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new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ka(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ir().disposeTensor(this),this.dataId=e.dataId,Ir().incRef(this,null)}dispose(){Ir().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(xd,Symbol.hasInstance,{value:e=>e instanceof Ze&&e.assign!=null&&e.assign instanceof Function});var ar={};Oe(ar,{assertTypesMatch:()=>B5,getTensorsInContainer:()=>p2,isTensorInList:()=>e9,makeTypesMatch:()=>Dt});var i2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(i2||(i2={}));var l2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(l2||(l2={}));var u2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(u2||(u2={}));var c2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(c2||(c2={}));var d2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(d2||(d2={}));var QE={float32:c2,int32:l2,bool:u2,complex64:d2};function zn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return QE[e][t]}function bd(e){return zn(e,"int32")}function Dt(e,t){if(e.dtype===t.dtype)return[e,t];let n=zn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function B5(e,t){O(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function e9(e,t){return t.some(n=>n.id===e.id)}function p2(e){let t=[],n=new Set;return W5(e,t,n),t}function W5(e,t,n){if(e==null)return;if(e instanceof Ze){t.push(e);return}if(!t9(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),W5(a,t,n))}}function t9(e){return Array.isArray(e)||typeof e=="object"}function h2(e){return e.kernelName!=null}var V5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},f2=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new V5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){qr(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Yl)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s(sthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return f2.nextTensorId++}nextVariableId(){return f2.nextVariableId++}clone(e){let t=W.runKernel(Ga,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return W.runKernel(Ra,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(jh(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=h2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(h2(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=jh(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,x);let y=x.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:k,dtype:C}=b;return this.makeTensorFromDataId(w,k,C)});if(s){let b=this.getTensorsForGradient(h,f,y);n=this.saveTensorsForBackwardMode(b)}return y}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:c,attrs:u}=e,d=h2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=s2(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,c)=>a[c]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&Sa(e[0])&&(r=e.map(i=>md(i)));let a=s.write(r,t,n),o=new Ze(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=S5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ze(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new xd(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*Yg(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 xd||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*Yg(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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=s2(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=fh(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return c}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}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=p2(e),n=new Set(t.map(r=>r.id));for(let r=0;r{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(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));O(r instanceof Ze,()=>"The result y returned by f() must be a tensor.");let a=GE(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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t;return e.rank===0||e.rank===1?t=G(e,[1,1,1,e.size]):e.rank===2?t=G(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function y$(e,t,n,s,r,a){a==null&&(a=.001);let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;s!=null&&(u=D(s,"offset","batchNorm")),O(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),O(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),O(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:A$(o),scale:c,offset:u,mean:i,variance:l},h={varianceEpsilon:a},f=W.runKernel(Va,p,h);return G(f,o.shape)}var Du=V({batchNorm_:y$});function x$(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),c;r!=null&&(c=D(r,"scale","batchNorm"));let u;return 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s.length>0&&(n=Se(n,s)),G(n,e.shape)}function If(e,t,n,s){if(t==="linear")return e;if(t==="relu")return Rr(e);if(t==="elu")return Td(e);if(t==="relu6")return i1(e);if(t==="prelu")return yf(e,n);if(t==="leakyrelu")return df(e,s);if(t==="sigmoid")return cs(e);throw new Error(`Unknown fused activation ${t}.`)}var Cf=(e,t)=>!(e>0)||t==="linear";function BP({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Cf(W.state.gradientDepth,l)===!1){let w=No(e,t,n,s,r,a,o);return i!=null&&(w=le(w,i)),If(w,l,c,u)}let d=D(e,"x","conv2d","float32"),p=D(t,"filter","conv2d","float32"),h=d,f=!1;d.rank===3&&(f=!0,h=G(d,[1,d.shape[0],d.shape[1],d.shape[2]])),O(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),O(p.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${p.rank}.`),o!=null&&O(pn(s),()=>`Error in fused conv2d: pad 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s=Math.exp(t*n*n);return n<=e?s:0}function Wv(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function gF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppressionAsync"),o=D(t,"scores","nonMaxSuppressionAsync"),i=Uu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),c=l[0],u=l[1],{selectedIndices:d}=zv(c,u,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Xt(d,"int32")}var AF=gF;function yF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=Uu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c={boxes:o,scores:i},u={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=W.runKernel(Ri,c,u);return{selectedIndices:d[0],selectedScores:d[1]}}var xF=V({nonMaxSuppressionWithScore_:yF});async function bF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let 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NF(e,t,n=!1,s=!1){let r=D(e,"images","resizeNearestNeighbor");O(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),O(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),O(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),O(s===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=G(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(vu,i,l);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var EF=V({resizeNearestNeighbor_:NF});function RF(e,t="binary",n=!1,s=.5){let r=D(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],c=L(Xt([s]),255),u,d,p,h;if(O(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),O(r.shape[2]===3||r.shape[2]===1,()=>`Error 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W.runKernel(qi,l,c)}var PF=V({transform_:DF});function FF(e,t,n){O(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),O(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=D(e,"a","bandPart");O(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=G(Bu(0,a,1,"int32"),[-1,1]),l=Bu(0,o,1,"int32"),c=me(i,l),u=ir(ll(c,Re(+t,"int32")),il(c,Re(-n,"int32"))),d=Gt([a,o],s.dtype);return G(An(ss(G(s,[-1,a,o])).map(p=>Bn(u,p,d))),r)}var OF=V({bandPart_:FF});function MF(e){let t;if(Array.isArray(e)){t=!1,O(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a`Gram-Schmidt: Non-unique lengths 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o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:q(()=>Je(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:q(()=>Je(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=le(L(c,this.beta1),L(l,1-this.beta1)),p=le(L(u,this.beta2),L(gt(l),1-this.beta2)),h=pe(d,n),f=pe(p,s);c.assign(d),u.assign(p);let m=le(L(pe(h,le(_n(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&se(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&se(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),q(()=>{this.accBeta1.assign(Ro(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ro(this.beta2,this.iterations_+1))});let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};$f.className="Adamax";Co($f);var Od=class extends Yr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=W.registeredVariables[n];q(()=>{let o=le(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=fn(Re(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};_f.className="Momentum";Co(_f);var Df=class extends Yr{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>Je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>Je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>Je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;q(()=>{let c=le(L(i,this.decay),L(gt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=le(L(u,this.decay),L(o,1-this.decay)),p=pe(L(o,this.learningRate),_n(me(c,le(gt(d),this.epsilon)))),h=le(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=me(r,h);r.assign(f)}else{let 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bw={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function vw(e,t={}){return Md(e,ue.SerializationMap.getMap().classNameMap,t,"initializer")}function Ot(e){return C1(e)}function Nt(e){if(typeof e=="string"){let t=e in bw?bw[e]:e;if(t==="GlorotNormal")return new Bf;if(t==="GlorotUniform")return new Lf;if(t==="HeNormal")return new Wf;if(t==="HeUniform")return new Vf;if(t==="LeCunNormal")return new Uf;if(t==="LeCunUniform")return new Gf;{let n={};return n.className=t,n.config={},vw(n)}}else return e instanceof Zs?e:vw(e)}function PL(){return new z1}function FL(){return new zf}function OL(e){return new L1(e)}function ML(e){return new B1(e)}function zL(e){return new 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},JL=0,Qe=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=JL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Qr(n)+"_"+jf(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(y.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let x=g.filter(y=>y===A).length;if(x!==1)throw new cr(`The name "${A}" is used ${x} times in the model. All layer names should be unique. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new H(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${s} weights are not set: ${a}`)}q1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${nA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=tA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=kt(e);let n=new Al;for(let s=0;s{e=kt(e);let n;return t==null?n=dl(null,e.length):n=kt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=qf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;oparseInt(o,10)).sort(Pf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;fparseInt(i,10)).sort(Pf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,x;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[y,b]=h[0];f.mask==null&&(f.mask=b),A=kt(u.call(y,f)),x=kt(u.computeMask(y,b)),m=[y],g=[b]}else m=h.map(y=>y[0]),g=h.map(y=>y[1]),f.mask==null&&(f.mask=g),A=kt(u.call(m,f)),x=kt(u.computeMask(m,g));if(u.activityRegularizer)throw new Le("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let y=0;y{let e=[];for(let t of this.layers)for(let n=0;n0){let f=[];for(let m=0;m0&&m.apply(rs(A),x)}function l(m){let g=m.name,A=fr(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(y=>{if(!(y instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${y}`);o(A,y)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!lL(a);)for(let m of u){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let x of A)i(g,x)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],x=m[2];$r(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],x=m[2];$r(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[x])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new H("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(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function _B(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===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!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Hw(e,t){return _B(e,t,"classWeight")}async function jw(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return Ln(e);if(e.shape.length===2){if(e.shape[1]>1)return Hs(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),a=Array.from(await r.data());se(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Xw(e,t,n){if(n instanceof Ze)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function FB(e){if(e.length===3)throw new Le("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function OB(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Kw(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=FB(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=Dw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=Pw(u,d,n.epochs,null,null,MB(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f=n.batchesPerEpoch:y.done){if(r){let b;Kw(n.validationData)?b=kt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=kt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?PB:n.validationBatchSize,verbose:0}));for(let w=0;w0)throw new Le("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=zB(t)?t:await t.iterator(),i=0,l=0;for(;s?l{if(c.value){let{xs:u,ys:d}=qw(e,c.value),p=u.concat(d),h=q(()=>r(p));if(se(p),l===0)for(let m=0;mle(a[m],L(f,g))),l>0&&se(A)}se(h),i+=f,++l}return a}),c.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let c=0;c0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function jd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>ml(s,t,n-t)):ml(e,t,n-t)}function aA(e,t){return q(()=>e==null?null:Array.isArray(e)?e.map(n=>aA(n,t)):yw(e,t.dtype==="int32"?t:de(t,"int32")))}function oA(e,t){let n=[],s=0,r=null;for(;s=e&&(r=e),n.push([s,r]),s=r;return n}async function BB(e,t,n,s,r,a,o,i,l,c,u,d,p,h,f){r==null&&(r=32),a==null&&(a=1),u==null&&(u=!0),p==null&&(p=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,h==null))throw new H("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),A;g!=null&&(A=dr(0,g)),o==null&&(o=1);let{callbackList:x,history:y}=Pw(i,o,a,p,g,h,r,m,d);x.setModel(e),e.history=y,await x.onTrainBegin(),e.stopTraining_=!1;for(let b=p;b{let F=C[N][0],_=C[N][1],P=ml(k,F,_-F);R.batch=N,R.size=_-F;let T=aA(n,P),M=t(T);for(let U=0;U0){if(f=!0,s.validationData.length===2)o=s.validationData[0],i=s.validationData[1];else throw s.validationData.length===3?new Le("validationData including sample weights is not supported yet."):new H(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);let C=!0,N=await e.standardizeUserData(o,i,null,null,C,d);l=N[0],c=N[1],m=l.concat(c)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){f=!0;let C=Math.floor(r[0].shape[0]*(1-s.validationSplit)),N=r[0].shape[0];l=jd(r,C,N),r=jd(r,0,C),c=jd(a,C,N),a=jd(a,0,C),m=l.concat(c)}else s.validationSteps!=null&&(f=!0);let g=r.concat(a).concat(u);e.checkTrainableWeightsConsistency();let A=e.makeTrainFunction(),x=e.getDedupedMetricsNames(),y,b;f?(e.makeTestFunction(),y=e.testFunction,b=x.slice().concat(x.map(C=>"val_"+C))):(y=null,m=[],b=x.slice());let w=Dw(s.callbacks,s.yieldEvery);return await BB(e,A,g,x,d,s.epochs,s.verbose,w,y,m,s.shuffle,b,s.initialEpoch,null,null)}finally{e.isTraining=!1,yl(r,t),yl(a,n),yl(l,o),yl(c,i),u!=null&&se(u)}}function Zw(e){let t=[];e instanceof Ze&&(e=[e]);for(let n=0;nn.push(r.id));else if(t!=null)for(let r in t){let a=t[r];n.push(a.id)}let s=[];if(e instanceof Ze)n.indexOf(e.id)===-1&&s.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&s.push(r)});else if(e!