(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var W9=class extends _9{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(L9(this.input)&&se().get("IS_NODE")){let e=di("fs");this.input=e.readFileSync(this.input.substr(7))}return new P9(this.input,this.options)}},B9=class extends _9{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return L9(this.url)?new W9(this.url,this.fileOptions).iterator():mse(this.url,this.fileOptions)}};function yse(e,t={}){return new F9(new B9(e),t)}function Ase(e){let t=T5(e);return pa(async()=>t)}function xse(e){return pa(async()=>{let t=await e();return T5(()=>t.next())})}async function bse(e,t){return D9.create(e,t)}async function vse(e){return O9.create(e)}var wse="3.7.0";function Se(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var kse=us.whereImpl,V9=class extends Wc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new f1(this,Ps())}nextDataId(){return V9.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,se().get("IS_NODE")&&M.warn(`
============================
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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Expected ${n}, got ${o}`)}let r=a-1,s=k.getArrayFromDType("int32",a);if(n===0||a===0){let o=new Array(n);for(let l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(d=>{u+=this.getNumNGrams(l,d)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(d=>{let h=t[o+1]-t[o],p=this.getNumNGrams(h,d);this.createNGrams(e,l,i,u,p,d),u+=p}),this.preserveShort&&u===s[o]){let d=t[o+1]-t[o];if(d===0)continue;let h=d+2*this.padWidth,p=1;this.createNGrams(e,l,i,u,p,h)}}return[i,s]}};function wN(e,t,n,a,r,s,i,o){return new fie(n,a,r,s,i,o).compute(e,t)}function mie(e,t,n){if(!e.length)return[];if(t.length===0){let s=new Array(e.length);for(let i=0;i<e.length;++i)s[i]=e.subarray(i,i+1);return s}if(t.length===1){let s=t[0],i=[],o=e.indexOf(s);for(;o!==-1;){let l=e.subarray(0,o);(!n||l.length!==0)&&i.push(l),e=e.subarray(o+1),o=e.indexOf(s)}return(!n||e.length!==0)&&i.push(e),i}let a=[],r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!n||i.length!==0)&&a.push(i),r=s+1}return a}function kN(e,t,n){let a=e.length,r=[],s=0,i=0,o=new Array(a);for(let p=0;p<a;++p){let c=mie(e[p],t,n),m=c.length;o[p]=m,s+=m,i=Math.max(i,m),r.push(...c)}let l=k.getArrayFromDType("int32",s*2),u=new Array(s),d=[a,i],h=0;for(let p=0;p<a;++p)for(let c=0;c<o[p];++c)l[h*2]=p,l[h*2+1]=c,u[h]=r[h],++h;return[l,u,d]}function IN(e,t){let n=k.getArrayFromDType("int32",e.length);for(let a=0;a<e.length;++a)n[a]=k.fingerPrint64(e[a]).modulo(t).getLowBitsUnsigned();return n}var SN=nn((e,t)=>e-t),gie=$5((e,t,n,a)=>({real:e-n,imag:t-a})),_5=bn(zi,SN,gie),yie={kernelName:zi,backendName:"cpu",kernelFunc:_5};function NN(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=Pe(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}function TN(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*a),u=k.getTypedArrayFromDType("int32",i*a);for(let h=0;h<i;h++){let p=h*o,c=e.subarray(p,p+o),m=[];for(let A=0;A<c.length;A++)m.push({value:c[A],index:A});m.sort((A,x)=>x.value-A.value);let f=h*a,g=l.subarray(f,f+a),y=u.subarray(f,f+a);for(let A=0;A<a;A++)g[A]=m[A].value,y[A]=m[A].index}let d=t.slice();return d[d.length-1]=a,[Pe(d,n,l),Pe(d,"int32",u)]}function EN(e,t,n,a){let r=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),l=new Qt(s,a,e),u=[],d=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(d)f=e[m].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let A=0;A<s[2];A++)g.push(l.get(y,m,A));f=g.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let g=Object.keys(i).length;i[f]=g,o[m]=g,u.push(m)}}let h=s.slice();h[1]=Object.keys(i).length;let p=new Qt(h,a);u.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)p.set(l.get(g,m,y),g,f,y)});let c=n.slice();return c[r]=h[1],{outputValues:p.values,outputShape:c,indices:o}}var Aie="3.7.0";MA("cpu",()=>new M5,1);var CN=xt(Fd,e=>e>=0?e:Math.exp(e)-1),xie={kernelName:Fd,backendName:"cpu",kernelFunc:CN};function MN(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;Se([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(r.shape,"float32",l)}var bie={kernelName:cl,backendName:"cpu",kernelFunc:MN},vie=nn((e,t)=>e<0?t*e:e);function $N(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;Se([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=vie(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var wie={kernelName:Sl,backendName:"cpu",kernelFunc:$N},RN=xt(Nl,e=>Math.max(0,e)),kie={kernelName:Nl,backendName:"cpu",kernelFunc:RN},FN=xt(El,e=>Math.min(Math.max(0,e),6)),Iie={kernelName:El,backendName:"cpu",kernelFunc:FN},ON=xt(Rl,e=>1/(1+Math.exp(-e))),Sie={kernelName:Rl,backendName:"cpu",kernelFunc:ON};function z5(e,t,n,a,r){if(n==="linear")return zr({inputs:{x:t},backend:e});if(n==="relu")return RN({inputs:{x:t},backend:e});if(n==="elu")return CN({inputs:{x:t},backend:e});if(n==="relu6")return FN({inputs:{x:t},backend:e});if(n==="prelu")return $N({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return MN({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return ON({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Ot(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. 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bt=0;for(let lt=Fe;lt<Be;lt++){let Ct=Math.min(ve,g-1)*X,Je=Math.min(ve,y-1)*ae,Hn=S[Ct+ft*q+lt*Q],Bt=D[lt*ee+mt*ie+Je];bt+=Hn*Bt}ce[ve*de+(ft*z+mt)]+=bt}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(x,te.dtype,te.values)}var Tie={kernelName:Qo,backendName:"cpu",kernelFunc:DN};function Eie(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=a,p,c,m,f=[];p=DN({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(c=tp({inputs:{a:p,b:i},backend:n}),f.push(p),p=c),d&&(m=z5(n,p,d,o,h),f.push(p),p=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return p}var Cie={kernelName:Ll,backendName:"cpu",kernelFunc:Eie},Mie=xt(bd,e=>Math.acos(e)),$ie={kernelName:bd,backendName:"cpu",kernelFunc:Mie},Rie=xt(vd,e=>Math.acosh(e)),Fie={kernelName:vd,backendName:"cpu",kernelFunc:Rie};function Oie(e){let{inputs:t,backend:n}=e,a=t;Se(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Pe(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var Die={kernelName:Zo,backendName:"cpu",kernelFunc:Oie};function _ie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Se(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,u=M.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=Da({inputs:{x:r},backend:n,attrs:{perm:u}}),l=M.getInnerMostAxes(l.length,r.shape.length)),M.assertAxesAreInnerMostDims("all",l,d.shape.length);let[h,p]=M.computeOutAndReduceShapes(d.shape,l),c=k.sizeFromShape(p),m=k.makeZerosTypedArray(k.sizeFromShape(h),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let v=0;v<c;++v){let b=f[A+v];x=x&&b}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(h,d.dtype,m);if(i){let y=M.expandShapeToKeepDim(h,o),A=Ot({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var zie={kernelName:wd,backendName:"cpu",kernelFunc:_ie};function Pie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Se(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,u=M.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=Da({inputs:{x:r},backend:n,attrs:{perm:u}}),l=M.getInnerMostAxes(l.length,r.shape.length)),M.assertAxesAreInnerMostDims("any",l,d.shape.length);let[h,p]=M.computeOutAndReduceShapes(d.shape,l),c=k.sizeFromShape(p),m=k.makeZerosTypedArray(k.sizeFromShape(h),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let v=0;v<c;++v){let b=f[A+v];x=x||b}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(h,d.dtype,m);if(i){let y=M.expandShapeToKeepDim(h,o),A=Ot({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var Lie={kernelName:kd,backendName:"cpu",kernelFunc:Pie};function Wie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Se(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=M.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Da({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=M.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],M.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,h]=M.computeOutAndReduceShapes(l.shape,i),p=k.sizeFromShape(d),c=k.makeZerosTypedArray(p,"int32"),m=k.sizeFromShape(h),f=n.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let v=0;v<m;++v){let b=f[y+v];b>A&&(A=b,x=v)}c[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",c)}var Bie={kernelName:Yo,backendName:"cpu",kernelFunc:Wie};function Vie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Se(r,"argMin");let 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loe(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Se([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=M.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,v=y-1-d.padInfo.top,b=Pe(i.shape,"float32"),w=1/(c*m),I=n.data.get(r.dataId).values,T=Pe(r.shape,"float32",I);for(let C=0;C<d.batchSize;++C)for(let z=0;z<d.inChannels;++z)for(let $=0;$<d.inHeight;++$)for(let S=0;S<d.inWidth;++S){let D=$-v,_=S-x,W=0;for(let X=0;X<y;X+=f){let q=(D+X)/h;if(!(q<0||q>=d.outHeight||Math.floor(q)!==q))for(let Q=0;Q<A;Q+=g){let ee=(_+Q)/p;ee<0||ee>=d.outWidth||Math.floor(ee)!==ee||(W+=T.get(C,q,ee,z))}}b.set(W*w,C,$,S,z)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var uoe={kernelName:v1,backendName:"cpu",kernelFunc:loe};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var hoe={kernelName:dl,backendName:"cpu",kernelFunc:doe};function poe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;Se([r],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=M.getReshaped(r.shape,s,o),u=M.getPermuted(l.length,s.length),d=M.getReshapedPermuted(r.shape,s,o),h=M.getSliceBeginCoords(i,s.length),p=M.getSliceSize(d,i,s.length),c=Ot({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Da({inputs:{x:c},backend:n,attrs:{perm:u}}),f=Ot({inputs:{x:m},backend:n,attrs:{shape:d}}),g=fo({inputs:{x:f},backend:n,attrs:{begin:h,size:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var coe={kernelName:Xc,backendName:"cpu",kernelFunc:poe};function foe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=R5(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var moe={kernelName:k1,backendName:"cpu",kernelFunc:foe},goe=xt(Ti,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),yoe={kernelName:Ti,backendName:"cpu",kernelFunc:goe},Aoe=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];a[u]=Math.hypot(d,h)}return n.makeOutput(a,t.shape,"float32")},xoe={kernelName:Zc,backendName:"cpu",kernelFunc:Aoe};function pu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var boe={kernelName:P1,backendName:"cpu",kernelFunc:pu};function cu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=M.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return 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u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var voe={kernelName:Cd,backendName:"cpu",kernelFunc:cu};function PN(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a;Se([r,s],"conv2d");let h=M.convertConv2DDataFormat(l),p=M.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,h),c=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",v=new Qt(p.outShape,r.dtype),b=k.computeStrides(r.shape),w=k.computeStrides(s.shape),I=b[0],T=x?b[1]:b[2],C=x?b[2]:1,z=x?1:b[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],D=x?v.strides[2]:1,_=x?1:v.strides[1],W=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,q=v.values;for(let Q=0;Q<p.batchSize;++Q){let ee=Q*I,ie=Q*$;for(let ae=0;ae<p.outHeight;++ae){let de=ie+ae*S,te=ae*p.strideHeight-A;for(let ce=0;ce<c;++ce){let he=te+ce*f;if(he<0||he>=p.inHeight)continue;let ve=ce*w[0],xe=ee+he*T;for(let 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Noe={kernelName:nl,backendName:"cpu",kernelFunc:Soe};function Toe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;Se([r,s],"conv3d");let u=M.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,v=new Qt(u.outShape,r.dtype),b=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,I=v.values,T=k.computeStrides(r.shape),C=k.computeStrides(s.shape);for(let z=0;z<u.batchSize;++z){let $=z*T[0],S=z*v.strides[0];for(let D=0;D<u.outDepth;++D){let _=S+D*v.strides[1],W=D*u.strideDepth-y;for(let X=0;X<d;++X){let q=W+X*c;if(q<0||q>=u.inDepth)continue;let Q=X*C[0],ee=$+q*T[1];for(let ie=0;ie<u.outHeight;++ie){let ae=_+ie*v.strides[2],de=ie*u.strideHeight-x;for(let te=0;te<h;++te){let ce=de+te*m;if(ce<0||ce>=u.inHeight)continue;let he=Q+te*C[1],ve=ee+ce*T[2];for(let xe=0;xe<u.outWidth;++xe){let 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Poe={kernelName:$d,backendName:"cpu",kernelFunc:zoe};function Loe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;Se(r,"cumsum");let l=M.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Da({inputs:{x:r},backend:n,attrs:{perm:l}}));let d=M.getInnerMostAxes(1,r.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Ga(u.dtype,"int32"),p=k.makeZerosTypedArray(k.sizeFromShape(u.shape),h),c=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?0:c[x];else{let v=f(y,A-1);p[x]=i?c[v]+p[v]:c[x]+p[v]}}let g=n.makeTensorInfo(u.shape,h,p);if(l!=null){let y=M.getUndoAxesPermutation(l),A=Da({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),A}return g}var Woe={kernelName:rl,backendName:"cpu",kernelFunc:Loe};function Boe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,d=R5(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=G9(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Voe={kernelName:E1,backendName:"cpu",kernelFunc:Boe};function Uoe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Goe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a;Se([r,s],"depthwiseConv2dNativeBackpropFilter");let h=M.computeConv2DInfo(r.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:m,filterWidth:f}=h,g=new Qt(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,v=n.data.get(r.dataId).values,b=new Qt(r.shape,r.dtype,v),w=n.data.get(s.dataId).values,I=new Qt(s.shape,s.dtype,w);for(let T=0;T<m;++T){let C=Math.max(0,Math.ceil((A-T)/p)),z=Math.min(h.outHeight,(h.inHeight+A-T)/p);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/c)),D=Math.min(h.outWidth,(h.inWidth+y-$)/c);for(let _=0;_<h.outChannels;++_){let W=Math.trunc(_/x),X=_%x,q=0;for(let Q=0;Q<h.batchSize;++Q)for(let ee=C;ee<z;++ee){let ie=T+ee*p-A;for(let ae=S;ae<D;++ae){let de=$+ae*c-y;q+=b.get(Q,ie,de,W)*I.get(Q,ee,ae,_)}}g.set(q,T,$,W,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var qoe={kernelName:C1,backendName:"cpu",kernelFunc:Goe};function Koe(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a;Se([r,s],"depthwiseConv2DNativeBackpropInput");let h=k.computeStrides(r.shape),p=k.computeStrides(s.shape),c=M.computeConv2DInfo(d,s.shape,i,o,l,u,!0),m=new Qt(c.inShape,"float32"),f=m.values,[g,y,A]=m.strides,x=n.data.get(r.dataId).values,[v,b,w]=h,I=n.data.get(s.dataId).values,[T,C,z]=p,{batchSize:$,filterHeight:S,filterWidth:D,inChannels:_,inHeight:W,inWidth:X,outChannels:q,outHeight:Q,outWidth:ee,strideHeight:ie,strideWidth:ae}=c,de=S-1-c.padInfo.top,te=D-1-c.padInfo.left,ce=q/_;for(let he=0;he<$;++he)for(let ve=0;ve<_;++ve)for(let xe=0;xe<W;++xe){let Ee=xe-de,Fe=Math.max(0,Math.ceil(Ee/ie)),We=Math.min(Q,(S+Ee)/ie);for(let qe=0;qe<X;++qe){let Be=qe-te,ft=Math.max(0,Math.ceil(Be/ae)),mt=Math.min(ee,(D+Be)/ae),bt=0;for(let lt=Fe;lt<We;++lt){let Ct=lt*ie-Ee;for(let Je=ft;Je<mt;++Je){let 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hue={kernelName:V1,backendName:"cpu",kernelFunc:due};function pue(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Se([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=a,p=M.computePool2DInfo(o.shape,l,u,1,d,h),c=n.data.get(o.dataId).values,m=Pe(p.outShape,o.dtype,_N(c,o.shape,o.dtype,p).values),f=p.strideHeight,g=p.strideWidth,y=p.dilationHeight,A=p.dilationWidth,x=p.effectiveFilterHeight,v=p.effectiveFilterWidth,b=v-1-p.padInfo.left,w=x-1-p.padInfo.top,I=Pe(o.shape,"float32"),T=n.data.get(r.dataId).values,C=Pe(r.shape,"float32",T);for(let z=0;z<p.batchSize;++z)for(let $=0;$<p.inChannels;++$)for(let S=0;S<p.inHeight;++S)for(let D=0;D<p.inWidth;++D){let _=S-w,W=D-b,X=0;for(let q=0;q<x;q+=y){let Q=(_+q)/f;if(!(Q<0||Q>=p.outHeight||Math.floor(Q)!==Q))for(let ee=0;ee<v;ee+=A){let ie=(W+ee)/g;if(ie<0||ie>=p.outWidth||Math.floor(ie)!==ie)continue;let 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l=o?r:VN({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],d=l.shape[1],h=n.data.get(l.dataId).values,p=[u,s],c=k.makeZerosTypedArray(k.sizeFromShape(p),"int32");for(let m=0;m<u;++m){let f=m*d,g=new Float32Array(d-1);g[0]=h[f];for(let x=1;x<g.length;++x)g[x]=g[x-1]+h[f+x];let y=Sue.alea(i.toString()),A=m*s;for(let x=0;x<s;++x){let v=y();c[A+x]=g.length;for(let b=0;b<g.length;b++)if(v<g[b]){c[A+x]=b;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",c)}var Eue={kernelName:j1,backendName:"cpu",kernelFunc:Tue},Cue=us.nonMaxSuppressionV3Impl;function Mue(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a;Se(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:h}=Cue(u,d,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var $ue={kernelName:Gd,backendName:"cpu",kernelFunc:Mue},Rue=us.nonMaxSuppressionV4Impl;function Fue(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a;Se(r,"NonMaxSuppressionPadded");let d=n.data.get(r.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:p,validOutputs:c}=Rue(d,h,i,o,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([c]))]}var Oue={kernelName:qd,backendName:"cpu",kernelFunc:Fue},Due=us.nonMaxSuppressionV5Impl;function _ue(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a;Se(r,"NonMaxSuppressionWithScore");let d=n.data.get(r.dataId).values,h=n.data.get(s.dataId).values,p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Due(d,h,p,c,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var zue={kernelName:Kd,backendName:"cpu",kernelFunc:_ue};function Pue(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;Se(r,"oneHot");let l=k.sizeFromShape(r.shape),u=new Float32Array(l*s);u.fill(o);let d=n.data.get(r.dataId).values;for(let h=0;h<l;++h)d[h]>=0&&d[h]<s&&(u[h*s+d[h]]=i);return n.makeTensorInfo([...r.shape,s],"int32",u)}var Lue={kernelName:wl,backendName:"cpu",kernelFunc:Pue};function R0(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=co({inputs:{input:a},backend:n}),s=R0({inputs:{x:r},backend:n}),i=pu({inputs:{input:a},backend:n}),o=R0({inputs:{x:i},backend:n}),l=ca({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return V5({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var Wue={kernelName:ph,backendName:"cpu",kernelFunc:R0};function UN(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=co({inputs:{input:a},backend:n}),s=UN({inputs:{x:r},backend:n}),i=pu({inputs:{input:a},backend:n}),o=R0({inputs:{x:i},backend:n}),l=ca({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return V5({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var Bue={kernelName:Xd,backendName:"cpu",kernelFunc:UN};function jN(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $0({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let 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Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});ze.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);function Wn(){let e,t,n,a,r,s,i,o,l,u;return se().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
      bool isnan_custom(float val) {
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      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
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      #define isnan(value) isnan_custom(value)
    `,l="",u=`
      #define round(value) newRound(value)
      int newRound(float value) {
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      }

      ivec4 newRound(vec4 value) {
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      }
    `):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
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    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
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      }
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        return equal(abs(val), vec4(INFINITY));
      }
    `,u=`
      int round(float value) {
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      ivec4 round(vec4 value) {
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      }
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  }
`}var bT=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
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    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
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    } else if(v > FLOAT_MAX) {
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    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
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    c[2] = floor(128.0 * m);
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    highp float ebias = e + 127.0;
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    return c / 255.0;
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      ivec3 outCoordsFromFlatIndex(int index) {
        ${Ao(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
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        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);
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      ivec3 outCoordsFromFlatIndex(int index) {
        ${Ao(["r","c","d"],e)}
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      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
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        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));
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        ${n.output} = result;
      }
    `}},Whe=class{constructor(e){this.variableNames=["A"],this.outTexUsage=_a.DOWNLOAD;let t=Wn();this.outputShape=e,this.userCode=`
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      void main() {
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        ${t.output} = encode_float(x);
      }
    `}},Bhe=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=_a.DOWNLOAD;let t=Wn();this.outputShape=e,this.userCode=`
      ${bT}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},Vhe=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=Wn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
      ${X5(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, ${r}.0);
        vec4 values = ${a.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];
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        ${a.output} = vec4(${i}, 0., 0., 0.);
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    `}},Uhe=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=Wn(),[r,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 u=0;u<=1;u++){let d=l*2+u;i+=`
          localCoords = coords;
          if(localCoords[2] + ${u} < ${e[2]}) {
            localCoords[2] += ${u};
            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, ${r}.0);
              values = ${a.texture2D}(A, uv);

              if(offset == 0) {
                result[${d}] = values[0];
              } else if(offset == 1) {
                result[${d}] = values[1];
              } else if(offset == 2) {
                result[${d}] = values[2];
              } else {
                result[${d}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${X5(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${i}

