human/dist/human.esm.js

5187 lines
1.3 MiB

/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new nN(this.backendInstance),!0}setupRegisteredKernels(){yl(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){yl(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Eu)&&typeof n.then=="function"){let 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 nd.nextTensorId++}nextVariableId(){return nd.nextVariableId++}clone(e){let t=P.runKernel(zs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return P.runKernel(ks,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(Mc(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 '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=r1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(r1(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Mc(h,this.backendName);D(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:N}=v;return this.makeTensorFromDataId(b,w,N)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,p=r1(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Xm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&$r(e[0])&&(r=e.map(o=>Yu(o)));let s=a.write(r,t,n),i=new Be(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=ub(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Be(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 td(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*Vm(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 td||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*Vm(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=Xm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let p=n[d],c=qp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.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=a1(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(D(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));D(r instanceof Be,()=>"The result y returned by f() must be a tensor.");let s=sN(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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r=M(e,"sparseIndices","sparseToDense","int32"),s=M(t,"sparseValues","sparseToDense"),i=M(a,"defaultValue","sparseToDense",s.dtype);LM(r,s,n,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:n};return P.runKernel(Sc,o,l)}var pg=L({sparseToDense_:WM});function BM(e,t){let n=M(t,"indices","gatherND","int32"),a={params:M(e,"x","gatherND","string_or_numeric"),indices:n};return P.runKernel(zo,a)}var G3=L({gatherND_:BM});function VM(e,t){if(t==null)return e.shape.slice();if(cr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let a=0;a<e.shape.length;a++)t[a]==null&&e.shape[a]!=null?n.push(e.shape[a]):n.push(t[a]);return n}return t}function jM(e,t,n,a){let r=M(e,"x","dropout");if(D(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),D(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Be?r.clone():r;let 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filter (${n[2]}.`),D(d===n[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${n[3]}).`),i!=null&&D(qt(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let p={x:o,dy:l},c={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,filterShape:n};return P.runKernel(Jp,p,c)}var hg=L({conv2DBackpropFilter_:GM});function yh(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return B(e,zl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Ah(e,t){let n=t,a=Bt(e.shape,t.shape);return a.length>0&&(n=Se(n,a)),q(n,e.shape)}function xh(e,t,n,a){if(t==="linear")return e;if(t==="relu")return Ka(e);if(t==="elu")return El(e);if(t==="relu6")return rh(e);if(t==="prelu")return gd(e,n);if(t==="leakyrelu")return pd(e,a);if(t==="sigmoid")return Rn(e);throw new Error(`Unknown fused activation ${t}.`)}var bh=(e,t)=>!(e>0)||t==="linear";function qM({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:d}){if(l=l||"linear",bh(P.state.gradientDepth,l)===!1){let b=mr(e,t,n,a,r,s,i);return o!=null&&(b=ie(b,o)),xh(b,l,u,d)}let p=M(e,"x","conv2d"),c=M(t,"filter","conv2d"),h=p,m=!1;p.rank===3&&(m=!0,h=q(p,[1,p.shape[0],p.shape[1],p.shape[2]])),D(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),D(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),i!=null&&D(qt(a),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),D(h.shape[3]===c.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${c.shape[2]}.`),D(Ga(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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s={skipEmpty:n},i={input:a,delimiter:r},o=P.runKernel(Tc,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var b$=L({stringSplit_:x$});function v$(e,t){let n=M(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 P.runKernel(Cc,r,a)}var w$=L({stringToHashBucketFast_:v$}),k$={fft:bd,ifft:Ol,rfft:vd,irfft:ch},I$={hammingWindow:nF,hannWindow:Y3,frame:J3,stft:iF},De={flipLeftRight:dF,resizeNearestNeighbor:r7,resizeBilinear:a7,rotateWithOffset:cF,cropAndResize:lF,nonMaxSuppression:fF,nonMaxSuppressionAsync:wF,nonMaxSuppressionWithScore:IF,nonMaxSuppressionWithScoreAsync:NF,nonMaxSuppressionPadded:CF,nonMaxSuppressionPaddedAsync:RF,threshold:OF,transform:_F},i7={bandPart:LF,gramSchmidt:BF,qr:jF},S$={absoluteDifference:GF,computeWeightedLoss:Ar,cosineDistance:XF,hingeLoss:ZF,huberLoss:JF,logLoss:e$,meanSquaredError:n$,sigmoidCrossEntropy:s$,softmaxCrossEntropy:l$},wd={sparseFillEmptyRows:d$,sparseReshape:c$,sparseSegmentMean:f$,sparseSegmentSum:g$},vh={stringNGrams:A$,stringSplit:b$,stringToHashBucketFast:w$},xr=class extends u3{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 he(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 M3(e,t)}dispose(){this.iterations_!=null&&he(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(xr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var wh=class extends xr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;V(()=>{let l=ie(B(i,this.rho),B(ot(s),1-this.rho)),u=B(me(an(ie(o,this.epsilon)),an(ie(i,this.epsilon))),s),d=ie(B(o,this.rho),B(ot(u),1-this.rho));i.assign(l),o.assign(d);let p=ie(B(u,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(he(this.accumulatedGrads.map(e=>e.variable)),he(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};wh.className="Adadelta";jr(wh);var kh=class extends xr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:V(()=>Rl(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;V(()=>{let i=ie(s,ot(r));s.assign(i);let o=ie(B(me(r,an(ie(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&he(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)}};kh.className="Adagrad";jr(kh);var Ih=class extends xr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:V(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:V(()=>Ge(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,p=ie(B(u,this.beta1),B(l,1-this.beta1)),c=ie(B(d,this.beta2),B(ot(l),1-this.beta2)),h=me(p,n),m=me(c,a);u.assign(p),d.assign(c);let f=ie(B(me(h,ie(an(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&he(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&he(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(yr(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)}};Ih.className="Adam";jr(Ih);var Sh=class extends xr{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=[],V(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=me(-this.learningRate,ie(B(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)}};Sh.className="Adamax";jr(Sh);var kd=class extends xr{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=P.registeredVariables[t];V(()=>{let s=ie(B(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Kt(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Nh.className="Momentum";jr(Nh);var Th=class extends xr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;V(()=>{let l=ie(B(i,this.decay),B(ot(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,d=ie(B(u,this.decay),B(s,1-this.decay)),p=me(B(s,this.learningRate),an(ye(l,ie(ot(d),this.epsilon)))),c=ie(B(o,this.momentum),p);i.assign(l),u.assign(d),o.assign(c);let h=ye(a,c);a.assign(h)}else{let 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t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Th.className="RMSProp";jr(Th);var Mi=class{static sgd(e){return new kd(e)}static momentum(e,t,n=!1){return new Nh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new Th(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new Ih(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new wh(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new Sh(e,t,n,a,r)}static 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it=0;for(let Ye=Te;Ye<ze;Ye++){let ht=Math.min(xe,g-1)*G,Ue=Math.min(xe,y-1)*Q,In=S[ht+tt*H+Ye*J],kt=z[Ye*K+nt*ne+Ue];it+=In*kt}le[xe*se+(tt*_+nt)]+=it}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(x,Z.dtype,Z.values)}var fO={kernelName:ws,backendName:"cpu",kernelFunc:tv};function mO(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:p}=a,c,h,m,f=[];c=tv({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(h=Id({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),d&&(m=Ng(n,c,d,o,p),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var gO={kernelName:hi,backendName:"cpu",kernelFunc:mO},yO=rt(go,e=>Math.acos(e)),AO={kernelName:go,backendName:"cpu",kernelFunc:yO},xO=rt(yo,e=>Math.acosh(e)),bO={kernelName:yo,backendName:"cpu",kernelFunc:xO};function vO(e){let{inputs:t,backend:n}=e,a=t;we(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Ve(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 wO={kernelName:xs,backendName:"cpu",kernelFunc:vO};function kO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,u=F.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("all",l,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*h,x=f[A];for(let v=0;v<h;++v){let b=f[A+v];x=x&&b}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),A=gt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var IO={kernelName:Ao,backendName:"cpu",kernelFunc:kO};function SO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;we(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,u=F.getAxesPermutation(l,r.shape.length),d=r;u!=null&&(d=ua({inputs:{x:r},backend:n,attrs:{perm:u}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("any",l,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*h,x=f[A];for(let v=0;v<h;++v){let b=f[A+v];x=x||b}m[y]=x}u!=null&&n.disposeIntermediateTensorInfo(d);let g=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),A=gt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var NO={kernelName:xo,backendName:"cpu",kernelFunc:SO};function TO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;we(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,p]=F.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.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)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var CO={kernelName:bs,backendName:"cpu",kernelFunc:TO};function EO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;we(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=ua({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,p]=F.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.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)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(d,"int32",h)}var RO={kernelName:Fu,backendName:"cpu",kernelFunc:EO},MO=rt(bo,e=>Math.asin(e)),FO={kernelName:bo,backendName:"cpu",kernelFunc:MO},$O=rt(vo,e=>Math.asinh(e)),DO={kernelName:vo,backendName:"cpu",kernelFunc:$O},OO=rt(wo,e=>Math.atan(e)),zO={kernelName:wo,backendName:"cpu",kernelFunc:OO},_O=Ot((e,t)=>Math.atan2(e,t)),PO=Yt(Io,_O),LO={kernelName:Io,backendName:"cpu",kernelFunc:PO},WO=rt(ko,e=>Math.atanh(e)),BO={kernelName:ko,backendName:"cpu",kernelFunc:WO};function Tg(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,d=r.effectiveFilterHeight,p=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Ve(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],A=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;v<r.batchSize;++v){let b=v*y,w=v*a[0];for(let N=0;N<r.inChannels;++N)for(let C=0;C<r.outHeight;++C){let 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W=O-N,G=f.get(g,S,O,y);G>_&&(_=G,r?$=s?((g*a.inHeight+S)*a.inWidth+O)*a.inChannels+y:(S*a.inWidth+O)*a.inChannels+y:$=z*c+W)}}i.set($,g,A,w,y)}}return i}function av(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,d=r.dilationHeight,p=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Ve(r.outShape,n),v=x.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],w=r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let E=0;E<r.batchSize;++E){let _=E*b,$=E*a[0];for(let S=0;S<r.inChannels;++S)for(let z=0;z<r.outDepth;++z){let O=z*i-f,W=O;for(;W<0;)W+=u;let G=Math.min(r.inDepth,c+O),H=_+z*w;for(let J=0;J<r.outHeight;++J){let K=J*o-g,ne=K;for(;ne<0;)ne+=d;let Q=Math.min(r.inHeight,h+K),se=H+J*N;for(let Z=0;Z<r.outWidth;++Z){let le=Z*l-y,oe=le;for(;oe<0;)oe+=p;let xe=Math.min(r.inWidth,m+le),fe=se+Z*C,Ne=A,Te=0,Oe=0;for(let ze=W;ze<G;ze+=u){let tt=$+ze*a[1];for(let nt=ne;nt<Q;nt+=d){let it=tt+nt*a[2];for(let Ye=oe;Ye<xe;Ye+=p){let ht=it+Ye*a[3],Ue=e[ht+S];if(s==="max"&&Ue>Ne?Ne=Ue:s==="avg"&&(Te+=Ue,Oe++),isNaN(Ne))break}if(isNaN(Ne))break}if(isNaN(Ne))break}let Pe=fe+S;v[Pe]=s==="avg"?Te/Oe:Ne}}}}return x}function VO(e,t){let n=Ve(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,d=t.effectiveFilterHeight,p=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*a-c,x=A;for(;x<0;)x+=i;let v=Math.min(t.inDepth,u+A);for(let b=0;b<t.outHeight;++b){let w=b*r-h,N=w;for(;N<0;)N+=o;let C=Math.min(t.inHeight,d+w);for(let E=0;E<t.outWidth;++E){let _=E*s-m,$=_;for(;$<0;)$+=l;let S=Math.min(t.inWidth,p+_),z=Number.NEGATIVE_INFINITY,O=-1;for(let W=x;W<v;W+=i){let G=W-A;for(let H=N;H<C;H+=o){let J=H-w;for(let K=$;K<S;K+=l){let ne=K-_,Q=e.get(f,W,H,K,g);Q>=z&&(z=Q,O=G*d*p+J*d+ne)}}}n.set(O,f,y,b,E,g)}}}return n}function jO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;we(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l),p;if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))p=Ya({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=k.computeStrides(r.shape),m=Tg(c,r.shape,r.dtype,h,d,"avg");p=n.makeTensorInfo(d.outShape,r.dtype,m.values)}return p}var UO={kernelName:vs,backendName:"cpu",kernelFunc:jO};function HO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;we(r,"avgPool3d");let d=F.computePool3DInfo(r.shape,s,i,1,o,l,u),p=n.data.get(r.dataId).values,c=av(p,r.shape,r.dtype,k.computeStrides(r.shape),d,"avg");return n.makeTensorInfo(c.shape,"float32",c.values)}var GO={kernelName:$u,backendName:"cpu",kernelFunc:HO};function qO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;we([r,s],"avgPool3DGrad");let d=F.computePool3DInfo(s.shape,i,o,1,l,u),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,v=d.effectiveFilterDepth,b=d.effectiveFilterHeight,w=d.effectiveFilterWidth,N=v-1-d.padInfo.front,C=w-1-d.padInfo.left,E=b-1-d.padInfo.top,_=Ve(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let z=0;z<d.batchSize;++z)for(let O=0;O<d.inChannels;++O)for(let W=0;W<d.inDepth;++W)for(let G=0;G<d.inHeight;++G)for(let H=0;H<d.inWidth;++H){let J=W-N,K=G-E,ne=H-C,Q=0;for(let se=0;se<v;se+=y){let Z=(J+se)/p;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let le=0;le<b;le+=A){let oe=(K+le)/c;if(!(oe<0||oe>=d.outHeight||Math.floor(oe)!==oe))for(let xe=0;xe<w;xe+=x){let fe=(ne+xe)/h;fe<0||fe>=d.outWidth||Math.floor(fe)!==fe||(Q+=S.get(z,Z,oe,fe,O))}}}_.set(Q*$,z,W,G,H,O)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var XO={kernelName:Kp,backendName:"cpu",kernelFunc:qO};function KO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;we([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=F.computePool2DInfo(i.shape,o,l,1,u),p=d.strideHeight,c=d.strideWidth,h=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=Ve(i.shape,"float32"),w=1/(h*m),N=n.data.get(r.dataId).values,C=Ve(r.shape,"float32",N);for(let E=0;E<d.batchSize;++E)for(let _=0;_<d.inChannels;++_)for(let $=0;$<d.inHeight;++$)for(let S=0;S<d.inWidth;++S){let z=$-v,O=S-x,W=0;for(let G=0;G<y;G+=f){let H=(z+G)/p;if(!(H<0||H>=d.outHeight||Math.floor(H)!==H))for(let J=0;J<A;J+=g){let K=(O+J)/c;K<0||K>=d.outWidth||Math.floor(K)!==K||(W+=C.get(E,H,K,_))}}b.set(W*w,E,$,S,_)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var ZO={kernelName:Xp,backendName:"cpu",kernelFunc:KO};function YO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=a;u==null&&(u=.001);let d=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),g=m.length,y=h.length,A=c.length,x=p.length,v=0,b=0,w=0,N=0;for(let C=0;C<d.length;++C)f[C]=m[v++]+(d[C]-p[b++])*h[w++]/Math.sqrt(c[N++]+u),v>=g&&(v=0),b>=x&&(b=0),w>=y&&(w=0),N>=A&&(N=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var JO={kernelName:Ds,backendName:"cpu",kernelFunc:YO};function QO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;we([r],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=F.getReshaped(r.shape,s,o),u=F.getPermuted(l.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=gt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ua({inputs:{x:h},backend:n,attrs:{perm:u}}),f=gt({inputs:{x:m},backend:n,attrs:{shape:d}}),g=Di({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var ez={kernelName:Du,backendName:"cpu",kernelFunc:QO};function tz(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=xg(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var nz={kernelName:Zp,backendName:"cpu",kernelFunc:tz},az=rt(zr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),rz={kernelName:zr,backendName:"cpu",kernelFunc:az},sz=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],p=l[u];a[u]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},iz={kernelName:Ou,backendName:"cpu",kernelFunc:sz};function Wl(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 oz={kernelName:pc,backendName:"cpu",kernelFunc:Wl};function Bl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return Ya({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(F.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(v=>$i({inputs:{input:v},backend:n})),g=o.map(v=>Wl({inputs:{input:v},backend:n})),y=Bl({inputs:f,backend:n,attrs:{axis:s}}),A=Bl({inputs:g,backend:n,attrs:{axis:s}}),x=qn({inputs:{real:y,imag:A},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),x}let u=o.map(f=>{let g=k.sizeFromShape(f.shape.slice(s));return gt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),d=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=F.computeOutShape(u.map(f=>f.shape),1);let p=u[0].shape[0]===1,c=bg(d,i,t[0].dtype,p),h=F.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var lz={kernelName:So,backendName:"cpu",kernelFunc:Bl};function rv(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;we([r,s],"conv2d");let p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,A=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Lt(c.outShape,r.dtype),b=k.computeStrides(r.shape),w=k.computeStrides(s.shape),N=b[0],C=x?b[1]:b[2],E=x?b[2]:1,_=x?1:b[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],z=x?v.strides[2]:1,O=x?1:v.strides[1],W=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,H=v.values;for(let J=0;J<c.batchSize;++J){let K=J*N,ne=J*$;for(let Q=0;Q<c.outHeight;++Q){let se=ne+Q*S,Z=Q*c.strideHeight-A;for(let le=0;le<h;++le){let oe=Z+le*f;if(oe<0||oe>=c.inHeight)continue;let xe=le*w[0],fe=K+oe*C;for(let 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Lt(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,A=n.data.get(s.dataId).values,[x,v,b]=p,{batchSize:w,filterHeight:N,filterWidth:C,inChannels:E,inHeight:_,inWidth:$,outChannels:S,outHeight:z,outWidth:O,strideHeight:W,strideWidth:G}=m;h=m.dataFormat;let H=N-1-m.padInfo.top,J=C-1-m.padInfo.left,K=h==="channelsLast",ne=f.strides[0],Q=K?f.strides[1]:f.strides[2],se=K?f.strides[2]:1,Z=K?1:f.strides[1],le=c[0],oe=K?c[1]:c[2],xe=K?c[2]:1,fe=K?1:c[1];for(let Ne=0;Ne<w;++Ne)for(let Te=0;Te<E;++Te)for(let Oe=0;Oe<_;++Oe){let Pe=Oe-H,ze=Math.max(0,Math.ceil(Pe/W)),tt=Math.min(z,(N+Pe)/W);for(let nt=0;nt<$;++nt){let it=nt-J,Ye=Math.max(0,Math.ceil(it/G)),ht=Math.min(O,(C+it)/G),Ue=0;for(let kt=ze;kt<tt;++kt){let ta=kt*W-Pe;for(let en=Ye;en<ht;++en){let Sn=en*G-it,na=le*Ne+oe*kt+xe*en,Pn=x*(N-1-ta)+v*(C-1-Sn)+b*Te;for(let dn=0;dn<S;++dn){let tn=y[na+fe*dn],Ba=A[Pn+dn];Ue+=tn*Ba}}}let In=ne*Ne+Q*Oe+se*nt+Z*Te;g[In]=Ue}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var hz={kernelName:Ns,backendName:"cpu",kernelFunc:cz};function fz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;we([r,s],"conv3d");let u=F.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:p,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=u,y=g.front,A=g.left,x=g.top,v=new Lt(u.outShape,r.dtype),b=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,N=v.values,C=k.computeStrides(r.shape),E=k.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let $=_*C[0],S=_*v.strides[0];for(let z=0;z<u.outDepth;++z){let O=S+z*v.strides[1],W=z*u.strideDepth-y;for(let G=0;G<d;++G){let H=W+G*h;if(H<0||H>=u.inDepth)continue;let J=G*E[0],K=$+H*C[1];for(let ne=0;ne<u.outHeight;++ne){let Q=O+ne*v.strides[2],se=ne*u.strideHeight-x;for(let Z=0;Z<p;++Z){let le=se+Z*m;if(le<0||le>=u.inHeight)continue;let oe=J+Z*E[1],xe=K+le*C[2];for(let fe=0;fe<u.outWidth;++fe){let Ne=Q+fe*u.outChannels,Te=fe*u.strideWidth-A;for(let 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oe=0;oe<g;++oe){let xe=Math.max(0,Math.ceil((ne-oe)/h)),fe=Math.min(p.outHeight,(p.inHeight+ne-oe)/h),Ne=oe*b+le;for(let Te=0;Te<y;++Te){let Oe=Math.max(0,Math.ceil((K-Te)/m)),Pe=Math.min(p.outWidth,(p.inWidth+K-Te)/m),ze=Te*w+Ne;for(let tt=0;tt<p.inChannels;++tt){let nt=tt*N+ze;for(let it=0;it<p.outChannels;++it){let Ye=0;for(let ht=0;ht<p.batchSize;++ht){let Ue=ht*O,In=ht*E;for(let kt=se;kt<Z;++kt){let ta=(Q+kt*c-J)*W+Ue,en=kt*_+In;for(let Sn=xe;Sn<fe;++Sn){let na=(oe+Sn*h-ne)*G+ta,Pn=Sn*$+en;for(let dn=Oe;dn<Pe;++dn){let tn=(Te+dn*m-K)*H+na,Ba=dn*S+Pn;Ye+=z[tn+tt]*C[Ba+it]}}}}x[nt+it]=Ye}}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var yz={kernelName:Qp,backendName:"cpu",kernelFunc:gz};function Az(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;we([r],"conv3dBackpropInputV2");let u=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(l,s.shape,o,1,i),c=new Lt(p.inShape,"float32"),h=c.values,[m,f,g,y]=c.strides,A=n.data.get(r.dataId).values,[x,v,b,w]=u,N=n.data.get(s.dataId).values,[C,E,_,$]=d,{batchSize:S,filterDepth:z,filterHeight:O,filterWidth:W,inChannels:G,inDepth:H,inHeight:J,inWidth:K,outChannels:ne,outDepth:Q,outHeight:se,outWidth:Z,strideDepth:le,strideHeight:oe,strideWidth:xe}=p,fe=z-1-p.padInfo.front,Ne=O-1-p.padInfo.top,Te=W-1-p.padInfo.left;for(let Oe=0;Oe<S;++Oe)for(let Pe=0;Pe<G;++Pe)for(let ze=0;ze<H;++ze){let tt=ze-fe,nt=Math.max(0,Math.ceil(tt/le)),it=Math.min(Q,(z+tt)/le);for(let Ye=0;Ye<J;++Ye){let ht=Ye-Ne,Ue=Math.max(0,Math.ceil(ht/oe)),In=Math.min(se,(O+ht)/oe);for(let kt=0;kt<K;++kt){let ta=kt-Te,en=Math.max(0,Math.ceil(ta/xe)),Sn=Math.min(Z,(W+ta)/xe),na=0;for(let Pn=nt;Pn<it;++Pn){let dn=Pn*le-tt;for(let tn=Ue;tn<In;++tn){let Ba=tn*oe-ht;for(let fa=en;fa<Sn;++fa){let ma=fa*xe-ta,Nr=x*Oe+v*Pn+b*tn+w*fa,or=C*(z-1-dn)+E*(O-1-Ba)+_*(W-1-ma)+$*Pe;for(let Tr=0;Tr<ne;++Tr){let eo=A[Nr+Tr],Va=N[or+Tr];na+=eo*Va}}}}h[m*Oe+f*ze+g*Ye+y*kt+Pe]=na}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var xz={kernelName:ec,backendName:"cpu",kernelFunc:Az},bz=rt(Ts,e=>Math.cos(e)),vz={kernelName:Ts,backendName:"cpu",kernelFunc:bz},wz=rt(No,e=>Math.cosh(e)),kz={kernelName:No,backendName:"cpu",kernelFunc:wz};function Iz(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[d,p,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Ve([m,f,g,h],"float32"),A=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,b=k.computeStrides(r.shape),w=k.computeStrides(y.shape);for(let N=0;N<m;N++){let C=N*4,E=A[C],_=A[C+1],$=A[C+2],S=A[C+3],z=x[N];if(z>=d)continue;let O=f>1?($-E)*(p-1)/(f-1):0,W=g>1?(S-_)*(c-1)/(g-1):0;for(let G=0;G<f;G++){let H=f>1?E*(p-1)+G*O:.5*(E+$)*(p-1);if(H<0||H>p-1){for(let J=0;J<g;J++)for(let K=0;K<h;K++){let ne=K+J*w[2]+G*w[1]+N*w[0];y.values[ne]=u}continue}if(l==="bilinear"){let J=Math.floor(H),K=Math.ceil(H),ne=H-J;for(let Q=0;Q<g;Q++){let se=g>1?_*(c-1)+Q*W:.5*(_+S)*(c-1);if(se<0||se>c-1){for(let xe=0;xe<h;xe++){let fe=xe+Q*w[2]+G*w[1]+N*w[0];y.values[fe]=u}continue}let Z=Math.floor(se),le=Math.ceil(se),oe=se-Z;for(let xe=0;xe<h;xe++){let fe=xe+Z*b[2]+J*b[1]+z*b[0],Ne=v[fe];fe=xe+le*b[2]+J*b[1]+z*b[0];let Te=v[fe];fe=xe+Z*b[2]+K*b[1]+z*b[0];let Oe=v[fe];fe=xe+le*b[2]+K*b[1]+z*b[0];let Pe=v[fe],ze=Ne+(Te-Ne)*oe,tt=Oe+(Pe-Oe)*oe;fe=xe+Q*w[2]+G*w[1]+N*w[0],y.values[fe]=ze+(tt-ze)*ne}}}else for(let J=0;J<g;++J){let K=g>1?_*(c-1)+J*W:.5*(_+S)*(c-1);if(K<0||K>c-1){for(let se=0;se<h;se++){let Z=se+J*w[2]+G*w[1]+N*w[0];y.values[Z]=u}continue}let ne=Math.round(K),Q=Math.round(H);for(let se=0;se<h;se++){let Z=se+ne*b[2]+Q*b[1]+z*b[0],le=se+J*w[2]+G*w[1]+N*w[0];y.values[le]=v[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var Sz={kernelName:To,backendName:"cpu",kernelFunc:Iz};function Nz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;we(r,"cumsum");let l=F.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=ua({inputs:{x:r},backend:n,attrs:{perm:l}}));let d=F.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 p=Aa(u.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(u.shape),p),h=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<h.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)c[x]=i?0:h[x];else{let v=f(y,A-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let g=n.makeTensorInfo(u.shape,p,c);if(l!=null){let y=F.getUndoAxesPermutation(l),A=ua({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),A}return g}var Tz={kernelName:Cs,backendName:"cpu",kernelFunc:Nz};function Cz(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=xg(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=f7(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${r.shape.length}.