=null)for(let r in e){let a=e[r];n.indexOf(a.id)===-1&&s.push(a)}s.forEach(r=>{r.isDisposed||r.dispose()})}function VB(e){return e instanceof Ze}function iA(e){return Array.isArray(e)}function Yw(e){return!VB(e)&&!iA(e)}function Jw(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(iA(e)&&e.length>0)o=!0;else if(Yw(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new H(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(Yw(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new H(`No data provided for "${o}". 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Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Nt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Et(e.alphaRegularizer),this.alphaConstraint=an(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 H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=pt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s(Ht(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function yk(e,t){return q(()=>(Ht(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function sW(e,t,n,s=1,r="valid",a,o=1){return q(()=>{if(a==null&&(a=ur()),Ht(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ye(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=H2(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=pr(i,n)),i})}function xk(e,t,n,s=[1,1],r="valid",a,o,i=null){return q(()=>{if(a==null&&(a=ur()),Ht(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=yA(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=$o.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Ye(l,[0,3,1,2])),l})}function rW(e,t,n,s=[1,1,1],r="valid",a,o){return q(()=>{if(a==null&&(a=ur()),Ht(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=yk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=X2(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=pr(i,n)),a==="channelsFirst"&&(i=Ye(i,[0,4,1,2,3])),i})}var xA=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",xA.verifyArgs(t),this.rank=e,yn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ku(t.kernelSize,e,"kernelSize"),this.strides=Ku(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,$s(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ht(this.dataFormat),this.activation=zo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Nt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=an(t.biasConstraint),this.biasRegularizer=Et(t.biasRegularizer),this.activityRegularizer=Et(t.activityRegularizer),this.dilationRate=Ku(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!N1(e.kernelSize,"number",1,3))throw new H(`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:Mo(this.activation),useBias:this.useBias,biasInitializer:Ot(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:rn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xd=class extends xA{constructor(e,t){super(e,t);this.kernel=null,Xd.verifyArgs(t),this.filters=t.filters,yn(this.filters,"filters"),this.kernelInitializer=Nt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=an(t.kernelConstraint),this.kernelRegularizer=Et(t.kernelRegularizer)}build(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 q(()=>{e=Be(e);let n,s=this.bias==null?null:this.bias.read(),r=lw(this.activation.getClassName());if(r!=null&&this.rank===2)n=xk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=sW(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=xk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=rW(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=pt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},bk=class extends Xd{constructor(e){super(2,e);bk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!N1(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},am=bk;am.className="Conv2D";ue.registerClass(am);var vk=class extends Xd{constructor(e){super(3,e);vk.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},om=vk;om.className="Conv3D";ue.registerClass(om);var bA=class extends am{constructor(e){super(e);if(this.inputSpec=[new Kt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Be(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Fr(i,d,c,this.padding),f=Fr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let g=q2(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ye(g,[0,3,1,2])),this.bias!=null&&(g=pr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=pt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Fr(t[s],i,a,this.padding),t[r]=Fr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};bA.className="Conv2DTranspose";ue.registerClass(bA);var vA=class extends om{constructor(e){super(e);if(this.inputSpec=[new Kt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Be(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=Fr(l,f,d,this.padding),x=Fr(c,m,p,this.padding),y=Fr(u,g,h,this.padding),b=[r,A,x,y,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,4,1]));let w=Q3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Ye(w,[0,4,1,2,3])),this.bias!==null&&(w=pr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=pt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Fr(t[s],c,o,this.padding),t[r]=Fr(t[r],u,i,this.padding),t[a]=Fr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vA.className="Conv3DTranspose";ue.registerClass(vA);var wk=class extends Xd{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`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=Nt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Et(t.depthwiseRegularizer),this.depthwiseConstraint=an(t.depthwiseConstraint),this.pointwiseInitializer=Nt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Et(t.pointwiseRegularizer),this.pointwiseConstraint=an(t.pointwiseConstraint)}build(e){if(e=pt(e),e.length{e=Be(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=bv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(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=Ot(this.depthwiseInitializer),e.pointwiseInitializer=Ot(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=rn(this.depthwiseConstraint),e.pointwiseConstraint=rn(this.pointwiseConstraint),e}};wk.className="SeparableConv";var wA=class extends wk{constructor(e){super(2,e)}};wA.className="SeparableConv2D";ue.registerClass(wA);var kk=class extends Xd{constructor(e){super(1,e);kk.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"&&!N1(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},kA=kk;kA.className="Conv1D";ue.registerClass(kA);var SA=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=Of(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Of(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Of(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Of(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}};SA.className="Cropping2D";ue.registerClass(SA);var IA=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,xL(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 q(()=>{let n=Be(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return Ye(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};IA.className="UpSampling2D";ue.registerClass(IA);function aW(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=ur()),Ht(r);let o=yA(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Cd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ye(o,[0,3,1,2])),o})}var CA=class extends xA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Nt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=an(e.depthwiseConstraint),this.depthwiseRegularizer=Et(e.depthwiseRegularizer)}build(e){if(e=pt(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 q(()=>{e=Be(e);let n=aW(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=pr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=mr(t,this.kernelSize[0],this.padding,this.strides[0]),a=mr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ot(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=rn(this.depthwiseRegularizer),e}};CA.className="DepthwiseConv2D";ue.registerClass(CA);function Sk(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function Ik(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(dr(2,l));if(t=Ye(t,c),a!=null)throw new Le("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=de(de(r,"bool"),"float32"),r.rank===l-1&&(r=mn(r,-1)),r=Ye(r,c)),s&&(t=Rs(t,0),r!=null&&(r=Rs(r,0)));let u=[],d,p=n,h=t.shape[0],f=ss(t),m;r!=null&&(m=ss(r));for(let A=0;Ae(x,p));if(r==null)d=y[0],p=y[1];else{let b=q(()=>{let w=m[A],k=me(Es(w),w),C=le(L(y[0],w),L(p[0],k)),N=p.map((R,F)=>le(L(y[1][F],w),L(R,k)));return{output:C,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=An(u,1)),[d,g,p]})}var Ck=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new um({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Kt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return dr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){H1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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;no.shape[o.shape.length-1]),a))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Kt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Jr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("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(s=>Gt([n,s])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)se(this.states_),this.keptStates!=null&&(se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):se(this.states_);for(let s=0;sfn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Sk(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Kt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof hr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Be(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new H(`RNN Layer has ${a} 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 o={training:s},l=Ik((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return q(()=>{let t=Gt(e.shape);return t=Se(t,[1,2]),t=Bd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?O1(t,[1,n]):t):this.cell.stateSize>1?[O1(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()===Ck.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fr(s,n);return new e(Object.assign(t,{cell:r}))}},ta=Ck;ta.className="RNN";ue.registerClass(ta);var Kd=class extends Qe{},im=class extends Kd{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,yn(this.units,"units"),this.activation=zo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=an(e.kernelConstraint),this.recurrentConstraint=an(e.recurrentConstraint),this.biasConstraint=an(e.biasConstraint),this.dropout=Hu([1,Fo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 q(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0Es(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0Es(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=_r(L(e,a),this.kernel.read()):r=_r(e,this.kernel.read()),this.bias!=null&&(r=pr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=le(r,_r(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Mo(this.activation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),recurrentInitializer:Ot(this.recurrentInitializer),biasInitializer:Ot(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:rn(this.kernelConstraint),recurrentConstraint:rn(this.recurrentConstraint),biasConstraint:rn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};im.className="SimpleRNNCell";ue.registerClass(im);var TA=class extends ta{constructor(e){e.cell=new im(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};TA.className="SimpleRNN";ue.registerClass(TA);var lm=class extends Kd{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,yn(this.units,"units"),this.activation=zo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=an(e.kernelConstraint),this.recurrentConstraint=an(e.recurrentConstraint),this.biasConstraint=an(e.biasConstraint),this.dropout=Hu([1,Fo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 q(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0Es(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0Es(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};NA.className="GRU";ue.registerClass(NA);var Zd=class extends Kd{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,yn(this.units,"units"),this.activation=zo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=an(e.kernelConstraint),this.recurrentConstraint=an(e.recurrentConstraint),this.biasConstraint=an(e.biasConstraint),this.dropout=Hu([1,Fo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=pt(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Zs{apply(o,i){let l=r.apply([a]),c=new zf().apply([a]),u=r.apply([a*2]);return Aw(Aw(l,c),u)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0Es(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Es(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0{this.cell.dropoutMask!=null&&(se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};EA.className="LSTM";ue.registerClass(EA);var um=class extends Kd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{fl(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fr(r,n));return new e({cells:s})}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 j1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;aa!=null?a(t(),n):xw(t(),n),i=()=>Vd(o,t,s);return!r||r<=1?fn(i().clone()):Array(r).fill(void 0).map(i).map(c=>fn(c.clone()))}var Tk=class extends ta{constructor(e){if(e.unroll)throw new Le("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Le("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Kt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,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 q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Gt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Jr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new H("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(()=>Gt(r)):this.states_=[Gt(r)];else if(e==null)se(this.states_),this.keptStates!=null&&(se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):se(this.states_);for(let o=0;ofn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=mr(l,s[0],r,a[0],o[0]),d=mr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};Tk.className="ConvRNN2D";var cm=class extends Zd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,yn(this.filters,"filters"),this.kernelSize=Ku(n,2,"kernelSize"),this.kernelSize.forEach(i=>yn(i,"kernelSize")),this.strides=Ku(s||1,2,"strides"),this.strides.forEach(i=>yn(i,"strides")),this.padding=r||"valid",$s(this.padding),this.dataFormat=a||"channelsLast",Ht(this.dataFormat),this.dilationRate=Ku(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>yn(i,"dilationRate"))}build(e){var t;e=pt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends Zs{apply(u,d){let p=l.apply([c]),h=hs([c]),f=l.apply([c*2]);return F1([p,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0Es(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:L(J[ee],Z),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0Es(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),x=3,[y,b,w,k]=nn(this.kernel.read(),o,x),[C,N,R,F]=this.useBias?nn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,y,C,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,F,this.padding);let[_,P,T,M]=nn(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,_),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),A=this.recurrentConv(A,M);let U=this.recurrentActivation.apply(le(c,f)),j=this.recurrentActivation.apply(le(u,m)),z=le(L(j,a),L(U,this.activation.apply(le(d,g)))),X=L(this.recurrentActivation.apply(le(p,A)),this.activation.apply(z));return[X,X,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=No(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?pr(r,n,this.dataFormat):r}recurrentConv(e,t){return No(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};cm.className="ConvLSTM2DCell";ue.registerClass(cm);var RA=class extends Tk{constructor(e){let t=new cm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};RA.className="ConvLSTM2D";ue.registerClass(RA);var dm=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s{this.invokeCallHook(e,t);let n=Be(e);if(0xw(n,this.rate,r,this.seed),()=>n,s)}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()}};dm.className="Dropout";ue.registerClass(dm);var $A=class extends dm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};$A.className="SpatialDropout1D";ue.registerClass($A);var _A=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,yn(this.units,"units"),this.activation=zo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=an(e.kernelConstraint),this.biasConstraint=an(e.biasConstraint),this.kernelRegularizer=Et(e.kernelRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=pt(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=pt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e),s=lw(this.activation.getClassName()),r;return s!