        ${a.output} = ${o};
      }
    `}},vT={};$e(vT,{bindVertexProgramAttributeStreams:()=>MT,createBufferFromOutputTexture:()=>FT,createFloat16MatrixTexture:()=>NT,createFloat16PackedMatrixTexture:()=>CT,createFloat32MatrixTexture:()=>ST,createIndexBuffer:()=>IT,createPackedMatrixTexture:()=>ET,createUnsignedBytesMatrixTexture:()=>TT,createVertexBuffer:()=>kT,createVertexShader:()=>wT,downloadByteEncodedFloatMatrixFromOutputTexture:()=>DT,downloadFloat32MatrixFromBuffer:()=>OT,downloadMatrixFromPackedOutputTexture:()=>zT,downloadPackedMatrixFromBuffer:()=>_T,getInternalFormatForFloat16MatrixTexture:()=>Y5,getInternalFormatForFloat16PackedMatrixTexture:()=>eb,getInternalFormatForFloat32MatrixTexture:()=>Z5,getInternalFormatForPackedMatrixTexture:()=>Q5,getInternalFormatForUnsignedBytesMatrixTexture:()=>J5,uploadDenseMatrixToTexture:()=>$T,uploadPixelDataToTexture:()=>RT});function wT(e){let t=Wn(),n=`${t.version}
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    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

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      resultUV = uv;
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this.throwIfDisposed(),CT(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),ET(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(G5(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>DT(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return _T(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return OT(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=FT(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(se().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>zT(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=QN(t,e);this.vertexShader==null&&(this.vertexShader=wT(t));let a=eT(t);return ke(t,()=>t.attachShader(a,this.vertexShader)),ke(t,()=>t.attachShader(a,n)),tT(t,a),this.debug&&O0(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=MT(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&O0(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?lT(this.gl,e,t):uT(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(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(),dT(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=fu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&O0(this.gl,this.program),lp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=op(this.gl,se().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(se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(se().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,se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,se().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=jhe(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(),D0(this.gl,e,this.framebuffer),this.debug&&lp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(D0(this.gl,this.outputTexture,this.framebuffer),this.debug&&lp(this.gl)):G5(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;D0(a,e,this.framebuffer),this.debug&&lp(a),this.outputTexture=e,ke(a,()=>a.viewport(0,0,t,n)),ke(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,a))}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 jhe(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:PT}=M;function Hhe(e,t,n,a){let r=[];e.forEach(c=>{let m=k.sizeFromShape(c.shapeInfo.logicalShape);c.shapeInfo.isUniform?r.push(`uniform float ${c.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${c.name};`),r.push(`uniform int offset${c.name};`))});let s=r.join(`
`),i=e.map(c=>Ghe(c,t,a)).join(`
`),o=t.texShape,l=Wn(),u=Xhe(l),d,h,p=Jhe(l);return t.isPacked?(d=qhe(t.logicalShape,o),h=Yhe(l)):(d=Khe(t.logicalShape,o),h=Zhe(l)),a&&(p+=npe),[p,u,h,s,d,i,n].join(`
`)}function gu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return fpe(e);case 1:return gpe(e);case 2:return Ape(e);case 3:return bpe(e);case 4:return wpe(e);case 5:return kpe(e);case 6:return Ipe(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function LT(e){switch(e.shapeInfo.logicalShape.length){case 0:return cpe(e);case 1:return mpe(e);case 2:return ype(e);case 3:return xpe(e);default:return vpe(e)}}function Ghe(e,t,n=!1){let a="";n?a+=LT(e):a+=gu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=Spe(e,t):a+=Npe(e,t)),a}function qhe(e,t){switch(e.length){case 0:return WT();case 1:return ape(e,t);case 2:return hpe(e,t);case 3:return spe(e,t);default:return ope(e,t)}}function Khe(e,t){switch(e.length){case 0:return WT();case 1:return rpe(e,t);case 2:return ppe(e,t);case 3:return ipe(e,t);case 4:return lpe(e,t);case 5:return upe(e,t);case 6:return dpe(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Xhe(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function Zhe(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function Yhe(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function Jhe(e){return`${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);
    }

    ${Qhe}
    ${epe}
    ${tpe}
  `}var Qhe=`
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);
}
`,epe=`
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);
}
`,tpe=`
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);
}
`,npe=`
  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 WT(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function ape(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 rpe(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 * ${t[1]} + resTexRC.y;
    }
  `}function spe(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      int b = index / ${r};
      index -= b * ${r};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec3(b, r, c);
    }
  `}function ipe(e,t){let n=Ao(["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;
      ${n}
      return ivec3(r, c, d);
    }
  `}function ope(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
      int b${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 / ${r};
      index -= b * ${r};

      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec${e.length}(${o});
    }
  `}function lpe(e,t){let n=Ao(["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;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function upe(e,t){let n=Ao(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function dpe(e,t){let n=Ao(["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 hpe(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]}, ${n[1]}));
      }
    `;let a=Math.ceil(e[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));

      int index = resTexRC.x * ${n[1]} + resTexRC.y;
      int r = 2 * (index / ${a});
      int c = imod(index, ${a}) * 2;

      return ivec2(r, c);
    }
  `}function ppe(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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function xo(e){return`offset${e}`}function cpe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Wn();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function fpe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
      float ${n}() {
        return sampleTexture(${t}, halfCR);
      }
    `;let[s,i]=e.shapeInfo.texShape,o=xo(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${s}, ${i}, ${o});
      return sampleTexture(${t}, uv);
    }
  `}function mpe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=Wn();return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${r[0]}, ${r[1]}, index);
      return ${s.texture2D}(${t}, uv);
    }
  `}function gpe(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${yu(e)}
      }
    `;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
      float ${n}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;let i=xo(t);return s===1?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
        return sampleTexture(${t}, uv);
      }
    `:r===1?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
      return sampleTexture(${t}, uv);
    }
  `}function ype(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=Wn();if(r!=null&&k.arraysEqual(t,r))return`
      vec4 ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);

        return ${o.texture2D}(${n}, uv);
      }
    `;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],u=Math.ceil(t[1]/2);return`
    vec4 ${a}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
      return ${o.texture2D}(${n}, uv);
    }
  `}function Ape(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let h=r[0],p=r[1];return`
    float ${a}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${p}.0, ${h}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let h=Au(e,o),p=["row","col"];return`
      ${gu(h)}
      float ${a}(int row, int col) {
        return ${a}(${xu(p,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${yu(e)}
      }
    `;let l=r[0],u=r[1],d=xo(n);return u===1?`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${n}, uv);
    }
  `:l===1?`
    float ${a}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:`
  float ${a}(int row, int col) {
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          return ${a}(${xu(f,o)});
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    }
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      ${gu(m)}
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        return ${a}(${xu(f,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
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        ${yu(e)}
      }
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      float ${a}(int row, int col, int depth, int depth2) {
        float texR = float(row);
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                vec3(${s}, ${r}, 1));
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      float ${a}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
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        vec2 uv = (vec2(texC, texR) + halfCR) /
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      // Explicitly use integer operations as dot() only works on floats.
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        return ${a}(${xu(g,u)});
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    `}if(e.shapeInfo.isUniform)return`
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      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
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          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
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      // Explicitly use integer operations as dot() only works on floats.
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      float ${a}(int row, int col, int depth,
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        int index = round(dot(
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      float ${a}(int row, int col, int depth,
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               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
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      ${d}
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        return sampleTexture(${n}, resultUV);
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      void main() {
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        setOutput(getChannel(packedInput, ${i}));
      }
    `}},ife=us.whereImpl,ofe=1e-7,lfe=1e-4,ob={};function ufe(e){return e in ob||(ob[e]={}),ob[e]}var dfe=()=>se().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),hfe=600;function pfe(){return se().global.screen==null?1024:se().global.screen.height*se().global.screen.width*window.devicePixelRatio*hfe/1024/1024}var xE=class extends Wc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!se().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Pr(se().getNumber("WEBGL_VERSION"));this.binaryCache=ufe(se().getNumber("WEBGL_VERSION")),this.gpgpu=new W0(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Hce(this.gpgpu),this.numMBBeforeWarning=pfe(),this.texData=new f1(this,Ps())}nextDataId(){return xE.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((se().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||se().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:_a.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(se().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:_a.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new wu(i,V0):h=new Qs(i,V0);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:a}],a),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=k.now());let d;if(a==="complex64"){let h=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);d=M.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(m=>c.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let c;o?c=new wu(a,V0):c=new Qs(a,V0);let m=this.runWebGLProgram(c,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!se().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&se().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&se().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture,...ip(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=c[0],f=c[1];d=M.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ps().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!ZN(n))throw se().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(se().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...ip(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(h),c}let s=se().getBool("WEBGL_PACK")&&a===!0,i=s?_0(t):t,o=s?new Bhe(i):new Whe(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(se().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=dfe){return se().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){M.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return ife(e.shape,t)}packedUnaryOp(e,t,n){let a=new wu(e.shape,t),r=this.compileAndRun(a,[e],n);return Ps().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=hE(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(se().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,AE,e.dtype);let t=new Qs(e.shape,AE),n=this.compileAndRun(t,[e]);return Ps().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return Ps().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new sfe(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Lce(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[go(e.shape),...yo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[go(t),...yo(t)],s=new fE(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=_0(a),i;n?i=new Lhe(s):i=new Phe(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===rp.DENSE){let f=ip(e.outputShape);i.texShape=f.map(g=>g*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 g=this.texData.get(f.dataId);if(g.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=se().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=f.shape)}else if(!!g.isPacked!=!!e.packedInputs)f=g.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),g=this.texData.get(f.dataId);else if(g.isPacked&&!up(g.shape,f.shape)){let y=f,A=f.shape;f.shape=g.shape,f=this.packedReshape(f,A),o.push(f),g=this.texData.get(f.dataId),y.shape=A}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:g,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},d=Cpe(e,l,u),h=this.getAndSaveBinary(d,()=>Tpe(this.gpgpu,e,l,u)),p=this.activeTimers!=null,c;p&&(c=this.startTimer()),Epe(this.gpgpu,h,l,u,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),p&&(c=this.endTimer(c),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(c)}));let m=se().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!se().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(se().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Z(()=>{if(!se().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=se().getBool("DEBUG");se().set("DEBUG",!1);let t=this.abs(Re(1e-8)).dataSync()[0];if(se().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ofe:lfe}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=k.now());let d=t.texShape;if(d==null&&(d=cT(n,o),t.texShape=d),r!=null){let h=_0(n),p,c=d[1],m=d[0],f=r instanceof Uint8Array;o?([c,m]=fu(d[0],d[1]),p=new Uhe(h,[m,c],f)):p=new Vhe(h,[m,c],f);let g=this.makeTensorInfo([m,c],a);f?this.texData.get(g.dataId).usage=_a.PIXELS:this.texData.get(g.dataId).usage=_a.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),c,m,r);let y=!0,A=this.runWebGLProgram(p,[g],a,null,y),x=this.texData.get(A.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(A.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-u)}else{let h=this.acquireTexture(d,i,a,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=cfe(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}},hp=xE;hp.nextDataId=0;function cfe(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var ffe="3.7.0";function bE(){se().set("WEBGL_FORCE_F16_TEXTURES",!0)}yf.isBrowser()&&MA("webgl",()=>new hp,2);var mfe={forceHalfFloat:bE},vE=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,ku=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=M.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},U0=`
  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;
`,pp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=M.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${kt(r)} coords = getOutputCoords();
        `,r===1)s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=Bn("coords",r);s+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${s}

        setOutput(result);
      }
    `}};function fa(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var gfe={kernelName:pl,backendName:"webgl",kernelFunc:fa};function ei(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=fa({inputs:{x:a},backend:n}),l=fa({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var yfe={kernelName:I1,backendName:"webgl",kernelFunc:ei},wE="return (a < 0.) ? b * a : a;",kE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Afe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=se().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pp(kE,r.shape,i.shape):new ku(wE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var xfe={kernelName:cl,backendName:"webgl",kernelFunc:Afe},IE="return (a < 0.) ? b * a : a;",SE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function bfe(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=se().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pp(SE,a.shape,r.shape):new ku(IE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var vfe={kernelName:Sl,backendName:"webgl",kernelFunc:bfe},NE="if (isnan(x)) return x;",wfe=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,kfe=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function ot({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),p=n(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=se().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new wu(i.shape,t):d=new Qs(i.shape,e),o.runWebGLProgram(d,[i],l)}}function Tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(a&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,b]=x,w={dataId:v.dataId,dtype:v.dtype,shape:l.shape},I={dataId:b.dataId,dtype:b.dtype,shape:u.shape},T=new ku(e,l.shape,u.shape);return d.runWebGLProgram(T,[w,I],Ga(v.dtype,b.dtype))}),A=ei({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Ga(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&r!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?M.fromUint8ToStringArray(m):m,y=l.dtype==="string"?M.fromUint8ToStringArray(f):f,[A,x]=r(l.shape,u.shape,g,y,h),v=d.makeTensorInfo(x,h),b=d.texData.get(v.dataId);return b.values=A,v}let p=se().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new pp(t,l.shape,u.shape,n):c=new ku(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function j0(e,t=!1){if(e==="linear")return t?efe:Xce;if(e==="relu")return t?nfe:Yce;if(e==="elu")return t?tfe:Zce;if(e==="relu6")return t?afe:Jce;if(e==="prelu")return t?SE:IE;if(e==="leakyrelu")return t?kE:wE;if(e==="sigmoid")return t?rfe:Qce;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var TE=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=a?e[1]:e[2],d=Math.ceil(u/2),h=a?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",c=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:l?f=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:f=`vec4 activation(vec4 x) {
          ${i}
        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${f}

      const float sharedDimension = ${d}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${d}; i++) {
          int batchA = ${A};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${h});
          vec4 b = getMatrixB(batchB, ${p});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${c[0]} * ${m[0]});
          result += (${c[1]} * ${m[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${y}

        ${g}

        setOutput(result);
      }
    `}},EE={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},CE=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=M.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));
      }
    `}},ME="return a * b;";function lb(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=M.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new CE(EE.REAL,a.shape,r.shape),d=new CE(EE.IMAG,a.shape,r.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(u,h,"float32"),c=n.runWebGLProgram(d,h,"float32"),m=ei({inputs:{real:p,imag:c},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,d]=vce(a.shape,r.shape,o.values,l.values,s),h=n.makeTensorInfo(d,s),p=n.texData.get(h.dataId);return p.values=u,h}let i;return se().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new pp(ME,a.shape,r.shape):i=new ku(ME,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var Ife={kernelName:Oi,backendName:"webgl",kernelFunc:lb};function Sfe(e,t,n){let a=[go(e.shape),...yo(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[go(t),...yo(t)],i=new fE(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function be(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),u=k.sizeFromShape(l);k.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(r.dataId);return d.isPacked&&!up(r.shape,l)&&!(d.texture!==null&&up(d.shape,l))?Sfe(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Nfe={kernelName:Qd,backendName:"webgl",kernelFunc:be},$E=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";r%n>0&&(u=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${u}
        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);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},Tfe=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,d=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 = ${o}(values, minMaxValue);
        if (${t==="min"} || ${t==="max"}) {
          minMaxValue = ${o}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,p="vec4";t==="all"?(i="1.0",h=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,p="bvec4"):t==="any"&&(i="0.0",h=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,p="bvec4");let c="";r%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${r}) {
          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) {
        ${c}
        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 < ${u}; i += 4) {
          int inIdx = inOffset + i;
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${h}
        }