`)}var Ez={kernelName:tc,backendName:"cpu",kernelFunc:Cz};function Rz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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$z(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;we([r,s],"depthwiseConv2dNativeBackpropFilter");let p=F.computeConv2DInfo(r.shape,d,i,o,l,u,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Lt(p.filterShape,"float32"),y=p.padInfo.left,A=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,b=new Lt(r.shape,r.dtype,v),w=n.data.get(s.dataId).values,N=new Lt(s.shape,s.dtype,w);for(let C=0;C<m;++C){let E=Math.max(0,Math.ceil((A-C)/c)),_=Math.min(p.outHeight,(p.inHeight+A-C)/c);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/h)),z=Math.min(p.outWidth,(p.inWidth+y-$)/h);for(let O=0;O<p.outChannels;++O){let W=Math.trunc(O/x),G=O%x,H=0;for(let J=0;J<p.batchSize;++J)for(let K=E;K<_;++K){let ne=C+K*c-A;for(let Q=S;Q<z;++Q){let se=$+Q*h-y;H+=b.get(J,ne,se,W)*N.get(J,K,Q,O)}}g.set(H,C,$,W,G)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Dz={kernelName:nc,backendName:"cpu",kernelFunc:$z};function Oz(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;we([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=F.computeConv2DInfo(d,s.shape,i,o,l,u,!0),m=new Lt(h.inShape,"float32"),f=m.values,[g,y,A]=m.strides,x=n.data.get(r.dataId).values,[v,b,w]=p,N=n.data.get(s.dataId).values,[C,E,_]=c,{batchSize:$,filterHeight:S,filterWidth:z,inChannels:O,inHeight:W,inWidth:G,outChannels:H,outHeight:J,outWidth:K,strideHeight:ne,strideWidth:Q}=h,se=S-1-h.padInfo.top,Z=z-1-h.padInfo.left,le=H/O;for(let oe=0;oe<$;++oe)for(let xe=0;xe<O;++xe)for(let fe=0;fe<W;++fe){let Ne=fe-se,Te=Math.max(0,Math.ceil(Ne/ne)),Oe=Math.min(J,(S+Ne)/ne);for(let Pe=0;Pe<G;++Pe){let ze=Pe-Z,tt=Math.max(0,Math.ceil(ze/Q)),nt=Math.min(K,(z+ze)/Q),it=0;for(let Ye=Te;Ye<Oe;++Ye){let ht=Ye*ne-Ne;for(let Ue=tt;Ue<nt;++Ue){let In=Ue*Q-ze,kt=v*oe+b*Ye+w*Ue,ta=C*(S-1-ht)+E*(z-1-In)+_*xe;for(let en=0;en<le;++en){let Sn=xe*le+en,na=x[kt+Sn],Pn=N[ta+en];it+=na*Pn}}}f[g*oe+y*fe+A*Pe+xe]=it}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var zz={kernelName:ac,backendName:"cpu",kernelFunc:Oz};function _z(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Ve([r,r],a.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*r+u]=s[u];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var Pz={kernelName:rc,backendName:"cpu",kernelFunc:_z},Lz={kernelName:_u,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,d=a.shape.length,p=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:v,strideWidth:b,filterHeight:w,filterWidth:N,dilationHeight:C,dilationWidth:E,outShape:_}=F.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=k.sizeFromShape(_),S=_.length,z=k.getArrayFromDType(a.dtype,$);for(let O=0;O<h;++O)for(let W=0;W<y;++W){let G=W*v-x.top;for(let H=0;H<A;++H){let J=H*b-x.left;for(let K=0;K<g;++K){let ne=Number.MIN_SAFE_INTEGER;for(let se=0;se<w;++se){let Z=G+se*C;if(Z>=0&&Z<m)for(let le=0;le<N;++le){let oe=J+le*E;if(oe>=0&&oe<f){let xe=k.locToIndex([O,Z,oe,K],d,k.computeStrides(a.shape)),fe=k.locToIndex([se,le,K],c,k.computeStrides(r.shape)),Ne=u[xe]+p[fe];Ne>ne&&(ne=Ne)}}}let Q=k.locToIndex([O,W,H,K],S,k.computeStrides(_));z[Q]=ne}}}return{dataId:l.write(k.toTypedArray(z,a.dtype),_,a.dtype),shape:_,dtype:a.dtype}}},Wz={kernelName:ic,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,d=k.toNestedArray(a.shape,u.data.get(a.dataId).values),p=k.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:v,filterHeight:b,filterWidth:w,dilationHeight:N,dilationWidth:C,outShape:E}=F.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===E.length,()=>`Error in ${ic}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let _=k.toNestedArray(E,u.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let z=0;z<g;++z){let O=z*x-A.top;for(let W=0;W<y;++W){let G=W*v-A.left;for(let H=0;H<f;++H){let 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g=f;f=Id({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Ng(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var m_={kernelName:fi,backendName:"cpu",kernelFunc:f_};function g_(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:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=sv({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let g=f;f=Id({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Ng(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var y_={kernelName:mi,backendName:"cpu",kernelFunc:g_};function A_(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,d,p]=F.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let 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U_(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;we(i,"LRNGrad");let p=k.sizeFromShape(i.shape),c=i.shape[3],h=n.data.get(i.dataId).values,m=n.data.get(r.dataId).values,f=n.data.get(s.dataId).values,g=new Float32Array(p),y=p;for(let A=0;A<y;A++){let x=A%c,v=A-x+Math.max(0,x-o),b=A-x+Math.min(c,x+o+1),w=0;for(let N=v;N<b;N++)w+=Math.pow(m[N],2);w=u*w+l;for(let N=v;N<b;N++){let C=-2*u*d*m[N]*f[A]/w;A===N&&(C+=Math.pow(w,-d)),C*=h[A],g[N]+=C}}return n.makeTensorInfo(i.shape,r.dtype,g)}var H_={kernelName:hc,backendName:"cpu",kernelFunc:U_};function ov(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,l=r.shape,u=l.length,d=k.parseAxisParam(s,l),p=d,c=F.getAxesPermutation(p,u),h=o.data.get(r.dataId).values;if(c!=null){let v=new Array(u);for(let 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d=F.computePool3DInfo(s.shape,i,o,1,l,u),p=n.bufferSync(s),c=VO(p,d),h=d.strideDepth,m=d.strideHeight,f=d.strideWidth,g=d.dilationDepth,y=d.dilationHeight,A=d.dilationWidth,x=d.effectiveFilterDepth,v=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=x-1-d.padInfo.front,N=b-1-d.padInfo.left,C=v-1-d.padInfo.top,E=Ve(s.shape,"float32"),_=n.bufferSync(r);for(let $=0;$<d.batchSize;++$)for(let S=0;S<d.inChannels;++S)for(let z=0;z<d.inDepth;++z)for(let O=0;O<d.inHeight;++O)for(let W=0;W<d.inWidth;++W){let G=z-w,H=O-C,J=W-N,K=0;for(let ne=0;ne<x;ne+=g){let Q=(G+ne)/h;if(!(Q<0||Q>=d.outDepth||Math.floor(Q)!==Q))for(let se=0;se<v;se+=y){let Z=(H+se)/m;if(!(Z<0||Z>=d.outHeight||Math.floor(Z)!==Z))for(let le=0;le<b;le+=A){let oe=(J+le)/f;if(oe<0||oe>=d.outWidth||Math.floor(oe)!==oe)continue;let xe=x*v*b-1-c.get($,Q,Z,oe,S),fe=ne*v*b+se*b+le,Ne=xe===fe?1:0;Ne!==0&&(K+=_.get($,Q,Z,oe,S)*Ne)}}}E.set(K,$,z,O,W,S)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var J_={kernelName:mc,backendName:"cpu",kernelFunc:Y_};function Q_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,l,u,1,d,p),h=n.data.get(o.dataId).values,m=Ve(c.outShape,o.dtype,nv(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,y=c.dilationHeight,A=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,b=v-1-c.padInfo.left,w=x-1-c.padInfo.top,N=Ve(o.shape,"float32"),C=n.data.get(r.dataId).values,E=Ve(r.shape,"float32",C);for(let _=0;_<c.batchSize;++_)for(let $=0;$<c.inChannels;++$)for(let S=0;S<c.inHeight;++S)for(let z=0;z<c.inWidth;++z){let O=S-w,W=z-b,G=0;for(let H=0;H<x;H+=y){let J=(O+H)/f;if(!(J<0||J>=c.outHeight||Math.floor(J)!==J))for(let K=0;K<v;K+=A){let ne=(W+K)/g;if(ne<0||ne>=c.outWidth||Math.floor(ne)!==ne)continue;let Q=x*v-1-m.get(_,J,ne,$),se=H*v+K,Z=Q===se?1:0;Z!==0&&(G+=E.get(_,J,ne,$)*Z)}}N.set(G,_,S,z,$)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var eP={kernelName:fc,backendName:"cpu",kernelFunc:Q_};function tP(e,t,n,a,r){let s=k.computeStrides(t),i=Tg(e,t,n,s,r,"max"),o=nv(e,t,n,r,!0,a);return[i.values,o.values]}var nP={kernelName:gc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;we(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,d=F.computePool2DInfo(a.shape,r,s,[1,1],i),[p,c]=tP(u,a.shape,a.dtype,o,d),h=l.write(p,d.outShape,a.dtype),m=l.write(c,d.outShape,a.dtype);return[{dataId:h,shape:d.outShape,dtype:a.dtype},{dataId:m,shape:d.outShape,dtype:"int32"}]}};function aP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),l=F.computeOutAndReduceShapes(r.shape,o)[1],u=k.sizeFromShape(l),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([u]));d.push(p);let c=Zr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(c);let 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bP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a;we(r,"NonMaxSuppressionPadded");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=xP(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var vP={kernelName:Ko,backendName:"cpu",kernelFunc:bP},wP=Za.nonMaxSuppressionV5Impl;function kP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a;we(r,"NonMaxSuppressionWithScore");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=wP(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var IP={kernelName:Zo,backendName:"cpu",kernelFunc:kP};function SP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;we(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 p=0;p<l;++p)d[p]>=0&&d[p]<s&&(u[p*s+d[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",u)}var NP={kernelName:qs,backendName:"cpu",kernelFunc:SP};function Dh(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=$i({inputs:{input:a},backend:n}),s=Dh({inputs:{x:r},backend:n}),i=Wl({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),l=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Mg({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var TP={kernelName:fl,backendName:"cpu",kernelFunc:Dh};function uv(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=$i({inputs:{input:a},backend:n}),s=uv({inputs:{x:r},backend:n}),i=Wl({inputs:{input:a},backend:n}),o=Dh({inputs:{x:i},backend:n}),l=qn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Mg({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var CP={kernelName:Yo,backendName:"cpu",kernelFunc:uv};function dv(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $h({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let 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t.makeTensorInfo([o.length],s,o)}var OP={kernelName:ju,backendName:"cpu",kernelFunc:DP},zP=rt(el,e=>1/e),_P={kernelName:el,backendName:"cpu",kernelFunc:zP};function PP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeBilinear");let l=k.computeStrides(r.shape),[u,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,u,d,m])),y=[s&&u>1?c-1:c,s&&d>1?h-1:h],A=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=0,v=y[0]/A[0],b=y[1]/A[1];for(let w=0;w<p;w++)for(let N=0;N<u;N++){let C;i?C=v*(N+.5)-.5:C=v*N;let E=Math.max(0,Math.floor(C)),_=C-E,$=Math.min(c-1,Math.ceil(C)),S=w*l[0]+E*l[1],z=w*l[0]+$*l[1];for(let O=0;O<d;O++){let W;i?W=b*(O+.5)-.5:W=b*O;let G=Math.max(0,Math.floor(W)),H=W-G,J=Math.min(h-1,Math.ceil(W)),K=S+G*l[2],ne=z+G*l[2],Q=S+J*l[2],se=z+J*l[2];for(let Z=0;Z<m;Z++){let le=f[K+Z],oe=f[ne+Z],xe=f[Q+Z],fe=f[se+Z],Ne=le+(xe-le)*H,Te=oe+(fe-oe)*H,Oe=Ne+(Te-Ne)*_;g[x++]=Oe}}}return n.makeTensorInfo([p,u,d,m],"float32",g)}var LP={kernelName:Js,backendName:"cpu",kernelFunc:PP};function WP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,u,d,p]=r.shape,[,c,h]=s.shape,m=new Float32Array(l*u*d*p),f=[i&&c>1?u-1:u,i&&h>1?d-1:d],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/g[0],A=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let b=0;b<l;b++){let w=b*o[0];for(let N=0;N<c;N++){let C=N*y,E=Math.floor(C),_=Math.min(Math.ceil(C),u-1),$=w+E*o[1],S=w+_*o[1],z=C-E,O=1-z;for(let W=0;W<h;W++){let G=W*A,H=Math.floor(G),J=Math.min(Math.ceil(G),d-1),K=G-H,ne=1-K,Q=$+H*o[2],se=$+J*o[2],Z=S+H*o[2],le=S+J*o[2],oe=O*ne,xe=O*K,fe=z*ne,Ne=z*K;for(let Te=0;Te<p;Te++){let Oe=x[v++];m[Q+Te]+=Oe*oe,m[se+Te]+=Oe*xe,m[Z+Te]+=Oe*fe,m[le+Te]+=Oe*Ne}}}}return n.makeTensorInfo([l,d,u,p],"float32",m)}var BP={kernelName:bc,backendName:"cpu",kernelFunc:WP};function VP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;we(r,"resizeNearestNeighbor");let l=k.computeStrides(r.shape),[u,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(p*u*d*m),y=[s&&u>1?c-1:c,s&&d>1?h-1:h],A=[s&&u>1?u-1:u,s&&d>1?d-1:d],x=y[0]/A[0],v=y[1]/A[1],b=0;for(let w=0;w<p;w++){let N=w*l[0];for(let C=0;C<u;C++){let E=i?x*(C+.5):x*C,_=Math.min(c-1,s?Math.round(E):Math.floor(E));i&&(_=Math.max(0,_));let $=N+_*l[1];for(let S=0;S<d;S++){let z=i?v*(S+.5):v*S,O=Math.min(h-1,s?Math.round(z):Math.floor(z));i&&(O=Math.max(0,O));let W=$+O*l[2];for(let G=0;G<m;G++){let H=f[W+G];g[b++]=H}}}}return n.makeTensorInfo([p,u,d,m],r.dtype,g)}var jP={kernelName:Uu,backendName:"cpu",kernelFunc:VP};function UP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;we([s,r],"resizeNearestNeighborGrad");let o=k.computeStrides(r.shape),l=k.computeStrides(s.shape),[u,d,p,c]=r.shape,[,h,m]=s.shape,f=new 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QP(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;we([a,r,s],"select");let i=a.shape.length,o=n.data.get(a.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,d=Aa(r.dtype,s.dtype),p=k.makeZerosTypedArray(k.sizeFromShape(r.shape),d),c=0,h=i===0||i>1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<h;f++)o[m]===1?p[c++]=l[m]:p[c++]=u[m];return n.makeTensorInfo(r.shape,d,p)}var eL={kernelName:al,backendName:"cpu",kernelFunc:QP},tL=F.SELU_SCALEALPHA,nL=F.SELU_SCALE,aL=rt(rl,e=>e>=0?nL*e:tL*(Math.exp(e)-1)),rL={kernelName:rl,backendName:"cpu",kernelFunc:aL},sL=rt(ol,e=>e<0?-1:e>0?1:0),iL={kernelName:ol,backendName:"cpu",kernelFunc:sL},oL=rt(ai,e=>Math.sin(e)),lL={kernelName:ai,backendName:"cpu",kernelFunc:oL},uL=rt(il,e=>Math.sinh(e)),dL={kernelName:il,backendName:"cpu",kernelFunc:uL},pL=11920928955078125e-23,hv=Math.log(pL)+2,cL=rt(ll,e=>{let t=e>-hv,n=e<hv,a=Math.exp(e),r;return 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TL={kernelName:ul,backendName:"cpu",kernelFunc:NL},CL=rt(si,e=>Math.sqrt(e)),EL={kernelName:si,backendName:"cpu",kernelFunc:CL},RL={kernelName:Gu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;we(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},ML=rt(Pr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),FL={kernelName:Pr,backendName:"cpu",kernelFunc:ML};function $L(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a;we(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=fn.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=gt({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let 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HL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;we(r,"tile");let i=H7(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var GL={kernelName:_r,backendName:"cpu",kernelFunc:HL};function qL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;we(r,"topk");let o=n.data.get(r.dataId).values,[l,u]=G7(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 XL={kernelName:pl,backendName:"cpu",kernelFunc:qL};function KL(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],g=[d,m,f,h],y=k.computeStrides(r.shape),A=y[0],x=y[1],v=y[2],b=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(g));b.fill(l);let w=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values;for(let C=0;C<d;++C){let E=s.shape[0]===1?N:N.subarray(C*8,C*8+8);for(let _=0;_<m;++_)for(let $=0;$<f;++$)for(let S=0;S<h;++S){let 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bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
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}
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#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
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uniform float INFINITY;
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return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
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const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
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return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
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return vec4(0.0, 0.0, 0.0, 0.0);
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c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
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ivec3 outCoordsFromFlatIndex(int index) {
${Pi(["r","c","d"],e)}
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}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
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int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
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${n.output} = result;
}
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${Pi(["r","c","d"],e)}
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int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
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${n.output} = result;
}
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${Wv}
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float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
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${Wv}
void main() {
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${t.output} = encode_float(x);
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`}},EW=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=An(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Pg(e)}
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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];
}
${a.output} = vec4(${i}, 0., 0., 0.);
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`}},RW=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=An(),[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+=`
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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 = ${a.texture2D}(A, uv);
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result[${d}] = values[0];
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result[${d}] = values[1];
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result[${d}] = values[2];
} else {
result[${d}] = values[3];
}
}
}
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${Pg(e)}
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vec4 result = vec4(0.);
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ivec3 localCoords;
vec2 uv;
vec4 values;
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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Rd(this.gl,te().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(te().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(te().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,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,te().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=MW(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(),_h(this.gl,e,this.framebuffer),this.debug&&Md(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(_h(this.gl,this.outputTexture,this.framebuffer),this.debug&&Md(this.gl)):Og(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;_h(a,e,this.framebuffer),this.debug&&Md(a),this.outputTexture=e,be(a,()=>a.viewport(0,0,t,n)),be(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),be(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 MW(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:rw}=F;function FW(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
`),i=e.map(h=>$W(h,t,a)).join(`
`),o=t.texShape,l=An(),u=zW(l),d,p,c=LW(l);return t.isPacked?(d=DW(t.logicalShape,o),p=PW(l)):(d=OW(t.logicalShape,o),p=_W(l)),a&&(c+=jW),[c,u,p,s,d,i,n].join(`
`)}function Ul(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return tB(e);case 1:return aB(e);case 2:return sB(e);case 3:return oB(e);case 4:return uB(e);case 5:return dB(e);case 6:return pB(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function sw(e){switch(e.shapeInfo.logicalShape.length){case 0:return eB(e);case 1:return nB(e);case 2:return rB(e);case 3:return iB(e);default:return lB(e)}}function $W(e,t,n=!1){let a="";n?a+=sw(e):a+=Ul(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=cB(e,t):a+=hB(e,t)),a}function DW(e,t){switch(e.length){case 0:return iw();case 1:return UW(e,t);case 2:return JW(e,t);case 3:return GW(e,t);default:return XW(e,t)}}function OW(e,t){switch(e.length){case 0:return iw();case 1:return HW(e,t);case 2:return QW(e,t);case 3:return qW(e,t);case 4:return KW(e,t);case 5:return ZW(e,t);case 6:return YW(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function zW(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function _W(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function PW(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function LW(e){return`${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);
}
${WW}
${BW}
${VW}
`}var WW=`
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);
}
`,BW=`
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);
}
`,VW=`
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);
}
`,jW=`
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 iw(){return`
int getOutputCoords() {
return 0;
}
`}function UW(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 HW(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 * ${t[1]} + resTexRC.y;
}
`}function GW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${a}) * 2;
return ivec3(b, r, c);
}
`}function qW(e,t){let n=Pi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function XW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${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}(${o});
}
`}function KW(e,t){let n=Pi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function ZW(e,t){let n=Pi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function YW(e,t){let n=Pi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function JW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${a}) * 2;
return ivec2(r, c);
}
`}function QW(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function Li(e){return`offset${e}`}function eB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=An();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function tB(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=Li(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function nB(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=An();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function aB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Hl(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=Li(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 rB(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=An();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 sB(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 p=r[0],c=r[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=Gl(e,o),c=["row","col"];return`
${Ul(p)}
float ${a}(int row, int col) {
return ${a}(${ql(c,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${Hl(e)}
}
`;let l=r[0],u=r[1],d=Li(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) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${d};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${n}, uv);
}
`}function iB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),c=[1,2],h=Gl(e,p),m=["b","row","col"];return`
${sw(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${ql(m,c)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),d=An();return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${u}, ${l}, b, row, col);
return ${d.texture2D}(${n}, uv);
}
`}function oB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=Gl(e,l),f=["row","col","depth"];return`
${Ul(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${ql(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${r}, ${s}, 1)));
${Hl(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],c=e.shapeInfo.flatOffset;if(p===r&&c==null)return`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(p===s&&c==null)return`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let h=Li(n);return`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r} + col * ${s} + depth + ${h};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function lB(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),d=u*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${d} + (row / 2) * ${u} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,d*=t[n-m-1],c=`b${m} * ${d} + `+c;let h=An();return`
vec4 ${r}(${p}) {
int index = ${c};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${h.texture2D}(${a}, uv);
}
`}function uB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=Gl(e,o),f=["row","col","depth","depth2"];return`
${Ul(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${ql(f,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${r}, 1)));
${Hl(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],c=d[1];if(c===i&&u==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(c===r&&u==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let h=Li(n);return`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${r} + depth2;
vec2 uv = uvFromFlat(${p}, ${c}, index + ${h});
return sampleTexture(${n}, uv);
}
`}function dB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=k.squeezeShape(t);if(l.length<t.length){let f=Gl(e,l),g=["row","col","depth","depth2","depth3"];return`
${Ul(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${ql(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${Hl(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;let m=Li(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${c}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function pB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=Gl(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${Ul(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${ql(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${d}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Hl(e)}
}
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===d&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=Li(n);return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${d} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function Hl(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function cB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=rw(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,d,p=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${p[g+u]} = 0;`).join(`
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return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
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return vec4(outputValue.x);
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vec4 ${r}() {
${l} coords = getOutputCoords();
${d}
vec4 outputValue = get${a}(${c});
${h}
}
`}function hB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let u=ut(l),d=rw(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${u} coords = getOutputCoords();
${c}
return get${a}(${m});
}
`}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Gl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ql(e,t){return t.map(n=>e[n]).join(", ")}function fB(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=FW(s,o,r,t.packedInputs),u=e.createProgram(l),d=null,p=e.getUniformLocation(u,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let c={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;c[m]=e.getUniformLocation(u,m,f),c[`offset${m}`]=e.getUniformLocation(u,`offset${m}`,f)}return{program:t,source:l,webGLProgram:u,uniformLocations:c,inShapeInfos:i,outShapeInfo:o,infLoc:d,nanLoc:p}}function ow(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function mB(e,t,n,a,r){ow(t.inShapeInfos,n),ow([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`];if(d!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(d,o.uniformValues[0]);else{let c=o.uniformValues;c instanceof Float32Array||(c=new Float32Array(c)),e.gl.uniform1fv(d,c)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,d,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function gB(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:yB,bincountImpl:lw,bincountReduceImpl:AB,ceilImpl:xB,concatImpl:bB,equalImpl:vB,expImpl:wB,expm1Impl:kB,floorImpl:IB,gatherNdImpl:SB,gatherV2Impl:NB,greaterImpl:TB,greaterEqualImpl:CB,lessImpl:EB,lessEqualImpl:RB,linSpaceImpl:MB,logImpl:FB,maxImpl:$B,maximumImpl:DB,minimumImpl:OB,multiplyImpl:zB,negImpl:_B,notEqualImpl:PB,prodImpl:LB,rangeImpl:WB,rsqrtImpl:BB,simpleAbsImpl:uw,sliceImpl:VB,sparseFillEmptyRowsImpl:jB,sparseReshapeImpl:UB,sparseSegmentReductionImpl:dw,stridedSliceImpl:HB,stringNGramsImpl:GB,stringSplitImpl:qB,stringToHashBucketFastImpl:XB,subImpl:KB,tileImpl:ZB,topKImpl:YB,transposeImpl:Ug,uniqueImpl:JB}=yg;function pw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function xn(e,t){return t===1?[e]:pw(e,t)}function QB(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var eV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