=null?r=_r(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=_r(n,this.kernel.read()),this.bias!=null&&(r=pr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Mo(this.activation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),biasInitializer:Ot(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:rn(this.kernelConstraint),biasConstraint:rn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_A.className="Dense";ue.registerClass(_A);var DA=class extends Qe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=pt(e);for(let t of e.slice(1))if(t==null)throw new H(`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],Po(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Be(e);return this.activation.apply(n)})}getConfig(){let e={activation:Mo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};PA.className="Activation";ue.registerClass(PA);var FA=class extends Qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Be(e),kL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};FA.className="RepeatVector";ue.registerClass(FA);var OA=class extends Qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Be(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};OA.className="Reshape";ue.registerClass(OA);var MA=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=dr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Kt({ndim:this.dims.length+1})]}computeOutputShape(e){e=pt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ye(Be(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};MA.className="Permute";ue.registerClass(MA);var zA=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Be(e),s=-1;return rf(zu(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e),s=-1,r=!0,a=rf(zu(n,this.maskValue),s,r);return L(n,de(a,n.dtype))})}};zA.className="Masking";ue.registerClass(zA);var LA=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(kt(e.inputLength))}this.inputDim=e.inputDim,yn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,yn(this.outputDim,"outputDim"),this.embeddingsInitializer=Nt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Et(e.embeddingsRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.embeddingsConstraint=an(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 q(()=>this.maskZero?(e=Be(e),zu(e,Je(e))):null)}computeOutputShape(e){if(e=pt(e),this.inputLength==null)return[...e,this.outputDim];let t=kt(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s{this.invokeCallHook(e,t);let n=Be(e);n.dtype!=="int32"&&(n=Ff(n,"int32"));let s=yw(this.embeddings.read(),G(n,[n.size]));return G(s,pt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ot(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:rn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};LA.className="Embedding";ue.registerClass(LA);var xl=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Le}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&Do(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Fo(s);for(let a of e){let o=a.rank;for(let i=0;i1){let c=dr(1,l).concat([0]);n.push(Ye(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(Ye(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(dr(0,o-1));a=Ye(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:mn(s,0));let n=t[0];for(let s=1;s{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return q(()=>F1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return q(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a3||t.shape.length>3)throw new Le("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Le("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;cs){o=r-s;let l=[];for(let c=0;c0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u"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 Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new H(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Yd(r,e[a].shape.length)):s=[Yd(this.axes,t.shape.length),Yd(this.axes,n.shape.length)],this.normalize&&(t=Zf(t,s[0]),n=Zf(n,s[1])),oW(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Yd(this.axes,e.length),Yd(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[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}};jA.className="Dot";ue.registerClass(jA);var qA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e);return Vd(()=>le(Mf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};qA.className="GaussianNoise";ue.registerClass(qA);var XA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?Vd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Mf(n.shape,1,r))},()=>n,t.training||!1):n})}};XA.className="GaussianDropout";ue.registerClass(XA);var KA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Be(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 q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Vd(()=>{let r=Be(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=il(Lu(n),this.rate);l=Ff(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=le(L(r,l),L(le(l,-1),i));return le(L(d,c),u)},()=>Be(e),t.training||!1)}return e})}};KA.className="AlphaDropout";ue.registerClass(KA);function Jd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=G3(e,t,n,s,r,a);else if(e.rank===3)o=H3(e,t,n,s,r,a);else if(e.rank===4)o=j3(e,t,n,s,r,a);else throw new Le(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function iW(e,t,n,s,r=.001){return q(()=>{let a=gf(e,s),o=a.mean,i=a.variance;return[Jd(e,o,i,n,t,r),o,i]})}function lW(e,t,n,s,r=.001){return q(()=>{let a=gf(e,s),o=a.mean,i=a.variance,l=[];for(let f of dr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Jd(e,c,u,p,d,r),o,i]})}function uW(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),dr(0,e.rank-1))?iW(e,t,n,s,r):lW(e,t,n,s,r)}var ZA=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Nt(e.betaInitializer||"zeros"),this.gammaInitializer=Nt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Nt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Nt(e.movingVarianceInitializer||"ones"),this.betaConstraint=an(e.betaConstraint),this.gammaConstraint=an(e.gammaConstraint),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer)}build(e){e=pt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Kt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Be(e),r=s.shape,a=r.length,o=dr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=dl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,dr(0,a).slice(0,a-1)),d=()=>{if(u){let A=G(this.movingMean.read(),l),x=G(this.movingVariance.read(),l),y=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Jd(s,A,x,y,b,this.epsilon)}else return Jd(s,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 d();let[p,h,f]=uW(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,x,y)=>{q(()=>{let b=1-y,w=A.read(),k=L(me(w,x),b);A.write(me(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ot(this.betaInitializer),gammaInitializer:Ot(this.gammaInitializer),movingMeanInitializer:Ot(this.movingMeanInitializer),movingVarianceInitializer:Ot(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:rn(this.betaConstraint),gammaConstraint:rn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ZA.className="BatchNormalization";ue.registerClass(ZA);var YA=class extends Qe{constructor(e){e==null&&(e={});super(e);if(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=Nt(e.betaInitializer||"zeros"),this.gammaInitializer=Nt(e.gammaInitializer||"ones"),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=pt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Do(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Be(e),s=n.shape,r=s.length;return q(()=>{let a=!0,{mean:o,variance:i}=gf(n,this.axis,a),l=dl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f{if(e.rank!==4)throw new H(`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 H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ur()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],qs(e,s)})}var JA=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ur():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=pt(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 q(()=>cW(Be(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};JA.className="ZeroPadding2D";ue.registerClass(JA);function pm(e,t,n,s,r,a){return q(()=>{Ht(r),pw(a),$s(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=ur()),a==null&&(a="max"),e=yA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=mf(e,t,n,i):o=of(e,t,n,i),r==="channelsFirst"&&(o=Ye(o,[0,3,1,2])),o})}function Nk(e,t,n,s,r,a){return q(()=>{Ht(r),pw(a),$s(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=ur()),a==null&&(a="max"),e=yk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=s1(e,t,n,i):o=U2(e,t,n,i),r==="channelsFirst"&&(o=Ye(o,[0,4,1,2,3])),o})}var Ek=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(yn(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);yn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,$s(this.padding),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){e=pt(e);let t=mr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Bd(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ot(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},QA=class extends Ek{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),pm(e,t,n,s,r,"max")}};QA.className="MaxPooling1D";ue.registerClass(QA);var ey=class extends Ek{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),pm(e,t,n,s,r,"avg")}};ey.className="AveragePooling1D";ue.registerClass(ey);var Rk=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(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 H(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];yn(this.poolSize,"poolSize"),yn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),$s(this.padding),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=mr(t,this.poolSize[0],this.padding,this.strides[0]),n=mr(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 q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},ty=class extends Rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),pm(e,t,n,s,r,"max")}};ty.className="MaxPooling2D";ue.registerClass(ty);var ny=class extends Rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),pm(e,t,n,s,r,"avg")}};ny.className="AveragePooling2D";ue.registerClass(ny);var $k=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 H(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];yn(this.poolSize,"poolSize"),yn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),$s(this.padding),this.inputSpec=[new Kt({ndim:5})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=mr(t,this.poolSize[0],this.padding,this.strides[0]),n=mr(n,this.poolSize[1],this.padding,this.strides[1]),s=mr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},sy=class extends $k{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),Nk(e,t,n,s,r,"max")}};sy.className="MaxPooling3D";ue.registerClass(sy);var ry=class extends $k{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),$s(s),Nk(e,t,n,s,r,"avg")}};ry.className="AveragePooling3D";ue.registerClass(ry);var _k=class extends Qe{constructor(e){super(e);this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},ay=class extends _k{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Be(e);return Bt(n,1)})}};ay.className="GlobalAveragePooling1D";ue.registerClass(ay);var oy=class extends _k{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Be(e);return gn(n,1)})}};oy.className="GlobalMaxPooling1D";ue.registerClass(oy);var Dk=class extends Qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Le}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},iy=class extends Dk{call(e,t){return q(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?Bt(n,[1,2]):Bt(n,[2,3])})}};iy.className="GlobalAveragePooling2D";ue.registerClass(iy);var ly=class extends Dk{call(e,t){return q(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?gn(n,[1,2]):gn(n,[2,3])})}};ly.className="GlobalMaxPooling2D";ue.registerClass(ly);var Pk=class extends Qe{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 s=t.layer,r=fr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},uy=class extends Pk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=pt(e),e.length<3)throw new H(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=pt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Be(e),Ik((a,o)=>[Be(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};uy.className="TimeDistributed";ue.registerClass(uy);function dW(e){hl(yL,"BidirectionalMergeMode",e)}var pW="concat",cy=class extends Pk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=fr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=fr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?pW:e.mergeMode,dW(this.mergeMode),e.weights)throw new Le("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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vU(a,o,i);case"convolution":return q(()=>wU(a,o,i));case"creation":return q(()=>kU(a,o,i));case"dynamic":return SU(a,o,i);case"evaluation":return q(()=>IU(a,o,i));case"image":return q(()=>EU(a,o,i));case"graph":return q(()=>CU(a,o,i));case"logical":return q(()=>RU(a,o,i));case"matrices":return q(()=>$U(a,o,i));case"normalization":return q(()=>_U(a,o,i));case"reduction":return q(()=>DU(a,o,i));case"slice_join":return q(()=>PU(a,o,i));case"sparse":return q(()=>FU(a,o,i));case"spectral":return q(()=>OU(a,o,i));case"string":return q(()=>MU(a,o,i));case"transformation":return q(()=>zU(a,o,i));case"hash_table":return NU(a,o,i,s);case"custom":let l=jk(a.op);if(l&&l.customExecutor)return l.customExecutor(new hU(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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 v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var x7=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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;tt.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 b7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>fs(p)[0]),u=[];s!=null&&(u=s.map(p=>fs(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((v7(p)||UU(p)||GU(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function LU(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>fs(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var BU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],WU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],VU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function v7(e){return BU.indexOf(e.op)>=0}function UU(e){return WU.indexOf(e.op)>=0}function GU(e){return VU.indexOf(e.op)>=0}var Ny=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,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 Ny(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(s=>s.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(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=b7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=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 [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return LU(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 s=n.map(u=>this.graph.nodes[fs(u)[0]]),r=t.map(u=>fs(u)[0]),a=r.map(u=>this.graph.nodes[u]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return q(()=>{let u=new x7(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=fs(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;fVn(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=jV(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];if(u===1){if(!this.keepTensorForDebug)c.dispose();else{let[d,p]=Or(t.name,s);this.intermediateTensors[d]?this.intermediateTensors[d][p]=c:(this.intermediateTensors[d]=[],this.intermediateTensors[d][p]=c)}delete o[c.id]}else u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=K().