        int inIdx = inOffset + ${u};
        if (${d===1}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${h}
        } else if (${d===2}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${h}
        } else if (${d===3}) {
          ${p} values = ${p}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${h}
        }
        setOutput(${l});
      }
    `}};function Efe(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=M.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function bo(e,t,n,a){let r=Efe(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],d,h;n==="mean"?d=i===0?new $E({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new $E({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Tfe({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=a.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(h)}return s}var Cfe=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 a=kt(this.rank),r=Mfe(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function Mfe(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var $fe=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=kt(this.rank),r=cE("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
    void main() {
      ${a} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${o}) {
        result[1] = ${l};
      }
      --${r[this.rank-1]};
      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function H0(e,t,n){let a=se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $fe(e.shape,t):new Cfe(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function Rfe(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=M.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=H0(e,l,a),o=M.getInnerMostAxes(o.length,s)),M.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=M.computeOutAndReduceShapes(d.shape,o),c=h;n&&(c=M.expandShapeToKeepDim(h,i));let m=k.sizeFromShape(p),f=k.sizeFromShape(e.shape)/m,g=be({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),y=cA(e.dtype),A=bo(g,y,"sum",a),x=be({inputs:{x:A},attrs:{shape:c},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),u&&a.disposeIntermediateTensorInfo(d),x}function G0(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return Rfe(r,s,i,n)}var Ffe={kernelName:Ol,backendName:"webgl",kernelFunc:G0};function Vn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=r.shape[s[d]];let u;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,h=ib(d,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=H0(r,s,i);return u}var Ofe={kernelName:Pl,backendName:"webgl",kernelFunc:Vn},RE=1e3;function q0({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],p=a?t.shape[d-1]:t.shape[d-2],c=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),A=k.sizeFromShape(g),x=y===A||y===1||A===1;k.assert(u>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([c,m]);k.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[y,h,c]:[y,c,h],w=a?[A,m,p]:[A,p,m],I=be({inputs:{x:e},backend:r,attrs:{shape:b}}),T=be({inputs:{x:t},backend:r,attrs:{shape:w}}),C=[I,T],z=Math.max(y,A),$=n?I.shape[1]:I.shape[2],S=s!=null,D=i!=null,_=l==="leakyrelu",W=l!=null?j0(l,!0):null,X=S||D||_||W!=null,q;if((c===1||m===1)&&$>RE&&X===!1){let ee=I,ie=T;n&&(ee=Vn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),C.push(ee)),a&&(ie=Vn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),C.push(ie));let ae=m!==1,de=m===1,te=ee;ae&&(te=be({inputs:{x:ee},backend:r,attrs:{shape:[z,$,1]}}),C.push(te));let ce=m===1?2:1,he=ie;de&&(he=be({inputs:{x:ie},backend:r,attrs:{shape:[z,1,$]}}),C.push(he));let ve=lb({inputs:{a:te,b:he},backend:r});q=G0({inputs:{x:ve},backend:r,attrs:{axis:ce,keepDims:!0}}),C.push(ve)}else{let ee=Ga(e.dtype,t.dtype),ie=new TE(b,w,[z,c,m],n,a,S,W,D,_),ae=[I,T];if(s!=null&&ae.push(s),D&&ae.push(i),_){let de=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ae.push(de),C.push(de)}q=r.runWebGLProgram(ie,ae,ee)}let Q=be({inputs:{x:q},backend:r,attrs:{shape:v}});C.push(q);for(let ee of C)r.disposeIntermediateTensorInfo(ee);return Q}function Dfe(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=a;return q0({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var _fe={kernelName:Ll,backendName:"webgl",kernelFunc:Dfe},FE="return abs(x);";function zfe(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=hE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return se().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new wu(a.shape,FE):r=new Qs(a.shape,FE),n.runWebGLProgram(r,[a],a.dtype)}var Pfe={kernelName:xd,backendName:"webgl",kernelFunc:zfe},Lfe=gr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,Wfe=ot({opSnippet:Lfe}),Bfe={kernelName:bd,backendName:"webgl",kernelFunc:Wfe},Vfe=gr+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Ufe=ot({opSnippet:Vfe}),jfe={kernelName:vd,backendName:"webgl",kernelFunc:Ufe},OE="return a + b;",Hfe=Tn({opSnippet:OE,packedOpSnippet:OE,supportsComplex:!0,cpuKernelImpl:nce}),Gfe={kernelName:Os,backendName:"webgl",kernelFunc:Hfe},qfe=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${a};
        setOutput(result);
      }
    `}},Kfe=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${a};
        setOutput(result);
      }
    `}};function K0(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return fa({inputs:{x:a[0]},backend:n});if(a.length>se().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=K0({inputs:a.slice(0,o),backend:n}),u=K0({inputs:a.slice(o),backend:n});return K0({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>Ga(o,l)),s=a.map(o=>o.shape),i=se().getBool("WEBGL_PACK")?new Kfe(a[0].shape,s):new qfe(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var Xfe={kernelName:Zo,backendName:"webgl",kernelFunc:K0};function Zfe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=M.getAxesPermutation(u,o),h=r;d!=null&&(h=Vn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=M.getInnerMostAxes(u.length,o)),M.assertAxesAreInnerMostDims("all",u,o);let[p,c]=M.computeOutAndReduceShapes(h.shape,u),m=k.sizeFromShape(c),f=be({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),g=bo(f,f.dtype,"all",n),y;if(i){let A=M.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(h),y}var Yfe={kernelName:wd,backendName:"webgl",kernelFunc:Zfe};function Jfe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=M.getAxesPermutation(u,o),h=r;d!=null&&(h=Vn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=M.getInnerMostAxes(u.length,o)),M.assertAxesAreInnerMostDims("any",u,o);let[p,c]=M.computeOutAndReduceShapes(h.shape,u),m=k.sizeFromShape(c),f=be({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),g=bo(f,f.dtype,"any",n),y;if(i){let A=M.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(h),y}var Qfe={kernelName:kd,backendName:"webgl",kernelFunc:Jfe},e0e=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,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 * ${a};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${a}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},t0e=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=kt(o),u=Bn("coords",o),d,h;if(s===1){h=o+1;let I=kt(h);d=`
        ${I} sourceLocR = ${I}(${u.join()}, 0);
        ++${u[o-1]};
        ${I} sourceLocG = ${I}(${u.join()}, 0);
        ++${u[o-2]};
        ${I} sourceLocA = ${I}(${u.join()}, 0);
        --${u[o-1]};
        ${I} sourceLocB = ${I}(${u.join()}, 0);
        --${u[o-2]};`}else h=o,d=`
        ${l} sourceLocR = coords;
        ++${u[o-1]};
        ${l} sourceLocG = coords;
        ++${u[o-2]};
        ${l} sourceLocA = coords;
        --${u[o-1]};
        ${l} sourceLocB = coords;
        --${u[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],m=p.map(I=>"int "+I),f=Bn("sourceLocR",h-1).concat("inIdx.r"),g=Bn("sourceLocG",h-1).concat("inIdx.g"),y=Bn("sourceLocB",h-1).concat("inIdx.b"),A=Bn("sourceLocA",h-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${A.join()})));`,b=`vec4(
            getAChannel(${f.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,w=a?"":`
      float getBestIndicesAChannel(${m.join()}) {
        return getChannel(getBestIndicesA(${p.join()}),
                                          vec2(${p.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${m.join()}) {
        return getChannel(getA(${p.join()}),
                               vec2(${p.slice(-2).join()}));
      }
      ${w}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
        ${d}
        ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},
          sourceLocB${c}, sourceLocA${c}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${b};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${v}
          vec4 candidate = ${b};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(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 DE(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=M.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new e0e(o,n,a==null),u=[t];a!=null&&u.push(a);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=DE(e,t,n,d);return e.disposeIntermediateTensorInfo(d),h}function _E(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=M.computeOptimalWindowSize(s),o=new t0e(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=_E(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}return u}function zE(e,t,n,a){let r=[n];if(M.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!se().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=M.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),u=be({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let d=DE(e,u,a);s.push(d);let h=be({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(p=>e.disposeIntermediateTensorInfo(p)),h}return _E(e,t,a)}function n0e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=M.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=M.getInnerMostAxes(i.length,l.shape.length)),M.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=zE(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),d}var a0e={kernelName:Yo,backendName:"webgl",kernelFunc:n0e};function r0e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=M.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=M.getInnerMostAxes(i.length,l.shape.length)),M.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=zE(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),d}var s0e={kernelName:qc,backendName:"webgl",kernelFunc:r0e},i0e=gr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,o0e=ot({opSnippet:i0e}),l0e={kernelName:Id,backendName:"webgl",kernelFunc:o0e},u0e=gr+"return log(x + sqrt(x * x + 1.0));",d0e=ot({opSnippet:u0e}),h0e={kernelName:Sd,backendName:"webgl",kernelFunc:d0e},p0e=gr+`
  return atan(x);
`,c0e=ot({opSnippet:p0e}),f0e={kernelName:Nd,backendName:"webgl",kernelFunc:c0e},m0e=wfe+`
  return atan(a, b);
`,g0e=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+kfe+`
  return result;
`,y0e=Tn({opSnippet:m0e,packedOpSnippet:g0e}),A0e={kernelName:Ed,backendName:"webgl",kernelFunc:y0e},x0e=gr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,b0e=ot({opSnippet:x0e}),v0e={kernelName:Td,backendName:"webgl",kernelFunc:b0e},cp=class{constructor(e,t,n,a=!1,r=!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,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let I=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${p}, ${c});

        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 < ${d};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${h};
                wC += ${u}) {
              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 ${I} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?r?f:g:`wR * ${h} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,b=s%4,w=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${A}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${p}, ${c});
      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 < ${d};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${v}; wC += 4) {
            int xC = xCCorner + wC * ${u};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              getValue(batch, xR, xC + 3 * ${u}, d)
            );

            ${w}
          }

          int xC = xCCorner + ${v};
          if (${b===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${w}
          } else if (${b===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              initializationValue,
              initializationValue
            );

            ${w}
          } else if (${b===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              initializationValue
            );

            ${w}
          }
        }
        setOutput(${x});
      }
    `}},ub=class{constructor(e,t,n,a=!1,r=!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,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${f}, ${g}, ${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 < ${p};
              wD += ${u}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${c};
                wR += ${d}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${m};
                  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 ${C} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${m} +
                      wR * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let v="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let w=Math.floor(s/4)*4,I=s%4,T=`
      if (${A}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${v}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${f}, ${g}, ${y});
      const float initializationValue = ${x};
      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(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${p};
            wD += ${u}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${c};
            wR += ${d}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${w}; 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)
              );

              ${T}
            }

            int xC = xCCorner + ${w};
            if (${I===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${T}
            } else if (${I===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${h}, ch),
                initializationValue,
                initializationValue
              );

              ${T}
            } else if (${I===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
              );

              ${T}
            }
          }
          setOutput(${b});
        }
      }
    `}};function w0e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;mu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(M.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=M.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return fa({inputs:{x:r},backend:n});let h=new cp(d,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}var k0e={kernelName:Jo,backendName:"webgl",kernelFunc:w0e};function I0e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,d=[1,1,1],h=M.computePool3DInfo(r.shape,s,i,d,o,l,u),p=new ub(h,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var S0e={kernelName:Kc,backendName:"webgl",kernelFunc:I0e},N0e=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${d});
      const float avgMultiplier = float(${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) / ${a}.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) / ${r}.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);
      }
    `}},T0e=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,m=h-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
      const ivec3 pads = ivec3(${c}, ${m}, ${f});
      const float avgMultiplier = float(${g});

      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 < ${d};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${r}.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 < ${p};
                wC += ${u}) {
              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 E0e(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,h=[1,1,1],p=M.computePool3DInfo(i.shape,o,l,h,u,d),c=new T0e(p);return n.runWebGLProgram(c,[r],i.dtype)}var C0e={kernelName:w1,backendName:"webgl",kernelFunc:E0e};function M0e(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;mu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=M.computePool2DInfo(i.shape,o,l,1,u),h=new N0e(d);return n.runWebGLProgram(h,[r],i.dtype)}var $0e={kernelName:v1,backendName:"webgl",kernelFunc:M0e};function R0e(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return q0({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var F0e={kernelName:Qo,backendName:"webgl",kernelFunc:R0e},O0e=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],M.assertAndGetBroadcastShape(e,t),M.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(M.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(M.assertAndGetBroadcastShape(e,r),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)));
      }
    `}},D0e=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],M.assertAndGetBroadcastShape(e,t),M.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(M.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(M.assertAndGetBroadcastShape(e,r),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);
      }
    `}},_0e=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.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 u=[a,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=se().getBool("WEBGL_PACK_NORMALIZATION")?new D0e(a.shape,r.shape,s.shape,d,h,l):new O0e(a.shape,r.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},z0e={kernelName:dl,backendName:"webgl",kernelFunc:_0e},P0e=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=kt(this.rank),n=`uniform int start[${this.rank}];`,a=L0e(this.rank),r,s=e.map((i,o)=>`sourceLoc.${db[o]} = start[${o}] + coords.${db[o]};`);r=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      ${n}
      void main() {
        ${r}
        setOutput(getSource(${a}));
      }
    `}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)}}},db=["x","y","z","w","u","v"];function L0e(e){if(e===1)return"sourceLoc";if(e<=6)return db.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var W0e=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=kt(this.rank),n=Bn("coords",this.rank),a=Bn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
      result.x = ${s};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${a[this.rank-1]};
        result.y = ${s};
        --${a[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${a[this.rank-2]};
        result.z = ${s};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${a[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${a[d]} = ${n[d]} + start[${d}];`).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 B0e(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Cn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function fp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Cn.parseSliceParams(r,s,i);if(Cn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let h=n.texData.get(r.dataId),p=Tce(h.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:u}=n.texData.get(r.dataId),d=Cn.isSliceContinous(r.shape,o,l);if(u||!d){let h=se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new W0e(l):new P0e(l),p=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),B0e(r,o,l,n)}var V0e={kernelName:ah,backendName:"webgl",kernelFunc:fp},U0e=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=M.getReshaped(r.shape,s,o),u=M.getPermuted(l.length,s.length),d=M.getReshapedPermuted(r.shape,s,o),h=M.getSliceBeginCoords(i,s.length),p=M.getSliceSize(d,i,s.length),c=[],m=be({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Vn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=be({inputs:{x:f},backend:n,attrs:{shape:d}}),y=fp({inputs:{x:g},backend:n,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},j0e={kernelName:Xc,backendName:"webgl",kernelFunc:U0e};function H0e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=dE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var G0e={kernelName:k1,backendName:"webgl",kernelFunc:H0e},q0e="return float(a != b);",PE=Tn({opSnippet:q0e,cpuKernelImpl:kce,dtype:"bool"}),K0e={kernelName:vl,backendName:"webgl",kernelFunc:PE};function mp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return fa({inputs:{x:r.complexTensorInfos.real},backend:n})}var X0e={kernelName:H1,backendName:"webgl",kernelFunc:mp},Z0e="return float(int(x));";function Y0e(e,t){let n=new Qs(e.shape,Z0e),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function hb(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return fa({inputs:{x:r},backend:n});let i=un(r.shape),o=hb({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ei({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=mp({inputs:{input:r},backend:n}),o=hb({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=fa({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Y0e(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=PE({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var J0e={kernelName:el,backendName:"webgl",kernelFunc:hb},LE="return ceil(x);",Q0e=ot({opSnippet:LE,packedOpSnippet:LE,cpuKernelImpl:rce}),eme={kernelName:Ni,backendName:"webgl",kernelFunc:Q0e},tme=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,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},nme=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,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function ame(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;se().getBool("WEBGL_PACK_CLIP")?o=new nme(r.shape):o=new tme(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var rme={kernelName:Ti,backendName:"webgl",kernelFunc:ame},sme=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 WE(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function ime(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new sme(a.shape),i=[WE(a,r.complexTensorInfos.real),WE(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var ome={kernelName:Zc,backendName:"webgl",kernelFunc:ime},lme=class{constructor(e){this.outputShape=[],this.outputShape=M.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-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},ume=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=M.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=kt(a),s=Bn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
        return getChannel(
            getT0(${d}), vec2(${u.join()}));
        }`;for(let m=1;m<o.length;m++){let f=o[m-1];h+=`
        if (${l} < ${o[m]}  && ${l} >= ${o[m-1]}) {
          return getChannel(
            getT${m}(${X0(i,l,f)}),
            vec2(${X0(u,l,f)}));
        }`}let p=o.length,c=o[o.length-1];h+=`
        return getChannel(
          getT${p}(${X0(i,l,c)}),
          vec2(${X0(u,l,c)}));`,this.userCode=`
      float getValue(${i.map(m=>"int "+m)}) {
        ${h}
      }

      void main() {
        ${r} coords = getOutputCoords();
        vec4 result = vec4(getValue(${s}), 0., 0., 0.);

        ${s[a-1]} = ${s[a-1]} + 1;
        if (${s[a-1]} < ${n[a-1]}) {
          result.g = getValue(${s});
        }

        ${s[a-2]} = ${s[a-2]} + 1;
        if (${s[a-2]} < ${n[a-2]}) {
          result.a = getValue(${s});
        }

        ${s[a-1]} = ${s[a-1]} - 1;
        if (${s[a-2]} < ${n[a-2]} &&
            ${s[a-1]} < ${n[a-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function X0(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Z0(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return fa({inputs:{x:r.complexTensorInfos.imag},backend:n})}var dme={kernelName:P1,backendName:"webgl",kernelFunc:Z0};function Iu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>mp({inputs:{input:f},backend:n})),h=e.map(f=>Z0({inputs:{input:f},backend:n})),p=Iu(d,t,n),c=Iu(h,t,n),m=ei({inputs:{real:p,imag:c},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),h.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(y=>{let A=k.sizeFromShape(y.shape.slice(t));return be({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=M.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,m=sce(h,p,a,c),f=M.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(f,a,m);return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>se().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=Iu(e.slice(0,d),t,n),p=Iu(e.slice(d),t,n),c=Iu([h,p],t,n);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),c}if(se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new ume(e.map(h=>h.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=hme(e,t,n),o=new lme(s.map(d=>d.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let u=be({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function hme(e,t,n){let a=M.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>be({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function BE(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=M.computeOutShape(t.map(u=>u.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>k.sizeFromShape(u.shape)>0);if(o.length===1)return fa({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return M.assertParamsConsistent(l,s),Iu(o,s,n)}var pme={kernelName:Cd,backendName:"webgl",kernelFunc:BE},VE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?3:1,x="",v="";n&&(a?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,v="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

      const ivec2 strides = ivec2(${o}, ${l});
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${A}];

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${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 * ${u};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${p}; wC++) {
            int xC = xCCorner + wC * ${d};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${c}; 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 (${f}) {
                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 (${m===1}) {

              if (${f}) {
                dotProd +=
                    getX(batch, xR, xC, ${c}) *
                    getW(wR, wC, ${c}, d2);
              } else {
                dotProd +=
                    getX(batch, ${c}, xR, xC) *
                    getW(wR, wC, ${c}, d2);
              }

            } else if (${m===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2)
              );

              if (${f}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${m===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2),
                getW(wR, wC, ${c} + 2, d2)
              );

              if (${f}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1),
                  getX(batch, xR, xC, ${c} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC),
                  getX(batch, ${c} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${b}
        ${v}
        setOutput(result);
      }
    `}},cme=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${r}, ${s}, ${i});
      const ivec3 pads = ivec3(${t}, ${n}, ${a});

      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 < ${d}; 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 < ${p}; wC++) {
              int xC = xCCorner + wC * ${u};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${c}; 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 (${m===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${c}) *
                  getW(wF, wR, wC, ${c}, d2);
              } else if (${m===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${m===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1),
                  getX(batch, xF, xR, xC, ${c} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2),
                  getW(wF, wR, wC, ${c} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},fme=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:d,dataFormat:h}=n,{left:p,top:c}=o,m=r*a,f=Wn(),g=h==="channelsLast",y=g?0:1,A=g?1:2,x="";for(let v=0;v<=1;v++)for(let b=0;b<=1;b++)x+=`
          blockIndex = rc.y + ${b};
          pos = rc.x + ${v};

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${l})) * ${i} - ${c};
            d0 = offsetY + ${d} * (pos / ${m});

            if(d0 < ${t[y]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${p}.);
              d1 = offsetX + ${u} * (int(mod(float(pos), ${m}.) / ${r}.));

              if(d1 < ${t[A]} && d1 >= 0) {

                ch = int(mod(float(pos), ${r}.));

                if (${g}) {
                  innerDims = vec2(d1, ch);
                  result[${v*2+b}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${v*2+b}] = 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;