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`;else{let n=xn("rc",t),a=ut(t),r=nV(t,e,n),s=aV(t,e[e.length-1],e[e.length-2],n),i=rV(e,n);this.userCode=`
void main() {
${a} rc = getOutputCoords();
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setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
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}
`}}};function tV(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function nV(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function aV(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
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ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
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result[${a}] =
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${sV(t)}
${Pg(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}};function sV(e){return`
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${Pi(["r","c","d"],e)}
return ivec3(r, c, d);
}
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void main() {
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float y = unaryOperation(x);
setOutput(y);
}
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vec4 result;
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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);
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vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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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;
`,AV=`
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;
`,xV="return 1.0 / (1.0 + exp(-1.0 * x));",Xl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},bV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=xn("rc",t),a=ut(t),r=QB(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},vV=Za.whereImpl,wV=1e-7,kV=1e-4,Hg={};function IV(e){return e in Hg||(Hg[e]={}),Hg[e]}var SV=te().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),NV=600;function TV(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*NV/1024/1024}var Kl=class extends Eu{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,!te().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ja(te().getNumber("WEBGL_VERSION"));this.binaryCache=IV(te().getNumber("WEBGL_VERSION")),this.gpgpu=new Vh(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 iV(this.gpgpu),this.numMBBeforeWarning=TV(),this.texData=new Up(this,fr())}nextDataId(){return Kl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((te().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||te().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:da.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(te().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:da.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 p;o?p=new Xl(i,jh):p=new Yr(i,jh);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}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 p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=F.mergeRealAndImagArrays(p,c)}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 h=this.pendingRead.get(e);return new Promise(m=>h.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 h;o?h=new Xl(a,jh):h=new Yr(a,jh);let m=this.runWebGLProgram(h,[{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(!te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&te().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"&&te().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Ed(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=F.mergeRealAndImagArrays(m,f)}else if(l==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}u!=null&&this.disposeIntermediateTensorInfo(u);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&fr().removeDataId(e,this),this.pendingDeletes--),p}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 Ve(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!gv(n))throw te().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...Ed(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?Ph(t):t,o=s?new CW(i):new TW(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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 te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(te().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=SV){return te().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){F.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return vV(e.shape,t)}packedUnaryOp(e,t,n){let a=new Xl(e.shape,t),r=this.compileAndRun(a,[e],n);return fr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=uw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(te().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,gw,e.dtype);let t=new Yr(e.shape,gw),n=this.compileAndRun(t,[e]);return fr().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 fr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new bV(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new eV(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[zi(e.shape),..._i(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[zi(t),..._i(t)],s=new cw(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=Ph(a),i;n?i=new NW(s):i=new SW(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===Td.DENSE){let f=Ed(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)<=te().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&&!Fd(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=gB(e,l,u),p=this.getAndSaveBinary(d,()=>fB(this.gpgpu,e,l,u)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),mB(this.gpgpu,p,l,u,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=te().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!te().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||(te().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=V(()=>{if(!te().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=te().getBool("DEBUG");te().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(te().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?wV:kV}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=$v(n,o),t.texShape=d),r!=null){let p=Ph(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Vl(d[0],d[1]),c=new RW(p,[m,h],f)):c=new EW(p,[m,h],f);let g=this.makeTensorInfo([m,h],a);f?this.texData.get(g.dataId).usage=da.PIXELS:this.texData.get(g.dataId).usage=da.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,m,r);let y=!0,A=this.runWebGLProgram(c,[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 p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=CV(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)}};Kl.nextDataId=0;function CV(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 yw="3.7.0";function Aw(){te().set("WEBGL_FORCE_F16_TEXTURES",!0)}ad.isBrowser()&&Il("webgl",()=>new Kl,2);var EV={forceHalfFloat:Aw},xw=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Zl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Uh=`
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;
`,Dd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=F.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=`
${ut(r)} coords = getOutputCoords();
`,r===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=xn("coords",r);s+=`
bool nextRowOutOfBounds =
(${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 Xn(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 RV={kernelName:zs,backendName:"webgl",kernelFunc:Xn};function Jr(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=Xn({inputs:{x:a},backend:n}),l=Xn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var MV={kernelName:Yp,backendName:"webgl",kernelFunc:Jr},bw="return (a < 0.) ? b * a : a;",vw=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function FV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(vw,r.shape,i.shape):new Zl(bw,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var $V={kernelName:_s,backendName:"webgl",kernelFunc:FV},ww="return (a < 0.) ? b * a : a;",kw=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function DV(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(kw,a.shape,r.shape):new Zl(ww,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var OV={kernelName:Zs,backendName:"webgl",kernelFunc:DV},Iw="if (isnan(x)) return x;",zV=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,_V=`
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 Ze({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 p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Xl(i.shape,t):d=new Yr(i.shape,e),o.runWebGLProgram(d,[i],l)}}function sn({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},N={dataId:b.dataId,dtype:b.dtype,shape:u.shape},C=new Zl(e,l.shape,u.shape);return d.runWebGLProgram(C,[w,N],Aa(v.dtype,b.dtype))}),A=Jr({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let p=s||Aa(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"?F.fromUint8ToStringArray(m):m,y=l.dtype==="string"?F.fromUint8ToStringArray(f):f,[A,x]=r(l.shape,u.shape,g,y,p),v=d.makeTensorInfo(x,p),b=d.texData.get(v.dataId);return b.values=A,v}let c=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Dd(t,l.shape,u.shape,n):h=new Zl(e,l.shape,u.shape),d.runWebGLProgram(h,[l,u],p)}}function Hh(e,t=!1){if(e==="linear")return t?mV:dV;if(e==="relu")return t?yV:cV;if(e==="elu")return t?gV:pV;if(e==="relu6")return t?AV:hV;if(e==="prelu")return t?kw:ww;if(e==="leakyrelu")return t?vw:bw;if(e==="sigmoid")return t?xV:fV;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Sw=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),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=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, ${p});
vec4 b = getMatrixB(batchB, ${c});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},Nw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Tw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},Cw="return a * b;";function Gg(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=F.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Tw(Nw.REAL,a.shape,r.shape),d=new Tw(Nw.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Jr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,d]=zB(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=u,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Dd(Cw,a.shape,r.shape):i=new Zl(Cw,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var PV={kernelName:Gs,backendName:"webgl",kernelFunc:Gg};function LV(e,t,n){let a=[zi(e.shape),..._i(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[zi(t),..._i(t)],i=new cw(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),u=k.sizeFromShape(l);k.assert(o===u,()=>`The new shape (${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&&!Fd(r.shape,l)&&!(d.texture!==null&&Fd(d.shape,l))?LV(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var WV={kernelName:tl,backendName:"webgl",kernelFunc:Ae},Ew=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);
${l}
}
setOutput(sumValue);
}
`}},BV=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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,p=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,c="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,c="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,c="bvec4");let h="";r%n>0&&(h=`
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) {
${h}
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;
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
${c} values = ${c}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${d===2}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${d===3}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function VV(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=F.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Wi(e,t,n,a){let r=VV(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],d,p;n==="mean"?d=i===0?new Ew({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Ew({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new BV({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var jV=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=ut(this.rank),r=UV(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function UV(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 HV=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 ${this.rank} is not yet supported.`);let a=ut(this.rank),r=pw("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${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] = ${l};
}
}
setOutput(result);
}
`}};function Gh(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HV(e.shape,t):new jV(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function GV(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=F.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Gh(e,l,a),o=F.getInnerMostAxes(o.length,s)),F.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=F.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=F.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,g=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),y=zc(e.dtype),A=Wi(g,y,"sum",a),x=Ae({inputs:{x:A},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),u&&a.disposeIntermediateTensorInfo(d),x}function qh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return GV(r,s,i,n)}var qV={kernelName:ii,backendName:"webgl",kernelFunc:qh};function bn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=r.shape[s[d]];let u;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=Ug(d,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=p}else u=Gh(r,s,i);return u}var XV={kernelName:ci,backendName:"webgl",kernelFunc:bn},Rw=1e3;function Xh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),A=k.sizeFromShape(g),x=y===A||y===1||A===1;k.assert(u>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[y,p,h]:[y,h,p],w=a?[A,m,c]:[A,c,m],N=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ae({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[N,C],_=Math.max(y,A),$=n?N.shape[1]:N.shape[2],S=s!=null,z=i!=null,O=l==="leakyrelu",W=l!=null?Hh(l,!0):null,G=S||z||O||W!=null,H;if((h===1||m===1)&&$>Rw&&G===!1){let K=N,ne=C;n&&(K=bn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(ne=bn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let Q=m!==1,se=m===1,Z=K;Q&&(Z=Ae({inputs:{x:K},backend:r,attrs:{shape:[_,$,1]}}),E.push(Z));let le=m===1?2:1,oe=ne;se&&(oe=Ae({inputs:{x:ne},backend:r,attrs:{shape:[_,1,$]}}),E.push(oe));let xe=Gg({inputs:{a:Z,b:oe},backend:r});H=qh({inputs:{x:xe},backend:r,attrs:{axis:le,keepDims:!0}}),E.push(xe)}else{let K=Aa(e.dtype,t.dtype),ne=new Sw(b,w,[_,h,m],n,a,S,W,z,O),Q=[N,C];if(s!=null&&Q.push(s),z&&Q.push(i),O){let se=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(se),E.push(se)}H=r.runWebGLProgram(ne,Q,K)}let J=Ae({inputs:{x:H},backend:r,attrs:{shape:v}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return J}function KV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:p}=a;return Xh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var ZV={kernelName:hi,backendName:"webgl",kernelFunc:KV},Mw="return abs(x);";function YV(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=uw(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Xl(a.shape,Mw):r=new Yr(a.shape,Mw),n.runWebGLProgram(r,[a],a.dtype)}var JV={kernelName:mo,backendName:"webgl",kernelFunc:YV},QV=Ca+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,ej=Ze({opSnippet:QV}),tj={kernelName:go,backendName:"webgl",kernelFunc:ej},nj=Ca+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,aj=Ze({opSnippet:nj}),rj={kernelName:yo,backendName:"webgl",kernelFunc:aj},Fw="return a + b;",sj=sn({opSnippet:Fw,packedOpSnippet:Fw,supportsComplex:!0,cpuKernelImpl:yB}),ij={kernelName:Or,backendName:"webgl",kernelFunc:sj},oj=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
setOutput(result);
}
`}},lj=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 Kh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Xn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Kh({inputs:a.slice(0,o),backend:n}),u=Kh({inputs:a.slice(o),backend:n});return Kh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>Aa(o,l)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new lj(a[0].shape,s):new oj(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var uj={kernelName:xs,backendName:"webgl",kernelFunc:Kh};function dj(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=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,o)),F.assertAxesAreInnerMostDims("all",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"all",n),y;if(i){let A=F.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var pj={kernelName:Ao,backendName:"webgl",kernelFunc:dj};function cj(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=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,o)),F.assertAxesAreInnerMostDims("any",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"any",n),y;if(i){let A=F.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var hj={kernelName:xo,backendName:"webgl",kernelFunc:cj},fj=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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},mj=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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=ut(o),u=xn("coords",o),d,p;if(s===1){p=o+1;let N=ut(p);d=`
${N} sourceLocR = ${N}(${u.join()}, 0);
++${u[o-1]};
${N} sourceLocG = ${N}(${u.join()}, 0);
++${u[o-2]};
${N} sourceLocA = ${N}(${u.join()}, 0);
--${u[o-1]};
${N} sourceLocB = ${N}(${u.join()}, 0);
--${u[o-2]};`}else p=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 c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=xn("sourceLocR",p-1).concat("inIdx.r"),g=xn("sourceLocG",p-1).concat("inIdx.g"),y=xn("sourceLocB",p-1).concat("inIdx.b"),A=xn("sourceLocA",p-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(${m.join()}) {
return getChannel(getBestIndicesA(${c.join()}),
vec2(${c.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${c.join()}),
vec2(${c.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};
${d}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${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 $w(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=F.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new fj(o,n,a==null),u=[t];a!=null&&u.push(a);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let p=$w(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function Dw(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=F.computeOptimalWindowSize(s),o=new mj(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=Dw(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}return u}function Ow(e,t,n,a){let r=[n];if(F.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=F.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let d=$w(e,u,a);s.push(d);let p=Ae({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return Dw(e,t,a)}function gj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=Ow(n,l,i[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var yj={kernelName:bs,backendName:"webgl",kernelFunc:gj};function Aj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=bn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=F.getInnerMostAxes(i.length,l.shape.length)),F.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=Ow(n,l,i[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var xj={kernelName:Fu,backendName:"webgl",kernelFunc:Aj},bj=Ca+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,vj=Ze({opSnippet:bj}),wj={kernelName:bo,backendName:"webgl",kernelFunc:vj},kj=Ca+"return log(x + sqrt(x * x + 1.0));",Ij=Ze({opSnippet:kj}),Sj={kernelName:vo,backendName:"webgl",kernelFunc:Ij},Nj=Ca+`
return atan(x);
`,Tj=Ze({opSnippet:Nj}),Cj={kernelName:wo,backendName:"webgl",kernelFunc:Tj},Ej=zV+`
return atan(a, b);
`,Rj=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+_V+`
return result;
`,Mj=sn({opSnippet:Ej,packedOpSnippet:Rj}),Fj={kernelName:Io,backendName:"webgl",kernelFunc:Mj},$j=Ca+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Dj=Ze({opSnippet:$j}),Oj={kernelName:ko,backendName:"webgl",kernelFunc:Dj},Od=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,p=e.effectiveFilterWidth,c=e.padInfo.top,h=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) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
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 < ${p};
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 ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let A="max",x=`${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}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
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});
}
`}},qg=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,p=e.dilationWidth,c=e.effectiveFilterDepth,h=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 E=">=";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 < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${p}) {
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 ${E} 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 * ${h} * ${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,N=s%4,C=`
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 < ${c};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
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 * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${C}
}
int xC = xCCorner + ${w};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${C}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${C}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${C}
}
}
setOutput(${b});
}
}
`}};function zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;jl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Xn({inputs:{x:r},backend:n});let p=new Od(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var _j={kernelName:vs,backendName:"webgl",kernelFunc:zj};function Pj(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],p=F.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new qg(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Lj={kernelName:$u,backendName:"webgl",kernelFunc:Pj},Wj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${d});
const float avgMultiplier = float(${p});
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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Bj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${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 < ${p};
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 < ${c};
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 Vj(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,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,l,p,u,d),h=new Bj(c);return n.runWebGLProgram(h,[r],i.dtype)}var jj={kernelName:Kp,backendName:"webgl",kernelFunc:Vj};function Uj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;jl([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,d=F.computePool2DInfo(i.shape,o,l,1,u),p=new Wj(d);return n.runWebGLProgram(p,[r],i.dtype)}var Hj={kernelName:Xp,backendName:"webgl",kernelFunc:Uj};function Gj(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Xh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var qj={kernelName:ws,backendName:"webgl",kernelFunc:Gj},Xj=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(F.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)));
}
`}},Kj=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(F.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);
}
`}},Zj=({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 p=null;o!=null&&(p=o.shape,u.push(o));let c=te().getBool("WEBGL_PACK_NORMALIZATION")?new Kj(a.shape,r.shape,s.shape,d,p,l):new Xj(a.shape,r.shape,s.shape,d,p,l);return t.runWebGLProgram(c,u,u[0].dtype)},Yj={kernelName:Ds,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=Qj(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Xg[o]} = start[${o}] + coords.${Xg[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)}}},Xg=["x","y","z","w","u","v"];function Qj(e){if(e===1)return"sourceLoc";if(e<=6)return Xg.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var eU=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=xn("coords",this.rank),a=xn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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 tU(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=fn.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 zd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=fn.parseSliceParams(r,s,i);if(fn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=VB(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),d=fn.isSliceContinous(r.shape,o,l);if(u||!d){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eU(l):new Jj(l),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),tU(r,o,l,n)}var nU={kernelName:sl,backendName:"webgl",kernelFunc:zd},aU=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=F.getReshaped(r.shape,s,o),u=F.getPermuted(l.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:l}}),f=bn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:d}}),y=zd({inputs:{x:g},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},rU={kernelName:Du,backendName:"webgl",kernelFunc:aU};function sU(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=lw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var iU={kernelName:Zp,backendName:"webgl",kernelFunc:sU},oU="return float(a != b);",zw=sn({opSnippet:oU,cpuKernelImpl:PB,dtype:"bool"}),lU={kernelName:qo,backendName:"webgl",kernelFunc:zw};function _d(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Xn({inputs:{x:r.complexTensorInfos.real},backend:n})}var uU={kernelName:Ac,backendName:"webgl",kernelFunc:_d},dU="return float(int(x));";function pU(e,t){let n=new Yr(e.shape,dU),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Kg(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Xn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Kg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Jr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=_d({inputs:{input:r},backend:n}),o=Kg({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Xn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return pU(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=zw({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 cU={kernelName:ks,backendName:"webgl",kernelFunc:Kg},_w="return ceil(x);",hU=Ze({opSnippet:_w,packedOpSnippet:_w,cpuKernelImpl:xB}),fU={kernelName:Is,backendName:"webgl",kernelFunc:hU},mU=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)}}},gU=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 yU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new gU(r.shape):o=new mU(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var AU={kernelName:zr,backendName:"webgl",kernelFunc:yU},xU=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 Pw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function bU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new xU(a.shape),i=[Pw(a,r.complexTensorInfos.real),Pw(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var vU={kernelName:Ou,backendName:"webgl",kernelFunc:bU},wU=class{constructor(e){this.outputShape=[],this.outputShape=F.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},kU=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=F.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=xn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${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(),p=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${u.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${Zh(i,l,f)}),