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let a=new x7(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(c=>Vn(c,this.tensorsMap,a)),i=o.map(c=>c.id),l=Object.keys(e).map(c=>e[c].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[fs(x)[0]]),o=n.map(x=>fs(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=b7(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(x=>{let[y,b]=fs(x),w=[];w[b]=e[x],h[y]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let x=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(x)}u==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=i.filter(x=>!v7(x)&&!Vn(x.name,h,t)).map(x=>x.name);if(A.length>0){let x="";throw u!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. 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c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Or(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=fs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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xn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},dG=class extends xn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;se(e.value)}}},pG=class extends xn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(K().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new z7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),Ut(n,t)}},L7=class extends xn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Xt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=lr([a,r,i,o],[1,4])}else this.cropBox=lr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(K().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new L7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=mn(de(e,"float32"),0),n;n=$e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},B7=class{},W7=class extends xn{split(e){return new vG(this,e)}},vG=class extends W7{constructor(e,t){super();this.upstream=e,this.impl=new wG(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wG=class extends Ry{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}},kG=class extends xn{decodeUTF8(){return new SG(this)}},SG=class extends W7{constructor(e){super();this.upstream=e,this.impl=new IG(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},IG=class extends Ry{constructor(e){super();if(this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=A5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return K().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},V7=class extends kG{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(K().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function CG(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=TG(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new V7(o,t)}else throw new Error(a.statusText)}var TG=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function U7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var G7=class extends B7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(U7(this.input)&&K().get("IS_NODE")){let e=Ws("fs");this.input=e.readFileSync(this.input.substr(7))}return new V7(this.input,this.options)}},H7=class extends B7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return U7(this.url)?new G7(this.url,this.fileOptions).iterator():CG(this.url,this.fileOptions)}};function NG(e,t={}){return new M7(new H7(e),t)}function EG(e){let t=Ey(e);return ms(async()=>t)}function RG(e){return ms(async()=>{let t=await e();return Ey(()=>t.next())})}async function $G(e,t){return L7.create(e,t)}async function _G(e){return z7.create(e)}var DG="0.0.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var PG=Xs.whereImpl,j7=class extends Yl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Xc(this,ns())}nextDataId(){return j7.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,K().get("IS_NODE")&&E.warn(` ============================ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. 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sj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ne(r,"argMin");let o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=_s({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],E.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[u,d]=E.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;gn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var rj={kernelName:ru,backendName:"cpu",kernelFunc:sj},aj=ht(au,e=>Math.asin(e)),oj={kernelName:au,backendName:"cpu",kernelFunc:aj},ij=ht(ou,e=>Math.asinh(e)),lj={kernelName:ou,backendName:"cpu",kernelFunc:ij},uj=ht(iu,e=>Math.atan(e)),cj={kernelName:iu,backendName:"cpu",kernelFunc:uj},dj=Zt((e,t)=>Math.atan2(e,t)),pj=bn(uu,dj),hj={kernelName:uu,backendName:"cpu",kernelFunc:pj},fj=ht(lu,e=>Math.atanh(e)),mj={kernelName:lu,backendName:"cpu",kernelFunc:fj};function Vy(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=ze(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],y=r.outShape[3];for(let b=0;bz?z=oe:a==="avg"&&(X+=oe,Z++)}if(isNaN(z))break}let J=P+T*y+C;g[J]=a==="avg"?X/Z:z}}}return m}function MS(e,t,n,s,r=!1,a=!1){let o=ze(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=ze(t,n,e);for(let g=0;gF&&(F=j,r?_=a?((g*s.inHeight+P)*s.inWidth+M)*s.inChannels+A:(P*s.inWidth+M)*s.inChannels+A:_=T*p+U)}}o.set(_,g,x,k,A)}}return o}function zS(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,c=r.dilationDepth,u=r.dilationHeight,d=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,A=r.padInfo.left,x=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,y=ze(r.outShape,n),b=y.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let R=0;Rwe?we=ft:a==="avg"&&(Ce+=ft,Me++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let We=be+P;b[We]=a==="avg"?Ce/Me:we}}}}return y}function gj(e,t){let n=ze(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m=T&&(T=ee,M=j*u*d+X*u+J)}}}n.set(M,m,A,w,R,g)}}}return n}function Aj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ne(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,A=u.dilationDepth,x=u.dilationHeight,y=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,C=b-1-u.padInfo.front,N=k-1-u.padInfo.left,R=w-1-u.padInfo.top,F=ze(a.shape,"float32"),_=1/(f*m*g),P=n.bufferSync(r);for(let T=0;T=u.outDepth||Math.floor(Q)!==Q))for(let te=0;te=u.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe=u.outWidth||Math.floor(be)!==be)continue;ee+=P.get(T,Q,oe,be,M)}}}F.set(ee*_,T,U,j,z,M)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var wj={kernelName:gh,backendName:"cpu",kernelFunc:vj};function kj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ne([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,A=u.effectiveFilterHeight,x=u.effectiveFilterWidth,y=x-1-u.padInfo.left,b=A-1-u.padInfo.top,w=ze(o.shape,"float32"),k=1/(h*f),C=n.data.get(r.dataId).values,N=ze(r.shape,"float32",C);for(let R=0;R=u.outHeight||Math.floor(z)!==z))for(let X=0;X=u.outWidth||Math.floor(Z)!==Z)continue;U+=N.get(R,z,Z,F)}}w.set(U*k,R,_,P,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var Sj={kernelName:mh,backendName:"cpu",kernelFunc:kj};function Ij(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;v.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires 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i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=Rt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=_s({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Rt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=vl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Nj={kernelName:di,backendName:"cpu",kernelFunc:Tj};function Ej(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=Py(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var Rj={kernelName:Ah,backendName:"cpu",kernelFunc:Ej};function $j(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var _j={kernelName:yh,backendName:"cpu",kernelFunc:$j},Dj=ht(Hr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;cm.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>v.sizeFromShape(m.shape)>0);if(i.length===1)return Mr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(E.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>bl({inputs:{input:b},backend:n})),g=i.map(b=>Qu({inputs:{input:b},backend:n})),A=ec({inputs:m,backend:n,attrs:{axis:a}}),x=ec({inputs:g,backend:n,attrs:{axis:a}}),y=gs({inputs:{real:A,imag:x},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x),y}let c=i.map(m=>{let g=v.sizeFromShape(m.shape.slice(a));return Rt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=E.computeOutShape(c.map(m=>m.shape),1);let d=c[0].shape[0]===1,p=Fy(u,o,t[0].dtype,d),h=E.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var zj={kernelName:pi,backendName:"cpu",kernelFunc:ec};function LS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s;Ne([r,a],"conv2d");let d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,x=p.padInfo.top,y=p.dataFormat==="channelsLast",b=new en(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),C=w[0],N=y?w[1]:w[2],R=y?w[2]:1,F=y?1:w[1],_=b.strides[0],P=y?b.strides[1]:b.strides[2],T=y?b.strides[2]:1,M=y?1:b.strides[1],U=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,z=b.values;for(let X=0;X=p.inHeight)continue;let fe=te*k[0],be=Z+oe*N;for(let we=0;we=p.inWidth)continue;let qe=fe+We*k[1],ct=be+He*R,dt=qe;for(let rt=0;rt=c.inDepth)continue;let X=j*R[0],Z=_+z*N[1];for(let J=0;J=c.inHeight)continue;let oe=X+Q*R[1],fe=Z+te*N[2];for(let be=0;be=c.inWidth)continue;let He=oe+Me*R[2],qe=fe+We*c.inChannels,ct=He;for(let dt=0;dtMath.cos(e)),Yj={kernelName:Pa,backendName:"cpu",kernelFunc:Zj},Jj=ht(Fa,e=>Math.cosh(e)),Qj={kernelName:Fa,backendName:"cpu",kernelFunc:Jj};function eq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=ze([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,y=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(A.shape);for(let C=0;C=u)continue;let M=m>1?(_-R)*(d-1)/(m-1):0,U=g>1?(P-F)*(p-1)/(g-1):0;for(let j=0;j1?R*(d-1)+j*M:.5*(R+_)*(d-1);if(z<0||z>d-1){for(let X=0;X1?F*(p-1)+ee*U:.5*(F+P)*(p-1);if(ne<0||ne>p-1){for(let fe=0;fe1?F*(p-1)+X*U:.5*(F+P)*(p-1);if(Z<0||Z>p-1){for(let ne=0;neA+f-x-1:(A,x)=>A+x;for(let A=0;A`Only NHWC dataFormat supported on CPU for depthToSpace. 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ne=v.locToIndex([U,j,X,J],P,v.computeStrides(F));T[ne]=ee}}}return{dataId:l.write(v.toTypedArray(T,s.dtype),F,s.dtype),shape:F,dtype:s.dtype}}},gq={kernelName:Th,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:x,strideHeight:y,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:C,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Th}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let F=v.toNestedArray(R,c.data.get(a.dataId).values),_=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let 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int offset = imod(flatIndex, 4); flatIndex = idiv(flatIndex, 4, 1.); int r = flatIndex / texShape[1]; int c = imod(flatIndex, texShape[1]); vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]); vec4 values = ${n.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]; } ${n.output} = vec4(${s}, 0., 0., 0.); } `}},HY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Un();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=` localCoords = coords; if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) { localCoords[2] += ${o}; if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) { localCoords[1] += ${a}; 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${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; ${t.varyingVs} vec2 resultUV; void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`;return QS(e,n)}function NI(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return sI(e,t)}function EI(e){let t=new Uint16Array([0,1,2,2,1,3]);return rI(e,t)}function dp(e,t,n,s,r,a){oI(t,n);let o=aI(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function nx(e){return e.internalFormatFloat}function RI(e,t,n,s){let[r,a]=ip(t,n);return dp(e,r,a,nx(s),s.textureFormatFloat,e.FLOAT)}function sx(e){return e.internalFormatHalfFloat}function $I(e,t,n,s){let[r,a]=ip(t,n);return dp(e,r,a,sx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function rx(e){return e.downloadTextureFormat}function _I(e,t,n,s){let[r,a]=ip(t,n);return dp(e,r,a,rx(s),e.RGBA,e.UNSIGNED_BYTE)}function ax(e){return e.internalFormatPackedFloat}function DI(e,t,n,s){let[r,a]=tc(t,n);return dp(e,r,a,ax(s),e.RGBA,e.FLOAT)}function ox(e){return e.internalFormatPackedHalfFloat}function PI(e,t,n,s){let[r,a]=tc(t,n);return dp(e,r,a,ox(s),e.RGBA,s.textureTypeHalfFloat)}function FI(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ky(e,t,"clipSpacePos",n,3,a,s)&&Ky(e,t,"uv",n,2,a,r)}function OI(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function MI(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function zI(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function LI(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function BI(e,t,n,s){let[r,a]=ip(t,n),o=4,i=new Uint8Array(BZ(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function WI(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(WZ(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function VI(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Fm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Cm(t,e)):this.gl=zr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=lp(this.gl,r),Ps(this.gl,a))this.textureHalfFloatExtension=lp(this.gl,a);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ps(this.gl,s))this.colorBufferHalfFloatExtension=lp(this.gl,s);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=NI(this.gl),this.indexBuffer=EI(this.gl),this.framebuffer=iI(this.gl),this.textureConfig=Xy(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),PI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),DI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Zy(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>BI(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return WI(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return LI(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=zI(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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Ie(t,()=>t.attachShader(n,this.vertexShader)),Ie(t,()=>t.attachShader(n,e)),nI(t,n),this.debug&&Nm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=FI(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Nm(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?uI(this.gl,e,t):cI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),dI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=tc(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Nm(this.gl,this.program),up(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=lp(this.gl,K().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(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().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 v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,K().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,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=jY(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)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Em(this.gl,e,this.framebuffer),this.debug&&up(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Em(this.gl,this.outputTexture,this.framebuffer),this.debug&&up(this.gl)):Zy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;Em(s,e,this.framebuffer),this.debug&&up(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,s))}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 jY(e){let t=0;for(;t`${e}.