        ${x}

        ${f.output} = result;
      }
    `}};function UE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),d=n.inChannels,h=l[0]*l[1]*l[2],p=n.outChannels,c=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],A=(h===1||p===1)&&d>RE,x=l[2]%2!=0&&!!u.isPacked;if(A||!se().getBool("WEBGL_LAZILY_UNPACK")||!se().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=be({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),w=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=q0({a:b,b:w,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=be({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(w),y.push(I)}else{let v=c?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,k.assert(up(u.shape,b.shape),()=>`packed reshape ${u.shape} to ${b.shape} isn't free`);let I=be({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let T=q0({a:b,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);k.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=fa({inputs:{x:T},backend:a}),g.shape=n.outShape,y.push(T)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function jE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=n,m=c==="channelsLast",f=l*u*d,g=p*h,y=[f,g],A=!0,x=!1,v=[],b=be({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=be({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let I=new fme(y,b.shape,n),T=a.runWebGLProgram(I,[b],"float32"),C=be({inputs:{x:T},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(T),v.push(C);let z=r!=null,$=s!=null,S=o==="leakyrelu",D=o?j0(o,!0):null,_=new TE(C.shape,w.shape,[1,g,n.outChannels],A,x,z,D,$,S),W=[C,w];if(r&&W.push(r),$&&W.push(s),S){let ee=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));W.push(ee),v.push(ee)}let X=a.runWebGLProgram(_,W,"float32"),q=m?[1,p,h,n.outChannels]:[1,n.outChannels,p,h],Q=be({inputs:{x:X},backend:a,attrs:{shape:q}});v.push(X);for(let ee of v)a.disposeIntermediateTensorInfo(ee);return Q}function mme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,h=M.convertConv2DDataFormat(l),p=M.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=UE({x:r,filter:s,convInfo:p,backend:n});else if(se().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)c=jE({x:r,filter:s,convInfo:p,backend:n});else{let f=new VE(p);c=n.runWebGLProgram(f,[r,s],"float32")}let m=be({inputs:{x:c},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(c),m}var gme={kernelName:tl,backendName:"webgl",kernelFunc:mme},yme=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=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} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              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);
      }
    `}},Ame=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${d}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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) / ${a}.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) / ${r}.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);
      }
    `}},xme=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=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} - ${r};

            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 * ${a} - ${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);
      }
    `}},bme=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${o}, ${l}, ${u});

      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) / ${r}.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 < ${a}; 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 = ${a} - 1 - wC;

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function vme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=a,h=M.convertConv2DDataFormat(l),p=M.computeConv2DInfo(r.shape,d,i,1,o,u,!1,h),c=new yme(p);return n.runWebGLProgram(c,[r,s],"float32")}var wme={kernelName:S1,backendName:"webgl",kernelFunc:vme};function kme(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=a,h=M.convertConv2DDataFormat(u),p=M.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new Ame(p);return n.runWebGLProgram(c,[r,s],"float32")}var Ime={kernelName:nl,backendName:"webgl",kernelFunc:kme};function Sme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=M.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new cme(u);return n.runWebGLProgram(d,[r,s],"float32")}var Nme={kernelName:Yc,backendName:"webgl",kernelFunc:Sme};function Tme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=M.computeConv3DInfo(r.shape,l,i,1,o),d=new xme(u);return n.runWebGLProgram(d,[r,s],"float32")}var Eme={kernelName:N1,backendName:"webgl",kernelFunc:Tme};function Cme(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=M.computeConv3DInfo(l,s.shape,o,1,i),d=new bme(u);return n.runWebGLProgram(d,[r,s],"float32")}var Mme={kernelName:T1,backendName:"webgl",kernelFunc:Cme},$me=NE+`
  return cos(x);
`,Rme=ot({opSnippet:$me}),Fme={kernelName:al,backendName:"webgl",kernelFunc:Rme},Ome=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,Dme=ot({opSnippet:Ome}),_me={kernelName:Md,backendName:"webgl",kernelFunc:Dme},zme=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=n;this.outputShape=[u,d,h,l];let p=a==="bilinear"?1:0,[c,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
      const float height_ratio = float(${f});
      const float width_ratio = float(${A});
      void main() {
        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 = ${g};
        float width_scale = ${x};

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${c} ) {
          setOutput(float(${r}));
          return;
        }
        float in_x = ${v};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${r}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${p} == 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);
        }
      }
    `}},Pme=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new zme(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},Lme={kernelName:$d,backendName:"webgl",kernelFunc:Pme},HE=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${GE(a,"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() {
        ${kt(a)} coords = getOutputCoords();
        int end = ${qE(a,"coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${qE(a,"coords")} = idx;
          val += getX(${GE(a,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function GE(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 ${e} is not yet supported`)}function qE(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Wme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=M.getAxesPermutation([s],l),d=r;u!=null&&(d=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let h=M.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=fa({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(p))-1;m++){let f=new HE(d.shape,!1,o),g=f.getCustomSetupFunc(m),y=c;c=n.runWebGLProgram(f,[c],c.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new HE(d.shape,i,o),f=c;c=n.runWebGLProgram(m,[c],c.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=M.getUndoAxesPermutation(u),f=Vn({inputs:{x:c},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),f}return c}var Bme={kernelName:rl,backendName:"webgl",kernelFunc:Wme};function Vme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=dE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=ace(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Ume={kernelName:E1,backendName:"webgl",kernelFunc:Vme},jme=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 Hme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=new jme(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Gme={kernelName:Rd,backendName:"webgl",kernelFunc:Hme},KE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,d=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?g=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:g=`
          float activation(float x) {
            ${n}
          }
        `,y="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${g}

      const ivec2 strides = ivec2(${u}, ${d});
      const ivec2 pads = ivec2(${o}, ${l});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${f};
        int q = d2 - d1 * ${f};

        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 < ${c}; wR++) {
          int xR = xRCorner + wR * ${h};

          if (xR < 0 || xR >= ${s}) {
            continue;
          }

          for (int wC = 0; wC < ${m}; wC++) {
            int xC = xCCorner + wC * ${p};

            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;
        ${A}
        ${y}
        setOutput(result);
      }
    `}},XE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,d=e.strideHeight,h=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,g=f,y=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
          vec4 xTexelC${b*2};
          int xTexelC${b*2}Ready;
          vec4 xC${b};`;for(let b=0;b<m;b++){for(let w=0;w<f;w++)y+=`
          xTexelC${w*2} = vec4(0.0);
          xTexelC${w*2}Ready = 0;
          xC${w} = vec4(0.0);`;y+=`
        xR = xRCorner + ${b*p};
        if (xR >=0 && xR < ${i}) {
      `;for(let w=0;w<(g+1)/2;w++){let I=w*2,T=I*c;if(y+=`
          xC = xCCorner + ${T};
          `,h===1){if(I<f&&(u%2==1?(y+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
                  xTexelC${T} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= ${o}) {
                    xTexelC${T}.zw = vec2(0.0);
                  }
                  xTexelC${T}Ready = 1;
                }
              `,c===1&&T>0?y+=`
                xC${I} = vec4(xTexelC${T-2}.zw, xTexelC${T}.xy);
                `:y+=`
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < ${o}) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= ${o}) {
                      previous.zw = vec2(0.0);
                    }

                    xC${I} = vec4(previous.zw, xTexelC${T}.xy);
                  } else {
                    xC${I} = vec4(0.0, 0.0, xTexelC${T}.xy);
                  }
                  `):y+=`
                if (xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) {
                  xTexelC${T} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${o}) {
                    xTexelC${T}.zw = vec2(0.0);
                  }
                  xTexelC${T}Ready = 1;
                }

                xC${I} = xTexelC${T};
                `,T+1<f)){let C=u%2==0?k.nearestLargerEven(c):c;c%2==0&&u%2==1||c%2!=0&&u%2!=1?(y+=`
                  xCOffset = xC + ${u%2} + ${C};

                  if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
                    xTexelC${T+2} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= ${o}) {
                      xTexelC${T+2}.zw = vec2(0.0);
                    }
                    xTexelC${T+2}Ready = 1;
                  }
                  `,c>1&&(y+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
                      xTexelC${T} = getX(batch, xR, xCOffset, d1);
                      xTexelC${T}Ready = 1;
                    }
                    `),y+=`
                  xC${I+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.xy);
                  `):C===1?y+=`
                    xC${I+1} = xTexelC${T};
                    `:y+=`
                    xCOffset = xC + ${C};

                    if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
                      xTexelC${T+2} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= ${o}) {
                        xTexelC${T+2}.zw = vec2(0.0);
                      }
                      xTexelC${T+2}Ready = 1;
                    }

                    xC${I+1} = xTexelC${T+2};
                    `}}else T<f&&(u%2==1?(y+=`
                xCOffset = xC + 1 - ${h};
                if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
                  xTexelC${T} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= ${o}) {
                    xTexelC${T}.zw = vec2(0.0);
                  }
                  xTexelC${T}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${T+2}Ready == 0) {
                  xTexelC${T+2} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= ${o}) {
                    xTexelC${T+2}.zw = vec2(0.0);
                  }
                  xTexelC${T+2}Ready = 1;
                }

                xC${I} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw);
              `,T+1<f&&(y+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + ${h};
                  if(xCOffset >= 0 && xCOffset < ${o}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${I+1} = vec4(xTexelC${T+2}.xy, final.xy);
                `)):(y+=`
                if(xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) {
                  xTexelC${T} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= ${o}) {
                    xTexelC${T}.zw = vec2(0.0);
                  }
                  xTexelC${T}Ready = 1;
                }

                xCOffset = xC + ${h};
                if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
                  xTexelC${T+2} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= ${o}) {
                    xTexelC${T+2}.zw = vec2(0.);
                  }
                  xTexelC${T+2}Ready = 1;
                }

                xC${I} = vec4(
                  xTexelC${T}.xy, xTexelC${T+2}.xy);
              `,T+1<f&&(y+=`
                  xC${I+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw);
                `)));I<f&&(y+=`
            wTexel = getW(${b}, ${T}, d1, q);
            dotProd += xC${I} * vec4(wTexel.xz, wTexel.xz);
          `,T+1<f&&(y+=`
              wTexel = getW(${b}, ${T+1}, d1, q);
              dotProd += xC${I+1} * vec4(wTexel.xz, wTexel.xz);
            `))}y+=`
        }
      `}let A="",x="";n&&(a?A=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?A=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:A=`vec4 activation(vec4 x) {
          ${n}
        }`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${A}

      const ivec2 strides = ivec2(${d}, ${h});
      const ivec2 pads = ivec2(${l}, ${u});

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${s};
        int q = d2 - d1 * ${s};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${y}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${v}
        ${x}
        setOutput(result);
      }
    `}};function qme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,d=l;d==null&&(d=[1,1]),k.assert(M.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=M.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),p;return se().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?p=new XE(h):p=new KE(h),n.runWebGLProgram(p,[r,s],"float32")}var Kme={kernelName:sl,backendName:"webgl",kernelFunc:qme},Xme=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=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} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              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);
      }
    `}},Zme=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=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) / ${a}.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) / ${r}.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 Yme(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,h=M.computeConv2DInfo(r.shape,d,i,o,l,u,!0),p=new Xme(h);return n.runWebGLProgram(p,[r,s],"float32")}var Jme={kernelName:C1,backendName:"webgl",kernelFunc:Yme};function Qme(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,h=M.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Zme(h);return n.runWebGLProgram(p,[r,s],"float32")}var ege={kernelName:M1,backendName:"webgl",kernelFunc:Qme},tge=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 nge(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=be({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new tge(s),l=n.runWebGLProgram(o,[i],i.dtype),u=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var age={kernelName:$1,backendName:"webgl",kernelFunc:nge},rge=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=a;this.userCode=`
      const ivec2 strides = ivec2(${r}, ${s});
      const ivec2 pads = ivec2(${d}, ${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 * ${u};

              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 sge(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=M.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,h=new rge(u);d=n.runWebGLProgram(h,[r,s],"float32");let p=be({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),p}var ige={kernelName:Jc,backendName:"webgl",kernelFunc:sge};function oge(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=M.decodeEinsumEquation(r,s.length);M.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=M.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=M.getEinsumPermutation(c,l[g]),x;M.isIdentityPermutation(y)?x=s[g]:(x=Vn({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let b=0;b<A.length;++b)v.splice(A[b],0,1);k.arraysEqual(x.shape,v)||(x=be({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),p===null?p=x:(p=lb({inputs:{a:x,b:p},backend:n}),m.push(p))}f<h-1&&(u[f]>=0&&(p=G0({inputs:{x:p},backend:n,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&n.disposeIntermediateTensorInfo(f);return p}var lge={kernelName:O1,backendName:"webgl",kernelFunc:oge},uge="return (x >= 0.0) ? x : (exp(x) - 1.0);",dge=`
  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;
`,hge=ot({opSnippet:uge,packedOpSnippet:dge}),pge={kernelName:Fd,backendName:"webgl",kernelFunc:hge},cge="return (b >= 1.0) ? a : a * (b + 1.0);",fge=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,mge=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=se().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pp(fge,a.shape,r.shape):new ku(cge,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},gge={kernelName:D1,backendName:"webgl",kernelFunc:mge},yge=`
  return vec4(equal(a, b));
`,Age="return float(a == b);",xge=Tn({opSnippet:Age,packedOpSnippet:yge,dtype:"bool",cpuKernelImpl:ice}),bge={kernelName:ol,backendName:"webgl",kernelFunc:xge},vge=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${M.ERF_P};
  float a1 = ${M.ERF_A1};
  float a2 = ${M.ERF_A2};
  float a3 = ${M.ERF_A3};
  float a4 = ${M.ERF_A4};
  float a5 = ${M.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));
`,wge=ot({opSnippet:vge}),kge={kernelName:Od,backendName:"webgl",kernelFunc:wge},ZE="return exp(x);",YE=ot({opSnippet:ZE,packedOpSnippet:ZE,cpuKernelImpl:oce}),Ige={kernelName:Ei,backendName:"webgl",kernelFunc:YE};function pb(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),be({inputs:{x:s},backend:a,attrs:{shape:o}})}var Sge={kernelName:Dd,backendName:"webgl",kernelFunc:pb},JE="return exp(x) - 1.0;",Nge=ot({opSnippet:JE,packedOpSnippet:JE,cpuKernelImpl:lce}),Tge={kernelName:ll,backendName:"webgl",kernelFunc:Nge},QE=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${r};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${i}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${a});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${a}; 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 eC(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=be({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new QE("real",l,t),d=new QE("imag",l,t),h=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(u,h,"float32"),c=n.runWebGLProgram(d,h,"float32"),m=ei({inputs:{real:p,imag:c},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c);let f=be({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Ege(e){let{inputs:t,backend:n}=e,{input:a}=t;return eC(a,!1,n)}var Cge={kernelName:_1,backendName:"webgl",kernelFunc:Ege},Mge=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 cb(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Mge(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var $ge={kernelName:Qc,backendName:"webgl",kernelFunc:cb},Rge=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);
        }
    `}},Fge={kernelName:_d,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Rge(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},tC="return floor(x);",Oge=ot({opSnippet:tC,packedOpSnippet:tC,cpuKernelImpl:uce}),Dge={kernelName:Ci,backendName:"webgl",kernelFunc:Oge},_ge=`
  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;
  }
`,zge=`
  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);
`,Pge=Tn({opSnippet:_ge,packedOpSnippet:zge,dtype:"int32"}),Lge={kernelName:ul,backendName:"webgl",kernelFunc:Pge},Wge=class{constructor(e){this.variableNames=["A"];let t=Wn(),[n,a]=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(${a}.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));
      }
    `}},Bge=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Wn(),[n,a]=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(${a}.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;
      }
    `}},Vge={kernelName:aA,backendName:"webgl",kernelFunc:Uge},Su;function Uge(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],h=[u,l,s];(o||i)&&(Su==null&&(Su=document.createElement("canvas").getContext("2d")),Su.canvas.width=l,Su.canvas.height=u,Su.drawImage(r,0,0,l,u),r=Su.canvas);let p=n.makeTensorInfo(d,"int32");n.texData.get(p.dataId).usage=_a.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let c=se().getBool("WEBGL_PACK")?new Bge(h):new Wge(h),m=n.runWebGLProgram(c,[p],"int32");return n.disposeData(p.dataId),m}function jge(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=a,f=M.convertConv2DDataFormat(d),g=M.computeConv2DInfo(r.shape,s.shape,l,h,u,p,!1,f),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=UE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else if(se().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=jE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,w=c==="leakyrelu",I=c?j0(c,!1):null,T=new VE(g,v,I,b,w),C=[r,s];if(i&&C.push(i),o&&C.push(o),w){let z=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));C.push(z),A.push(z)}y=n.runWebGLProgram(T,C,"float32")}let x=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Hge={kernelName:Wl,backendName:"webgl",kernelFunc:jge};function Gge(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(M.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=M.computeConv2DInfo(r.shape,s.shape,l,f,u,h,!0),y=se().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=p?j0(p,y):null,x=[r,s],v=i!=null,b=o!=null,w=p==="leakyrelu";if(v&&x.push(i),b&&x.push(o),w){let C=n.makeTensorInfo([],"float32",k.createScalarValue(c,"float32"));x.push(C),m.push(C)}let I;y?I=new XE(g,v,A,b,w):I=new KE(g,v,A,b,w);let T=n.runWebGLProgram(I,x,"float32");return m.forEach(C=>n.disposeIntermediateTensorInfo(C)),T}var qge={kernelName:Bl,backendName:"webgl",kernelFunc:Gge},Kge=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=kt(t.length),r=kt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${a} strides = ${a}(${this.strides});
         void main() {
          ${r} 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 Xge(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=k.sizeFromShape(a.shape),[l,u,d,h]=M.prepareAndValidate(a,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),c=be({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/d,d]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),A=n.bufferSync(a),x=dce(y,A,a.dtype,u,i,d,h,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new Kge(i,h,[u,d]),f=n.runWebGLProgram(m,[c,p],c.dtype),g=be({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(f),g}var Zge={kernelName:Pd,backendName:"webgl",kernelFunc:Xge},Yge=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=kt(this.rank),a=Jge(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${a}));
      }
    `}};function Jge(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function Qge(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=M.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=k.sizeFromShape(s.shape),h=[],p=be({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=be({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let A=n.bufferSync(c),x=n.bufferSync(p),v=hce(x,A,m);return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new Yge(p.shape,m),g=n.runWebGLProgram(f,[p,c],p.dtype);h.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var eye={kernelName:zd,backendName:"webgl",kernelFunc:Qge},tye="return float(a > b);",nye=`
  return vec4(greaterThan(a, b));
`,aye=Tn({opSnippet:tye,packedOpSnippet:nye,cpuKernelImpl:pce,dtype:"bool"}),rye={kernelName:hl,backendName:"webgl",kernelFunc:aye},sye="return float(a >= b);",iye=`
  return vec4(greaterThanEqual(a, b));
`,oye=Tn({opSnippet:sye,packedOpSnippet:iye,dtype:"bool",cpuKernelImpl:cce}),lye={kernelName:Mi,backendName:"webgl",kernelFunc:oye};function uye(e){let{inputs:t,backend:n}=e,{input:a}=t;return eC(a,!0,n)}var dye={kernelName:z1,backendName:"webgl",kernelFunc:uye},hye="return float(!isnan(x) && !isinf(x));",pye=ot({opSnippet:hye,dtype:"bool"}),cye={kernelName:Ld,backendName:"webgl",kernelFunc:pye},fye="return float(isinf(x));",mye=ot({opSnippet:fye,dtype:"bool"}),gye={kernelName:Wd,backendName:"webgl",kernelFunc:mye},yye="return float(isnan(x));",Aye=ot({opSnippet:yye,dtype:"bool"}),xye={kernelName:Bd,backendName:"webgl",kernelFunc:Aye},bye="return float(a < b);",vye=`
  return vec4(lessThan(a, b));
`,wye=Tn({opSnippet:bye,packedOpSnippet:vye,cpuKernelImpl:fce,dtype:"bool"}),kye={kernelName:fl,backendName:"webgl",kernelFunc:wye},Iye="return float(a <= b);",Sye=`
  return vec4(lessThanEqual(a, b));
`,Nye=Tn({opSnippet:Iye,packedOpSnippet:Sye,cpuKernelImpl:mce,dtype:"bool"}),Tye={kernelName:ml,backendName:"webgl",kernelFunc:Nye};function Eye(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=gce(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Cye={kernelName:L1,backendName:"webgl",kernelFunc:Eye},Mye=`if (x < 0.0) return NAN;
  return log(x);`,$ye=`
  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;
`,Rye=ot({opSnippet:Mye,packedOpSnippet:$ye,cpuKernelImpl:yce}),Fye={kernelName:$i,backendName:"webgl",kernelFunc:Rye},Oye="return log(1.0 + x);",Dye=ot({opSnippet:Oye}),_ye={kernelName:Vd,backendName:"webgl",kernelFunc:Dye},zye="return float(a >= 1.0 && b >= 1.0);",Pye=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Lye=Tn({opSnippet:zye,packedOpSnippet:Pye,dtype:"bool"}),Wye={kernelName:Ud,backendName:"webgl",kernelFunc:Lye},Bye="return float(!(x >= 1.0));",Vye=ot({opSnippet:Bye}),Uye={kernelName:ef,backendName:"webgl",kernelFunc:Vye},jye="return float(a >= 1.0 || b >= 1.0);",Hye=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Gye=Tn({opSnippet:jye,packedOpSnippet:Hye,dtype:"bool"}),qye={kernelName:tf,backendName:"webgl",kernelFunc:Gye},Kye=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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 * ${o};
        setOutput(val);
      }
    `}},Xye=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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);
      }
    `}},Zye=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=se().getBool("WEBGL_PACK_NORMALIZATION")?new Xye(r.shape,s,i,o,l):new Kye(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Yye={kernelName:nf,backendName:"webgl",kernelFunc:Zye},Jye=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,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(${a}) * norm + float(${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(${a})
                * float(${r})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},Qye=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,h=new Jye(r.shape,o,l,u,d);return n.runWebGLProgram(h,[r,s,i],r.dtype)},e1e={kernelName:W1,backendName:"webgl",kernelFunc:Qye};function t1e(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=bo(i,e.dtype,"max",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function nC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=M.getAxesPermutation(u,o),h=d!=null,p=n.shouldExecuteOnCPU([r]),c=r;if(h){if(p){let A=n.texData.get(c.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[d[w]];let v=ib(A,r.shape,r.dtype,d,x);c=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(c.dataId);b.values=v}else c=H0(r,d,n);u=M.getInnerMostAxes(u.length,o)}M.assertAxesAreInnerMostDims("max",u,o);let[m,f]=M.computeOutAndReduceShapes(c.shape,u),g=m;i&&(g=M.expandShapeToKeepDim(m,l));let y;if(p){let A=n.texData.get(c.dataId).values,x=Ace(A,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=t1e(c,f,g,n);return h&&n.disposeIntermediateTensorInfo(c),y}var n1e={kernelName:gl,backendName:"webgl",kernelFunc:nC},a1e=vE+`
  return max(a, b);
`,r1e=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+U0+`
  return result;
`,s1e=Tn({opSnippet:a1e,packedOpSnippet:r1e,cpuKernelImpl:xce}),i1e={kernelName:Ri,backendName:"webgl",kernelFunc:s1e};function o1e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;mu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(M.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=M.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return fa({inputs:{x:r},backend:n});let h=new cp(d,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}var l1e={kernelName:yl,backendName:"webgl",kernelFunc:o1e};function u1e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,d=[1,1,1],h=M.computePool3DInfo(r.shape,s,i,d,o,u,l),p=new ub(h,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var d1e={kernelName:af,backendName:"webgl",kernelFunc:u1e},h1e=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        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 < ${r};
          wR += ${a}) {
          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);
      }
    `}},p1e=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${d}, ${h}, ${p});