vec2(${Zh(u,l,f)}));
}`}let c=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${c}(${Zh(i,l,h)}),
vec2(${Zh(u,l,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${p}
}
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 Zh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Yh(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Xn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var IU={kernelName:pc,backendName:"webgl",kernelFunc:Yh};function Yl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>_d({inputs:{input:f},backend:n})),p=e.map(f=>Yh({inputs:{input:f},backend:n})),c=Yl(d,t,n),h=Yl(p,t,n),m=Jr({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),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 Ae({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),p=d.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=F.computeOutShape(d.map(y=>y.shape),1),h=d[0].shape[0]===1,m=bB(p,c,a,h),f=F.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(f,a,m);return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=Yl(e.slice(0,d),t,n),c=Yl(e.slice(d),t,n),h=Yl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new kU(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=SU(e,t,n),o=new wU(s.map(d=>d.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let u=Ae({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function SU(e,t,n){let a=F.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function Lw(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.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 Xn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return F.assertParamsConsistent(l,s),Yl(o,s,n)}var NU={kernelName:So,backendName:"webgl",kernelFunc:Lw},Ww=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,p=e.filterHeight,c=e.filterWidth,h=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 < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${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, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${b}
${v}
setOutput(result);
}
`}},TU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},CU=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:p}=n,{left:c,top:h}=o,m=r*a,f=An(),g=p==="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} - ${h};
d0 = offsetY + ${d} * (pos / ${m});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${c}.);
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 Bw({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,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],A=(p===1||c===1)&&d>Rw,x=l[2]%2!=0&&!!u.isPacked;if(A||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),w=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Xh({a:b,b:w,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=Ae({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(w),y.push(N)}else{let v=h?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(Fd(u.shape,b.shape),()=>`packed reshape ${u.shape} to ${b.shape} isn't free`);let N=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let C=Xh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=a.texData.get(C.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,E.shape=n.outShape,g=Xn({inputs:{x:C},backend:a}),g.shape=n.outShape,y.push(C)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function Vw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*d,g=c*p,y=[f,g],A=!0,x=!1,v=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let N=new CU(y,b.shape,n),C=a.runWebGLProgram(N,[b],"float32"),E=Ae({inputs:{x:C},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(C),v.push(E);let _=r!=null,$=s!=null,S=o==="leakyrelu",z=o?Hh(o,!0):null,O=new Sw(E.shape,w.shape,[1,g,n.outChannels],A,x,_,z,$,S),W=[E,w];if(r&&W.push(r),$&&W.push(s),S){let K=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));W.push(K),v.push(K)}let G=a.runWebGLProgram(O,W,"float32"),H=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],J=Ae({inputs:{x:G},backend:a,attrs:{shape:H}});v.push(G);for(let K of v)a.disposeIntermediateTensorInfo(K);return J}function EU(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,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Bw({x:r,filter:s,convInfo:c,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=Vw({x:r,filter:s,convInfo:c,backend:n});else{let f=new Ww(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var RU={kernelName:Ss,backendName:"webgl",kernelFunc:EU},MU=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);
}
`}},FU=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);
}
`}},$U=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},DU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 OU(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,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(r.shape,d,i,1,o,u,!1,p),h=new MU(c);return n.runWebGLProgram(h,[r,s],"float32")}var zU={kernelName:Jp,backendName:"webgl",kernelFunc:OU};function _U(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,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(i,s.shape,o,1,l,d,!1,p),h=new FU(c);return n.runWebGLProgram(h,[r,s],"float32")}var PU={kernelName:Ns,backendName:"webgl",kernelFunc:_U};function LU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=F.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new TU(u);return n.runWebGLProgram(d,[r,s],"float32")}var WU={kernelName:zu,backendName:"webgl",kernelFunc:LU};function BU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=F.computeConv3DInfo(r.shape,l,i,1,o),d=new $U(u);return n.runWebGLProgram(d,[r,s],"float32")}var VU={kernelName:Qp,backendName:"webgl",kernelFunc:BU};function jU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=F.computeConv3DInfo(l,s.shape,o,1,i),d=new DU(u);return n.runWebGLProgram(d,[r,s],"float32")}var UU={kernelName:ec,backendName:"webgl",kernelFunc:jU},HU=Iw+`
return cos(x);
`,GU=Ze({opSnippet:HU}),qU={kernelName:Ts,backendName:"webgl",kernelFunc:GU},XU=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,KU=Ze({opSnippet:XU}),ZU={kernelName:No,backendName:"webgl",kernelFunc:KU},YU=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,p]=n;this.outputShape=[u,d,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,v]=p>1?[`${(o-1)/(p-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 > ${h} ) {
setOutput(float(${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(${c} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},JU=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,d=new YU(r.shape,s.shape,o,l,u);return n.runWebGLProgram(d,[r,s,i],"float32")},QU={kernelName:To,backendName:"webgl",kernelFunc:JU},jw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Uw(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() {
${ut(a)} coords = getOutputCoords();
int end = ${Hw(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${Hw(a,"coords")} = idx;
val += getX(${Uw(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 Uw(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 ${e} is not yet supported`)}function Hw(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function eH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,u=F.getAxesPermutation([s],l),d=r;u!=null&&(d=bn({inputs:{x:r},backend:n,attrs:{perm:u}}));let p=F.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Xn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new jw(d.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new jw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=F.getUndoAxesPermutation(u),f=bn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var tH={kernelName:Cs,backendName:"webgl",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),d=lw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),d=AB(l,u,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var aH={kernelName:tc,backendName:"webgl",kernelFunc:nH},rH=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 sH(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],p=l*s,c=u*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new rH(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var iH={kernelName:Co,backendName:"webgl",kernelFunc:sH},Gw=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,p=e.dilationHeight,c=e.dilationWidth,h=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 < ${h}; wR++) {
int xR = xRCorner + wR * ${p};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${m}; wC++) {
int xC = xCCorner + wC * ${c};
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}
${y}
setOutput(result);
}
`}},qw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,g=f,y=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
vec4 xTexelC${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*c};
if (xR >=0 && xR < ${i}) {
`;for(let w=0;w<(g+1)/2;w++){let N=w*2,C=N*h;if(y+=`
xC = xCCorner + ${C};
`,p===1){if(N<f&&(u%2==1?(y+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
xTexelC${C} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${C}.zw = vec2(0.0);
}
xTexelC${C}Ready = 1;
}
`,h===1&&C>0?y+=`
xC${N} = vec4(xTexelC${C-2}.zw, xTexelC${C}.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 >= ${o}) {
previous.zw = vec2(0.0);
}
xC${N} = vec4(previous.zw, xTexelC${C}.xy);
} else {
xC${N} = vec4(0.0, 0.0, xTexelC${C}.xy);
}
`):y+=`
if (xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
xTexelC${C} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${C}.zw = vec2(0.0);
}
xTexelC${C}Ready = 1;
}
xC${N} = xTexelC${C};
`,C+1<f)){let E=u%2==0?k.nearestLargerEven(h):h;h%2==0&&u%2==1||h%2!=0&&u%2!=1?(y+=`
xCOffset = xC + ${u%2} + ${E};
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${C+2}.zw = vec2(0.0);
}
xTexelC${C+2}Ready = 1;
}
`,h>1&&(y+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
xTexelC${C} = getX(batch, xR, xCOffset, d1);
xTexelC${C}Ready = 1;
}
`),y+=`
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.xy);
`):E===1?y+=`
xC${N+1} = xTexelC${C};
`:y+=`
xCOffset = xC + ${E};
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${C+2}.zw = vec2(0.0);
}
xTexelC${C+2}Ready = 1;
}
xC${N+1} = xTexelC${C+2};
`}}else C<f&&(u%2==1?(y+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
xTexelC${C} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${C}.zw = vec2(0.0);
}
xTexelC${C}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${C+2}Ready == 0) {
xTexelC${C+2} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= ${o}) {
xTexelC${C+2}.zw = vec2(0.0);
}
xTexelC${C+2}Ready = 1;
}
xC${N} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
`,C+1<f&&(y+=`
final = vec4(0.0);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xC${N+1} = vec4(xTexelC${C+2}.xy, final.xy);
`)):(y+=`
if(xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
xTexelC${C} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${C}.zw = vec2(0.0);
}
xTexelC${C}Ready = 1;
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${C+2}.zw = vec2(0.);
}
xTexelC${C+2}Ready = 1;
}
xC${N} = vec4(
xTexelC${C}.xy, xTexelC${C+2}.xy);
`,C+1<f&&(y+=`
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
`)));N<f&&(y+=`
wTexel = getW(${b}, ${C}, d1, q);
dotProd += xC${N} * vec4(wTexel.xz, wTexel.xz);
`,C+1<f&&(y+=`
wTexel = getW(${b}, ${C+1}, d1, q);
dotProd += xC${N+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}, ${p});
const ivec2 pads = ivec2(${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 oH(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(F.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=F.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),c;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new qw(p):c=new Gw(p),n.runWebGLProgram(c,[r,s],"float32")}var lH={kernelName:Es,backendName:"webgl",kernelFunc:oH},uH=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},dH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function pH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=a,p=F.computeConv2DInfo(r.shape,d,i,o,l,u,!0),c=new uH(p);return n.runWebGLProgram(c,[r,s],"float32")}var cH={kernelName:nc,backendName:"webgl",kernelFunc:pH};function hH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=a,p=F.computeConv2DInfo(d,s.shape,i,o,l,u,!0),c=new dH(p);return n.runWebGLProgram(c,[r,s],"float32")}var fH={kernelName:ac,backendName:"webgl",kernelFunc:hH},mH=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function gH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new mH(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var yH={kernelName:rc,backendName:"webgl",kernelFunc:gH},AH=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${d}, ${p});
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 xH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=F.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,p=new AH(u);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(d),c}var bH={kernelName:_u,backendName:"webgl",kernelFunc:xH};function vH(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=F.getEinsumComputePath(o,l),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=F.getEinsumPermutation(h,l[g]),x;F.isIdentityPermutation(y)?x=s[g]:(x=bn({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=Ae({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=Gg({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(u[f]>=0&&(c=qh({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var wH={kernelName:oc,backendName:"webgl",kernelFunc:vH},kH="return (x >= 0.0) ? x : (exp(x) - 1.0);",IH=`
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;
`,SH=Ze({opSnippet:kH,packedOpSnippet:IH}),NH={kernelName:Eo,backendName:"webgl",kernelFunc:SH},TH="return (b >= 1.0) ? a : a * (b + 1.0);",CH=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,EH=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dd(CH,a.shape,r.shape):new Zl(TH,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},RH={kernelName:lc,backendName:"webgl",kernelFunc:EH},MH=`
return vec4(equal(a, b));
`,FH="return float(a == b);",$H=sn({opSnippet:FH,packedOpSnippet:MH,dtype:"bool",cpuKernelImpl:vB}),DH={kernelName:Mo,backendName:"webgl",kernelFunc:$H},OH=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${F.ERF_P};
float a1 = ${F.ERF_A1};
float a2 = ${F.ERF_A2};
float a3 = ${F.ERF_A3};
float a4 = ${F.ERF_A4};
float a5 = ${F.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,zH=Ze({opSnippet:OH}),_H={kernelName:Ro,backendName:"webgl",kernelFunc:zH},Xw="return exp(x);",Kw=Ze({opSnippet:Xw,packedOpSnippet:Xw,cpuKernelImpl:wB}),PH={kernelName:Ms,backendName:"webgl",kernelFunc:Kw};function Zg(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var LH={kernelName:Fo,backendName:"webgl",kernelFunc:Zg},Zw="return exp(x) - 1.0;",WH=Ze({opSnippet:Zw,packedOpSnippet:Zw,cpuKernelImpl:kB}),BH={kernelName:$o,backendName:"webgl",kernelFunc:WH},Yw=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 Jw(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=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new Yw("real",l,t),d=new Yw("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Jr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function VH(e){let{inputs:t,backend:n}=e,{input:a}=t;return Jw(a,!1,n)}var jH={kernelName:uc,backendName:"webgl",kernelFunc:VH},UH=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 Yg(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 UH(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var HH={kernelName:Pu,backendName:"webgl",kernelFunc:Yg},GH=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},qH={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new GH(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Qw="return floor(x);",XH=Ze({opSnippet:Qw,packedOpSnippet:Qw,cpuKernelImpl:IB}),KH={kernelName:Fs,backendName:"webgl",kernelFunc:XH},ZH=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,YH=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,JH=sn({opSnippet:ZH,packedOpSnippet:YH,dtype:"int32"}),QH={kernelName:$s,backendName:"webgl",kernelFunc:JH},eG=class{constructor(e){this.variableNames=["A"];let t=An(),[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 = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},tG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=An(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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;
}
`}},nG={kernelName:Rc,backendName:"webgl",kernelFunc:aG},Jl;function aG(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],p=[u,l,s];(o||i)&&(Jl==null&&(Jl=document.createElement("canvas").getContext("2d")),Jl.canvas.width=l,Jl.canvas.height=u,Jl.drawImage(r,0,0,l,u),r=Jl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=da.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=te().getBool("WEBGL_PACK")?new tG(p):new eG(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function rG(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:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=F.convertConv2DDataFormat(d),g=F.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!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=Bw({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=Vw({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,w=h==="leakyrelu",N=h?Hh(h,!1):null,C=new Ww(g,v,N,b,w),E=[r,s];if(i&&E.push(i),o&&E.push(o),w){let _=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));E.push(_),A.push(_)}y=n.runWebGLProgram(C,E,"float32")}let x=Ae({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var sG={kernelName:fi,backendName:"webgl",kernelFunc:rG};function iG(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:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=F.computeConv2DInfo(r.shape,s.shape,l,f,u,p,!0),y=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=c?Hh(c,y):null,x=[r,s],v=i!=null,b=o!=null,w=c==="leakyrelu";if(v&&x.push(i),b&&x.push(o),w){let E=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(E),m.push(E)}let N;y?N=new qw(g,v,A,b,w):N=new Gw(g,v,A,b,w);let C=n.runWebGLProgram(N,x,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var oG={kernelName:mi,backendName:"webgl",kernelFunc:iG},lG=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(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 uG(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,p]=F.prepareAndValidate(a,r),c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=Ae({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=SB(y,A,a.dtype,u,i,d,p,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new lG(i,p,[u,d]),f=n.runWebGLProgram(m,[h,c],h.dtype),g=Ae({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var dG={kernelName:zo,backendName:"webgl",kernelFunc:uG},pG=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=cG(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function cG(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(`${n[r]}`);return a.join()}function hG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],u=F.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=k.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,d/u.batchSize]}});p.push(c),p.push(h);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let A=n.bufferSync(h),x=n.bufferSync(c),v=NB(x,A,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new pG(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);p.push(g);let y=Ae({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var fG={kernelName:Oo,backendName:"webgl",kernelFunc:hG},mG="return float(a > b);",gG=`
return vec4(greaterThan(a, b));
`,yG=sn({opSnippet:mG,packedOpSnippet:gG,cpuKernelImpl:TB,dtype:"bool"}),AG={kernelName:_o,backendName:"webgl",kernelFunc:yG},xG="return float(a >= b);",bG=`
return vec4(greaterThanEqual(a, b));
`,vG=sn({opSnippet:xG,packedOpSnippet:bG,dtype:"bool",cpuKernelImpl:CB}),wG={kernelName:Os,backendName:"webgl",kernelFunc:vG};function kG(e){let{inputs:t,backend:n}=e,{input:a}=t;return Jw(a,!0,n)}var IG={kernelName:dc,backendName:"webgl",kernelFunc:kG},SG="return float(!isnan(x) && !isinf(x));",NG=Ze({opSnippet:SG,dtype:"bool"}),TG={kernelName:Po,backendName:"webgl",kernelFunc:NG},CG="return float(isinf(x));",EG=Ze({opSnippet:CG,dtype:"bool"}),RG={kernelName:Lo,backendName:"webgl",kernelFunc:EG},MG="return float(isnan(x));",FG=Ze({opSnippet:MG,dtype:"bool"}),$G={kernelName:Wo,backendName:"webgl",kernelFunc:FG},DG="return float(a < b);",OG=`
return vec4(lessThan(a, b));
`,zG=sn({opSnippet:DG,packedOpSnippet:OG,cpuKernelImpl:EB,dtype:"bool"}),_G={kernelName:Bo,backendName:"webgl",kernelFunc:zG},PG="return float(a <= b);",LG=`
return vec4(lessThanEqual(a, b));
`,WG=sn({opSnippet:PG,packedOpSnippet:LG,cpuKernelImpl:RB,dtype:"bool"}),BG={kernelName:Vo,backendName:"webgl",kernelFunc:WG};function VG(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=MB(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var jG={kernelName:cc,backendName:"webgl",kernelFunc:VG},UG=`if (x < 0.0) return NAN;
return log(x);`,HG=`
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;
`,GG=Ze({opSnippet:UG,packedOpSnippet:HG,cpuKernelImpl:FB}),qG={kernelName:Ps,backendName:"webgl",kernelFunc:GG},XG="return log(1.0 + x);",KG=Ze({opSnippet:XG}),ZG={kernelName:jo,backendName:"webgl",kernelFunc:KG},YG="return float(a >= 1.0 && b >= 1.0);",JG=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,QG=sn({opSnippet:YG,packedOpSnippet:JG,dtype:"bool"}),eq={kernelName:Uo,backendName:"webgl",kernelFunc:QG},tq="return float(!(x >= 1.0));",nq=Ze({opSnippet:tq}),aq={kernelName:Lu,backendName:"webgl",kernelFunc:nq},rq="return float(a >= 1.0 || b >= 1.0);",sq=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,iq=sn({opSnippet:rq,packedOpSnippet:sq,dtype:"bool"}),oq={kernelName:Wu,backendName:"webgl",kernelFunc:iq},lq=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 * ${o};
setOutput(val);
}
`}},uq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
}
`}},dq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=te().getBool("WEBGL_PACK_NORMALIZATION")?new uq(r.shape,s,i,o,l):new lq(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},pq={kernelName:Bu,backendName:"webgl",kernelFunc:dq},cq=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 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},hq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=a,p=new cq(r.shape,o,l,u,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},fq={kernelName:hc,backendName:"webgl",kernelFunc:hq};function mq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,e.dtype,"max",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function e6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),u=l,d=F.getAxesPermutation(u,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let A=n.texData.get(h.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[d[w]];let v=Ug(A,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=v}else h=Gh(r,d,n);u=F.getInnerMostAxes(u.length,o)}F.assertAxesAreInnerMostDims("max",u,o);let[m,f]=F.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=F.expandShapeToKeepDim(m,l));let y;if(c){let A=n.texData.get(h.dataId).values,x=$B(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=mq(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var gq={kernelName:Ls,backendName:"webgl",kernelFunc:e6},yq=xw+`
return max(a, b);
`,Aq=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Uh+`
return result;
`,xq=sn({opSnippet:yq,packedOpSnippet:Aq,cpuKernelImpl:DB}),bq={kernelName:Ws,backendName:"webgl",kernelFunc:xq};function vq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;jl(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;k.assert(F.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=F.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Xn({inputs:{x:r},backend:n});let p=new Od(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var wq={kernelName:Bs,backendName:"webgl",kernelFunc:vq};function kq(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],p=F.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new qg(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Iq={kernelName:Vu,backendName:"webgl",kernelFunc:kq},Sq=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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Nq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${d}, ${p}, ${c});
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 = ${h} -