${n}`)}function Gn(e,t){return t===1?[e]:jI(e,t)}function $J(e,t){if(e===1)return"rc";let n="";for(let s=0;s ${t[0]}`;let s="";for(let r=e-2;r= ${t[r]}`,r= ${t}; bool rEdge = rp1 >= ${n}; `}function OJ(e,t){let n=e.length,s=DJ(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${s[0]}), cEdge ? 0. : getA(${s[1]}), rEdge ? 0. : getA(${s[2]}), rEdge || cEdge ? 0. : getA(${s[3]})`}var qI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${s}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${s>0?"}":""} `}this.userCode=` ${MJ(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?ex():Qy(e)} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.); ivec3 thisRC; int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; ${n} setOutput(result); } `}};function MJ(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${t?eY(["r","c","d"],"inputShape"):Il(["r","c","d"],e)} return ivec3(r, c, d); } `}var zJ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=KI(t,n),r=ZI(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=XI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===In.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===In.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===In.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=KI(n,s),a=ZI(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=XI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function LJ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function XI(e,t,n,s,r){let a=BJ(t,s),o;if(r){let[l,c]=tc(e[0],e[1]);o=l*c}else{let[l,c]=ip(e[0],e[1]);o=l*c}let i=LJ(n,a);return o*i}function BJ(e,t){switch(e){case In.PACKED_2X2_FLOAT32:return ax(t);case In.PACKED_2X2_FLOAT16:return ox(t);case In.UNPACKED_FLOAT32:return nx(t);case In.UNPACKED_FLOAT16:return sx(t);case In.PACKED_4X1_UNSIGNED_BYTE:return rx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function WJ(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?In.PACKED_2X2_FLOAT32:In.UNPACKED_FLOAT32:e?In.PACKED_2X2_FLOAT16:In.UNPACKED_FLOAT16}function KI(e,t){if(e===Ds.UPLOAD)return In.PACKED_2X2_FLOAT32;if(e===Ds.RENDER||e==null)return WJ(t);if(e===Ds.DOWNLOAD||e===Ds.PIXELS)return In.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ZI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Vo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Ar="if (isnan(x)) return x;",VJ="return x;",YI="return abs(x);",UJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",GJ=Ar+` return (x < 0.0) ? 0.0 : x; `,HJ=Ar+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Om="return x;",jJ="return 1.0 / (1.0 + exp(-1.0 * x));",qJ="return x;",XJ=` 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; `,KJ=` 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; `,ZJ=` 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; `,YJ="return 1.0 / (1.0 + exp(-1.0 * x));",ic=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}},JJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Gn("rc",t),s=yt(t),r=$J(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${s} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${o})); } `}},QJ=Xs.whereImpl,eQ=1e-7,tQ=1e-4,Mm={};function nQ(e){return e in Mm||(Mm[e]={}),Mm[e]}var sQ=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),rQ=600;function aQ(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*rQ/1024/1024}var JI=class extends Yl{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,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=zr(K().getNumber("WEBGL_VERSION"));this.binaryCache=nQ(K().getNumber("WEBGL_VERSION")),this.gpgpu=new Fm(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 zJ(this.gpgpu),this.numMBBeforeWarning=aQ(),this.texData=new Xc(this,ns())}nextDataId(){return JI.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().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 s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Ds.UPLOAD,refCount:1}),s}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,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Ds.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new ic(o,Om):d=new Vo(o,Om);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=E.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new ic(s,Om):h=new Vo(s,Om);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(a!=="complex64"&&K().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Tm(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=E.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ns().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(K().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:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=sQ){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ns().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new JJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new _J(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[kl(e.shape),...Sl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[kl(t),...Sl(t)],a=new qI(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Rm(s),o,i=Tm(a);n?o=new WY(a):o=new BY(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===op.DENSE){let m=Tm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!cp(g.shape,m.shape)){let A=m,x=m.shape;m.shape=g.shape,m=this.packedReshape(m,x),i.push(m),g=this.texData.get(m.dataId),A.shape=x}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=LY(e,l,c),d=this.getAndSaveBinary(u,()=>MY(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),zY(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=K().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(Re(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?eQ:tQ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=fI(n,i),t.texShape=u),r!=null){let d=Rm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;i?([h,f]=tc(u[0],u[1]),p=new HY(d,m)):p=new GY(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=Ds.PIXELS:this.texData.get(g.dataId).usage=Ds.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],x=!0,y=this.runWebGLProgram(p,[g],s,A,x),b=this.texData.get(y.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(y.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=oQ(t,s)),n.values}acquireTexture(e,t,n,s){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,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}},pp=JI;pp.nextDataId=0;function oQ(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 s=0;snew pp,2);var lQ={forceHalfFloat:QI},e4=` if (isnan(a)) return a; if (isnan(b)) return b; `,lc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},zm=` 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; `,hp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Fs(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` ${yt(r)} coords = getOutputCoords(); `,r===1)this.enableShapeUniforms?a+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=Gn("coords",r);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = (${i[r-1]} + 1) >= outShape[${r} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:a+=` bool nextRowOutOfBounds = (${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); ${a} setOutput(result); } `}};function As(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var uQ={kernelName:Ga,backendName:"webgl",kernelFunc:As};function Uo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=As({inputs:{x:s},backend:n}),l=As({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var cQ={kernelName:Jc,backendName:"webgl",kernelFunc:Uo},t4="return (a < 0.) ? b * a : a;",n4=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function dQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(n4,r.shape,o.shape):new lc(t4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var pQ={kernelName:ki,backendName:"webgl",kernelFunc:dQ},s4="return (a < 0.) ? b * a : a;",r4=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function hQ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(r4,s.shape,r.shape):new lc(s4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var fQ={kernelName:no,backendName:"webgl",kernelFunc:hQ},a4="if (isnan(x)) return x;",mQ=` if (isnan(a)) return a; if (isnan(b)) return b; `,gQ=` result.r = isNaN.r > 0. ? 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NAN : result.a; `;function st({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new ic(o.shape,t):u=new Vo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Cn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(y=>{let[b,w]=y,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new lc(e,l.shape,c.shape);return u.runWebGLProgram(N,[k,C],zn(b.dtype,w.dtype))}),x=Uo({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),x}let d=a||zn(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?E.fromUint8ToStringArray(f):f,A=l.dtype==="string"?E.fromUint8ToStringArray(m):m,[x,y]=r(l.shape,c.shape,g,A,d),b=u.makeTensorInfo(y,d),w=u.texData.get(b.dataId);return w.values=x,b}let p=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new hp(t,l.shape,c.shape,n):h=new lc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Lm(e,t=!1){if(e==="linear")return t?qJ:VJ;if(e==="relu")return t?KJ:GJ;if(e==="elu")return t?XJ:UJ;if(e==="relu6")return t?ZJ:HJ;if(e==="prelu")return t?r4:s4;if(e==="leakyrelu")return t?n4:t4;if(e==="sigmoid")return t?YJ:jJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o4=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Fs(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${o} }`:l?m=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${o} }`:m=`vec4 activation(vec4 x) { ${o} }`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",y="rc.x";e[0]`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. 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vec4 minMaxValue = vec4(${o}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; ${p} values = ${p}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${d} } int inIdx = inOffset + ${c}; if (${u===1}) { ${p} values = ${p}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${d} } else if (${u===2}) { ${p} values = ${p}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${d} } else if (${u===3}) { ${p} values = ${p}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${d} } setOutput(${l}); } `}};function vQ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Tl(e,t,n,s){let r=vQ(e.shape),a=e;for(let o=0;o6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=yt(this.rank),r=jI("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),N=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,N],F=Math.max(A,x),_=n?C.shape[1]:C.shape[2],P=a!=null,T=o!=null,M=l==="leakyrelu",U=l!=null?Lm(l,!0):null,j=P||T||M||U!=null,z;if((h===1||f===1)&&_>d4&&j===!1){let Z=C,J=N;n&&(Z=Hn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),R.push(Z)),s&&(J=Hn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),R.push(J));let ee=f!==1,ne=f===1,Q=Z;ee&&(Q=ve({inputs:{x:Z},backend:r,attrs:{shape:[F,_,1]}}),R.push(Q));let te=f===1?2:1,oe=J;ne&&(oe=ve({inputs:{x:J},backend:r,attrs:{shape:[F,1,_]}}),R.push(oe));let fe=lx({inputs:{a:Q,b:oe},backend:r});z=Wm({inputs:{x:fe},backend:r,attrs:{axis:te,keepDims:!0}}),R.push(fe)}else{let Z=zn(e.dtype,t.dtype),J=new o4(w,k,[F,h,f],n,s,P,U,T,M),ee=[C,N];if(a!=null&&ee.push(a),T&&ee.push(o),M){let ne=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ee.push(ne),R.push(ne)}z=r.runWebGLProgram(J,ee,Z)}let X=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let Z of R)r.disposeIntermediateTensorInfo(Z);return X}function NQ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Vm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var EQ={kernelName:yo,backendName:"webgl",kernelFunc:NQ},p4="return abs(x);";function RQ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=GI(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ic(s.shape,p4):r=new Vo(s.shape,p4),n.runWebGLProgram(r,[s],s.dtype)}var $Q={kernelName:ci,backendName:"webgl",kernelFunc:RQ},_Q=Ar+` if (abs(x) > 1.) { return NAN; 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setOutput(result); } `}};function Um(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return As({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Um({inputs:s.slice(0,l),backend:n}),u=Um({inputs:s.slice(l),backend:n});return Um({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>zn(l,c)),a=s.map(l=>l.shape),i=K().getBool("WEBGL_PACK")?new WQ(s[0].shape,a):new BQ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var VQ={kernelName:Ca,backendName:"webgl",kernelFunc:Um};function UQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("all",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"all",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var GQ={kernelName:nu,backendName:"webgl",kernelFunc:UQ};function HQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("any",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"any",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var jQ={kernelName:su,backendName:"webgl",kernelFunc:HQ},qQ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=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 * ${s}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${s}; i++) { int inIdx = ${i}; float candidate = getA(batch, inIdx); if (candidate ${o} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},XQ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=yt(i),c=Gn("coords",i),u,d;if(a===1){d=i+1;let C=yt(d);u=` ${C} sourceLocR = ${C}(${c.join()}, 0); ++${c[i-1]}; ${C} sourceLocG = ${C}(${c.join()}, 0); ++${c[i-2]}; ${C} sourceLocA = ${C}(${c.join()}, 0); --${c[i-1]}; ${C} sourceLocB = ${C}(${c.join()}, 0); --${c[i-2]};`}else d=i,u=` ${l} sourceLocR = coords; 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} return asin(x); `,eee=st({opSnippet:QQ}),tee={kernelName:au,backendName:"webgl",kernelFunc:eee},nee=Ar+"return log(x + sqrt(x * x + 1.0));",see=st({opSnippet:nee}),ree={kernelName:ou,backendName:"webgl",kernelFunc:see},aee=Ar+` return atan(x); `,oee=st({opSnippet:aee}),iee={kernelName:iu,backendName:"webgl",kernelFunc:oee},lee=mQ+` return atan(a, b); `,uee=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+gQ+` return result; `,cee=Cn({opSnippet:lee,packedOpSnippet:uee}),dee={kernelName:uu,backendName:"webgl",kernelFunc:cee},pee=Ar+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,hee=st({opSnippet:pee}),fee={kernelName:lu,backendName:"webgl",kernelFunc:hee},fp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${o}, ${i}); const ivec2 pads = ivec2(${p}, ${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 < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC += ${c}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${C} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let x="max",y=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(y="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${x}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${o}, ${i}); const ivec2 pads = ivec2(${p}, ${h}); const float initializationValue = ${A}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${A}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${k} } int xC = xCCorner + ${b}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${k} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${k} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${k} } } setOutput(${y}); } `}},ux=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",y="0.0";if(x||(y="-1.0 / 1e-20"),n){let R=">=";this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC += ${d}) { 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 ${R} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?