      void main() {
        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 += ${r}) {
          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 < ${u};
                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(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${c} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${u} +
                  wR * ${u} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function c1e(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=a,h=[1,1,1],p=M.computePool3DInfo(i.shape,o,l,h,u,d),c=new ub(p,"max",!0),m=n.runWebGLProgram(c,[i],i.dtype),f=new p1e(p),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var f1e={kernelName:V1,backendName:"webgl",kernelFunc:c1e};function m1e(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;mu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=a,p=M.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,m=new cp(p,"max",c),f=n.runWebGLProgram(m,[o],o.dtype),g=new h1e(p),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var g1e={kernelName:B1,backendName:"webgl",kernelFunc:m1e};function y1e(e,t,n,a){let r=new cp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new cp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var A1e={kernelName:U1,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];k.assert(M.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=M.computePool2DInfo(a.shape,r,s,u,i),[h,p]=y1e(a,o,d,l);return[h,p]}};function x1e(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=bo(i,"float32","mean",a),l=be({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var b1e={kernelName:Al,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,d=M.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([a]),c=[],m=a;if(h){if(p){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let I=0;I<v.length;I++)v[I]=a.shape[d[I]];let b=ib(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=H0(a,d,i);c.push(m),u=M.getInnerMostAxes(u.length,o)}M.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=M.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=M.expandShapeToKeepDim(f,l));let A=x1e(m,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function v1e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=M.getAxesPermutation(u,o),h=r;d!=null&&(h=Vn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=M.getInnerMostAxes(u.length,r.shape.length)),M.assertAxesAreInnerMostDims("min",u,o);let[p,c]=M.computeOutAndReduceShapes(h.shape,u),m=k.sizeFromShape(c),f=be({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),g=bo(f,f.dtype,"min",n),y;if(i){let A=M.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(h),y}var w1e={kernelName:xl,backendName:"webgl",kernelFunc:v1e},k1e=vE+`
  return min(a, b);
`,I1e=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+U0+`
  return result;
`,S1e=Tn({opSnippet:k1e,packedOpSnippet:I1e,cpuKernelImpl:bce}),N1e={kernelName:Fi,backendName:"webgl",kernelFunc:S1e},T1e=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let a=e.length,r=kt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===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=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});

      void main() {
        ${r} outC = getOutputCoords();
        for (int i = 0; i < ${a}; 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};
          }
        }
        ${r} coords = outC - start;
        setOutput(getX(${o}));
      }
    `}},E1e=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,m)=>c[0]+e[m]+c[1]);let a=e.length,r=kt(a),s=t.map(c=>c[0]).join(","),i=t.map((c,m)=>c[0]+e[m]).join(","),o=Bn("rc",a),l=Bn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,p="";if(a===1){let c=`
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${h};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${h};
        }
        source -= start;
      `;p=`
        ${r} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${d});
        ${o[a-1]} += 1;
        if(${u}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${d});
        }
      `}else{let c=`
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${h}) +
                gte * ((end - 1) * 2 - source + ${h});
        source -= start;
      `;p=`
        ${r} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${d});
        ${o[a-1]} += 1;
        if(${u}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${d});
        }
        rc = outputLoc;
        ${o[a-2]} += 1;
        if(${o[a-2]} < ${this.outputShape[a-2]}) {
          ${c}
          result[2] = getChannel(getX(${l.join()}), ${d});
          ${o[a-1]} += 1;
          if(${u}) {
            ${c}
            result[3] = getChannel(getX(${l.join()}), ${d});
          }
        }
      `}this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${p}
        setOutput(result);
      }
    `}},C1e=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new E1e(a.shape,r,s):new T1e(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},M1e={kernelName:bl,backendName:"webgl",kernelFunc:C1e},$1e=`if (b == 0.0) return NAN;
  return mod(a, b);`,R1e=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+U0+`
  return result;
`,F1e=Tn({opSnippet:$1e,packedOpSnippet:R1e}),O1e={kernelName:jd,backendName:"webgl",kernelFunc:F1e},D1e=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)}}},_1e=`
if (a == b) {
  return 1.0;
};
return a / b;`,z1e=`
  // 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;
`,aC=Tn({opSnippet:_1e,packedOpSnippet:z1e,checkOutOfBounds:!0}),P1e={kernelName:il,backendName:"webgl",kernelFunc:aC},rC="return a - b;",sC=Tn({opSnippet:rC,packedOpSnippet:rC,supportsComplex:!0,cpuKernelImpl:Oce}),L1e={kernelName:zi,backendName:"webgl",kernelFunc:sC};function iC(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=nC({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=M.expandShapeToKeepDim(o.shape,i),u=be({inputs:{x:o},backend:n,attrs:{shape:l}}),d=sC({inputs:{a:r,b:u},backend:n}),h=YE({inputs:{x:d},backend:n}),p=G0({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),c=be({inputs:{x:p},backend:n,attrs:{shape:l}}),m=aC({inputs:{a:h,b:c},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}var W1e={kernelName:Dl,backendName:"webgl",kernelFunc:iC};function B1e(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:iC({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new D1e(u,d,s),p=h.getCustomSetupFunc(i),c=n.runWebGLProgram(h,[l],"int32",p);return o||n.disposeIntermediateTensorInfo(l),c}var V1e={kernelName:j1,backendName:"webgl",kernelFunc:B1e},oC="return -x;";function U1e(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=wce(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return se().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new wu(a.shape,oC):r=new Qs(a.shape,oC),n.runWebGLProgram(r,[a],a.dtype)}var j1e={kernelName:Hd,backendName:"webgl",kernelFunc:U1e},H1e=us.nonMaxSuppressionV3Impl;function G1e(e){M.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:h}=H1e(u,d,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var q1e={kernelName:Gd,backendName:"webgl",kernelFunc:G1e},K1e=us.nonMaxSuppressionV4Impl;function X1e(e){M.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,d=n.readSync(r.dataId),h=n.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=K1e(d,h,i,o,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([c]))]}var Z1e={kernelName:qd,backendName:"webgl",kernelFunc:X1e},Y1e=us.nonMaxSuppressionV5Impl;function J1e(e){M.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,d=n.readSync(r.dataId),h=n.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Y1e(d,h,p,c,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Q1e={kernelName:Kd,backendName:"webgl",kernelFunc:J1e},eAe=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${a}), float(${n}),
                      float(index == coords.y)));
      }
    `}},tAe=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),u=new eAe(l,s,i,o),d=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let p=[...r.shape,s],c=be({inputs:{x:h},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(h),c},nAe={kernelName:wl,backendName:"webgl",kernelFunc:tAe};function Y0(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=mp({inputs:{input:a},backend:n}),s=Y0({inputs:{x:r},backend:n}),i=Z0({inputs:{input:a},backend:n}),o=Y0({inputs:{x:i},backend:n}),l=ei({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return cb({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var aAe={kernelName:ph,backendName:"webgl",kernelFunc:Y0};function lC(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=mp({inputs:{input:a},backend:n}),s=lC({inputs:{x:r},backend:n}),i=Z0({inputs:{input:a},backend:n}),o=Y0({inputs:{x:i},backend:n}),l=ei({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return cb({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var rAe={kernelName:Xd,backendName:"webgl",kernelFunc:lC};function sAe(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return pb({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=pb({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(h),h}),u=BE({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var iAe={kernelName:Zd,backendName:"webgl",kernelFunc:sAe},oAe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=kt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};
        uniform float value;

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});
      uniform float value;

      void main() {
        ${r} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${r} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},lAe=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=kt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Bn("rc",a),l=Bn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
       if(${u}) {
      `,a===1?"":`}
       rc = outputLoc;
       ${o[a-2]} += 1;
       if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":`  ${o[a-1]} += 1;
         if(${u}) {`],p=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let m=0,f=a===1?2:4;m<f;m++)c+=`
        ${h[m]}
        if (${p}) {
          result[${m}] = float(value);
        } else {
          ${r} source = rc - start;
          result[${m}] = getChannel(getX(${l.join()}), ${d});
        }
      `;c+=a===1?"} ":"}}",this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});
      uniform float value;

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${c}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},uC=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lAe(r.shape,s,i):new oAe(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},uAe={kernelName:kl,backendName:"webgl",kernelFunc:uC},dAe=`
  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);
`,hAe=`
  // 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));
  `+U0+`
  return result;
`,pAe=Tn({opSnippet:dAe,packedOpSnippet:hAe}),cAe={kernelName:Il,backendName:"webgl",kernelFunc:pAe};function fAe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=k.parseAxisParam(s,r.shape),d=u,h=M.getAxesPermutation(d,o),p=r;h!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:h}}),d=M.getInnerMostAxes(d.length,o),l.push(p)),M.assertAxesAreInnerMostDims("prod",d,o);let c;if(n.shouldExecuteOnCPU([p])){let m=n.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:y}=Ice(p.shape,p.dtype,m,d);c=n.makeTensorInfo(g,y,f)}else{let[m,f]=M.computeOutAndReduceShapes(p.shape,d),g=k.sizeFromShape(f),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),A=cA(r.dtype),x=bo(y,A,"prod",n);c=be({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(c);let m=M.expandShapeToKeepDim(c.shape,u);c=be({inputs:{x:c},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),c}var mAe={kernelName:Yd,backendName:"webgl",kernelFunc:fAe},dC=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Sce(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},gAe={kernelName:rf,backendName:"webgl",kernelFunc:dC},yAe="return 1.0 / x;",AAe=ot({opSnippet:yAe}),xAe={kernelName:Jd,backendName:"webgl",kernelFunc:AAe},bAe=gr+`
  return (x < 0.0) ? 0.0 : x;
`,vAe=`
  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;
`,wAe=ot({opSnippet:bAe,packedOpSnippet:vAe}),kAe={kernelName:Nl,backendName:"webgl",kernelFunc:wAe},IAe=gr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,SAe=`
  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;
`,NAe=ot({opSnippet:IAe,packedOpSnippet:SAe}),TAe={kernelName:El,backendName:"webgl",kernelFunc:NAe},EAe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],h;r?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/d[0]},
          ${u[1]/d[1]});
      const vec2 inputShapeRC = vec2(${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);
      }
    `}},CAe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],h;r?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/d[0]},
          ${u[1]/d[1]},
          ${u[1]/d[1]});
      const vec3 inputShapeRC = vec3(${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 MAe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=se().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new CAe(r.shape,l,u,s,i):new EAe(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var $Ae={kernelName:Tl,backendName:"webgl",kernelFunc:MAe},RAe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${d});

        const float invHeightScale = float(${h});
        const float invWidthScale = float(${p});

        const int winHeight = int(${c});
        const int winWidth = int(${m});

        // 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), ${a-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), ${r-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 FAe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new RAe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var OAe={kernelName:q1,backendName:"webgl",kernelFunc:FAe},DAe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],h=a?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/d[0]},
          ${u[1]/d[1]});
      const vec2 inputShapeRC = vec2(${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 = ${p};

        // 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);
      }
    `}},_Ae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],h=a?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/d[0]},
          ${u[1]/d[1]},
          ${u[1]/d[1]});
      const vec3 inputShapeRC = vec3(${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 = ${p};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${n-1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `}};function zAe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=se().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _Ae(r.shape,l,u,s,i):new DAe(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var PAe={kernelName:sf,backendName:"webgl",kernelFunc:zAe},LAe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${d});

        const float invHeightScale = float(${h});
        const float invWidthScale = float(${p});

        const int winHeight = int(${c});
        const int winWidth = int(${m});

        // 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(${a}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${r}) - 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 WAe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new LAe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var BAe={kernelName:G1,backendName:"webgl",kernelFunc:WAe},VAe=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 a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=kt(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},UAe=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-${n} tensor is not yet supported`);this.outputShape=e;let a=Bn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=kt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              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 = ${o(a.slice())};
          if(${r}){
            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${u(a.slice())};
            if(${r}) {
              result.a = ${d(a.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(c){return h(c)}function l(c){return c[n-1]="("+c[n-1]+" + 1)",h(c)}function u(c){return c[n-2]="("+c[n-2]+" + 1)",h(c)}function d(c){return c[n-1]="("+c[n-1]+" + 1)",c[n-2]="("+c[n-2]+" + 1)",h(c)}function h(c){let m=e.map((y,A)=>p(A,c)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(c,m){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${m[c]} - 1`:`${m[c]}`}}};function jAe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return fa({inputs:{x:r},backend:n});let l=se().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UAe(r.shape,o):new VAe(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var HAe={kernelName:Cl,backendName:"webgl",kernelFunc:jAe},GAe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        uniform vec4 params;
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${r}
          if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},qAe={kernelName:ch,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new GAe(a.shape,s),[u,d]=M.getImageCenter(i,a.shape[1],a.shape[2]),h=l.getCustomSetupFunc(u,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,h)}},KAe=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,XAe=ot({opSnippet:KAe}),ZAe={kernelName:Ml,backendName:"webgl",kernelFunc:XAe},YAe="return inversesqrt(x);",JAe=ot({opSnippet:YAe,cpuKernelImpl:Nce}),QAe={kernelName:Di,backendName:"webgl",kernelFunc:JAe},hC=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=kt(r.length),l=kt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let d=`getIndices(${u})`,h="";a===1?h="i":a===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${r});