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 Tq(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,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,l,p,u,d),h=new qg(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Nq(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Cq={kernelName:mc,backendName:"webgl",kernelFunc:Tq};function Eq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;jl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,l,u,1,d,p),h=!0,m=new Od(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Sq(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Rq={kernelName:fc,backendName:"webgl",kernelFunc:Eq};function Mq(e,t,n,a){let r=new Od(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Od(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Fq={kernelName:gc,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(F.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=F.computePool2DInfo(a.shape,r,s,u,i),[p,c]=Mq(a,o,d,l);return[p,c]}};function $q(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,"float32","mean",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Dq={kernelName:Vs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),u=l,d=F.getAxesPermutation(u,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let N=0;N<v.length;N++)v[N]=a.shape[d[N]];let b=Ug(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=Gh(a,d,i);h.push(m),u=F.getInnerMostAxes(u.length,o)}F.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=F.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=F.expandShapeToKeepDim(f,l));let A=$q(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return A}};function Oq(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=F.getAxesPermutation(u,o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",u,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,u),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Wi(f,f.dtype,"min",n),y;if(i){let A=F.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=Ae({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),d!=null&&n.disposeIntermediateTensorInfo(p),y}var zq={kernelName:js,backendName:"webgl",kernelFunc:Oq},_q=xw+`
return min(a, b);
`,Pq=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Uh+`
return result;
`,Lq=sn({opSnippet:_q,packedOpSnippet:Pq,cpuKernelImpl:OB}),Wq={kernelName:Us,backendName:"webgl",kernelFunc:Lq},Bq=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=ut(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}));
}
`}},Vq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=xn("rc",a),l=xn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${d});
}
`}else{let h=`
${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 - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${d});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${d});
${o[a-1]} += 1;
if(${u}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${d});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},jq=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vq(a.shape,r,s):new Bq(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Uq={kernelName:Hs,backendName:"webgl",kernelFunc:jq},Hq=`if (b == 0.0) return NAN;
return mod(a, b);`,Gq=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Uh+`
return result;
`,qq=sn({opSnippet:Hq,packedOpSnippet:Gq}),Xq={kernelName:Ho,backendName:"webgl",kernelFunc:qq},Kq=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)}}},Zq=`
if (a == b) {
return 1.0;
};
return a / b;`,Yq=`
// 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;
`,t6=sn({opSnippet:Zq,packedOpSnippet:Yq,checkOutOfBounds:!0}),Jq={kernelName:Rs,backendName:"webgl",kernelFunc:t6},n6="return a - b;",a6=sn({opSnippet:n6,packedOpSnippet:n6,supportsComplex:!0,cpuKernelImpl:KB}),Qq={kernelName:ui,backendName:"webgl",kernelFunc:a6};function r6(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=e6({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=F.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),d=a6({inputs:{a:r,b:u},backend:n}),p=Kw({inputs:{x:d},backend:n}),c=qh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:l}}),m=t6({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var eX={kernelName:oi,backendName:"webgl",kernelFunc:r6};function tX(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:r6({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],d=l.shape[1],p=new Kq(u,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var nX={kernelName:yc,backendName:"webgl",kernelFunc:tX},s6="return -x;";function aX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=_B(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Xl(a.shape,s6):r=new Yr(a.shape,s6),n.runWebGLProgram(r,[a],a.dtype)}var rX={kernelName:Go,backendName:"webgl",kernelFunc:aX},sX=Za.nonMaxSuppressionV3Impl;function iX(e){F.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:p}=sX(u,d,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var oX={kernelName:Xo,backendName:"webgl",kernelFunc:iX},lX=Za.nonMaxSuppressionV4Impl;function uX(e){F.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),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=lX(d,p,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var dX={kernelName:Ko,backendName:"webgl",kernelFunc:uX},pX=Za.nonMaxSuppressionV5Impl;function cX(e){F.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),p=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=pX(d,p,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var hX={kernelName:Zo,backendName:"webgl",kernelFunc:cX},fX=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)));
}
`}},mX=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 fX(l,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},gX={kernelName:qs,backendName:"webgl",kernelFunc:mX};function Jh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=_d({inputs:{input:a},backend:n}),s=Jh({inputs:{x:r},backend:n}),i=Yh({inputs:{input:a},backend:n}),o=Jh({inputs:{x:i},backend:n}),l=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var yX={kernelName:fl,backendName:"webgl",kernelFunc:Jh};function i6(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=_d({inputs:{input:a},backend:n}),s=i6({inputs:{x:r},backend:n}),i=Yh({inputs:{input:a},backend:n}),o=Jh({inputs:{x:i},backend:n}),l=Jr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Yg({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var AX={kernelName:Yo,backendName:"webgl",kernelFunc:i6};function xX(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Zg({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 p=Zg({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),u=Lw({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var bX={kernelName:Jo,backendName:"webgl",kernelFunc:xX},vX=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=ut(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)}}},wX=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=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=xn("rc",a),l=xn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${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}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${c}) {
result[${m}] = float(value);
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${d});
}
`;h+=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.);
${h}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},o6=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wX(r.shape,s,i):new vX(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},kX={kernelName:Xs,backendName:"webgl",kernelFunc:o6},IX=`
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);
`,SX=`
// 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));
`+Uh+`
return result;
`,NX=sn({opSnippet:IX,packedOpSnippet:SX}),TX={kernelName:Ks,backendName:"webgl",kernelFunc:NX};function CX(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,p=F.getAxesPermutation(d,o),c=r;p!=null&&(c=bn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=F.getInnerMostAxes(d.length,o),l.push(c)),F.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=LB(c.shape,c.dtype,m,d);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=F.computeOutAndReduceShapes(c.shape,d),g=k.sizeFromShape(f),y=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),A=zc(r.dtype),x=Wi(y,A,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=F.expandShapeToKeepDim(h.shape,u);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var EX={kernelName:Qo,backendName:"webgl",kernelFunc:CX},l6=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=WB(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},RX={kernelName:ju,backendName:"webgl",kernelFunc:l6},MX="return 1.0 / x;",FX=Ze({opSnippet:MX}),$X={kernelName:el,backendName:"webgl",kernelFunc:FX},DX=Ca+`
return (x < 0.0) ? 0.0 : x;
`,OX=`
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;
`,zX=Ze({opSnippet:DX,packedOpSnippet:OX}),_X={kernelName:Ys,backendName:"webgl",kernelFunc:zX},PX=Ca+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,LX=`
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;
`,WX=Ze({opSnippet:PX,packedOpSnippet:LX}),BX={kernelName:Qs,backendName:"webgl",kernelFunc:WX},VX=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],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":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 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);
}
`}},jX=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],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":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 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 UX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jX(r.shape,l,u,s,i):new VX(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],"float32")}var HX={kernelName:Js,backendName:"webgl",kernelFunc:UX},GX=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],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
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 qX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new GX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var XX={kernelName:bc,backendName:"webgl",kernelFunc:qX},KX=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],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="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 = ${c};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},ZX=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],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="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 = ${c};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function YX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZX(r.shape,l,u,s,i):new KX(r.shape,l,u,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var JX={kernelName:Uu,backendName:"webgl",kernelFunc:YX},QX=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],p=1/u,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function eK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new QX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var tK={kernelName:xc,backendName:"webgl",kernelFunc:eK},nK=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=ut(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},aK=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-${n} tensor is not yet supported`);this.outputShape=e;let a=xn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${d(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,A)=>c(A,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function rK(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 Xn({inputs:{x:r},backend:n});let l=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aK(r.shape,o):new nK(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var sK={kernelName:ei,backendName:"webgl",kernelFunc:rK},iK=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 < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},oK={kernelName:ml,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new iK(a.shape,s),[u,d]=F.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(u,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},lK=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,uK=Ze({opSnippet:lK}),dK={kernelName:ti,backendName:"webgl",kernelFunc:uK},pK="return inversesqrt(x);",cK=Ze({opSnippet:pK,cpuKernelImpl:BB}),hK={kernelName:ni,backendName:"webgl",kernelFunc:cK},u6=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let d=`getIndices(${u})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=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 * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${c};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function fK(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:p}=F.calculateShapes(s,r,i),c=[p/u,u];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new u6(l,o,h.shape.length,m.shape.length,d,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),A=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),A}var mK={kernelName:nl,backendName:"webgl",kernelFunc:fK},gK=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=ut(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function yK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new gK(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Aa(r.dtype,s.dtype))}var AK={kernelName:al,backendName:"webgl",kernelFunc:yK},xK=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${F.SELU_SCALEALPHA};
float scale = ${F.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,bK=Ze({opSnippet:xK}),vK={kernelName:rl,backendName:"webgl",kernelFunc:bK},wK="return 1.0 / (1.0 + exp(-1.0 * x));",kK=Ze({opSnippet:wK}),IK={kernelName:ri,backendName:"webgl",kernelFunc:kK},SK=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,NK=Ze({opSnippet:SK}),TK={kernelName:ol,backendName:"webgl",kernelFunc:NK},CK=Iw+`
return sin(x);
`,EK=Ze({opSnippet:CK}),RK={kernelName:ai,backendName:"webgl",kernelFunc:EK},MK=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,FK=Ze({opSnippet:MK}),$K={kernelName:il,backendName:"webgl",kernelFunc:FK},DK=`
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;
`,OK=Ze({opSnippet:DK}),zK={kernelName:ll,backendName:"webgl",kernelFunc:OK},_K=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=o6({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=F.getReshaped(d.shape,s,o,!1),c=F.getPermuted(p.length,s.length,!1),h=F.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:n,attrs:{shape:p}}),f=bn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=Ae({inputs:{x:f},backend:n,attrs:{shape:h}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},PK={kernelName:Hu,backendName:"webgl",kernelFunc:_K};function LK(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],[p,c,h,m,f]=jB(o,a.shape,a.dtype,l,r.dtype,u,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var WK={kernelName:vc,backendName:"webgl",kernelFunc:LK};function BK(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,p]=UB(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(d,a.dtype,u),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var VK={kernelName:wc,backendName:"webgl",kernelFunc:BK};function jK(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]=dw(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(d,a.dtype,u)}var UK={kernelName:kc,backendName:"webgl",kernelFunc:jK};function HK(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
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,d]=dw(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(d,a.dtype,u)}var GK={kernelName:Ic,backendName:"webgl",kernelFunc:HK};function qK(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,strides:d,outputSize:p}=F.calculateShapes(s,r,o),c=!1,h=new u6(u,l,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var XK={kernelName:Sc,backendName:"webgl",kernelFunc:qK};function KK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=F.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=zd({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var ZK={kernelName:ul,backendName:"webgl",kernelFunc:KK},YK="return sqrt(x);",JK=Ze({opSnippet:YK}),QK={kernelName:si,backendName:"webgl",kernelFunc:JK},eZ="return x * x;",tZ=Ze({opSnippet:eZ}),nZ={kernelName:Gu,backendName:"webgl",kernelFunc:tZ},d6="return (a - b) * (a - b);",aZ=sn({opSnippet:d6,packedOpSnippet:d6}),rZ={kernelName:li,backendName:"webgl",kernelFunc:aZ};function sZ({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ca+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Yr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var iZ={kernelName:Pr,backendName:"webgl",kernelFunc:sZ},oZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(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 lZ(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:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:A}=fn.sliceInfo(r.shape,s,i,o,l,u,d,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=zd({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=Ae({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,N=Ve(x.shape,x.dtype,w),C=HB(A,N,f,m);v=n.makeTensorInfo(A,x.dtype,C.values)}else{let w=new oZ(m,f,A);v=n.runWebGLProgram(w,[x],x.dtype)}let b=Ae({inputs:{x:v},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var uZ={kernelName:dl,backendName:"webgl",kernelFunc:lZ};function dZ(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:p}=t,c=n.readSync(d.dataId),h=n.readSync(p.dataId),[m,f]=GB(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var pZ={kernelName:Nc,backendName:"webgl",kernelFunc:dZ};function cZ(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,p]=qB(o,l,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var hZ={kernelName:Tc,backendName:"webgl",kernelFunc:cZ};function fZ(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=XB(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var mZ={kernelName:Cc,backendName:"webgl",kernelFunc:fZ},gZ="return tan(x);",yZ=Ze({opSnippet:gZ}),AZ={kernelName:di,backendName:"webgl",kernelFunc:yZ},xZ=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,bZ=Ze({opSnippet:xZ}),vZ={kernelName:pi,backendName:"webgl",kernelFunc:bZ},wZ=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=ut(this.rank),r=kZ(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function kZ(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]}, ${e[r]})`);return a.join()}function p6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,u=Ve(r.shape,r.dtype,l),d=ZB(u,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new wZ(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var IZ={kernelName:_r,backendName:"webgl",kernelFunc:p6};function SZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,u]=YB(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var NZ={kernelName:pl,backendName:"webgl",kernelFunc:SZ},TZ=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${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 CZ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[d,p,c,h]=r.shape,[m,f]=u!=null?u:[p,c],g=[d,m,f,h],y=new TZ(p,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var EZ={kernelName:cl,backendName:"webgl",kernelFunc:CZ};function RZ(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;jl(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}=JB(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var MZ={kernelName:Ec,backendName:"webgl",kernelFunc:RZ};function FZ(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 p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){c[s]=f;let g=zd({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=Ae({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var $Z={kernelName:hl,backendName:"webgl",kernelFunc:FZ},DZ=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,p=`
sumValue += dot(values, segFilter);
`,c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
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
);
${p}
}
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
);
${p}
} else if (${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
);
${p}
} else if (${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
);
${p}
}
setOutput(${l});
}
`}};function OZ(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=F.getAxesPermutation([u],o),p=r;d!=null&&(p=bn({inputs:{x:r},backend:n,attrs:{perm:d}}),l.push(p),u=F.getInnerMostAxes(1,o)[0]);let c=F.segment_util.computeOutShape(p.shape,u,i),h=k.sizeFromShape([p.shape[u]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=zc(r.dtype),g=(v,b,w,N,C)=>{let E=v.shape[0],_=v.shape[1],$=F.segment_util.segOpComputeOptimalWindowSize(_,C),S={windowSize:$,inSize:_,batchSize:E,numSegments:C},z=new DZ(S,b),O=n.compileAndRun(z,[v,w],N);if(l.push(O),O.shape[1]===C)return O;let W=l6({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),G=p6({inputs:{x:W},backend:n,attrs:{reps:[_/$]}});return l.push(W),l.push(G),g(O,b,G,N,C)},y=g(m,"unsortedSegmentSum",s,f,i),A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}}),x=A;if(d!=null){l.push(A);let v=F.getUndoAxesPermutation(d);x=bn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var zZ={kernelName:qu,backendName:"webgl",kernelFunc:OZ},_Z=[pq,fq,ZV,JV,tj,rj,ij,uj,pj,hj,yj,xj,wj,Sj,Fj,Cj,Oj,Lj,_j,jj,Hj,qj,Yj,rU,iU,cU,fU,AU,vU,MV,NU,zU,PU,RU,VU,UU,WU,qU,ZU,QU,tH,aH,iH,cH,fH,lH,yH,bH,wH,NH,RH,DH,_H,PH,LH,BH,jH,HH,qH,KH,QH,nG,sG,oG,dG,fG,AG,wG,RV,IG,IU,TG,RG,$G,$V,_G,BG,jG,ZG,qG,eq,aq,oq,gq,Iq,wq,Cq,Rq,Fq,bq,Dq,zq,Wq,Uq,Xq,nX,PV,rX,oX,dX,hX,lU,gX,AX,bX,kX,TX,OV,EX,RX,uU,Jq,$X,BX,_X,WV,HX,XX,JX,tK,sK,oK,dK,hK,mK,AK,vK,IK,TK,RK,$K,nU,eX,zK,PK,WK,VK,UK,GK,XK,ZK,QK,nZ,rZ,iZ,uZ,pZ,hZ,mZ,Qq,qV,AZ,vZ,IZ,NZ,EZ,XV,MZ,$Z,zZ,yX];for(let e of _Z)gi(e);var Fn;(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"})(Fn||(Fn={}));var Pd;(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"})(Pd||(Pd={}));var c6;function PZ(e){c6=e.wasm.cwrap(hi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function LZ(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:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let C=n.dataIdMap.get(i.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);m=C.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Pd[d];if(g==null)throw new Error(`${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),N=new Uint8Array(new Int32Array(s.shape).buffer);return c6(c,w,r.shape.length,h,N,s.shape.length,l,u,g,m,f,p||0,b),v}var WZ={kernelName:hi,backendName:"wasm",setupFunc:PZ,kernelFunc:LZ};function vn(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 BZ=vn(mo);function wn(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,p=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:u.dtype,m=F.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,h);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(p,g,u.shape.length,c,y,d.shape.length,Fn[u.dtype],A);if(t&&u.dtype==="float32")return x(),f;let v=F.getBroadcastDims(u.shape,m),b=F.getBroadcastDims(d.shape,m),w=v.every((C,E)=>C===E),N=b.every((C,E)=>C===E);if(w&&N)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 VZ=!0,jZ=wn(Or,VZ),h6;function UZ(e){h6=e.wasm.cwrap(xs,null,["array","number","number","number"])}function HZ(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 h6(s,r.length,Fn[a.dtype],i),a}var GZ={kernelName:xs,backendName:"wasm",setupFunc:UZ,kernelFunc:HZ};function Qh(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 qZ={kernelName:zs,backendName:"wasm",kernelFunc:Qh},f6;function XZ(e){f6=e.wasm.cwrap(ci,null,["number","array","number","number","number","array","number"])}function e0(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=ZZ(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=KZ(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Qh({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),d=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(u.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return f6(d,h,l.shape.length,Fn[l.dtype],p,c,s.length),u}function KZ(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function ZZ(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 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);es(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Qa(x===0,"input layer has >1 nodes"),Qa(v===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof tu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. 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 N=v.inboundNodes[b];if(x.indexOf(N)!==-1)throw new Ma(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(A.indexOf(N)!==-1)return;this.containerNodes.add(tr.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let C=N.inboundLayers.length;for(let E=0;E<C;E++){let _=N.inputTensors[E],$=N.inboundLayers[E],S=N.nodeIndices[E],z=N.tensorIndices[E];o(_,A,x,$,S,z)}for(A.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},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],N=b.inboundNodes[w],C=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(A+1,C),n[N.id]=N}}let p={};for(let y in t){let A=t[y];A in p||(p[A]=[]),p[A].push(n[y])}let c={};for(let y in a){let A=a[y];A in c||(c[A]=[]),c[A].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(r0);this.layers=[];for(let y of h){let A=c[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 tr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(y=>parseInt(y,10)).sort(r0);let m=this.inputs.slice(),f=[];for(let y of h)for(let A of p[y]){let x=A.outboundLayer;if(x!=null){for(let v of A.inputTensors)if(m.indexOf(v)===-1)throw new Ma(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of A.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new Ma(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new A0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new U(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new U(`${s.length} of ${a} weights are not set: ${s}`)}Ny(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=$y(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return V(()=>{e=yt(e);let n=new qi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Yd(this.outputs,n,t)})}computeMask(e,t){return V(()=>{e=yt(e);let n;return t==null?n=Bi(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=g0(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(r0);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let d=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],A=`${f.name}_${g}_${y}`,x=n[A];d.push(x)}let p=u.computeOutputShape($n(d)),c=g0(p),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Qa(o in n),r.push(n[o])}return $n(r)}runInternalGraph(e,t){t==null&&(t=Bi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];n[l.id]=[u,d]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(r0);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,p=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,A;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),y=yt(d.call(x,m)),A=yt(d.