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} * ${f} + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,C=a%4,N=` if (${x}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${A}); 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 xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${p}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; wC += 4) { int xC = xCCorner + wC * ${d}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), getValue(batch, xD, xR, xC + 2 * ${d}, ch), getValue(batch, xD, xR, xC + 3 * ${d}, ch) ); ${N} } int xC = xCCorner + ${k}; if (${C===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${N} } else if (${C===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), initializationValue, initializationValue ); ${N} } else if (${C===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), getValue(batch, xD, xR, xC + 2 * ${d}, ch), initializationValue ); ${N} } } setOutput(${w}); } } `}};function mee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return As({inputs:{x:r},backend:n});let d=new fp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var gee={kernelName:Na,backendName:"webgl",kernelFunc:mee};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new ux(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var yee={kernelName:Yc,backendName:"webgl",kernelFunc:Aee},xee=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${u}); const float avgMultiplier = float(${d}); 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 < ${i}; wR += ${a}) { 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 < ${l}; wC+= ${o}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},bee=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=` const ivec3 pads = ivec3(${h}, ${f}, ${m}); const float avgMultiplier = float(${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 < ${u}; wD += ${i}) { 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 < ${d}; wR += ${l}) { 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 < ${p}; wC += ${c}) { float dyC = float(dyCCorner + wC) / ${o}.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 vee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new bee(p);return n.runWebGLProgram(h,[r],o.dtype)}var wee={kernelName:gh,backendName:"webgl",kernelFunc:vee};function kee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;nc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new xee(u);return n.runWebGLProgram(d,[r],o.dtype)}var See={kernelName:mh,backendName:"webgl",kernelFunc:kee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Vm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Cee={kernelName:Ea,backendName:"webgl",kernelFunc:Iee},Tee=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${o}; float scale = ${i}; float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},Nee=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${o}; vec4 scale = ${i}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } `}},Eee=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=K().getBool("WEBGL_PACK_NORMALIZATION")?new Nee(s.shape,r.shape,a.shape,u,d,l):new Tee(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},Ree={kernelName:Va,backendName:"webgl",kernelFunc:Eee},$ee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=_ee(this.rank),s,r=e.map((a,o)=>`sourceLoc.${cx[o]} = start[${o}] + coords.${cx[o]};`);s=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` `)} `,this.userCode=` void main() { ${s} setOutput(getSource(${n})); } `}},cx=["x","y","z","w","u","v"];function _ee(e){if(e===1)return"sourceLoc";if(e<=6)return cx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Dee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=Gn("coords",this.rank),s=Gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=` result.x = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${s[this.rank-1]}; result.y = ${a}; --${s[this.rank-1]}; } `,i=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { ++${s[this.rank-2]}; result.z = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${s[this.rank-1]}; result.w = ${a}; } } `,l=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(` `);this.userCode=` void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${l} vec4 result = vec4(0.); ${o} ${i} setOutput(result); } `}};function Pee(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Pt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function uc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Pt.parseSliceParams(r,a,o);if(Pt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=xJ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Pt.isSliceContinous(r.shape,i,l);if(c||!u){let d=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dee(l):new $ee(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Pee(r,i,l,n)}var Fee={kernelName:Bi,backendName:"webgl",kernelFunc:uc},Oee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Hn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),A=uc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),A},Mee={kernelName:di,backendName:"webgl",kernelFunc:Oee};function zee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=UI(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var Lee={kernelName:Ah,backendName:"webgl",kernelFunc:zee};function Bee(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Wee={kernelName:yh,backendName:"webgl",kernelFunc:Bee},Vee="return float(a != b);",A4=Cn({opSnippet:Vee,cpuKernelImpl:fJ,dtype:"bool"}),Uee={kernelName:Ni,backendName:"webgl",kernelFunc:A4};function mp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return As({inputs:{x:r.complexTensorInfos.real},backend:n})}var Gee={kernelName:id,backendName:"webgl",kernelFunc:mp},Hee="return float(int(x));";function jee(e,t){let n=new Vo(e.shape,Hee),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function dx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return As({inputs:{x:r},backend:n});let o=Gt(r.shape),i=dx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Uo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=mp({inputs:{input:r},backend:n}),i=dx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=As({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return jee(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=A4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var qee={kernelName:Ra,backendName:"webgl",kernelFunc:dx},y4="return ceil(x);",Xee=st({opSnippet:y4,packedOpSnippet:y4,cpuKernelImpl:KY}),Kee={kernelName:$a,backendName:"webgl",kernelFunc:Xee},Zee=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}},Yee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}};function Jee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new Yee(r.shape):i=new Zee(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Qee={kernelName:Hr,backendName:"webgl",kernelFunc:Jee},ete=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 x4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new ete(s.shape),o=[x4(s,r.complexTensorInfos.real),x4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var nte={kernelName:Qc,backendName:"webgl",kernelFunc:tte},ste=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f= ${i[f-1]}) { return getChannel( getT${f}(${Gm(o,l,m)}), vec2(${Gm(c,l,m)})); }`}let p=i.length,h=i[i.length-1];d+=` return getChannel( getT${p}(${Gm(o,l,h)}), vec2(${Gm(c,l,h)}));`,this.userCode=` float getValue(${o.map(f=>"int "+f)}) { ${d} } void main() { ${r} coords = getOutputCoords(); vec4 result = vec4(getValue(${a}), 0., 0., 0.); ${a[s-1]} = ${a[s-1]} + 1; if (${a[s-1]} < ${n[s-1]}) { result.g = getValue(${a}); } ${a[s-2]} = ${a[s-2]} + 1; if (${a[s-2]} < ${n[s-2]}) { result.a = getValue(${a}); } ${a[s-1]} = ${a[s-1]} - 1; if (${a[s-2]} < ${n[s-2]} && ${a[s-1]} < ${n[s-1]}) { result.b = getValue(${a}); } setOutput(result); } `}};function Gm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Hm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return As({inputs:{x:r.complexTensorInfos.imag},backend:n})}var ate={kernelName:sd,backendName:"webgl",kernelFunc:Hm};function cc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>mp({inputs:{input:m},backend:n})),d=e.map(m=>Hm({inputs:{input:m},backend:n})),p=cc(u,t,n),h=cc(d,t,n),f=Uo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=ZY(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=cc(e.slice(0,u),t,n),p=cc(e.slice(u),t,n),h=cc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new rte(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=ote(e,t,n),i=new ste(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function ote(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function b4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return As({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),cc(i,a,n)}var ite={kernelName:pi,backendName:"webgl",kernelFunc:b4},v4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,x=m?3:1,y="",b="";n&&(s?y=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?y=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:y=` float activation(float x) { ${n} } `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${y} const ivec2 strides = ivec2(${i}, ${l}); const ivec2 pads = ivec2(${a}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${x}]; ivec2 xRCCorner = ivec2(coords[${g}], coords[${A}]) * 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 < ${d}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${m}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${f===1}) { if (${m}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${m}) { 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, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${m}) { 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; ${w} ${b} setOutput(result); } `}},lte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${a}, ${o}); const ivec3 pads = ivec3(${t}, ${n}, ${s}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${u}; wF++) { int xF = xFCorner + wF * ${i}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${f===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${f===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, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${f===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); } `}},ute=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let{dataFormat:n}=t,s=Un(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=` blockIndex = rc.y + ${u}; pos = rc.x + ${c}; ${i} offsetY = int(blockIndex / outWidth) * stride[0] - pad[0]; d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow); if(d0 < inputShape[${a}] && d0 >= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${o}] && d1 >= 0) { ch = imod(pos, inChannels); if (${r}) { innerDims = vec2(d1, ch); result[${c*2+u}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${c*2+u}] = 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; ${l} ${s.output} = result; } `}};function w4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||p===1)&&u>d4)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(cp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let C=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(C);let N=Vm({a:w,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=As({inputs:{x:N},backend:s}),g.shape=n.outShape,A.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Vm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),A.push(w),A.push(k),A.push(C)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function k4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,A=[m,g],x=!0,y=!1,b=[],w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let C=new ute(A,n),N=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(C,[w],"float32",N),F=ve({inputs:{x:R},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(R),b.push(F);let _=r!=null,P=a!=null,T=i==="leakyrelu",M=i?Lm(i,!0):null,U=new o4(F.shape,k.shape,[1,g,n.outChannels],x,y,_,M,P,T),j=[F,k];if(r&&j.push(r),P&&j.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));j.push(J),b.push(J)}let z=s.runWebGLProgram(U,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Z=ve({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return Z}function cte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=w4({x:r,filter:a,convInfo:p,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=k4({x:r,filter:a,convInfo:p,backend:n});else{let m=new v4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var dte={kernelName:_a,backendName:"webgl",kernelFunc:cte},pte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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} - ${s}; 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 >= ${e.inWidth}) { continue; } if (${a}) { 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); } `}},hte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${u}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${s}.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 < ${e.outChannels}; d2++) { if (${a}) { 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); } `}},fte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=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} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${s} - ${o}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},mte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${i}, ${l}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${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) / ${a}.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 < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${s} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function gte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new pte(p);return n.runWebGLProgram(h,[r,a],"float32")}var Ate={kernelName:xh,backendName:"webgl",kernelFunc:gte};function yte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new hte(p);return n.runWebGLProgram(h,[r,a],"float32")}var xte={kernelName:Da,backendName:"webgl",kernelFunc:yte};function bte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new lte(c);return n.runWebGLProgram(u,[r,a],"float32")}var vte={kernelName:ed,backendName:"webgl",kernelFunc:bte};function wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new fte(c);return n.runWebGLProgram(u,[r,a],"float32")}var kte={kernelName:bh,backendName:"webgl",kernelFunc:wte};function Ste(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new mte(c);return n.runWebGLProgram(u,[r,a],"float32")}var Ite={kernelName:vh,backendName:"webgl",kernelFunc:Ste},Cte=a4+` return cos(x); `,Tte=st({opSnippet:Cte}),Nte={kernelName:Pa,backendName:"webgl",kernelFunc:Tte},Ete=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,Rte=st({opSnippet:Ete}),$te={kernelName:Fa,backendName:"webgl",kernelFunc:Rte},_te=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,y,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); const float width_ratio = float(${x}); 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 >= ${a}) { return; } float height_scale = ${g}; float width_scale = ${y}; float in_y = ${A}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${f} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${p} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},Dte=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new _te(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Pte={kernelName:fi,backendName:"webgl",kernelFunc:Dte},S4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${I4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${yt(s)} coords = getOutputCoords(); int end = ${C4(s,"coords")}; float val = ${r}; int pow2 = int(pow(2.0, index)); if (${o}) { int idx = ${i}; ${C4(s,"coords")} = idx; val += getX(${I4(s,"coords")}); } setOutput(val); } `}};function I4(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 ${e} is not yet supported`)}function C4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Fte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=Hn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=As({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new S4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new