        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 < ${t}; j++) {
              int index = round(${d});
              flattenedIndex += index * ${c};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${p};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function e2e(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=M.calculateShapes(s,r,i),p=[h/u,u];if(h===0)return n.makeTensorInfo(i,r.dtype);let c=be({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=be({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new hC(l,o,c.shape.length,m.shape.length,d,p),y=n.runWebGLProgram(g,[m,c,f],m.dtype),A=be({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),A}var t2e={kernelName:eh,backendName:"webgl",kernelFunc:e2e},n2e=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.join()}let s=kt(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `}};function a2e(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new n2e(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Ga(r.dtype,s.dtype))}var r2e={kernelName:th,backendName:"webgl",kernelFunc:a2e},s2e=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${M.SELU_SCALEALPHA};
  float scale = ${M.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,i2e=ot({opSnippet:s2e}),o2e={kernelName:nh,backendName:"webgl",kernelFunc:i2e},l2e="return 1.0 / (1.0 + exp(-1.0 * x));",u2e=ot({opSnippet:l2e}),d2e={kernelName:Rl,backendName:"webgl",kernelFunc:u2e},h2e=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,p2e=ot({opSnippet:h2e}),c2e={kernelName:sh,backendName:"webgl",kernelFunc:p2e},f2e=NE+`
  return sin(x);
`,m2e=ot({opSnippet:f2e}),g2e={kernelName:$l,backendName:"webgl",kernelFunc:m2e},y2e=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,A2e=ot({opSnippet:y2e}),x2e={kernelName:rh,backendName:"webgl",kernelFunc:A2e},b2e=`
  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;
`,v2e=ot({opSnippet:b2e}),w2e={kernelName:ih,backendName:"webgl",kernelFunc:v2e},k2e=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],d=uC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),h=M.getReshaped(d.shape,s,o,!1),p=M.getPermuted(h.length,s.length,!1),c=M.getReshapedPermuted(d.shape,s,o,!1),m=be({inputs:{x:d},backend:n,attrs:{shape:h}}),f=Vn({inputs:{x:m},backend:n,attrs:{perm:p}}),g=be({inputs:{x:f},backend:n,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},I2e={kernelName:of,backendName:"webgl",kernelFunc:k2e};function S2e(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[h,p,c,m,f]=Ece(o,a.shape,a.dtype,l,r.dtype,u,d);return[n.makeTensorInfo(p,a.dtype,h),n.makeTensorInfo([p[0]],r.dtype,c),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var N2e={kernelName:K1,backendName:"webgl",kernelFunc:S2e};function T2e(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,d,h]=Cce(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var E2e={kernelName:X1,backendName:"webgl",kernelFunc:T2e};function C2e(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=pE(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(d,a.dtype,u)}var M2e={kernelName:Z1,backendName:"webgl",kernelFunc:C2e};function $2e(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=pE(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(d,a.dtype,u)}var R2e={kernelName:Y1,backendName:"webgl",kernelFunc:$2e};function F2e(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=M.calculateShapes(s,r,o),p=!1,c=new hC(u,l,r.shape.length,s.shape.length,d,[h,1],p),m=n.runWebGLProgram(c,[s,r,i],s.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var O2e={kernelName:J1,backendName:"webgl",kernelFunc:F2e};function D2e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=M.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),h=r.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=fp({inputs:{x:r},backend:n,attrs:{begin:d,size:c}});return d[o]+=p,m})}var _2e={kernelName:oh,backendName:"webgl",kernelFunc:D2e},z2e="return sqrt(x);",P2e=ot({opSnippet:z2e}),L2e={kernelName:Fl,backendName:"webgl",kernelFunc:P2e},W2e="return x * x;",B2e=ot({opSnippet:W2e}),V2e={kernelName:lf,backendName:"webgl",kernelFunc:B2e},pC="return (a - b) * (a - b);",U2e=Tn({opSnippet:pC,packedOpSnippet:pC}),j2e={kernelName:_i,backendName:"webgl",kernelFunc:U2e};function H2e({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=gr+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Qs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var G2e={kernelName:Li,backendName:"webgl",kernelFunc:H2e},q2e=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=kt(n.length),s=kt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function K2e(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=a,{nonStrided:c,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=Cn.sliceInfo(r.shape,s,i,o,l,u,d,h,p),x=be({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(c){let w=fp({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=be({inputs:{x:w},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(w)}else if(A.some(w=>w===0))v=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let w=n.texData.get(x.dataId).values,I=Pe(x.shape,x.dtype,w),T=Mce(A,I,f,m);v=n.makeTensorInfo(A,x.dtype,T.values)}else{let w=new q2e(m,f,A);v=n.runWebGLProgram(w,[x],x.dtype)}let b=be({inputs:{x:v},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var X2e={kernelName:lh,backendName:"webgl",kernelFunc:K2e};function Z2e(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:d,dataSplits:h}=t,p=n.readSync(d.dataId),c=n.readSync(h.dataId),[m,f]=$ce(p,c,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(h.shape,"int32",f)]}var Y2e={kernelName:Q1,backendName:"webgl",kernelFunc:Z2e};function J2e(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,d,h]=Rce(o,l,r),p=d.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(h))]}var Q2e={kernelName:eA,backendName:"webgl",kernelFunc:J2e};function exe(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=Fce(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var txe={kernelName:tA,backendName:"webgl",kernelFunc:exe},nxe="return tan(x);",axe=ot({opSnippet:nxe}),rxe={kernelName:_l,backendName:"webgl",kernelFunc:axe},sxe=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,ixe=ot({opSnippet:sxe}),oxe={kernelName:zl,backendName:"webgl",kernelFunc:ixe},lxe=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 a=kt(this.rank),r=uxe(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function uxe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function cC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(h=>k.decodeString(h)):o,u=Pe(r.shape,r.dtype,l),d=Dce(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new lxe(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var dxe={kernelName:Pi,backendName:"webgl",kernelFunc:cC};function hxe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=_ce(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var pxe={kernelName:uh,backendName:"webgl",kernelFunc:hxe},cxe=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${r});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${r});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${i} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function fxe(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,h,p,c]=r.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new cxe(h,p,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var mxe={kernelName:dh,backendName:"webgl",kernelFunc:fxe};function gxe(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;mu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=zce(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var yxe={kernelName:nA,backendName:"webgl",kernelFunc:gxe};function Axe(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=fp({inputs:{x:i},backend:n,attrs:{begin:p,size:c}}),y=be({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var xxe={kernelName:hh,backendName:"webgl",kernelFunc:Axe},bxe=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,d=n%4,h=`
        sumValue += dot(values, segFilter);
    `,p="";r%n>0&&(p=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `);let c="";r%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${o};

      float getValue(int batch, int inIdx) {
        ${p}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${c}
        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 < ${u}; 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 + ${u};
        if (${d===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
          );

          ${h}
        } else if (${d===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
          );