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=yt(d.call(f,m)),A=yt(d.computeMask(f,g));if(d.activityRegularizer)throw new _e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],b=y[x],w=A[x];n[v.id]=[b,w]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Qa(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof tr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=tr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=tr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let p=s.inboundNodes[d],c=tr.nodeKey(s,d),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],A=p.tensorIndices[f],x=tr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,A,h])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];a.push([i.name,u,d])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=tr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];r.push([i.name,u,d])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],A;for(let x of g){let v=x[0],b=x[1],w=x[2];if(A=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let N=r[v];if(N.inboundNodes.length<=b){i(f,g);return}let C=N.inboundNodes[b];y.push(C.outputTensors[w])}y.length>0&&f.apply($n(y),A)}function l(f){let g=f.name,y=Oa(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let f of d)l(f);for(;!qne(s);)for(let f of d){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],A=f[2];Qa(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[A])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],A=f[2];Qa(g in r);let x=r[g].inboundNodes[y].outputTensors;c.push(x[A])}return new e({inputs:p,outputs:c,name:u})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function yre(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function s8(e,t){return yre(e,t,"classWeight")}async function i8(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=V(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());he(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])}),Dt(i,"float32")}else return null}function Are(e,t){return B(e,t)}var xre=32;function o8(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=l8("input",e.inputNames,n),i=l8("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 l8(e,t,n){if(n instanceof Be)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 U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function bre(e){if(e.length===3)throw new _e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function vre(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 ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let h=0;h<this.inputs.length;++h)u.push({key:this.inputs[h],value:n[h]});let d=new qi(u),p=Yd(this.outputs,d,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=Are(m,r[h]));let f=Nt(m);t.push(f),h===0?c=m:c=ie(c,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Nt(f(a[g],p[g]))}Kt(m),s.push(m)}return c=Nt(c),this.calculateLosses().forEach(h=>{c=ie(c,h)}),c},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>V(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new qi(s),o=Yd(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Nt(u(r[l],o[l]));l===0?n=d:n=ie(n,d),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],p=Nt(u(r[d],o[d]));t.push(p)}return t})}async fit(e,t,n={}){return Nre(this,e,t,n)}async fitDataset(e,t){return vre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return he(s),$n(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=Bc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=wr(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=>wr(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]=wr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[wr(S0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>wr(S0(e)));{let e={};for(let t in this.metrics)e[t]=wr(S0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Zd(e.optimizer_config),n=Oa(t),a;if(typeof e.loss=="string")a=Vi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Vi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Vi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Vi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Vi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=En.getSaveHandlers(e);if(i.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new U(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await En.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:Mre,generatedBy:`TensorFlow.js tfjs-layers v${Dy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await En.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=En.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;t8(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){t8(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};kr.className="Model";re.registerClass(kr);var f8=class extends kr{};f8.className="Functional";re.registerClass(f8);async function Fre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Zd(n),r=Oa(a,t);if(e.weightsManifest!=null){let s=await En.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),he(s)}return r}async function $re(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Dre(e,void 0,t)}async function Dre(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=Oa(Zd(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=Ore(a.weightData,a.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),he(u),he(d.map(p=>p.tensor))}return o}function Ore(e,t){let n=En.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var ru=class extends kr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:m0("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof ru||e instanceof kr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=B4({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=W4(this.outputs[0])}this.inboundNodes=[],new A0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new kr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ma("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ma("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ma("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ma("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof ru))throw new _e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Oa(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};ru.className="Sequential";re.registerClass(ru);function zre(e){return new kr(e)}function _re(e){return new ru(e)}function Pre(e,t){return t==null&&(t={}),$re(e,t)}function m8(e){return B4(e)}function Lre(e,t){wa.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},g8=class extends On{apply(e,t=1){return pae(e,t)}};g8.className="elu";re.registerClass(g8);var y8=class extends On{apply(e){return oh(e)}};y8.className="selu";re.registerClass(y8);var A8=class extends On{apply(e){return Ka(e)}};A8.className="relu";re.registerClass(A8);var x8=class extends On{apply(e){return V(()=>Fl(6,Ka(e)))}};x8.className="relu6";re.registerClass(x8);var b8=class extends On{apply(e){return e}};b8.className="linear";re.registerClass(b8);var v8=class extends On{apply(e){return Rn(e)}};v8.className="sigmoid";re.registerClass(v8);var w8=class extends On{apply(e){return hae(e)}};w8.className="hardSigmoid";re.registerClass(w8);var k8=class extends On{apply(e){return Ci(e)}};k8.className="softplus";re.registerClass(k8);var I8=class extends On{apply(e){return cae(e)}};I8.className="softsign";re.registerClass(I8);var S8=class extends On{apply(e){return Si(e)}};S8.className="tanh";re.registerClass(S8);var Wy=class extends On{apply(e,t=-1){return xd(e,t)}};Wy.className="softmax";re.registerClass(Wy);var N8=class extends On{apply(e,t=-1){return eh(e,t)}};N8.className="logSoftmax";re.registerClass(N8);var T8=class extends On{apply(e,t=1){return V(()=>Rn(e.mul(t)).mul(e))}};T8.className="swish";re.registerClass(T8);var C8=class extends On{apply(e){return V(()=>B(e,Si(Ci(e))))}};C8.className="mish";re.registerClass(C8);function rs(e){return e.getClassName()}function By(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ss(e){if(e==null){let t={};return t.className="linear",t.config={},By(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},By(t)}else return e instanceof On?e:By(e)}function Vy(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var E8=class extends re.Serializable{},Qd=class extends E8{constructor(e){super();Vy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,Se(B(this.l1,Wt(e))))),this.hasL2&&(t=ie(t,Se(B(this.l2,qd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Qd.className="L1L2";re.registerClass(Qd);function Wre(e){return Vy(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function Bre(e){return Vy(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var R8={l1l2:"L1L2"};function dt(e){return sy(e)}function M8(e,t={}){return Vd(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function vt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in R8?R8[e]:e,config:{}};return M8(t)}else return e instanceof E8?e:M8(e)}var jy=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Le(e);let n=Ka(e);return this.maxValue!=null&&(n=Mn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};jy.className="ReLU";re.registerClass(jy);var Uy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Le(e);return pd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="LeakyReLU";re.registerClass(Uy);var Hy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=bt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=vt(e.alphaRegularizer),this.alphaConstraint=Ht(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Le(e),gd(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Tt(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Ut(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="PReLU";re.registerClass(Hy);var Gy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new _e(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Le(e);return El(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="ELU";re.registerClass(Gy);var qy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Le(e);return n.mul(Hd(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qy.className="ThresholdedReLU";re.registerClass(qy);var Xy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Le(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Softmax";re.registerClass(Xy);function su(e,t,n){if(typeof e=="number")return Bi(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!oae(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function za(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function nr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ns([n-t,0]);else if(a==="same")e=e*t;else throw new U(`Unsupport padding mode: ${a}.`);return e}function Ky(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function F8(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function Vre(e,t,n,a=1,r="valid",s,i=1){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Qe(e,[0,2,1])),r==="causal")throw new _e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Gc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=$a(o,n)),o})}function $8(e,t,n,a=[1,1],r="valid",s,i,o=null){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Ky(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Kr.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Qe(l,[0,3,1,2])),l})}function jre(e,t,n,a=[1,1,1],r="valid",s,i){return V(()=>{if(s==null&&(s=Ra()),Ft(s),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=F8(e,s);if(r==="causal")throw new _e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=L1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=$a(o,n)),s==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var Zy=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zy.verifyArgs(t),this.rank=e,Jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=su(t.kernelSize,e,"kernelSize"),this.strides=su(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ca(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=ss(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=bt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ht(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=su(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Qa("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:rs(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ep=class extends Zy{constructor(e,t){super(e,t);this.kernel=null,ep.verifyArgs(t),this.filters=t.filters,Jt(this.filters,"filters"),this.kernelInitializer=bt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ht(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],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 V(()=>{e=Le(e);let n,a=this.bias==null?null:this.bias.read(),r=w4(this.activation.getClassName());if(r!=null&&this.rank===2)n=$8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Vre(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=$8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=jre(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(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=za(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:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Ut(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},tp=class extends ep{constructor(e){super(2,e);tp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};tp.className="Conv2D";re.registerClass(tp);var np=class extends ep{constructor(e){super(3,e);np.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};np.className="Conv3D";re.registerClass(np);var Yy=class extends tp{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],p=this.strides[0],c=this.strides[1],h=nr(o,p,u,this.padding),m=nr(l,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,1]));let g=qc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Qe(g,[0,3,1,2])),this.bias!=null&&(g=$a(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=st(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]=nr(t[a],o,s,this.padding),t[r]=nr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Yy.className="Conv2DTranspose";re.registerClass(Yy);var Jy=class extends np{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Le(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=nr(l,m,p,this.padding),A=nr(u,f,c,this.padding),x=nr(d,g,h,this.padding),v=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,4,1]));let b=I3(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Qe(b,[0,4,1,2,3])),this.bias!==null&&(b=$a(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=nr(t[a],u,i,this.padding),t[r]=nr(t[r],d,o,this.padding),t[s]=nr(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jy.className="Conv3DTranspose";re.registerClass(Jy);var D8=class extends ep{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Ht(t.depthwiseConstraint),this.pointwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Ht(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Le(e);let n;if(this.rank===1)throw new _e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Qe(e,[0,2,3,1])),n=ag(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=$a(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Qe(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=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};D8.className="SeparableConv";var Qy=class extends D8{constructor(e){super(2,e)}};Qy.className="SeparableConv2D";re.registerClass(Qy);var T0=class extends ep{constructor(e){super(1,e);T0.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"&&!oy(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};T0.className="Conv1D";re.registerClass(T0);var eA=class extends Xe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=s0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return s0(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=s0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return s0(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}};eA.className="Cropping2D";re.registerClass(eA);var tA=class extends Xe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,rae(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Le(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Qe(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 Qe(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}};tA.className="UpSampling2D";re.registerClass(tA);function Ure(e,t,n=[1,1],a="valid",r,s){return V(()=>{r==null&&(r=Ra()),Ft(r);let i=Ky(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Cl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}var nA=class extends Zy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=bt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ht(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 V(()=>{e=Le(e);let n=Ure(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=$a(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=za(t,this.kernelSize[0],this.padding,this.strides[0]),s=za(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=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};nA.className="DepthwiseConv2D";re.registerClass(nA);function O8(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("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 z8(e,t,n,a=!1,r,s,i=!1,o=!1){return V(()=>{let l=t.shape.length;if(l<3)throw new U(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Fa(2,l));if(t=Qe(t,u),s!=null)throw new _e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=mn(r,-1)),r=Qe(r,u)),a&&(t=Hn(t,0),r!=null&&(r=Hn(r,0)));let d=[],p,c=n,h=t.shape[0],m=Gn(t),f;r!=null&&(f=Gn(r));for(let y=0;y<h;++y){let A=m[y],x=V(()=>e(A,c));if(r==null)p=x[0],c=x[1];else{let v=V(()=>{let b=f[y],w=Un(b).sub(b),N=x[0].mul(b).add(c[0].mul(w)),C=c.map((E,_)=>x[1][_].mul(b).add(E.mul(w)));return{output:N,newStates:C}});p=v.output,c=v.newStates}o&&d.push(p)}let g;return o&&(g=gn(d,1)),[p,g,c]})}var ar=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new R0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Fa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Iy(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 V(()=>{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 _e("Constants support is not implemented in RNN yet.");Iy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new zt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new _e("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 U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)he(this.states_),this.keptStates!=null&&(he(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):he(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 U(`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=>Kt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=O8(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 zt({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 Da){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 V(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Le(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 U(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=z8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?u:l;return this.returnState?[p].concat(d):p})}getInitialState(e){return V(()=>{let t=$t(e.shape);return t=Se(t,[1,2]),t=Gd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?my(t,[1,n]):t):this.cell.stateSize>1?[my(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()===ar.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Oa(a,n);return new e(Object.assign(t,{cell:r}))}};ar.className="RNN";re.registerClass(ar);var ap=class extends Xe{},C0=class extends ap{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,Jt(this.units,"units"),this.activation=ss(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=er(B(e,s),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=$a(r,this.bias.read())),i!=null&&(n=B(n,i));let o=ie(r,er(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:rs(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};C0.className="SimpleRNNCell";re.registerClass(C0);var aA=class extends ar{constructor(e){e.cell=new C0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(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)}};aA.className="SimpleRNN";re.registerClass(aA);var E0=class extends ap{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Jt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let u=er(e,this.kernel.read());this.useBias&&(u=$a(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let d=this.recurrentKernel.read(),[p,c]=Zt(d,[2*this.units,this.units],d.rank-1),h=er(a,p),[m,f,g]=Zt(u,3,u.rank-1),[y,A]=Zt(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(m,y)),o=this.recurrentActivation.apply(ie(f,A));let x=er(B(o,a),c);l=this.activation.apply(ie(g,x));let v=ie(B(i,a),B(ie(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};E0.className="GRUCell";re.registerClass(E0);var rA=class extends ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new E0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(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 t.implmentation===0&&(t.implementation=1),new e(t)}};rA.className="GRU";re.registerClass(rA);var rp=class extends ap{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Jt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=eu([1,ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eu([1,ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends va{apply(i,o){let l=r.apply([s]),u=new o0().apply([s]),d=r.apply([s*2]);return M4(M4(l,u),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),p=ie(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=$a(p,this.bias.read()));let[c,h,m,f]=Zt(p,4,p.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=ie(B(l,r),B(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=B(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};rp.className="LSTMCell";re.registerClass(rp);var sA=class extends ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new rp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(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 t.implmentation===0&&(t.implementation=1),new e(t)}};sA.className="LSTM";re.registerClass(sA);var R0=class extends ap{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Iy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Oa(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Sy(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Ny(t)}};R0.className="StackedRNNCells";re.registerClass(R0);function is(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>$4(t(),n),i=()=>Xd(s,t,a);return!r||r<=1?Kt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Kt(o.clone()))}var Hre=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},_8=class extends ar{constructor(e){if(e.unroll)throw new _e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new _e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new zt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(he(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(he(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)he(this.states_),this.keptStates!=null&&(he(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):he(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 U(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Kt(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=za(l,a[0],r,s[0],i[0]),p=za(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};_8.className="ConvRNN2D";var M0=class extends rp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Jt(this.filters,"filters"),this.kernelSize=su(n,2,"kernelSize"),this.kernelSize.forEach(o=>Jt(o,"kernelSize")),this.strides=su(a||1,2,"strides"),this.strides.forEach(o=>Jt(o,"strides")),this.padding=r||"valid",ca(this.padding),this.dataFormat=s||"channelsLast",Ft(this.dataFormat),this.dilationRate=su(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Jt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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 va{apply(d,p){let c=l.apply([u]),h=jn([u]),m=l.apply([u*2]);return fy([c,h,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 V(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>Un(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(K,ne,Q)=>!ne||!ne[Q]?K:B(ne[Q],K),u=l(a,o,0),d=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>Un(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,v,b,w]=Zt(this.kernel.read(),i,A),[N,C,E,_]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,v,C,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,w,_,this.padding);let[$,S,z,O]=Zt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let W=this.recurrentActivation.apply(ie(u,m)),G=this.recurrentActivation.apply(ie(d,f)),H=ie(B(G,s),B(W,this.activation.apply(ie(p,g)))),J=B(this.recurrentActivation.apply(ie(c,y)),this.activation.apply(H));return[J,J,H]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Hre(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=mr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$a(r,n,this.dataFormat):r}recurrentConv(e,t){return mr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};M0.className="ConvLSTM2DCell";re.registerClass(M0);var iA=class extends _8{constructor(e){let t=new M0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};iA.className="ConvLSTM2D";re.registerClass(iA);var F0=class extends Xe{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 V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Xd(()=>$4(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()}};F0.className="Dropout";re.registerClass(F0);var oA=class extends F0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};oA.className="SpatialDropout1D";re.registerClass(oA);var lA=class extends Xe{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,Jt(this.units,"units"),this.activation=ss(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ht(e.kernelConstraint),this.biasConstraint=Ht(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),a=w4(this.activation.getClassName()),r;return a!=null?r=er(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=$a(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};lA.className="Dense";re.registerClass(lA);var uA=class extends Xe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ts(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return dae(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};uA.className="Flatten";re.registerClass(uA);var dA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ss(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:rs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};dA.className="Activation";re.registerClass(dA);var pA=class extends Xe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Le(e),lae(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};pA.className="RepeatVector";re.registerClass(pA);var cA=class extends Xe{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 U("Can only specifiy one unknown dimension.");else r*=l}let i=ts(e);if(s!==null){if(r===0||i%r!=0)throw new U(n);a[s]=i/r}else if(i!==r)throw new U(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 V(()=>{this.invokeCallHook(e,t);let n=Le(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}};cA.className="Reshape";re.registerClass(cA);var hA=class extends Xe{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=Fa(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 zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Qe(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};hA.className="Permute";re.registerClass(hA);var fA=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Le(e),a=-1;return id(Ri(n,this.maskValue),a)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e),a=-1,r=!0,s=id(Ri(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};fA.className="Masking";re.registerClass(fA);var mA=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Jt(this.outputDim,"outputDim"),this.embeddingsInitializer=bt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Ht(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Le(e),Ri(e,Ge(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return n.dtype!