S4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=Hn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var Ote={kernelName:hi,backendName:"webgl",kernelFunc:Fte};function Mte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=UI(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=XY(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var zte={kernelName:wh,backendName:"webgl",kernelFunc:Mte},Lte=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 Bte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new Lte(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Wte={kernelName:mi,backendName:"webgl",kernelFunc:Bte},T4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Fs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:l=` float activation(float x) { ${n} } `,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${l} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${i}; int q = d2 - d1 * ${i}; 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 < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${o}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${u} ${c} setOutput(result); } `}},N4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Fs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(d+1)/2;g++){let A=g*2;if(p+=` xC = xCCorner + ${A*l}; `,i===1){if(A= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } `,l===1&&A>0?p+=` xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.xy); `:p+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${A} = vec4(previous.zw, xTexelC${A}.xy); } else { xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy); } `):p+=` if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } xC${A} = xTexelC${A}; `,A+1= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } `,l>1&&(p+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); xTexelC${A}Ready = 1; } `),p+=` xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy); `):x===1?p+=` xC${A+1} = xTexelC${A}; `:p+=` xCOffset = xC + ${x}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } xC${A+1} = xTexelC${A+1}; `}}else A= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.0); } xTexelC${A+1}Ready = 1; } xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw); `,A+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy); `)):(p+=` if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) { xTexelC${A} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${A}.zw = vec2(0.0); } xTexelC${A}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) { xTexelC${A+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${A+1}.zw = vec2(0.); } xTexelC${A+1}Ready = 1; } xC${A} = vec4( xTexelC${A}.xy, xTexelC${A+1}.xy); `,A+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new N4(d):p=new T4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Ute={kernelName:Oa,backendName:"webgl",kernelFunc:Vte},Gte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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 * ${a} + 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} - ${s}; 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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Hte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${o}); 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) / ${s}.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 < ${i}; dm++) { int d2 = d1 * ${i} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function jte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Gte(d);return n.runWebGLProgram(p,[r,a],"float32")}var qte={kernelName:kh,backendName:"webgl",kernelFunc:jte};function Xte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Hte(d);return n.runWebGLProgram(p,[r,a],"float32")}var Kte={kernelName:Sh,backendName:"webgl",kernelFunc:Xte},Zte=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function Yte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Zte(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var Jte={kernelName:Ih,backendName:"webgl",kernelFunc:Yte},Qte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=` const ivec2 strides = ivec2(${r}, ${a}); const ivec2 pads = ivec2(${u}, ${d}); 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 < ${o}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${i}; w++) { int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function ene(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Qte(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var tne={kernelName:td,backendName:"webgl",kernelFunc:ene};function nne(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m=0&&(p=Wm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var sne={kernelName:nd,backendName:"webgl",kernelFunc:nne},rne="return (x >= 0.0) ? x : (exp(x) - 1.0);",ane=` 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; `,one=st({opSnippet:rne,packedOpSnippet:ane}),ine={kernelName:za,backendName:"webgl",kernelFunc:one},lne="return (b >= 1.0) ? a : a * (b + 1.0);",une=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,cne=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(une,s.shape,r.shape):new lc(lne,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},dne={kernelName:Nh,backendName:"webgl",kernelFunc:cne},pne=` return vec4(equal(a, b)); `,hne="return float(a == b);",fne=Cn({opSnippet:hne,packedOpSnippet:pne,dtype:"bool",cpuKernelImpl:YY}),mne={kernelName:gi,backendName:"webgl",kernelFunc:fne},gne=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${E.ERF_P}; float a1 = ${E.ERF_A1}; float a2 = ${E.ERF_A2}; float a3 = ${E.ERF_A3}; float a4 = ${E.ERF_A4}; float a5 = ${E.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,Ane=st({opSnippet:gne}),yne={kernelName:cu,backendName:"webgl",kernelFunc:Ane},E4="return exp(x);",R4=st({opSnippet:E4,packedOpSnippet:E4,cpuKernelImpl:JY,dtype:"float32"}),xne={kernelName:La,backendName:"webgl",kernelFunc:R4};function px(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var bne={kernelName:Ai,backendName:"webgl",kernelFunc:px},$4="return exp(x) - 1.0;",vne=st({opSnippet:$4,packedOpSnippet:$4,cpuKernelImpl:QY}),wne={kernelName:yi,backendName:"webgl",kernelFunc:vne},_4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="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) { ${o} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${s}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${s}; 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) / ${a}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function D4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new _4("real",l,t),u=new _4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Uo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function kne(e){let{inputs:t,backend:n}=e,{input:s}=t;return D4(s,!1,n)}var Sne={kernelName:Eh,backendName:"webgl",kernelFunc:kne},Ine=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function gp(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Ine(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Cne={kernelName:du,backendName:"webgl",kernelFunc:gp},Tne=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 - 1; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},Nne={kernelName:xi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Tne(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},P4="return floor(x);",Ene=st({opSnippet:P4,packedOpSnippet:P4,cpuKernelImpl:eJ}),Rne={kernelName:Ba,backendName:"webgl",kernelFunc:Ene},$ne=` 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; } `,_ne=` 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); `,Dne=Cn({opSnippet:$ne,packedOpSnippet:_ne,dtype:"int32"}),Pne={kernelName:Wa,backendName:"webgl",kernelFunc:Dne},Fne=class{constructor(e){this.variableNames=["A"];let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},One=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.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; } `}},Mne={kernelName:dd,backendName:"webgl",kernelFunc:zne},dc;function zne(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(dc==null&&(dc=document.createElement("canvas").getContext("2d")),dc.canvas.width=l,dc.canvas.height=c,dc.drawImage(r,0,0,l,c),r=dc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Ds.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=K().getBool("WEBGL_PACK")?new One(d):new Fne(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function Lne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A,x=[];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"))A=w4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=k4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",C=h?Lm(h,!1):null,N=new v4(g,b,C,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(F),x.push(F)}A=n.runWebGLProgram(N,R,"float32")}let y=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return x.push(A),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Bne={kernelName:xo,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=E.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),A=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?Lm(p,A):null,y=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&y.push(o),w&&y.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));y.push(F),f.push(F)}let C;A?C=new N4(g,b,x,w,k):C=new T4(g,b,x,w,k);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(C,y,"float32",N);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),R}var Vne={kernelName:bo,backendName:"webgl",kernelFunc:Wne},Une=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=yt(t.length),r=yt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${s} strides = ${s}(${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 * ${a}; } setOutput(getX(flattenIndex, coords[1])); } `}};function Gne(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),y=tJ(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,y.values)}let f=new Une(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Hne={kernelName:vi,backendName:"webgl",kernelFunc:Gne},jne=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),s=qne(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${s})); } `}};function qne(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=nJ(w,b,g);return h.forEach(C=>n.disposeIntermediateTensorInfo(C)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new jne(f.shape,g),x=n.runWebGLProgram(A,[f,m],f.dtype);h.push(x);let y=ve({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Xne={kernelName:bi,backendName:"webgl",kernelFunc:F4},Kne="return float(a > b);",Zne=` return vec4(greaterThan(a, b)); `,Yne=Cn({opSnippet:Kne,packedOpSnippet:Zne,cpuKernelImpl:sJ,dtype:"bool"}),Jne={kernelName:wi,backendName:"webgl",kernelFunc:Yne},Qne="return float(a >= b);",ese=` return vec4(greaterThanEqual(a, b)); `,tse=Cn({opSnippet:Qne,packedOpSnippet:ese,dtype:"bool",cpuKernelImpl:rJ}),nse={kernelName:Ua,backendName:"webgl",kernelFunc:tse};function sse(e){let{inputs:t,backend:n}=e,{input:s}=t;return D4(s,!0,n)}var rse={kernelName:Rh,backendName:"webgl",kernelFunc:sse},ase="return float(!isnan(x) && !isinf(x));",ose=st({opSnippet:ase,dtype:"bool"}),ise={kernelName:pu,backendName:"webgl",kernelFunc:ose},lse="return float(isinf(x));",use=st({opSnippet:lse,dtype:"bool"}),cse={kernelName:hu,backendName:"webgl",kernelFunc:use},dse="return float(isnan(x));",pse=st({opSnippet:dse,dtype:"bool"}),hse={kernelName:fu,backendName:"webgl",kernelFunc:pse},fse="return float(a < b);",mse=` return vec4(lessThan(a, b)); `,gse=Cn({opSnippet:fse,packedOpSnippet:mse,cpuKernelImpl:aJ,dtype:"bool"}),Ase={kernelName:Si,backendName:"webgl",kernelFunc:gse},yse="return float(a <= b);",xse=` return vec4(lessThanEqual(a, b)); `,bse=Cn({opSnippet:yse,packedOpSnippet:xse,cpuKernelImpl:oJ,dtype:"bool"}),vse={kernelName:Ii,backendName:"webgl",kernelFunc:bse};function wse(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=iJ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var kse={kernelName:$h,backendName:"webgl",kernelFunc:wse},Sse=`if (x < 0.0) return NAN; return log(x);`,Ise=` 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; `,Cse=st({opSnippet:Sse,packedOpSnippet:Ise,cpuKernelImpl:lJ}),Tse={kernelName:Ha,backendName:"webgl",kernelFunc:Cse},Nse="return log(1.0 + x);",Ese=st({opSnippet:Nse}),Rse={kernelName:mu,backendName:"webgl",kernelFunc:Ese},$se="return float(a >= 1.0 && b >= 1.0);",_se=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,Dse=Cn({opSnippet:$se,packedOpSnippet:_se,dtype:"bool"}),Pse={kernelName:Ci,backendName:"webgl",kernelFunc:Dse},Fse="return float(!(x >= 1.0));",Ose=st({opSnippet:Fse}),Mse={kernelName:gu,backendName:"webgl",kernelFunc:Ose},zse="return float(a >= 1.0 || b >= 1.0);",Lse=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,Bse=Cn({opSnippet:zse,packedOpSnippet:Lse,dtype:"bool"}),Wse={kernelName:rd,backendName:"webgl",kernelFunc:Bse},Vse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`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 = -${a}; j <= ${a}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${o}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${i}; setOutput(val); } `}},Use=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${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 - ${a}; 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 = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o})); 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 * ${i}; setOutput(result); } `}},Gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=K().getBool("WEBGL_PACK_NORMALIZATION")?new Use(r.shape,a,o,i,l):new Vse(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Hse={kernelName:ad,backendName:"webgl",kernelFunc:Gse},jse=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,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 = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${s}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${s}) * 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); } `}},qse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new jse(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Xse={kernelName:_h,backendName:"webgl",kernelFunc:qse};function Kse(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Tl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function O4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let y=n.texData.get(h.dataId).values,b=new Array(i);for(let C=0;C`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return As({inputs:{x:r},backend:n});let d=new fp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var nre={kernelName:Xa,backendName:"webgl",kernelFunc:tre};function sre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new ux(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var rre={kernelName:od,backendName:"webgl",kernelFunc:sre},are=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); 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 += ${s}) { 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 < ${a}; 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 * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},ore=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=` const ivec3 pads = ivec3(${u}, ${d}, ${p}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${i}; 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 += ${a}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${o}) { float dyC = float(dyCCorner + wC) / ${s}.