          ${h}
        } else if (${d===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 vxe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,d=M.getAxesPermutation([u],o),h=r;d!=null&&(h=Vn({inputs:{x:r},backend:n,attrs:{perm:d}}),l.push(h),u=M.getInnerMostAxes(1,o)[0]);let p=M.segment_util.computeOutShape(h.shape,u,i),c=k.sizeFromShape([h.shape[u]]),m=be({inputs:{x:h},backend:n,attrs:{shape:[-1,c]}});l.push(m);let f=cA(r.dtype),g=(v,b,w,I,T)=>{let C=v.shape[0],z=v.shape[1],$=M.segment_util.segOpComputeOptimalWindowSize(z,T),S={windowSize:$,inSize:z,batchSize:C,numSegments:T},D=new bxe(S,b),_=n.compileAndRun(D,[v,w],I);if(l.push(_),_.shape[1]===T)return _;let W=dC({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=cC({inputs:{x:W},backend:n,attrs:{reps:[z/$]}});return l.push(W),l.push(X),g(_,b,X,I,T)},y=g(m,"unsortedSegmentSum",s,f,i),A=be({inputs:{x:y},backend:n,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let v=M.getUndoAxesPermutation(d);x=Vn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var wxe={kernelName:uf,backendName:"webgl",kernelFunc:vxe},kxe=[Yye,e1e,_fe,Pfe,Bfe,jfe,Gfe,Xfe,Yfe,Qfe,a0e,s0e,l0e,h0e,A0e,f0e,v0e,S0e,k0e,C0e,$0e,F0e,z0e,j0e,G0e,J0e,eme,rme,ome,yfe,pme,wme,Ime,gme,Eme,Mme,Nme,Fme,_me,Lme,Bme,Ume,Gme,Jme,ege,Kme,age,ige,lge,pge,gge,bge,kge,Ige,Sge,Tge,Cge,$ge,Fge,Dge,Lge,Vge,Hge,qge,Zge,eye,rye,lye,gfe,dye,dme,cye,gye,xye,xfe,kye,Tye,Cye,_ye,Fye,Wye,Uye,qye,n1e,d1e,l1e,f1e,g1e,A1e,i1e,b1e,w1e,N1e,M1e,O1e,V1e,Ife,j1e,q1e,Z1e,Q1e,K0e,nAe,rAe,iAe,uAe,cAe,vfe,mAe,gAe,X0e,P1e,xAe,TAe,kAe,Nfe,$Ae,OAe,PAe,BAe,HAe,qAe,ZAe,QAe,t2e,r2e,o2e,d2e,c2e,g2e,x2e,V0e,W1e,w2e,I2e,N2e,E2e,M2e,R2e,O2e,_2e,L2e,V2e,j2e,G2e,X2e,Y2e,Q2e,txe,L1e,Ffe,rxe,oxe,dxe,pxe,mxe,Ofe,yxe,xxe,wxe,aAe];for(let e of kxe)iA(e);var na;(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"})(na||(na={}));var gp;(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",e[e.sigmoid=5]="sigmoid"})(gp||(gp={}));var fC;function Ixe(e){fC=e.wasm.cwrap(Ll,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Sxe(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=a,p=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);m=T.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=gp[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],A=u?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,A],r.dtype),b=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),I=new Uint8Array(new Int32Array(s.shape).buffer);return fC(p,w,r.shape.length,c,I,s.shape.length,l,u,g,m,f,h||0,b),v}var Nxe={kernelName:Ll,backendName:"wasm",setupFunc:Ixe,kernelFunc:Sxe};function Un(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Txe=Un(xd);function jn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=n!=null?n:u.dtype,m=M.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,c);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id,x=()=>a(h,g,u.shape.length,p,y,d.shape.length,na[u.dtype],A);if(t&&u.dtype==="float32")return x(),f;let v=M.getBroadcastDims(u.shape,m),b=M.getBroadcastDims(d.shape,m),w=v.every((T,C)=>T===C),I=b.every((T,C)=>T===C);if(w&&I)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Exe=!0,Cxe=jn(Os,Exe),mC;function Mxe(e){mC=e.wasm.cwrap(Zo,null,["array","number","number","number"])}function $xe(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return mC(s,r.length,na[a.dtype],i),a}var Rxe={kernelName:Zo,backendName:"wasm",setupFunc:Mxe,kernelFunc:$xe};function J0(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Fxe={kernelName:pl,backendName:"wasm",kernelFunc:J0},gC;function Oxe(e){gC=e.wasm.cwrap(Pl,null,["number","array","number","number","number","array","number"])}function Q0(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=_xe(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Dxe(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=J0({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),d=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return gC(d,c,l.shape.length,na[l.dtype],h,p,s.length),u}function Dxe(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function _xe(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var zxe={kernelName:Pl,backendName:"wasm",kernelFunc:Q0,setupFunc:Oxe};function ti(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=M.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let d=new Array(r);for(let p=0;p<d.length;p++)d[p]=a[o[p]];i=M.getInnerMostAxes(i.length,r),l=Q0({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var yC;function Pxe(e){yC=e.wasm.cwrap(wd,null,["number, number, number"])}function Lxe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=ti(i,r,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;M.assertAxesAreInnerMostDims("all",d,c);let[m,f]=M.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;yC(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=M.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Wxe={kernelName:wd,backendName:"wasm",setupFunc:Pxe,kernelFunc:Lxe},AC;function Bxe(e){AC=e.wasm.cwrap(kd,null,["number, number, number"])}function Vxe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=ti(i,r,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;M.assertAxesAreInnerMostDims("any",d,c);let[m,f]=M.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;AC(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=M.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Uxe={kernelName:kd,backendName:"wasm",setupFunc:Bxe,kernelFunc:Vxe},xC;function jxe(e){xC=e.wasm.cwrap(Yo,null,["number","number","number","number","number"])}function Hxe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=ti(s,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),m=t.dataIdMap.get(c.dataId).id,f=k.sizeFromShape(c.shape),g=l.shape[d[0]];return xC(o,na[l.dtype],f,g,m),h&&t.disposeData(u.dataId),c}var Gxe={kernelName:Yo,backendName:"wasm",kernelFunc:Hxe,setupFunc:jxe},bC;function qxe(e){bC=e.wasm.cwrap(Jo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kxe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=M.computePool2DInfo(r.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let v=a.makeOutput(d.outShape,"float32"),b=a.dataIdMap.get(v.dataId).id;return bC(s,r.shape[0],r.shape[1],r.shape[2],h,p,c,m,f,g,y,A,x,b),v}var Xxe={kernelName:Jo,backendName:"wasm",setupFunc:qxe,kernelFunc:Kxe};function yr(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Zxe={kernelName:Qd,backendName:"wasm",kernelFunc:yr},vC;function Yxe(e){vC=e.wasm.cwrap(Qo,null,["number","array","number","number","array","number","number","number","number"])}function Jxe(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,d=i?r.shape[l-2]:r.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?r.shape[l-1]:r.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),A=g===y||g===1||y===1;k.assert(l>=2&&u>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let $=a.makeOutput(c.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return CC(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,A,x,z,v,b,w,I,T,C,S),$}var I5e={kernelName:sl,backendName:"wasm",setupFunc:w5e,kernelFunc:k5e},S5e=!1,N5e=jn(ol,S5e,"bool"),T5e=Un(Ei);function mb(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),yr({inputs:{x:r},backend:a,attrs:{shape:o}})}var E5e={kernelName:Dd,backendName:"wasm",kernelFunc:mb};function C5e(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var M5e={kernelName:Qc,backendName:"wasm",kernelFunc:C5e},MC;function $5e(e){MC=e.wasm.cwrap(_d,null,["number","number","number","number","number","number"])}function R5e(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,d]=a.shape;return MC(s,o,l,u,d,i),r}var F5e={kernelName:_d,backendName:"wasm",kernelFunc:R5e,setupFunc:$5e},O5e=Un(Ci),D5e=!1,_5e=jn(ul,D5e),$C;function z5e(e){$C=e.wasm.cwrap(dl,null,["number","number","number","number","number","number","number"])}function P5e(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return $C(d,h,p,c,m,r,g),f}var L5e={kernelName:dl,backendName:"wasm",setupFunc:z5e,kernelFunc:P5e},RC;function W5e(e){RC=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function B5e(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=M.computeConv2DInfo(r.shape,s.shape,l,d,u,p),g=gp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let b=f.filterHeight,w=f.filterWidth,I=f.padInfo.top,T=f.padInfo.right,C=f.padInfo.bottom,z=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,D=f.strideHeight,_=f.strideWidth,W=f.inChannels,X=f.padInfo.type==="SAME"?1:0,q=f.batchSize,Q=f.inHeight,ee=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ae=a.dataIdMap.get(ie.dataId).id,de=o==null?0:a.dataIdMap.get(o.dataId).id;return RC(y,q,Q,ee,A,b,w,v,I,T,C,z,X,$,S,D,_,W,x,g,de,m||0,ae),ie}var V5e={kernelName:Wl,backendName:"wasm",setupFunc:W5e,kernelFunc:B5e},FC;function U5e(e){FC=e.wasm.cwrap(Bl,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 j5e(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=M.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),g=gp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let b=f.filterHeight,w=f.filterWidth,I=f.padInfo.top,T=f.padInfo.right,C=f.padInfo.bottom,z=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,D=f.strideHeight,_=f.strideWidth,W=f.inChannels,X=f.padInfo.type==="SAME"?1:0,q=f.batchSize,Q=f.inHeight,ee=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ae=a.dataIdMap.get(ie.dataId).id,de=o==null?0:a.dataIdMap.get(o.dataId).id;return FC(y,q,Q,ee,A,b,w,v,I,T,C,z,X,$,S,D,_,W,x,g,de,m||0,ae),ie}var H5e={kernelName:Bl,backendName:"wasm",setupFunc:U5e,kernelFunc:j5e},OC;function G5e(e){OC=e.wasm.cwrap(Pd,null,["number","number","number","number","number","number","array","number"])}function q5e(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=S4.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let d=r.shape,h=d[d.length-1],p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return OC(p,na[a.dtype],c,i,h,o,m,f),u}var K5e={kernelName:Pd,backendName:"wasm",setupFunc:G5e,kernelFunc:q5e},DC;function X5e(e){DC=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Z5e(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=M.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=yr({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=k.sizeFromShape(s.shape),p=yr({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),c=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],m=t.makeOutput(c,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=d.shape.length-1,g=t.dataIdMap.get(d.dataId).id,y=t.dataIdMap.get(p.dataId).id,A=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(k.computeStrides(d.shape)).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(c)).buffer);return DC(g,na[r.dtype],x,f,y,u.batchSize,v,A),t.disposeData(d.dataId),t.disposeData(p.dataId),m.shape=u.outputShape,m}var Y5e={kernelName:zd,backendName:"wasm",setupFunc:X5e,kernelFunc:Z5e},J5e=!1,Q5e=jn(hl,J5e,"bool"),ebe=!1,tbe=jn(Mi,ebe,"bool"),_C;function nbe(e){_C=e.wasm.cwrap(cl,null,["number","number","number"])}function abe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;_C(r,n,i)}return s}var rbe={kernelName:cl,backendName:"wasm",setupFunc:nbe,kernelFunc:abe},sbe=!1,ibe=jn(fl,sbe,"bool"),obe=!1,lbe=jn(ml,obe,"bool"),ube=Un($i),dbe=!1,hbe=jn(Ud,dbe,"bool"),zC;function pbe(e){zC=e.wasm.cwrap(gl,null,["number, number, number"])}function cbe(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=ti(i,r,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;M.assertAxesAreInnerMostDims("max",d,c);let[m,f]=M.computeOutAndReduceShapes(l.shape,d),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;zC(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=M.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var fbe={kernelName:gl,backendName:"wasm",setupFunc:pbe,kernelFunc:cbe},mbe=!1,gbe=jn(Ri,mbe),PC;function ybe(e){PC=e.wasm.cwrap(yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Abe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,d=M.computePool2DInfo(r.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,v=d.strideWidth,b=d.inChannels,w=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. 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v=M.expandShapeToKeepDim(x.shape,p);x.shape=v}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var wbe={kernelName:Al,backendName:"wasm",setupFunc:bbe,kernelFunc:vbe},WC;function kbe(e){WC=e.wasm.cwrap(xl,null,["number, number, number"])}function Ibe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=ti(i,r,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let m=u.shape.length;M.assertAxesAreInnerMostDims("min",h,m);let[f,g]=M.computeOutAndReduceShapes(u.shape,h),y=k.sizeFromShape(g),A=t.makeOutput(f,u.dtype);if(k.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;WC(l,y,x)}if(c&&t.disposeData(d.dataId),s){let x=M.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Sbe={kernelName:xl,backendName:"wasm",setupFunc:kbe,kernelFunc:Ibe},Nbe=!1,Tbe=jn(Fi,Nbe),gb;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(gb||(gb={}));var BC;function Ebe(e){BC=e.wasm.cwrap(bl,null,["number","array","number","number","array","array","number","number"])}function Cbe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),h=a.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return BC(i,u,t.shape.length,na[t.dtype],p,c,gb[r],l),o}var Mbe={kernelName:bl,backendName:"wasm",kernelFunc:Cbe,setupFunc:Ebe},$be=!0,Rbe=jn(Oi,$be),Fbe=Un(Hd);function yb(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var VC;function Obe(e){VC=e.wasm.cwrap(Gd,"number",["number","number","number","number","number"])}function Dbe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=VC(u,d,s,r,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:m,pValidOutputs:f}=yb(t,h);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([c],"int32",p)}var _be={kernelName:Gd,backendName:"wasm",setupFunc:Obe,kernelFunc:Dbe},UC;function zbe(e){UC=e.wasm.cwrap(qd,"number",["number","number","number","number","number","bool"])}function Pbe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=UC(d,h,s,r,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=yb(t,p);t.wasm._free(f);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var Lbe={kernelName:qd,backendName:"wasm",setupFunc:zbe,kernelFunc:Pbe},jC;function Wbe(e){jC=e.wasm.cwrap(Kd,"number",["number","number","number","number","number","number"])}function Bbe(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=jC(d,h,s,r,i,o),{pSelectedIndices:c,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=yb(t,p);t.wasm._free(g);let y=t.makeOutput([m],"int32",c),A=t.makeOutput([m],"float32",f);return[y,A]}var Vbe={kernelName:Kd,backendName:"wasm",setupFunc:Wbe,kernelFunc:Bbe},Ube=!1,jbe=jn(vl,Ube,"bool"),HC;function Hbe(e){HC=e.wasm.cwrap(wl,null,["number","number","number","number","number"])}function Gbe(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return HC(d,s,i,o,u),l}var qbe={kernelName:wl,backendName:"wasm",setupFunc:Hbe,kernelFunc:Gbe};function Kbe(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Xbe={kernelName:Xd,backendName:"wasm",kernelFunc:Kbe};function Zbe(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mb({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=mb({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(h),h}),u=kC({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeData(d.dataId)),u}var Ybe={kernelName:Zd,backendName:"wasm",kernelFunc:Zbe},GC;function Jbe(e){GC=e.wasm.cwrap(kl,null,["number","array","number","number","array","array","number","number"])}function Qbe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),h=a.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return GC(i,u,t.shape.length,na[t.dtype],p,c,r,l),o}var e3e={kernelName:kl,backendName:"wasm",kernelFunc:Qbe,setupFunc:Jbe},t3e=!1,n3e=jn(Il,t3e),qC;function a3e(e){qC=e.wasm.cwrap(Sl,null,["number","number","number"])}function r3e(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return qC(s,i,l),o}var s3e={kernelName:Sl,backendName:"wasm",setupFunc:a3e,kernelFunc:r3e},KC;function i3e(e){KC=e.wasm.cwrap(Yd,null,["number","number","number","number"])}function 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_ve=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],zve=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Pve=[33,133,362,263,1,78,308],y7e=_ve.map(e=>wp[e]),A7e=zve.map(e=>wp[e]),x7e=Pve.map(e=>wp[e]);var kb=Lr.leftEyeLower0,Ib=Lr.rightEyeLower0,Eu={leftBounds:[kb[0],kb[kb.length-1]],rightBounds:[Ib[0],Ib[Ib.length-1]]},om={count:468,mouth:13,symmetryLine:[13,Lr.midwayBetweenEyes[0]]},wM={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Cu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function lm(e,t,n,a){for(let r=0;r<wb.length;r++){let{key:s,indices:i}=wb[r],o=Lr[`${n}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var Sb=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=vp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=a!==0?im(a,[0,0]):sm,l=a!==0?i.map(h=>[...yM(h,o),h[2]]):i,u=a!==0?gM(r):sm,d=[...Nu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[Math.round(h[0]+ni(d,u[0])),Math.round(h[1]+ni(d,u[1])),Math.round(h[2])])}getLeftToRightEyeDepthDifference(t){let n=t[Eu.leftBounds[0]][2],a=t[Eu.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=rm(am(bb([t[a],t[r]]),this.irisEnlarge)),o=vp(i),l=Ye.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&ka.flags.IS_BROWSER&&(l=Ye.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<Cu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],l/this.irisSize*a[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(Cu.index)}}getAdjustedIrisCoords(t,n,a){let r=t[Lr[`${a}EyeUpper0`][Cu.upperCenter]][2],s=t[Lr[`${a}EyeLower0`][Cu.lowerCenter]][2],i=(r+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=r:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=pM({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),l=am(o),u=rm(l),d=this.storedBoxes[i].landmarks,h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=Ue(()=>this.storedBoxes.map((i,o)=>{let l,u=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&ka.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=om.count?om.symmetryLine:wM.symmetryLine;u=vb(i.landmarks[x],i.landmarks[v]);let b=Nu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],I=Ye.rotateWithOffset(t,u,0,w);d=im(-u,b),n.face.mesh.enabled?l=Tu({startPoint:i.startPoint,endPoint:i.endPoint},I,[this.meshSize,this.meshSize]).div(255):l=Tu({startPoint:i.startPoint,endPoint:i.endPoint},I,[this.boxSize,this.boxSize]).div(255)}else{d=sm;let x=t.clone();n.face.mesh.enabled?l=Tu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=Tu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l};let[,h,p]=this.meshDetector.execute(l),c=h.dataSync()[0];if(c<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=c,null;let f=le(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:v,crop:b}=this.getEyeBox(f,l,Eu.leftBounds[0],Eu.leftBounds[1],!0),{box:w,boxSize:I,crop:T}=this.getEyeBox(f,l,Eu.rightBounds[0],Eu.rightBounds[1]),z=this.irisModel.predict(sn([b,T])).dataSync(),$=z.slice(0,Cu.numCoordinates*3),{rawCoords:S,iris:D}=this.getEyeCoords($,x,v,!0),_=z.slice(Cu.numCoordinates*3),{rawCoords:W,iris:X}=this.getEyeCoords(_,w,I),q=this.getLeftToRightEyeDepthDifference(f);Math.abs(q)<30?(lm(f,S,"left",null),lm(f,W,"right",null)):q<1?lm(f,S,"left",["EyeUpper0","EyeLower0"]):lm(f,W,"right",["EyeUpper0","EyeLower0"]);let Q=this.getAdjustedIrisCoords(f,D,"left"),ee=this.getAdjustedIrisCoords(f,X,"right");f=f.concat(Q).concat(ee)}let g=this.transformRawCoords(f,i,u,d),y=i.confidence;if(i=am(bb(g),1.5),i.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&ka.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=om.count?om.symmetryLine:wM.symmetryLine;u=vb(i.landmarks[x],i.landmarks[v]);let b=Nu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],I=Ye.rotateWithOffset(t.toFloat(),u,0,w);d=im(-u,b),l=Tu({startPoint:i.startPoint,endPoint:i.endPoint},I,[this.meshSize,this.meshSize]).div(255)}let A={mesh:g,box:i,faceConfidence:c,boxConfidence:i.confidence,image:l};return this.storedBoxes[o]={...rm(i),confidence:i.confidence,faceConfidence:c},A}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Zt=[null,null,null],Nb;async function kM(e,t){let n=await Nb.predict(e,t),a=[],r=0;for(let s of n||[]){if(!s||s.isDisposedInternal)continue;let i=s.mesh.map(d=>[d[0]/(e.shape[2]||0),d[1]/(e.shape[1]||0),d[2]/Nb.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(Lr))o[d]=Lr[d].map(h=>s.mesh[h]);let l=s.box?[Math.trunc(Math.max(0,s.box.startPoint[0])),Math.trunc(Math.max(0,s.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,s.box.endPoint[0])-Math.max(0,s.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,s.box.endPoint[1])-Math.max(0,s.box.startPoint[1]))]:[0,0,0,0],u=s.box?[s.box.startPoint[0]/(e.shape[2]||0),s.box.startPoint[1]/(e.shape[1]||0),(s.box.endPoint[0]-s.box.startPoint[0])/(e.shape[2]||0),(s.box.endPoint[1]-s.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];a.push({id:r++,score:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxScore:Math.round(100*s.boxConfidence)/100,faceScore:Math.round(100*s.faceConfidence)/100,box:l,boxRaw:u,mesh:s.mesh,meshRaw:i,annotations:o,image:s.image,tensor:s.image}),s.coords&&s.coords.dispose()}return a}async function Tb(e){return!Zt[0]&&e.face.enabled||!Zt[1]&&e.face.mesh.enabled||!Zt[2]&&e.face.iris.enabled?(Zt=await Promise.all([!Zt[0]&&e.face.enabled?vM(e):null,!Zt[1]&&e.face.mesh.enabled?Et(Mt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Zt[2]&&e.face.iris.enabled?Et(Mt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Zt[1]||!Zt[1].modelUrl?ge("load model failed:",e.face.mesh.modelPath):e.debug&&ge("load model:",Zt[1].modelUrl)),e.face.iris.enabled&&(!Zt[2]||!Zt[2].modelUrl?ge("load model failed:",e.face.iris.modelPath):e.debug&&ge("load model:",Zt[2].modelUrl))):e.debug&&(Zt[0]&&ge("cached model:",Zt[0].model.modelUrl),Zt[1]&&ge("cached model:",Zt[1].modelUrl),Zt[2]&&ge("cached model:",Zt[2].modelUrl)),Nb=new Sb(Zt[0],Zt[1],Zt[2]),Zt}var IM=vo,SM=wp;var Lve=["angry","disgust","fear","happy","sad","surprise","neutral"],Ar,um=[],NM=0,Eb=Number.MAX_SAFE_INTEGER,Cb=[.2989,.587,.114];async function Mb(e){return Ar?e.debug&&ge("cached model:",Ar.modelUrl):(Ar=await Et(Mt(e.modelBasePath,e.face.emotion.modelPath)),!Ar||!Ar.modelUrl?ge("load model failed:",e.face.emotion.modelPath):e.debug&&ge("load model:",Ar.modelUrl)),Ar}async function $b(e,t,n,a){return Ar?Eb<t.face.emotion.skipFrames&&t.skipFrame&&NM===a&&um[n]&&um[n].length>0?(Eb++,um[n]):(Eb=0,new Promise(async r=>{let s=Ye.resizeBilinear(e,[Ar.inputs[0].shape[2],Ar.inputs[0].shape[1]],!1),[i,o,l]=es(s,3,3);s.dispose();let u=fe(i,Cb[0]),d=fe(o,Cb[1]),h=fe(l,Cb[2]);i.dispose(),o.dispose(),l.dispose();let p=Xy([u,d,h]);u.dispose(),d.dispose(),h.dispose();let c=Ue(()=>p.sub(.5).mul(2));p.dispose();let m=[];if(t.face.emotion.enabled){let f=await Ar.predict(c),g=f.dataSync();Ve(f);for(let y=0;y<g.length;y++)g[y]>t.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*g[y])/100),emotion:Lve[y]});m.sort((y,A)=>A.score-y.score)}c.dispose(),um[n]=m,NM=a,r(m)})):null}var xr,dm=[],TM=0,Rb=Number.MAX_SAFE_INTEGER;async function Fb(e){let t=Mt(e.modelBasePath,e.face.description.modelPath);return xr?e.debug&&ge("cached model:",t):(xr=await Et(t),xr?e.debug&&ge("load model:",t):ge("load model failed:",e.face.description.modelPath)),xr}function Ob(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function EM(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=Ob(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function Db(e){return Ue(()=>{let n=e.image||e.tensor||e;if(!(n instanceof St))return null;let a=[[.05,.15,.85,.85]];return xr.inputs[0].shape?(n.shape.length===3?Ye.cropAndResize(Qr(n,0),a,[0],[xr.inputs[0].shape[2],xr.inputs[0].shape[1]]):Ye.cropAndResize(n,a,[0],[xr.inputs[0].shape[2],xr.inputs[0].shape[1]])).mul(255):null})}async function _b(e,t,n,a){var r,s;return xr?Rb<t.face.description.skipFrames&&t.skipFrame&&TM===a&&((r=dm[n])==null?void 0:r.age)&&((s=dm[n])==null?void 0:s.age)>0?(Rb++,dm[n]):(Rb=0,new Promise(async i=>{let o=Db(e),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(l=await xr.predict(o)),Ve(o),l&&(Ue(()=>{let d=l.find(f=>f.shape[1]===1).dataSync(),h=Math.trunc(200*Math.abs(d[0]-.5))/100;h>t.face.description.minConfidence&&(u.gender=d[0]<=.5?"female":"male",u.genderScore=Math.min(.99,h));let p=l.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],c=l.find(f=>f.shape[1]===100).dataSync();u.age=Math.round(c[p-1]>c[p+1]?10*p-100*c[p-1]:10*p+100*c[p+1])/10;let m=l.find(f=>f.shape[1]===1024);u.descriptor=[...m.dataSync()]}),l.forEach(d=>Ve(d))),dm[n]=u,TM=a,i(u)})):null}var Wve=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],a=1,r=e.mesh[33][2]>e.mesh[263][2],s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Bve=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},a=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],v=g[2]-y[2];return[A,x,v]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],v=g[0]*y[1]-g[1]*y[0];return[A,x,v]},s=g=>{let[y,A,x,v,b,w,I,T,C]=g,z,$,S;return v<1?v>-1?(S=Math.asin(v),$=Math.atan2(-I,y),z=Math.atan2(-w,b)):(S=-Math.PI/2,$=-Math.atan2(T,C),z=0):(S=Math.PI/2,$=Math.atan2(T,C),z=0),{pitch:2*-z,yaw:2*-$,roll:2*-S}},i=g=>{let y=(x,v,b,w)=>Math.atan2(w-v,b-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[o[10],o[152],o[234],o[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),d=n(a(u[1],u[0])),h=n(a(u[3],u[2])),p=n(r(h,d));h=r(d,p);let c=[h[0],h[1],h[2],d[0],d[1],d[2],p[0],p[1],p[2]],m=s(c),f=o.length===478?Wve(e):{bearing:0,strength:0};return{angle:m,matrix:c,gaze:f}},zb=async(e,t)=>{var d,h,p,c,m,f;let n,a,r,s,i,o,l=[];e.state="run:face",n=st();let u=await kM(t,e.config);if(e.performance.face=Math.trunc(st()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let g=0;g<u.length;g++){if(e.analyze("Get Face"),!u[g].image||u[g].image.isDisposedInternal){ge("Face object is disposed:",u[g].image);continue}let y=Bve(u[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?$b(u[g].image||er([]),e.config,g,u.length):{}:(e.state="run:emotion",n=st(),s=e.config.face.emotion.enabled?await $b(u[g].image||er([]),e.config,g,u.length):{},e.performance.emotion=Math.trunc(st()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?