=="int32"&&(n=Hd(n,"int32")),F4(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};mA.className="Embedding";re.registerClass(mA);var Ki=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new _e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=es(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&es(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ns(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Gd(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],p=u.slice(1).concat([d]),c=o.reshape([d].concat(ts(u.slice(1))));c=Qe(c,[1,0]),c=c.reshape(p),n.push(c),r=!0}else if(l>1){let u=Fa(1,l).concat([0]);n.push(Qe(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=Qe(s.reshape([-1,u]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Fa(0,i-1));s=Qe(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=es(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:mn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=xa(n,t[a]);return n})}},gA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};gA.className="Add";re.registerClass(gA);var yA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};yA.className="Multiply";re.registerClass(yA);var AA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return B(1/e.length,t)})}};AA.className="Average";re.registerClass(AA);var xA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xa(t,e[n]);return t})}};xA.className="Maximum";re.registerClass(xA);var bA=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Fl(t,e[n]);return t})}};bA.className="Minimum";re.registerClass(bA);var vA=class extends Ki{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>fy(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Un(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(mn(t[s],-1)):a.push(t[s]);let r=lt(a,this.axis);return Uc(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};vA.className="Concatenate";re.registerClass(vA);function sp(e,t){for(;e<0;)e+=t;return e}function Gre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new _e("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new _e("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return V(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,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 wA=class extends Ki{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 _e("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 U(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>sp(r,e[s].shape.length)):a=[sp(this.axes,t.shape.length),sp(this.axes,n.shape.length)],this.normalize&&(t=x0(t,a[0]),n=x0(n,a[1])),Gre(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[sp(this.axes,e.length),sp(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new _e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};wA.className="Dot";re.registerClass(wA);var kA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return Xd(()=>i0(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};kA.className="GaussianNoise";re.registerClass(kA);var IA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?Xd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(i0(n.shape,1,a))},()=>n,t.training||!1):n})}};IA.className="GaussianDropout";re.registerClass(IA);var SA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Xd(()=>{let a=Le(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=qr($l(n),this.rate);o=Hd(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Le(e),t.training||!1)}return e})}};SA.className="AlphaDropout";re.registerClass(SA);function ip(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=y3(e,t,n,a,r,s);else if(e.rank===3)i=A3(e,t,n,a,r,s);else if(e.rank===4)i=x3(e,t,n,a,r,s);else throw new _e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function qre(e,t,n,a,r=.001){return V(()=>{let s=nh(e,a),i=s.mean,o=s.variance;return[ip(e,i,o,n,t,r),i,o]})}function Xre(e,t,n,a,r=.001){return V(()=>{let s=nh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Fa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let u=i.reshape(l),d=o.reshape(l),p=t==null?null:t.reshape(l),c=n==null?null:n.reshape(l);return[ip(e,u,d,c,p,r),i,o]})}function Kre(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Fa(0,e.rank-1))?qre(e,t,n,a,r):Xre(e,t,n,a,r)}var NA=class extends Xe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.movingMeanInitializer=bt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=bt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ht(e.betaConstraint),this.gammaConstraint=Ht(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,a=Le(e),r=a.shape,s=r.length,i=Fa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Bi(1,s);l[o]=r[o];let u=i.slice();u.sort();let d=!k.arraysEqual(u,Fa(0,s).slice(0,s-1)),p=()=>{if(d){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),A=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return ip(a,g,y,A,x,this.epsilon)}else return ip(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=Kre(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,A)=>{V(()=>{let x=1-A,v=g.read(),b=v.sub(y).mul(x);g.write(v.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),movingMeanInitializer:Tt(this.movingMeanInitializer),movingVarianceInitializer:Tt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};NA.className="BatchNormalization";re.registerClass(NA);var TA=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Le(e),a=n.shape,r=a.length;return V(()=>{let s=!0,{mean:i,variance:o}=nh(n,this.axis,s),l=Bi(1,r);for(let m of this.axis)l[m]=a[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,d=u(this.gamma.read()),p=u(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=i.tile(c),o=o.tile(c),d=d.tile(h),p=p.tile(h),ip(n,i,o,p,d,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};TA.className="LayerNormalization";re.registerClass(TA);function Zre(e,t,n){return V(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ra()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],gr(e,a)})}var CA=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ra():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>Zre(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ZeroPadding2D";re.registerClass(CA);function $0(e,t,n,a,r,s){return V(()=>{Ft(r),N4(s),ca(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ra()),s==null&&(s="max"),e=Ky(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=hd(e,t,n,o):i=ld(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}function P8(e,t,n,a,r,s){return V(()=>{Ft(r),N4(s),ca(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ra()),s==null&&(s="max"),e=F8(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Y1(e,t,n,o):i=O1(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var L8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Jt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ca(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=st(e);let t=za(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=Gd(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Vt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},EA=class extends L8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"max")}};EA.className="MaxPooling1D";re.registerClass(EA);var RA=class extends L8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"avg")}};RA.className="AveragePooling1D";re.registerClass(RA);var W8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ca(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=za(t,this.poolSize[0],this.padding,this.strides[0]),n=za(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},MA=class extends W8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"max")}};MA.className="MaxPooling2D";re.registerClass(MA);var FA=class extends W8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),$0(e,t,n,a,r,"avg")}};FA.className="AveragePooling2D";re.registerClass(FA);var B8=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ca(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=za(t,this.poolSize[0],this.padding,this.strides[0]),n=za(n,this.poolSize[1],this.padding,this.strides[1]),a=za(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},$A=class extends B8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),P8(e,t,n,a,r,"max")}};$A.className="MaxPooling3D";re.registerClass($A);var DA=class extends B8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ca(a),P8(e,t,n,a,r,"avg")}};DA.className="AveragePooling3D";re.registerClass(DA);var V8=class extends Xe{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new _e}},OA=class extends V8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Le(e);return Nt(n,1)})}};OA.className="GlobalAveragePooling1D";re.registerClass(OA);var zA=class extends V8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Le(e);return Vn(n,1)})}};zA.className="GlobalMaxPooling1D";re.registerClass(zA);var j8=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new _e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_A=class extends j8{call(e,t){return V(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};_A.className="GlobalAveragePooling2D";re.registerClass(_A);var PA=class extends j8{call(e,t){return V(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Vn(n,[1,2]):Vn(n,[2,3])})}};PA.className="GlobalMaxPooling2D";re.registerClass(PA);var U8=class extends Xe{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=Oa(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},LA=class extends U8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(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 V(()=>(e=Le(e),z8((n,a)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};LA.className="TimeDistributed";re.registerClass(LA);function Yre(e){ji(aae,"BidirectionalMergeMode",e)}var Jre="concat",WA=class extends U8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Oa(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Oa(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Jre:e.mergeMode,Yre(this.mergeMode),e.weights)throw new _e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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()):$n(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=O8(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 U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(d=>new zt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new _e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Da;for(let l of s)if(l instanceof Da!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Hn(r,1));let i;return this.mergeMode==="concat"?i=fy([a,r]):this.mergeMode==="sum"?i=ie(a,r):this.mergeMode==="ave"?i=B(.5,ie(a,r)):this.mergeMode==="mul"?i=B(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){Ui(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ui(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 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TypeError(`Node type ${e.op} is not implemented`)}};function ka(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){k.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let a=0;a<e.length;a++){let r=e[a],s=t[a];k.assert(r<0||s<0||r===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Nk(e){return!(typeof e=="number"||e.some(t=>t<0))}function op(e,t,n){let a=n2(e,n),r=!Nk(a);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${a}`);if(r&&t.forEach(s=>{a=n2(s.shape,a)}),!Nk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function n2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let a=0;a<e.length;++a){let r=e[a],s=t[a];if(r>=0&&s>=0&&r!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[a]=r>=0?r:s}return n}var toe=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Kt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),d=new toe(u,r,a,s,l,i,o);return n.addTensorArray(d),[d.idTensor,ke(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let 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a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=aoe(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=noe(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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g=I("leakyreluAlpha",e,t,n);return{stride:d,pad:p,dataFormat:c,dilations:h,biasArg:m,preluArg:f,activationFunc:r,leakyreluAlpha:g}}var ooe=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Gc(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=z0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[mr(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=Tk(e,t,n);return[Kr.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=Tk(e,t,n);return[Kr.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=_3(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,l]}case"AvgPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[O1(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"MaxPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Y1(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"Dilation2D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dilations",e,t,n),i=a[1],o=a[2],l=s[1],u=s[2];return[B1(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},loe=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[Rl(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[R3(a,r,s)]}case"Multinomial":{let 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u=k.arraysEqual(l.shape,s);if(!u&&!k.arraysEqual(Vt(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:q(l,s)});return[gn(o,a)]});case"Unpack":{let a=I("axis",e,t,n),r=I("tensor",e,t,n);return Gn(r,a)}case"Tile":{let a=I("reps",e,t,n);return[Gr(I("x",e,t,n),a)]}case"Split":case"SplitV":{let a=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return Zt(s,r,a)}case"ScatterNd":{let a=I("indices",e,t,n),r=I("values",e,t,n),s=I("shape",e,t,n);return[H3(a,r,s)]}case"GatherNd":{let a=I("x",e,t,n),r=I("indices",e,t,n);return[G3(a,r)]}case"SparseToDense":{let a=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[pg(a,s,r,s.dtype===i.dtype?i:ge(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},boe=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:r,emptyRowIndicator:s,reverseIndexMap:i}=wd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[a,r,s,i]}case"SparseReshape":{let{outputIndices:a,outputShape:r}=wd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}case"SparseSegmentMean":return[wd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[wd.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},voe=(e,t,n)=>{switch(e.op){case"FFT":return[bd(I("x",e,t,n))];case"IFFT":return[Ol(I("x",e,t,n))];case"RFFT":return[vd(I("x",e,t,n))];case"IRFFT":return[ch(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},woe=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:r}=vh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[a,r]}case"StringSplit":{let{indices:a,values:r,shape:s}=vh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[a,r,s]}case"StringToHashBucketFast":return[vh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},koe=(e,t,n)=>{switch(e.op){case"Cast":return[ge(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[mn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[Vt(I("x",e,t,n),a)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[J1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[gr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let a=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[md(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[ud(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[W1(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Nl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ck(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return V(()=>Qie(s,i,o));case"basic_math":return V(()=>eoe(s,i,o));case"control":return ioe(s,i,o);case"convolution":return V(()=>ooe(s,i,o));case"creation":return V(()=>loe(s,i,o));case"dynamic":return uoe(s,i,o);case"evaluation":return V(()=>doe(s,i,o));case"image":return V(()=>foe(s,i,o));case"graph":return V(()=>poe(s,i,o));case"logical":return V(()=>moe(s,i,o));case"matrices":return V(()=>goe(s,i,o));case"normalization":return V(()=>yoe(s,i,o));case"reduction":return V(()=>Aoe(s,i,o));case"slice_join":return V(()=>xoe(s,i,o));case"sparse":return V(()=>boe(s,i,o));case"spectral":return V(()=>voe(s,i,o));case"string":return V(()=>woe(s,i,o));case"transformation":return V(()=>koe(s,i,o));case"hash_table":return hoe(s,i,o,a);case"custom":let l=nk(s.op);if(l&&l.customExecutor)return l.customExecutor(new Jie(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var Ek=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Rk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Kn(c)[0]),d=[];a!=null&&(d=a.map(c=>Kn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((Mk(c)||Coe(c)||Eoe(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ioe(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>Kn(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(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return u}var Soe=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Noe=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Toe=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Mk(e){return Soe.indexOf(e.op)>=0}function Coe(e){return Noe.indexOf(e.op)>=0}function Eoe(e){return Toe.indexOf(e.op)>=0}var r2=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 r2(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 this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Rk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return Ioe(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Kn(d)[0]]),r=t.map(d=>Kn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return V(()=>{let d=new Ek(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Kn(m),y=[];y[g]=e[m],p[f]=y});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=Ck(f,p,d,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=g,this.checkTensorForDisposal(f.name,f,p,d,c,r,h)}}return this.parent==null&&d.dispose(c),t.map(m=>kn(m,p,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Mie(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let d=i[u.id];d===1?(u.dispose(),delete i[u.id]):d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new Ek(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>kn(p,i,s)),l=o.map(p=>p.id),u=Object.keys(e).map(p=>e[p].id),d=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!d.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[Kn(A)[0]]),i=n.map(A=>Kn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:p}=Rk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,v]=Kn(A),b=[];b[v]=e[A],h[x]=b});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let A=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(A)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!Mk(A)&&!kn(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${A}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&I("isConstant",d.node,a,n)&&([p]=Ir(d.node.name,n)),a[d.node.name]==null){let c=Ck(d.node,a,n,this._resourceManager);p||([p]=Ir(d.node.name,n));let h=n.currentContext;k.isPromise(c)?u.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,l))}else this.processChildNodes(d.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ir(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Kn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Kn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Kn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Roe=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Moe="?tfjs-format=file",Foe="model.json",Fk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Roe}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=En.browserHTTPRequest(e,this.loadOptions);else{let t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new r2(wk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=wk.Instance.transformGraph(e.modelInitializer);this.initializer=new r2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=En.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Be)&&!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 ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},Gk=class extends Qt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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Kk{constructor(e,t){super();this.upstream=e,this.impl=new ole(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ole=class extends o2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},lle=class extends Qt{decodeUTF8(){return new ule(this)}},ule=class extends Kk{constructor(e){super();this.upstream=e,this.impl=new dle(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dle=class extends o2{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new 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kle=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Ile=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Sle=[33,133,362,263,1,78,308],fue=kle.map(e=>cp[e]),mue=Ile.map(e=>cp[e]),gue=Sle.map(e=>cp[e]);var c2=rr.leftEyeLower0,h2=rr.rightEyeLower0,du={leftBounds:[c2[0],c2[c2.length-1]],rightBounds:[h2[0],h2[h2.length-1]]},U0={count:468,mouth:13,symmetryLine:[13,rr.midwayBetweenEyes[0]]},p9={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},pu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function H0(e,t,n,a){for(let r=0;r<p2.length;r++){let{key:s,indices:i}=p2[r],o=rr[`${n}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var f2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=pp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?j0(a,[0,0]):V0,l=a!==0?i.map(p=>[...i9(p,o),p[2]]):i,u=a!==0?s9(r):V0,d=[...lu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+ls(d,u[0])),Math.round(p[1]+ls(d,u[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[du.leftBounds[0]][2],a=t[du.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=B0(W0(u2([t[a],t[r]]),this.irisEnlarge)),o=pp(i),l=De.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&sa.flags.IS_BROWSER&&(l=De.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<pu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],l/this.irisSize*a[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(pu.index)}}getAdjustedIrisCoords(t,n,a){let r=t[rr[`${a}EyeUpper0`][pu.upperCenter]][2],s=t[rr[`${a}EyeLower0`][pu.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=t9({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),l=W0(o),u=B0(l),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=V(()=>this.storedBoxes.map((i,o)=>{let l,u=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&sa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=U0.count?U0.symmetryLine:p9.symmetryLine;u=d2(i.landmarks[x],i.landmarks[v]);let b=lu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t,u,0,w);d=j0(-u,b),n.face.mesh.enabled?l=uu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):l=uu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=V0;let x=t.clone();n.face.mesh.enabled?l=uu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):l=uu({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[,p,c]=this.meshDetector.execute(l),h=p.dataSync()[0];if(h<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=h,null;let f=q(c,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:v,crop:b}=this.getEyeBox(f,l,du.leftBounds[0],du.leftBounds[1],!0),{box:w,boxSize:N,crop:C}=this.getEyeBox(f,l,du.rightBounds[0],du.rightBounds[1]),_=this.irisModel.predict(lt([b,C])).dataSync(),$=_.slice(0,pu.numCoordinates*3),{rawCoords:S,iris:z}=this.getEyeCoords($,x,v,!0),O=_.slice(pu.numCoordinates*3),{rawCoords:W,iris:G}=this.getEyeCoords(O,w,N),H=this.getLeftToRightEyeDepthDifference(f);Math.abs(H)<30?(H0(f,S,"left",null),H0(f,W,"right",null)):H<1?H0(f,S,"left",["EyeUpper0","EyeLower0"]):H0(f,W,"right",["EyeUpper0","EyeLower0"]);let J=this.getAdjustedIrisCoords(f,z,"left"),K=this.getAdjustedIrisCoords(f,G,"right");f=f.concat(J).concat(K)}let g=this.transformRawCoords(f,i,u,d),y=i.confidence;if(i=W0(u2(g),1.5),i.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&sa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=U0.count?U0.symmetryLine:p9.symmetryLine;u=d2(i.landmarks[x],i.landmarks[v]);let b=lu({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=De.rotateWithOffset(t.toFloat(),u,0,w);d=j0(-u,b),l=uu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255)}let A={mesh:g,box:i,faceConfidence:h,boxConfidence:i.confidence,image:l};return this.storedBoxes[o]={...B0(i),confidence:i.confidence,faceConfidence:h},A}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Rt=[null,null,null],m2;async function c9(e,t){let n=await m2.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]/m2.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(rr))o[d]=rr[d].map(p=>s.mesh[p]);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 g2(e){return!Rt[0]&&e.face.enabled||!Rt[1]&&e.face.mesh.enabled||!Rt[2]&&e.face.iris.enabled?(Rt=await Promise.all([!Rt[0]&&e.face.enabled?d9(e):null,!Rt[1]&&e.face.mesh.enabled?ct(ft(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Rt[2]&&e.face.iris.enabled?ct(ft(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Rt[1]||!Rt[1].modelUrl?de("load model failed:",e.face.mesh.modelPath):e.debug&&de("load model:",Rt[1].modelUrl)),e.face.iris.enabled&&(!Rt[2]||!Rt[2].modelUrl?de("load model failed:",e.face.iris.modelPath):e.debug&&de("load model:",Rt[2].modelUrl))):e.debug&&(Rt[0]&&de("cached model:",Rt[0].model.modelUrl),Rt[1]&&de("cached model:",Rt[1].modelUrl),Rt[2]&&de("cached model:",Rt[2].modelUrl)),m2=new f2(Rt[0],Rt[1],Rt[2]),Rt}var h9=Zi,f9=cp;var Nle=["angry","disgust","fear","happy","sad","surprise","neutral"],Pa,G0=[],m9=0,y2=Number.MAX_SAFE_INTEGER,A2=[.2989,.587,.114];async function x2(e){return Pa?e.debug&&de("cached model:",Pa.modelUrl):(Pa=await ct(ft(e.modelBasePath,e.face.emotion.modelPath)),!Pa||!Pa.modelUrl?de("load model failed:",e.face.emotion.modelPath):e.debug&&de("load model:",Pa.modelUrl)),Pa}async function b2(e,t,n,a){return Pa?y2<t.face.emotion.skipFrames&&t.skipFrame&&m9===a&&G0[n]&&G0[n].length>0?(y2++,G0[n]):(y2=0,new Promise(async r=>{let s=De.resizeBilinear(e,[Pa.inputs[0].shape[2],Pa.inputs[0].shape[1]],!1),[i,o,l]=Zt(s,3,3);s.dispose();let u=B(i,A2[0]),d=B(o,A2[1]),p=B(l,A2[2]);i.dispose(),o.dispose(),l.dispose();let c=jc([u,d,p]);u.dispose(),d.dispose(),p.dispose();let h=V(()=>c.sub(.5).mul(2));c.dispose();let m=[];if(t.face.emotion.enabled){let f=await Pa.predict(h),g=f.dataSync();he(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:Nle[y]});m.sort((y,A)=>A.score-y.score)}h.dispose(),G0[n]=m,m9=a,r(m)})):null}var La,q0=[],g9=0,v2=Number.MAX_SAFE_INTEGER;async function w2(e){let t=ft(e.modelBasePath,e.face.description.modelPath);return La?e.debug&&de("cached model:",t):(La=await ct(t),La?e.debug&&de("load model:",t):de("load model failed:",e.face.description.modelPath)),La}function k2(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 y9(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=k2(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function I2(e){return V(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Be))return null;let a=[[.05,.15,.85,.85]];return La.inputs[0].shape?(n.shape.length===3?De.cropAndResize(mn(n,0),a,[0],[La.inputs[0].shape[2],La.inputs[0].shape[1]]):De.cropAndResize(n,a,[0],[La.inputs[0].shape[2],La.inputs[0].shape[1]])).mul(255):null})}async function S2(e,t,n,a){var r,s;return La?v2<t.face.description.skipFrames&&t.skipFrame&&g9===a&&((r=q0[n])==null?void 0:r.age)&&((s=q0[n])==null?void 0:s.age)>0?(v2++,q0[n]):(v2=0,new Promise(async i=>{let o=I2(e),l,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(l=await La.predict(o)),he(o),l&&(V(()=>{let d=l.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(u.gender=d[0]<=.5?"female":"male",u.genderScore=Math.min(.99,p));let c=l.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=l.find(f=>f.shape[1]===100).dataSync();u.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=l.find(f=>f.shape[1]===1024);u.descriptor=[...m.dataSync()]}),l.forEach(d=>he(d))),q0[n]=u,g9=a,i(u)})):null}var Tle=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[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}},Cle=(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,N,C,E]=g,_,$,S;return v<1?v>-1?(S=Math.asin(v),$=Math.atan2(-N,y),_=Math.atan2(-w,b)):(S=-Math.PI/2,$=-Math.atan2(C,E),_=0):(S=Math.PI/2,$=Math.atan2(C,E),_=0),{pitch:2*-_,yaw:2*-$,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])),p=n(a(u[3],u[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?Tle(e):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}},N2=async(e,t)=>{var d,p,c,h,m,f;let n,a,r,s,i,o,l=[];e.state="run:face",n=Ke();let u=await c9(t,e.config);if(e.performance.face=Math.trunc(Ke()-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){de("Face object is disposed:",u[g].image);continue}let y=Cle(u[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?b2(u[g].image||ln([]),e.config,g,u.length):{}:(e.state="run:emotion",n=Ke(),s=e.config.face.emotion.enabled?await b2(u[g].image||ln([]),e.config,g,u.length):{},e.performance.emotion=Math.trunc(Ke()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?S2(u[g].image||ln([]),e.config,g,u.length):[]:(e.state="run:description",n=Ke(),o=e.config.face.description.enabled?await S2(u[g].image||ln([]),e.config,g,u.length):[],e.performance.embedding=Math.trunc(Ke()-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&&((p=(d=u[g])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((h=(c=u[g])==null?void 0:c.annotations)==null?void 0:h.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?Vt(u[g].image):null}),he(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 hp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A9=hp.length,fp=hp.reduce((e,t,n)=>(e[t]=n,e),{}),Ele=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rle=Ele.map(([e,t])=>[fp[e],fp[t]]),x9=[["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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De.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=u.arraySync();s.dispose(),u.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(l,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=V(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),l.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=V(()=>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),p=u.slice(2,4),c=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(T9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function _le(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function E9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return _le(n)}var R9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function us(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function Ple(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function M9(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(us(e[r],Ple(t,s)))}return n}function O2(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=R9(t[0],t[1]),i=M9(s,r),o=R9(-t[0],-t[1]);return M9(i,o)}function F9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-us(t[0],n),-us(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function z2(e,t){return[us(e,t[0]),us(e,t[1])]}var Lle=5,$9=1.65,D9=[0,5,9,13,17,1,2],Wle=0,Ble=2,_2=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=>z2([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return Z0(Y0(r),Lle)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=Z0(Y0(n),$9);a.palmLandmarks=[];for(let r=0;r<D9.length;r++)a.palmLandmarks.push(t[D9[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=K0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=O2(a,[0,0]),u=o.map(h=>[...z2(h,l),h[2]]),d=F9(r),p=[...mp(n),1],c=[us(p,d[0]),us(p,d[1])];return u.map(h=>[Math.trunc(h[0]+c[0]),Math.trunc(h[1]+c[1]),Math.trunc(h[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?E9(o.palmLandmarks[Wle],o.palmLandmarks[Ble]):0,u=mp(o),d=[u[0]/t.shape[2],u[1]/t.shape[1]],p=n.hand.rotation&&sa.flags.IS_BROWSER?De.rotateWithOffset(t,l,0,d):t.clone(),c=O2(-l,u),h=a?this.getBoxForPalmLandmarks(o.palmLandmarks,c):o,m=N9(h,p,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),p.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=q(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let b=this.transformRawCoords(v,h,l,c),w=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...w,confidence:A};let N={landmarks:b,confidence:A,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=Z0(Y0(o),$9),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 O9={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]},ds,ps,z9;async function P2(e,t){let n=await z9.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(O9))s[u]=O9[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 L2(e){!ds||!ps?([ds,ps]=await Promise.all([e.hand.enabled?ct(ft(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?ct(ft(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ds||!ds.modelUrl?de("load model failed:",e.hand.detector.modelPath):e.debug&&de("load model:",ds.modelUrl),!ps||!ps.modelUrl?de("load model failed:",e.hand.skeleton.modelPath):e.debug&&de("load model:",ps.modelUrl))):(e.debug&&de("cached model:",ds.modelUrl),e.debug&&de("cached model:",ps.modelUrl));let t=new D2(ds);return z9=new _2(t,ps),[ds,ps]}var _9=["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"],P9=["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 zn;async function J0(e){return zn?e.debug&&de("cached model:",zn.modelUrl):(zn=await ct(ft(e.modelBasePath,e.body.modelPath)),zn.width=parseInt(zn.signature.inputs["input_1:0"].tensorShape.dim[2].size),zn.height=parseInt(zn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!zn||!zn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",zn.modelUrl)),zn}async function W2(e,t){var f;if(!zn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},a=De.resizeBilinear(e,[zn.width,zn.height],!1),r=me(a,[255]);a.dispose();let s=await zn.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?_9:P9,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]),p=o.map(g=>g.position[1]),c=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],h=[0,0,0,0],m=o.reduce((g,y)=>y.score>g?y.score:g,0);return[{id:0,score:m,box:c,boxRaw:h,keypoints:o}]}var _n,sr=[],B2=[0,0,0,0],V2=[0,0,0,0],Q0=0,j2=Number.MAX_SAFE_INTEGER,Vle=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function L9(e){return _n?e.debug&&de("cached model:",_n.modelUrl):(_n=await ct(ft(e.modelBasePath,e.body.modelPath)),!_n||!_n.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",_n.modelUrl)),_n}function jle(e,t){let[n,a]=e.shape;return V(()=>{let r=(o,l)=>ye(o,B(me(o,ke(l,"int32")),ke(l,"int32"))),s=q(e,[a*n]),i=Vn(s,0).dataSync()[0];if(i>t){let o=ki(s,0),l=r(o,n).dataSync()[0],u=me(o,ke(n,"int32")).dataSync()[0];return[l,u,i]}return[0,0,i]})}async function U2(e,t){return j2<t.body.skipFrames&&t.skipFrame&&Object.keys(sr).length>0?(j2++,[{id:0,score:Q0,box:B2,boxRaw:V2,keypoints:sr}]):(j2=0,new Promise(async n=>{let a=V(()=>{if(!_n.inputs[0].shape)return null;let u=De.resizeBilinear(e,[_n.inputs[0].shape[2],_n.inputs[0].shape[1]],!1);return B(u,2).sub(1)}),r;if(t.body.enabled&&(r=await _n.predict(a)),a.dispose(),r){sr.length=0;let u=r.squeeze();he(r);let d=u.unstack(2);he(u);for(let p=0;p<d.length;p++){let[c,h,m]=jle(d[p],t.body.minConfidence);Q0>t.body.minConfidence&&sr.push({score:Math.round(100*m)/100,part:Vle[p],positionRaw:[c/_n.inputs[0].shape[2],h/_n.inputs[0].shape[1]],position:[Math.round(e.shape[2]*c/_n.inputs[0].shape[2]),Math.round(e.shape[1]*h/_n.inputs[0].shape[1])]})}d.forEach(p=>he(p))}Q0=sr.reduce((u,d)=>d.score>u?d.score:u,0);let s=sr.map(u=>u.position[0]),i=sr.map(u=>u.position[1]);B2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=sr.map(u=>u.positionRaw[0]),l=sr.map(u=>u.positionRaw[1]);V2=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:Q0,box:B2,boxRaw:V2,keypoints:sr}])}))}var Wa,ir=[],H2=[0,0,0,0],G2=[0,0,0,0],hu=0,q2=Number.MAX_SAFE_INTEGER,Ule=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function X2(e){return Wa?e.debug&&de("cached model:",Wa.modelUrl):(Wa=await ct(ft(e.modelBasePath,e.body.modelPath)),!Wa||!Wa.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Wa.modelUrl)),Wa}async function K2(e,t){return q2<t.body.skipFrames&&t.skipFrame&&Object.keys(ir).length>0?(q2++,[{id:0,score:hu,box:H2,boxRaw:G2,keypoints:ir}]):(q2=0,new Promise(async n=>{let a=V(()=>{if(!Wa.inputs[0].shape)return null;let u=De.resizeBilinear(e,[Wa.inputs[0].shape[2],Wa.inputs[0].shape[1]],!1);return ge(u,"int32")}),r;if(t.body.enabled&&(r=await Wa.predict(a)),a.dispose(),r){ir.length=0;let u=r.arraySync();he(r);let d=u[0][0];for(let p=0;p<d.length;p++)hu=d[p][2],hu>t.body.minConfidence&&ir.push({score:Math.round(100*hu)/100,part:Ule[p],positionRaw:[d[p][1],d[p][0]],position:[Math.round((e.shape[2]||0)*d[p][1]),Math.round((e.shape[1]||0)*d[p][0])]})}hu=ir.reduce((u,d)=>d.score>u?d.score:u,0);let s=ir.map(u=>u.position[0]),i=ir.map(u=>u.position[1]);H2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=ir.map(u=>u.positionRaw[0]),l=ir.map(u=>u.positionRaw[1]);G2=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)],n([{id:0,score:hu,box:H2,boxRaw:G2,keypoints:ir}])}))}var fu=[{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 Qn,Z2=[],Y2=Number.MAX_SAFE_INTEGER,ef=2.5;async function J2(e){if(Qn)e.debug&&de("cached model:",Qn.modelUrl);else{Qn=await ct(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(Qn.modelSignature.inputs);if(Qn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Qn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Qn||!Qn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",Qn.modelUrl)}return Qn}async function Hle(e,t,n,a){let r=0,s=[];for(let u of[1,2,4])V(()=>{var g,y;let d=u*13,p=(g=e.find(A=>A.shape[1]===d**2&&A.shape[2]===fu.length))==null?void 0:g.squeeze(),c=(y=e.find(A=>A.shape[1]===d**2&&A.shape[2]<fu.length))==null?void 0:y.squeeze(),m=c.reshape([-1,4,c.shape[1]/4]).argMax(2).arraySync(),f=p.arraySync();for(let A=0;A<p.shape[0];A++)for(let x=0;x<p.shape[1];x++){let v=f[A][x];if(v>a.object.minConfidence&&x!==61){let b=(.5+Math.trunc(A%d))/d,w=(.5+Math.trunc(A/d))/d,N=m[A].map(W=>W*(d/u/t)),[C,E]=[b-ef/u*N[0],w-ef/u*N[1]],[_,$]=[b+ef/u*N[2]-C,w+ef/u*N[3]-E],S=[C,E,_,$];S=S.map(W=>Math.max(0,Math.min(W,1)));let z=[S[0]*n[0],S[1]*n[1],S[2]*n[0],S[3]*n[1]],O={id:r++,score:Math.round(100*v)/100,class:x+1,label:fu[x].label,box:z.map(W=>Math.trunc(W)),boxRaw:S};s.push(O)}}});e.forEach(u=>he(u));let i=s.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),o=s.map(u=>u.score),l=[];if(i&&i.length>0){let u=await De.nonMaxSuppressionAsync(i,o,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);l=u.dataSync(),he(u)}return s=s.filter((u,d)=>l.includes(d)).sort((u,d)=>d.score-u.score),s}async function Q2(e,t){return Y2<t.object.skipFrames&&t.skipFrame&&Z2.length>0?(Y2++,Z2):(Y2=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[Qn.inputSize,Qn.inputSize],!1),s=r.div(255),i=s.transpose([0,3,1,2]);s.dispose(),r.dispose();let o;t.object.enabled&&(o=await Qn.predict(i)),i.dispose();let l=await Hle(o,Qn.inputSize,a,t);Z2=l,n(l)}))}var ea,e5=[],t5=Number.MAX_SAFE_INTEGER;async function n5(e){if(ea)e.debug&&de("cached model:",ea.modelUrl);else{ea=await ct(ft(e.modelBasePath,e.object.modelPath));let t=Object.values(ea.modelSignature.inputs);if(ea.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!ea.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!ea||!ea.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",ea.modelUrl)}return ea}async function Gle(e,t,n,a){if(!e)return[];let r=[],s=e.arraySync(),i=Vt(e);e.dispose();let o=Zt(i,6,1);i.dispose();let u=gn([o[1],o[0],o[3],o[2]],1).squeeze(),d=o[4].squeeze(),p=o[5].squeeze();o.forEach(f=>f.dispose());let c=await De.nonMaxSuppressionAsync(u,d,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);u.dispose(),d.dispose(),p.dispose();let h=c.dataSync();c.dispose();let m=0;for(let f of h){let 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`)}var tf=2048,Ee,wt,_t;function Yi(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Be)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof Be)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Ha(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let 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:Ee};let i=r,o=s;if(i>tf&&(i=tf,o=i*s/r),o>tf&&(o=tf,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");(!Ee||(Ee==null?void 0:Ee.width)!==i||(Ee==null?void 0:Ee.height)!==o)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==i&&(Ee.width=i),(Ee==null?void 0:Ee.height)!==o&&(Ee.height=o));let l=Ee.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,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!_t||!wt||Ee.width!==wt.width||(Ee==null?void 0:Ee.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Ee==null?void 0:Ee.width)&&(wt.width=Ee==null?void 0:Ee.width),(wt==null?void 0:wt.height)!==(Ee==null?void 0:Ee.height)&&(wt.height=Ee==null?void 0:Ee.height),_t=sa.flags.IS_BROWSER?new U9({canvas:wt}):null),!_t)return{tensor:null,canvas:Ee};_t.reset(),_t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&_t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&_t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&_t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&_t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&_t.addFilter("hue",t.filter.hue),t.filter.negative&&_t.addFilter("negative"),t.filter.sepia&&_t.addFilter("sepia"),t.filter.vintage&&_t.addFilter("brownie"),t.filter.sepia&&_t.addFilter("sepia"),t.filter.kodachrome&&_t.addFilter("kodachrome"),t.filter.technicolor&&_t.addFilter("technicolor"),t.filter.polaroid&&_t.addFilter("polaroid"),t.filter.pixelate!==0&&_t.addFilter("pixelate",t.filter.pixelate),_t.apply(Ee)}else wt=Ee,_t&&(_t=null);let u;if(wt.data){let d=[wt.height,wt.width,3];u=Lc(wt.data,d,"int32")}else if(wt instanceof ImageData)u=oa?oa.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 p=d.getContext("2d");p==null||p.drawImage(wt,0,0),u=oa?oa.fromPixels(d):null}else{let d=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");d.width=i,d.height=o;let p=d.getContext("2d");p==null||p.drawImage(wt,0,0);let c=p==null?void 0:p.getImageData(0,0,i,o);u=oa?oa.fromPixels(c):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 i5={};v5(i5,{all:()=>Zle,body:()=>q9,canvas:()=>Kle,face:()=>G9,gesture:()=>H9,hand:()=>X9,object:()=>K9,options:()=>cs,person:()=>Xle});var cs={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},nf=e=>Math.round(e*180/Math.PI);function r5(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function gp(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function s5(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function yp(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){s5(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function H9(e,t,n){let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function G9(e,t,n){var s,i,o,l;let a=Ln(cs,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let u of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&gp(r,u.box[0],u.box[1],u.box[2],u.box[3],a);let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${nf(u.rotation.angle.roll)}\xB0 yaw:${nf(u.rotation.angle.yaw)}\xB0 pitch:${nf(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${nf(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=a.color;for(let p=d.length-1;p>=0;p--){let c=Math.max(u.box[0],0),h=p*a.lineHeight+u.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d[p],c+5,h+16)),r.fillStyle=a.labelColor,r.fillText(d[p],c+4,h+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(a.drawPoints)for(let p of u.mesh)r5(r,p[0],p[1],p[2],a);if(a.drawPolygons){r.lineWidth=1;for(let p=0;p<Zi.length/3;p++){let c=[Zi[p*3+0],Zi[p*3+1],Zi[p*3+2]].map(h=>u.mesh[h]);s5(r,c,a)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,c=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],p,c,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 p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,c=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],p,c,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 p=[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(p[0],p[1]);let c=[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(c[0],c[1]),r.stroke()}}}}}async function q9(e,t,n){var s;let a=Ln(cs,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&&(gp(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)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,r5(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${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]]),yp(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&&s5(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]]),yp(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]]),yp(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]]),yp(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]]),yp(r,l,a)}}}}async function X9(e,t,n){let a=Ln(cs,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,gp(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]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,r5(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,l)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)r.beginPath(),r.strokeStyle=a.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:a.color,r.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),r.lineTo(o[l][0],o[l][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function K9(e,t,n){let a=Ln(cs,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){if(r.strokeStyle=a.color,r.fillStyle=a.color,gp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let 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2Q==`;var Q9="2.0.0";var mu,Ap,xp,Ji,Qi,gu,of,bp,lf,uf,df,pf,Jle=class{constructor(t){ra(this,mu,void 0);ra(this,Ap,void 0);ra(this,xp,void 0);ra(this,Ji,void 0);ra(this,Qi,void 0);ra(this,gu,void 0);this.analyze=(...t)=>{if(!pn(this,Ap))return;let n=this.tf.engine().state.numTensors,a=pn(this,mu);Ia(this,mu,n);let r=n-a;r!==0&&de(...t,r)};ra(this,of,t=>{if(!pn(this,xp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Be))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ra(this,bp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=Ke();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&de("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&&de("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&de("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&&de(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&de("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&e9();try{await this.tf.setBackend(this.config.backend)}catch(r){de("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(de("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&de(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(Ke()-a)}});this.next=t=>Y9(t||this.result);ra(this,lf,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let l=0;l<r.length/3;l++)s+=r[3*l+2];a.dispose();let i=100*(Math.max(s,pn(this,Qi))/Math.min(s,pn(this,Qi))-1);Ia(this,Qi,s);let o=i<Math.max(this.config.cacheSensitivity,pn(this,gu));return Ia(this,gu,i>10*this.config.cacheSensitivity?0:i),o});ra(this,uf,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(rf);break;case"full":n=await t(sf);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});ra(this,df,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+rf;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+sf;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)}));ra(this,pf,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(rf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(sf)),!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&&de("Warmup tfjs-node not loaded");return a});this.config=Ln(k5,t||{}),this.tf=dp,this.draw=i5,this.version=Q9,this.state="idle",Ia(this,mu,0),Ia(this,Ap,!1),Ia(this,xp,!1),Ia(this,Ji,!0),Ia(this,gu,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=>Yi(n,this.config),this.faceTriangulation=h9,this.faceUVMap=f9,this.sysinfo=I5(),Ia(this,Qi,1)}similarity(t,n){return k2(t,n)}segmentation(t,n){return J9(t,n,this.config)}enhance(t){return I2(t)}match(t,n,a=0){return y9(t,n,a)}async load(t){this.state="load";let n=Ke();t&&(this.config=Ln(this.config,t)),pn(this,Ji)&&(this.config.debug&&de(`version: ${this.version}`),this.config.debug&&de(`tfjs version: ${this.tf.version_core}`),this.config.debug&&de("platform:",this.sysinfo.platform),this.config.debug&&de("agent:",this.sysinfo.agent),await pn(this,bp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&de("configuration:",this.config),this.config.debug&&de("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?g2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?x2(this.config):null),this.models.handpose||(this.config.hand.enabled?L2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?$2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?J0(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?L9(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?X2(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?J2(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?n5(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?w2(this.config):null),this.models.segmentation||(this.config.segmentation.enabled?af(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await g2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await x2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await L2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await $2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await J0(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await J0(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await X2(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await J2(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await n5(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await w2(this.config)),this.config.segmentation.enabled&&!this.models.segmentation&&(this.models.segmentation=await af(this.config))),pn(this,Ji)&&(this.config.debug&&de("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ia(this,Ji,!1));let a=Math.trunc(Ke()-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=Ln(this.config,n),this.state="check";let i=pn(this,of).call(this,t);i&&(de(i,t),a({error:i}));let o=Ke();await pn(this,bp).call(this),await this.load(),r=Ke();let l=Yi(t,this.config);if(this.performance.image=Math.trunc(Ke()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ke(),await l5(l),s=Math.trunc(Ke()-r),s>0&&(this.performance.segmentation=s),l.canvas&&(l.tensor.dispose(),l=Yi(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){de("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}r=Ke(),this.config.skipFrame=await pn(this,lf).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(Ke()-r),this.analyze("Check Changed:");let u,d,p,c;this.config.async?(u=this.config.face.enabled?N2(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ke(),u=this.config.face.enabled?await N2(this,l.tensor):[],s=Math.trunc(Ke()-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?F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?U2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?K2(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ke(),this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?await F2(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?d=this.config.body.enabled?await W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?d=this.config.body.enabled?await U2(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(d=this.config.body.enabled?await K2(l.tensor,this.config):[]),s=Math.trunc(Ke()-r),s>0&&(this.performance.body=s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(p=this.config.hand.enabled?P2(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ke(),p=this.config.hand.enabled?await P2(l.tensor,this.config):[],s=Math.trunc(Ke()-r),s>0&&(this.performance.hand=s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?Q2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?a5(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ke(),this.config.object.modelPath.includes("nanodet")?c=this.config.object.enabled?await Q2(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(c=this.config.object.enabled?await a5(l.tensor,this.config):[]),s=Math.trunc(Ke()-r),s>0&&(this.performance.object=s)),this.analyze("End Object:"),this.config.async&&([u,d,p,c]=await Promise.all([u,d,p,c]));let h=[];this.config.gesture.enabled&&(r=Ke(),h=[...B9(u),...W9(d),...j9(p),...V9(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ke()-r)),this.performance.total=Math.trunc(Ke()-o),this.state="idle",this.result={face:u,body:d,hand:p,gesture:h,object:c,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var m;return Z9(u,d,p,h,(m=l==null?void 0:l.tensor)==null?void 0:m.shape)}},he(l.tensor),a(this.result)})}async warmup(t){let n=Ke();if(t&&(this.config=Ln(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await pn(this,uf).call(this):typeof Image!="undefined"?a=await pn(this,df).call(this):a=await pn(this,pf).call(this);let r=Ke();return this.config.debug&&de("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};mu=new WeakMap,Ap=new WeakMap,xp=new WeakMap,Ji=new WeakMap,Qi=new WeakMap,gu=new WeakMap,of=new WeakMap,bp=new WeakMap,lf=new WeakMap,uf=new WeakMap,df=new WeakMap,pf=new WeakMap;export{Jle as Human,Jle as default};
/**
* @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 See the LICENSE file. */
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