_b(u[g].image||er([]),e.config,g,u.length):[]:(e.state="run:description",n=st(),o=e.config.face.description.enabled?await _b(u[g].image||er([]),e.config,g,u.length):[],e.performance.embedding=Math.trunc(st()-n)),e.analyze("End Description:"),e.config.async&&([a,r,s,i,o]=await Promise.all([a,r,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=(d=u[g])==null?void 0:d.annotations)==null?void 0:h.leftEyeIris)&&((c=(p=u[g])==null?void 0:p.annotations)==null?void 0:c.rightEyeIris)&&(delete u[g].annotations.leftEyeIris,delete u[g].annotations.rightEyeIris);let A=((m=u[g].annotations)==null?void 0:m.leftEyeIris)&&((f=u[g].annotations)==null?void 0:f.rightEyeIris)?Math.max(Math.abs(u[g].annotations.leftEyeIris[3][0]-u[g].annotations.leftEyeIris[1][0]),Math.abs(u[g].annotations.rightEyeIris[4][1]-u[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0;l.push({...u[g],id:g,age:o.age,gender:o.gender,genderScore:o.genderScore,embedding:o.descriptor,emotion:s,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:y,tensor:e.config.face.detector.return?Yn(u[g].image):null}),Ve(u[g].image),u[g].image&&delete u[g].image,e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var kp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],CM=kp.length,Ip=kp.reduce((e,t,n)=>(e[t]=n,e),{}),Vve=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Uve=Vve.map(([e,t])=>[Ip[e],Ip[t]]),MM=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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Ye.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=u.arraySync();s.dispose(),u.dispose();let h=[];for(let p of d)if(i[p]>=n.hand.minConfidence){let c=Ze(l,[p,0],[1,-1]),m=Ze(r,[p,5],[1,14]),f=Ue(()=>this.normalizeLandmarks(m,p).reshape([-1,2]));m.dispose(),h.push({box:c,palmLandmarks:f,confidence:i[p]})}return r.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=Ue(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),d=u.slice(0,2),h=u.slice(2,4),p=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(PM({startPoint:d,endPoint:h,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function Zve(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function WM(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Zve(n)}var BM=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ai(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function Yve(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function VM(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(ai(e[r],Yve(t,s)))}return n}function Gb(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=BM(t[0],t[1]),i=VM(s,r),o=BM(-t[0],-t[1]);return VM(i,o)}function UM(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-ai(t[0],n),-ai(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function qb(e,t){return[ai(e,t[0]),ai(e,t[1])]}var Jve=5,jM=1.65,HM=[0,5,9,13,17,1,2],Qve=0,ewe=2,Kb=class{constructor(t,n){var a;this.handDetector=t,this.handPoseModel=n,this.inputSize=(a=this.handPoseModel)==null?void 0:a.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>qb([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return cm(fm(r),Jve)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=cm(fm(n),jM);a.palmLandmarks=[];for(let r=0;r<HM.length;r++)a.palmLandmarks.push(t[HM[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=pm(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Gb(a,[0,0]),u=o.map(c=>[...qb(c,l),c[2]]),d=UM(r),h=[...Sp(n),1],p=[ai(h,d[0]),ai(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(a=!0));let s=[];for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?WM(o.palmLandmarks[Qve],o.palmLandmarks[ewe]):0,u=Sp(o),d=[u[0]/t.shape[2],u[1]/t.shape[1]],h=n.hand.rotation&&ka.flags.IS_BROWSER?Ye.rotateWithOffset(t,l,0,d):t.clone(),p=Gb(-l,u),c=a?this.getBoxForPalmLandmarks(o.palmLandmarks,p):o,m=zM(c,h,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),h.dispose();let[g,y]=await this.handPoseModel.predict(f);f.dispose();let A=g.dataSync()[0];if(g.dispose(),A>=n.hand.minConfidence){let x=le(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let b=this.transformRawCoords(v,c,l,p),w=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...w,confidence:A};let I={landmarks:b,confidence:A,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};s.push(I)}else this.storedBoxes[i]=null;y.dispose()}else{let l=cm(fm(o),jM),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var GM={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},ri,si,qM;async function Xb(e,t){let n=await qM.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let u of Object.keys(GM))s[u]=GM[u].map(d=>n[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let u of i)u[0]<o[0]&&(o[0]=u[0]),u[1]<o[1]&&(o[1]=u[1]),u[0]>o[2]&&(o[2]=u[0]),u[1]>o[3]&&(o[3]=u[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:l,keypoints:i,annotations:s})}return a}async function Zb(e){!ri||!si?([ri,si]=await Promise.all([e.hand.enabled?Et(Mt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Et(Mt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ri||!ri.modelUrl?ge("load model failed:",e.hand.detector.modelPath):e.debug&&ge("load model:",ri.modelUrl),!si||!si.modelUrl?ge("load model failed:",e.hand.skeleton.modelPath):e.debug&&ge("load model:",si.modelUrl))):(e.debug&&ge("cached model:",ri.modelUrl),e.debug&&ge("cached model:",si.modelUrl));let t=new Hb(ri);return qM=new Kb(t,si),[ri,si]}var KM=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],XM=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var aa;async function mm(e){return aa?e.debug&&ge("cached model:",aa.modelUrl):(aa=await Et(Mt(e.modelBasePath,e.body.modelPath)),aa.width=parseInt(aa.signature.inputs["input_1:0"].tensorShape.dim[2].size),aa.height=parseInt(aa.signature.inputs["input_1:0"].tensorShape.dim[1].size),!aa||!aa.modelUrl?ge("load model failed:",e.body.modelPath):e.debug&&ge("load model:",aa.modelUrl)),aa}async function Yb(e,t){var f;if(!aa)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},a=Ye.resizeBilinear(e,[aa.width,aa.height],!1),r=Qe(a,[255]);a.dispose();let s=await aa.predict(r),i=((f=s.find(g=>g.size===195||g.size===155))==null?void 0:f.dataSync())||[];s.forEach(g=>g.dispose()),r.dispose();let o=[],l=(i==null?void 0:i.length)===195?KM:XM,u=5;for(let g=0;g<i.length/u;g++)o.push({id:g,part:l[g],position:[Math.trunc(n.width*i[u*g+0]/255),Math.trunc(n.height*i[u*g+1]/255),Math.trunc(i[u*g+2])+0],positionRaw:[i[u*g+0]/255,i[u*g+1]/255,i[u*g+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[u*g+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[u*g+4]))))/100});let d=o.map(g=>g.position[0]),h=o.map(g=>g.position[1]),p=[Math.min(...d),Math.min(...h),Math.max(...d)-Math.min(...d),Math.max(...h)-Math.min(...d)],c=[0,0,0,0],m=o.reduce((g,y)=>y.score>g?y.score:g,0);return[{id:0,score:m,box:p,boxRaw:c,keypoints:o}]}var ra,Wr=[],Jb=[0,0,0,0],Qb=[0,0,0,0],gm=0,e3=Number.MAX_SAFE_INTEGER,twe=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function ZM(e){return ra?e.debug&&ge("cached model:",ra.modelUrl):(ra=await Et(Mt(e.modelBasePath,e.body.modelPath)),!ra||!ra.modelUrl?ge("load model failed:",e.body.modelPath):e.debug&&ge("load model:",ra.modelUrl)),ra}function nwe(e,t){let[n,a]=e.shape;return Ue(()=>{let r=(o,l)=>je(o,fe(Qe(o,dt(l,"int32")),dt(l,"int32"))),s=le(e,[a*n]),i=$s(s,0).dataSync()[0];if(i>t){let o=Zy(s,0),l=r(o,n).dataSync()[0],u=Qe(o,dt(n,"int32")).dataSync()[0];return[l,u,i]}return[0,0,i]})}async function t3(e,t){return e3<t.body.skipFrames&&t.skipFrame&&Object.keys(Wr).length>0?(e3++,[{id:0,score:gm,box:Jb,boxRaw:Qb,keypoints:Wr}]):(e3=0,new Promise(async n=>{let a=Ue(()=>{if(!ra.inputs[0].shape)return null;let u=Ye.resizeBilinear(e,[ra.inputs[0].shape[2],ra.inputs[0].shape[1]],!1);return fe(u,2).sub(1)}),r;if(t.body.enabled&&(r=await ra.predict(a)),a.dispose(),r){Wr.length=0;let u=r.squeeze();Ve(r);let d=u.unstack(2);Ve(u);for(let h=0;h<d.length;h++){let[p,c,m]=nwe(d[h],t.body.minConfidence);gm>t.body.minConfidence&&Wr.push({score:Math.round(100*m)/100,part:twe[h],positionRaw:[p/ra.inputs[0].shape[2],c/ra.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/ra.inputs[0].shape[2]),Math.round(e.shape[1]*c/ra.inputs[0].shape[1])]})}d.forEach(h=>Ve(h))}gm=Wr.reduce((u,d)=>d.score>u?d.score:u,0);let s=Wr.map(u=>u.position[0]),i=Wr.map(u=>u.position[1]);Jb=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=Wr.map(u=>u.positionRaw[0]),l=Wr.map(u=>u.positionRaw[1]);Qb=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:gm,box:Jb,boxRaw:Qb,keypoints:Wr}])}))}var br,Br=[],n3=[0,0,0,0],a3=[0,0,0,0],$u=0,r3=Number.MAX_SAFE_INTEGER,awe=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function s3(e){return br?e.debug&&ge("cached model:",br.modelUrl):(br=await Et(Mt(e.modelBasePath,e.body.modelPath)),!br||!br.modelUrl?ge("load model failed:",e.body.modelPath):e.debug&&ge("load model:",br.modelUrl)),br}async function i3(e,t){return r3<t.body.skipFrames&&t.skipFrame&&Object.keys(Br).length>0?(r3++,[{id:0,score:$u,box:n3,boxRaw:a3,keypoints:Br}]):(r3=0,new Promise(async n=>{let a=Ue(()=>{if(!br.inputs[0].shape)return null;let u=Ye.resizeBilinear(e,[br.inputs[0].shape[2],br.inputs[0].shape[1]],!1);return zt(u,"int32")}),r;if(t.body.enabled&&(r=await br.predict(a)),a.dispose(),r){Br.length=0;let u=r.arraySync();Ve(r);let d=u[0][0];for(let h=0;h<d.length;h++)$u=d[h][2],$u>t.body.minConfidence&&Br.push({score:Math.round(100*$u)/100,part:awe[h],positionRaw:[d[h][1],d[h][0]],position:[Math.round((e.shape[2]||0)*d[h][1]),Math.round((e.shape[1]||0)*d[h][0])]})}$u=Br.reduce((u,d)=>d.score>u?d.score:u,0);let s=Br.map(u=>u.position[0]),i=Br.map(u=>u.position[1]);n3=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=Br.map(u=>u.positionRaw[0]),l=Br.map(u=>u.positionRaw[1]);a3=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:$u,box:n3,boxRaw:a3,keypoints:Br}])}))}var Ru=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ya,o3=[],l3=Number.MAX_SAFE_INTEGER,ym=2.5;async function u3(e){if(ya)e.debug&&ge("cached model:",ya.modelUrl);else{ya=await Et(Mt(e.modelBasePath,e.object.modelPath));let t=Object.values(ya.modelSignature.inputs);if(ya.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!ya.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!ya||!ya.modelUrl?ge("load model failed:",e.object.modelPath):e.debug&&ge("load model:",ya.modelUrl)}return ya}async function rwe(e,t,n,a){let r=0,s=[];for(let u of[1,2,4])Ue(()=>{var g,y;let d=u*13,h=(g=e.find(A=>A.shape[1]===d**2&&A.shape[2]===Ru.length))==null?void 0:g.squeeze(),p=(y=e.find(A=>A.shape[1]===d**2&&A.shape[2]<Ru.length))==null?void 0:y.squeeze(),m=p.reshape([-1,4,p.shape[1]/4]).argMax(2).arraySync(),f=h.arraySync();for(let A=0;A<h.shape[0];A++)for(let x=0;x<h.shape[1];x++){let v=f[A][x];if(v>a.object.minConfidence&&x!==61){let 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r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!s)return{tensor:null,canvas:_e};let i=r,o=s;if(i>Am&&(i=Am,o=i*s/r),o>Am&&(o=Am,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!_e||(_e==null?void 0:_e.width)!==i||(_e==null?void 0:_e.height)!==o)&&(_e=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(_e==null?void 0:_e.width)!==i&&(_e.width=i),(_e==null?void 0:_e.height)!==o&&(_e.height=o));let l=_e.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,s,0,0,_e==null?void 0:_e.width,_e==null?void 0:_e.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,s,0,0,_e==null?void 0:_e.width,_e==null?void 0:_e.height),t.filter.enabled){if((!an||!Wt||_e.width!==Wt.width||(_e==null?void 0:_e.height)!==(Wt==null?void 0:Wt.height))&&(Wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(_e==null?void 0:_e.width,_e==null?void 0:_e.height):document.createElement("canvas"),(Wt==null?void 0:Wt.width)!==(_e==null?void 0:_e.width)&&(Wt.width=_e==null?void 0:_e.width),(Wt==null?void 0:Wt.height)!==(_e==null?void 0:_e.height)&&(Wt.height=_e==null?void 0:_e.height),an=ka.flags.IS_BROWSER?new t$({canvas:Wt}):null),!an)return{tensor:null,canvas:_e};an.reset(),an.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&an.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&an.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&an.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&an.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&an.addFilter("hue",t.filter.hue),t.filter.negative&&an.addFilter("negative"),t.filter.sepia&&an.addFilter("sepia"),t.filter.vintage&&an.addFilter("brownie"),t.filter.sepia&&an.addFilter("sepia"),t.filter.kodachrome&&an.addFilter("kodachrome"),t.filter.technicolor&&an.addFilter("technicolor"),t.filter.polaroid&&an.addFilter("polaroid"),t.filter.pixelate!==0&&an.addFilter("pixelate",t.filter.pixelate),an.apply(_e)}else Wt=_e,an&&(an=null);let u;if(Wt.data){let d=[Wt.height,Wt.width,3];u=mc(Wt.data,d,"int32")}else if(Wt instanceof ImageData)u=Ua?Ua.fromPixels(Wt):null;else if(t.backend==="webgl"||t.backend==="humangl"){let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let h=d.getContext("2d");h==null||h.drawImage(Wt,0,0),u=Ua?Ua.fromPixels(d):null}else{let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let h=d.getContext("2d");h==null||h.drawImage(Wt,0,0);let p=h==null?void 0:h.getImageData(0,0,i,o);u=Ua?Ua.fromPixels(p):null}if(u){let d=u.toFloat();n=d.expandDims(0),u.dispose(),d.dispose()}}let a=t.filter.return?Wt:null;return{tensor:n,canvas:a}}var y3={};Qg(y3,{all:()=>uwe,body:()=>r$,canvas:()=>lwe,face:()=>a$,gesture:()=>n$,hand:()=>s$,object:()=>i$,options:()=>ii,person:()=>owe});var ii={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe 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r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Tp(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){g3(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function n$(e,t,n){let a=ia(ii,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: 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h=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],h,p,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let h=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],h,p,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=u.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((l=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let h=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(h[0],h[1]);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(p[0],p[1]),r.stroke()}}}}}async function r$(e,t,n){var s;let a=ia(ii,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Np(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,m3(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4);if(a.drawPolygons&&t[i].keypoints){let o,l=[];l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),Tp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),l.length===4&&g3(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftFoot"),o&&l.push([o.position[0],o.position[1]]),Tp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightHip"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightKnee"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightAnkle"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightHeel"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightFoot"),o&&l.push([o.position[0],o.position[1]]),Tp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="leftShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="leftPalm"),o&&l.push([o.position[0],o.position[1]]),Tp(r,l,a),l.length=0,o=t[i].keypoints.find(u=>u.part==="rightShoulder"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightElbow"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightWrist"),o&&l.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(u=>u.part==="rightPalm"),o&&l.push([o.position[0],o.position[1]]),Tp(r,l,a)}}}}async function s$(e,t,n){let a=ia(ii,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,Np(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,m3(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 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n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function uwe(e,t,n){let a=st(),r=ia(ii,n);!t||!e||e instanceof HTMLCanvasElement&&(a$(e,t.face,r),r$(e,t.body,r),s$(e,t.hand,r),i$(e,t.object,r),n$(e,t.gesture,r),t.performance.draw=Math.trunc(st()-a))}function o$(e,t,n,a,r){var o,l,u,d,h,p,c,m,f,g,y,A,x,v,b,w;let s=0,i=[];for(let I of e){let T={id:s++,face:I,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let _ of t)I.box[0]>_.box[0]&&I.box[0]<_.box[0]+_.box[2]&&I.box[1]+I.box[3]>_.box[1]&&I.box[1]+I.box[3]<_.box[1]+_.box[3]&&(T.body=_);if(T.body)for(let _ of n)_.box[0]+_.box[2]>T.body.box[0]&&_.box[0]+_.box[2]<T.body.box[0]+T.body.box[2]&&_.box[1]+_.box[3]>T.body.box[1]&&_.box[1]+_.box[3]<T.body.box[1]+T.body.box[3]&&T.hands&&(T.hands.left=_),_.box[0]<T.body.box[0]+T.body.box[2]&&_.box[0]>T.body.box[0]&&_.box[1]+_.box[3]>T.body.box[1]&&_.box[1]+_.box[3]<T.body.box[1]+T.body.box[3]&&T.hands&&(T.hands.right=_);for(let _ of a)_.face!==void 0&&_.face===I.id?(o=T.gestures)==null||o.push(_):_.iris!==void 0&&_.iris===I.id?(l=T.gestures)==null||l.push(_):_.body!==void 0&&_.body===((u=T.body)==null?void 0:u.id)?(d=T.gestures)==null||d.push(_):_.hand!==void 0&&_.hand===((p=(h=T.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=T.gestures)==null||c.push(_):_.hand!==void 0&&_.hand===((f=(m=T.hands)==null?void 0:m.right)==null?void 0:f.id)&&((g=T.gestures)==null||g.push(_));let C=[],z=[],$=_=>{_&&_.length===4&&(C.push(_[0],_[0]+_[2]),z.push(_[1],_[1]+_[3]))};$((y=T.face)==null?void 0:y.box),$((A=T.body)==null?void 0:A.box),$((v=(x=T.hands)==null?void 0:x.left)==null?void 0:v.box),$((w=(b=T.hands)==null?void 0:b.right)==null?void 0:w.box);let S=Math.min(...C),D=Math.min(...z);T.box=[S,D,Math.max(...C)-S,Math.max(...z)-D],r&&r.length===4&&(T.boxRaw=[T.box[0]/r[2],T.box[1]/r[1],T.box[2]/r[2],T.box[3]/r[1]]),i.push(T)}return i}var Le={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function l$(e){var r,s,i,o,l,u,d,h,p,c,m,f,g,y,A,x,v,b,w,I,T;let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if(Le.canvas=e.canvas,!Le.body||e.body.length!==Le.body.length)Le.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C<e.body.length;C++){let 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2Q==`;var d$="2.0.0";var Fu,Ep,Cp,ko,Io,Ou,km,Mp,Im,Sm,Nm,Tm,h$=class{constructor(t){wa(this,Fu,void 0);wa(this,Ep,void 0);wa(this,Cp,void 0);wa(this,ko,void 0);wa(this,Io,void 0);wa(this,Ou,void 0);this.analyze=(...t)=>{if(!Fn(this,Ep))return;let n=this.tf.engine().state.numTensors,a=Fn(this,Fu);Ja(this,Fu,n);let r=n-a;r!==0&&ge(...t,r)};wa(this,km,t=>{if(!Fn(this,Cp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof St))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});wa(this,Mp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=st();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&ge("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&ge("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&ge("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&ge(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&ge("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&hM();try{await this.tf.setBackend(this.config.backend)}catch(r){ge("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(ge("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&ge(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(st()-a)}});this.next=t=>l$(t||this.result);wa(this,Im,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let l=0;l<r.length/3;l++)s+=r[3*l+2];a.dispose();let i=100*(Math.max(s,Fn(this,Io))/Math.min(s,Fn(this,Io))-1);Ja(this,Io,s);let o=i<Math.max(this.config.cacheSensitivity,Fn(this,Ou));return Ja(this,Ou,i>10*this.config.cacheSensitivity?0:i),o});wa(this,Sm,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(vm);break;case"full":n=await t(wm);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});wa(this,Nm,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+vm;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+wm;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));wa(this,Tm,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(vm)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(wm)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&ge("Warmup tfjs-node not loaded");return a});this.config=ia(F3,t||{}),this.tf=bp,this.draw=y3,this.version=d$,this.state="idle",Ja(this,Fu,0),Ja(this,Ep,!1),Ja(this,Cp,!1),Ja(this,ko,!0),Ja(this,Ou,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.image=n=>wo(n,this.config),this.faceTriangulation=IM,this.faceUVMap=SM,this.sysinfo=O3(),Ja(this,Io,1)}similarity(t,n){return Ob(t,n)}segmentation(t,n){return u$(t,n,this.config)}enhance(t){return Db(t)}match(t,n,a=0){return EM(t,n,a)}async load(t){this.state="load";let n=st();t&&(this.config=ia(this.config,t)),Fn(this,ko)&&(this.config.debug&&ge(`version: ${this.version}`),this.config.debug&&ge(`tfjs version: ${this.tf.version_core}`),this.config.debug&&ge("platform:",this.sysinfo.platform),this.config.debug&&ge("agent:",this.sysinfo.agent),await Fn(this,Mp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&ge("configuration:",this.config),this.config.debug&&ge("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.movenet,this.models.nanodet,this.models.centernet,this.models.faceres,this.models.segmentation]=await Promise.all([this.models.face||(this.config.face.enabled?Tb(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Mb(this.config):null),this.models.handpose||(this.config.hand.enabled?Zb(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?jb(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?mm(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?ZM(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?s3(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?u3(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?c3(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Fb(this.config):null),this.models.segmentation||(this.config.segmentation.enabled?bm(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await Tb(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Mb(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Zb(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await jb(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await mm(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await mm(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await s3(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await u3(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await c3(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Fb(this.config)),this.config.segmentation.enabled&&!this.models.segmentation&&(this.models.segmentation=await bm(this.config))),Fn(this,ko)&&(this.config.debug&&ge("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ja(this,ko,!1));let a=Math.trunc(st()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r,s;this.config=ia(this.config,n),this.state="check";let i=Fn(this,km).call(this,t);i&&(ge(i,t),a({error:i}));let o=st();await Fn(this,Mp).call(this),await this.load(),r=st();let l=wo(t,this.config);if(this.performance.image=Math.trunc(st()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=st(),await x3(l),s=Math.trunc(st()-r),s>0&&(this.performance.segmentation=s),l.canvas&&(l.tensor.dispose(),l=wo(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){ge("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}r=st(),this.config.skipFrame=await Fn(this,Im).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(st()-r),this.analyze("Check Changed:");let u,d,h,p;this.config.async?(u=this.config.face.enabled?zb(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=st(),u=this.config.face.enabled?await zb(this,l.tensor):[],s=Math.trunc(st()-r),s>0&&(this.performance.face=s)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?Ub(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?Yb(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?t3(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?i3(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=st(),this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?await Ub(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?await Yb(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?await t3(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?await i3(l.tensor,this.config):[]),s=Math.trunc(st()-r),s>0&&(this.performance.body=s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(h=this.config.hand.enabled?Xb(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=st(),h=this.config.hand.enabled?await Xb(l.tensor,this.config):[],s=Math.trunc(st()-r),s>0&&(this.performance.hand=s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?d3(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?f3(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=st(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await d3(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await f3(l.tensor,this.config):[]),s=Math.trunc(st()-r),s>0&&(this.performance.object=s)),this.analyze("End Object:"),this.config.async&&([u,d,h,p]=await Promise.all([u,d,h,p]));let c=[];this.config.gesture.enabled&&(r=st(),c=[...JM(u),...YM(d),...e$(h),...QM(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(st()-r)),this.performance.total=Math.trunc(st()-o),this.state="idle",this.result={face:u,body:d,hand:h,gesture:c,object:p,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var m;return o$(u,d,h,c,(m=l==null?void 0:l.tensor)==null?void 0:m.shape)}},Ve(l.tensor),a(this.result)})}async warmup(t){let n=st();if(t&&(this.config=ia(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await Fn(this,Sm).call(this):typeof Image!="undefined"?a=await Fn(this,Nm).call(this):a=await Fn(this,Tm).call(this);let r=st();return this.config.debug&&ge("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};Fu=new WeakMap,Ep=new WeakMap,Cp=new WeakMap,ko=new WeakMap,Io=new WeakMap,Ou=new WeakMap,km=new WeakMap,Mp=new WeakMap,Im=new WeakMap,Sm=new WeakMap,Nm=new WeakMap,Tm=new WeakMap;return hwe;})();
/**
 * @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
 * 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
 *
 * https://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 See the LICENSE file. */