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} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function ire(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new ux(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new ore(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var lre={kernelName:Ph,backendName:"webgl",kernelFunc:ire};function ure(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;nc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new fp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new are(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var cre={kernelName:Dh,backendName:"webgl",kernelFunc:ure};function dre(e,t,n,s){let r=new fp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new fp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var pre={kernelName:Fh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=dre(s,i,u,l);return[d,p]}};function hre(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Tl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var fre={kernelName:Ka,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let N=0;Nc[0]+e[u]+c[1]);let s=e.length,r=yt(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=` int start = ${a}; int end = ${o}; 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 = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${s}; 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}; } } ${r} coords = outC - start; setOutput(getX(${i})); } `}},wre=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=yt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Gn("rc",s),l=Gn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; } else if (source >= end) { source = (end - 1) * 2 - source + ${d}; } source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[1] = getChannel(getX(${l.join()}), ${u}); } `}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 - ${d}) + gte * ((end - 1) * 2 - source + ${d}); source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[1] = getChannel(getX(${l.join()}), ${u}); } rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${u}); ${i[s-1]} += 1; if(${c}) { ${h} result[3] = getChannel(getX(${l.join()}), ${u}); } } `}this.userCode=` const ${r} start = ${r}(${a}); const ${r} end = ${r}(${o}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${p} setOutput(result); } `}},kre=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wre(s.shape,r,a):new vre(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Sre={kernelName:Ja,backendName:"webgl",kernelFunc:kre},Ire=`if (b == 0.0) return NAN; return mod(a, b);`,Cre=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+zm+` return result; `,Tre=Cn({opSnippet:Ire,packedOpSnippet:Cre}),Nre={kernelName:Au,backendName:"webgl",kernelFunc:Tre},Ere=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` 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})); } `}},Rre=` if (a == b) { return 1.0; }; return a / b;`,$re=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,M4=Cn({opSnippet:Rre,packedOpSnippet:$re,checkOutOfBounds:!0}),_re={kernelName:Ma,backendName:"webgl",kernelFunc:M4},z4="return a - b;",L4=Cn({opSnippet:z4,packedOpSnippet:z4,supportsComplex:!0,cpuKernelImpl:TJ}),Dre={kernelName:fo,backendName:"webgl",kernelFunc:L4};function B4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=O4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=L4({inputs:{a:r,b:c},backend:n}),d=R4({inputs:{x:u},backend:n}),p=Wm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=M4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var Pre={kernelName:po,backendName:"webgl",kernelFunc:B4};function Fre(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:B4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Ere(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Ore={kernelName:Oh,backendName:"webgl",kernelFunc:Fre},W4="return -x;";function Mre(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=hJ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ic(s.shape,W4):r=new Vo(s.shape,W4),n.runWebGLProgram(r,[s],s.dtype)}var zre={kernelName:Ti,backendName:"webgl",kernelFunc:Mre},Lre=Xs.nonMaxSuppressionV3Impl;function Bre(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Lre(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Wre={kernelName:Ei,backendName:"webgl",kernelFunc:Bre},Vre=Xs.nonMaxSuppressionV4Impl;function Ure(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Vre(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Gre={kernelName:yu,backendName:"webgl",kernelFunc:Ure},Hre=Xs.nonMaxSuppressionV5Impl;function jre(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Hre(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var qre={kernelName:Ri,backendName:"webgl",kernelFunc:jre},Xre=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${s}), float(${n}), float(index == coords.y))); } `}},Kre=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new Xre(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Zre={kernelName:_i,backendName:"webgl",kernelFunc:Kre};function jm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=jm({inputs:{x:r},backend:n}),o=Hm({inputs:{input:s},backend:n}),i=jm({inputs:{x:o},backend:n}),l=Uo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Yre={kernelName:Ki,backendName:"webgl",kernelFunc:jm};function V4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=V4({inputs:{x:r},backend:n}),o=Hm({inputs:{input:s},backend:n}),i=jm({inputs:{x:o},backend:n}),l=Uo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Jre={kernelName:$i,backendName:"webgl",kernelFunc:V4};function Qre(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return px({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=px({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=b4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var eae={kernelName:Di,backendName:"webgl",kernelFunc:Qre},tae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=yt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=` int start = ${a}; int end = ${o}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${r} start = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${i})); } } `}},nae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=yt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Gn("rc",s),l=Gn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1; if(${c}) { `,s===1?"":`} rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1; if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return gp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nae(r.shape,a,o):new tae(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},sae={kernelName:eo,backendName:"webgl",kernelFunc:U4},rae=` 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); `,aae=` // 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)); `+zm+` return result; `,oae=Cn({opSnippet:rae,packedOpSnippet:aae}),iae={kernelName:to,backendName:"webgl",kernelFunc:oae};function lae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=E.getAxesPermutation(u,i),p=r;d!=null&&(p=Hn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=mJ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=bd(r.dtype),y=Tl(A,x,"prod",n);h=ve({inputs:{x:y},backend:n,attrs:{shape:f}}),l.push(A),l.push(y)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var uae={kernelName:Pi,backendName:"webgl",kernelFunc:lae},G4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=gJ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},cae={kernelName:xu,backendName:"webgl",kernelFunc:G4},dae="return 1.0 / x;",pae=st({opSnippet:dae}),hae={kernelName:bu,backendName:"webgl",kernelFunc:pae},fae=Ar+` return (x < 0.0) ? 0.0 : x; `,mae=` 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; `,gae=st({opSnippet:fae,packedOpSnippet:mae}),Aae={kernelName:so,backendName:"webgl",kernelFunc:gae},yae=Ar+` return (x < 0.0) ? 0.0 : min(6.0, x); `,xae=` 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; `,bae=st({opSnippet:yae,packedOpSnippet:xae}),vae={kernelName:ao,backendName:"webgl",kernelFunc:bae},wae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // Compute the 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); } `}},kae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, ${i}.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 = ${d}; // 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 Sae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kae(r.shape,l,c,a,o):new wae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var Iae={kernelName:ro,backendName:"webgl",kernelFunc:Sae},Cae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { 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 >= ${o}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${s-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 Tae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Cae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Nae={kernelName:zh,backendName:"webgl",kernelFunc:Tae},Eae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},Rae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, ${i}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); // 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 $ae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Rae(r.shape,l,c,a,o):new Eae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var _ae={kernelName:vu,backendName:"webgl",kernelFunc:$ae},Dae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { 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 >= ${o}) { continue; } float sourceFracRow = float(${i[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${i[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${s}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Pae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Dae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Fae={kernelName:Mh,backendName:"webgl",kernelFunc:Pae},Oae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=yt(n);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${r})); } `}},Mae=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-${n} tensor is not yet supported`);this.outputShape=e;let s=Gn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${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() { ${o} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${i(s.slice())}; if(${r}){ result.g = ${l(s.slice())}; } if(${a}) { result.b = ${c(s.slice())}; if(${r}) { result.a = ${u(s.slice())}; } } setOutput(result); } `;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,x)=>p(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function zae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return As({inputs:{x:r},backend:n});let l=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Mae(r.shape,i):new Oae(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Lae={kernelName:Oi,backendName:"webgl",kernelFunc:zae},Bae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=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=` 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 < ${s} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},Wae={kernelName:Zi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Bae(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Vae=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Uae=st({opSnippet:Vae}),Gae={kernelName:Mi,backendName:"webgl",kernelFunc:Uae},Hae="return inversesqrt(x);",jae=st({opSnippet:Hae,cpuKernelImpl:AJ}),qae={kernelName:oo,backendName:"webgl",kernelFunc:jae},H4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(r.length),l=yt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` ${i} strides = ${i}(${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 < ${t}; j++) { int index = round(${u}); flattenedIndex += index * ${h}; } if (flattenedIndex == coords[0]) { sum += ${p}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new H4(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),x}var Kae={kernelName:zi,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function Yae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Zae(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var Jae={kernelName:Li,backendName:"webgl",kernelFunc:Yae},Qae=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${E.SELU_SCALEALPHA}; float scale = ${E.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,eoe=st({opSnippet:Qae}),toe={kernelName:wu,backendName:"webgl",kernelFunc:eoe},j4="return 1.0 / (1.0 + exp(-1.0 * x));",noe=st({opSnippet:j4,packedOpSnippet:j4,cpuKernelImpl:yJ}),soe={kernelName:lo,backendName:"webgl",kernelFunc:noe},roe=` if (isnan(x)) { return 0.0; } return sign(x); `,aoe=st({opSnippet:roe}),ooe={kernelName:ku,backendName:"webgl",kernelFunc:aoe},ioe=a4+` return sin(x); `,loe=st({opSnippet:ioe}),uoe={kernelName:io,backendName:"webgl",kernelFunc:loe},coe=` float e2x = exp(x); 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// We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},rie=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Nl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Z4(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[_,P]=EJ(F,c,r.dtype,a,o);return[n.makeTensorInfo(_.shape,_.dtype,_.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,gp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&Nl(n,h);let A=Z4(a),x=Z4(u),y=null,b=()=>y===null?[g,g]:[g,y],w=(F,_,P)=>{let T=b(),M=new sie(P),j=[[u],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[_]],z=y;y=n.runWebGLProgram(M,T,"int32",j),Nl(n,z)};for(let F=1;F=1;P/=2)w(_,P,[m,x])}for(let F=x;F>A;F/=2){let _=b(),P=new rie([m,F/2]),M=[[u],[y===null?1:0],[A]],U=y;y=n.runWebGLProgram(P,_,"int32",M),Nl(n,U);let j=A/2,z=j*2;for(let X=j;X>=1;X/=2)w(z,X,y.shape)}let k=y;y=uc({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,a]}}),Nl(n,k);let C=F4({inputs:{x:g,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Nl(n,g);let N=c.slice(0,-1);N.push(a),k=y,y=ve({inputs:{x:y},attrs:{shape:N},backend:n}),Nl(n,k);let R=C;return C=ve({inputs:{x:C},attrs:{shape:N},backend:n}),Nl(n,R),[C,y]}var oie={kernelName:ji,backendName:"webgl",kernelFunc:aie},iie=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${i} == 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 (${i} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${i} == 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(${e})); if (${o} == 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 lie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new iie(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var uie={kernelName:qi,backendName:"webgl",kernelFunc:lie};function cie(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=RJ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var die={kernelName:Hh,backendName:"webgl",kernelFunc:cie};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var hie={kernelName:Xi,backendName:"webgl",kernelFunc:pie},fie=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=` sumValue += dot(values, segFilter); `,p="";r%n>0&&(p=` 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 = ${i}; float getValue(int batch, int inIdx) { ${p} 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( ${a})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${a}))); float sumValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${d} } int inIdx = inOffset + ${c}; if (${u===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${d} } else if (${u===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${d} } else if (${u===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${d} } setOutput(${l}); } `}};function mie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=bd(r.dtype),g=(b,w,k,C,N)=>{let R=b.shape[0],F=b.shape[1],_=E.segment_util.segOpComputeOptimalWindowSize(F,N),P={windowSize:_,inSize:F,batchSize:R,numSegments:N},T=new fie(P,w),M=n.compileAndRun(T,[b,k],C);if(l.push(M),M.shape[1]===N)return M;let U=G4({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),j=K4({inputs:{x:U},backend:n,attrs:{reps:[F/_]}});return l.push(U),l.push(j),g(M,w,j,C,N)},A=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),y=x;if(u!=null){l.push(x);let b=E.getUndoAxesPermutation(u);y=Hn({inputs:{x:y},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var gie={kernelName:cd,backendName:"webgl",kernelFunc:mie},Aie=[Hse,Xse,EQ,$Q,PQ,MQ,LQ,VQ,GQ,jQ,ZQ,JQ,tee,ree,dee,iee,fee,yee,gee,wee,See,Cee,Ree,Mee,Lee,Wee,qee,Kee,Qee,nte,cQ,ite,Ate,xte,dte,kte,Ite,vte,Nte,$te,Pte,Ote,zte,Wte,qte,Kte,Ute,Jte,tne,sne,ine,dne,mne,yne,xne,bne,wne,Sne,Cne,Nne,Rne,Pne,Mne,Bne,Vne,Hne,Xne,Jne,nse,uQ,rse,ate,ise,cse,hse,pQ,Ase,vse,kse,Rse,Tse,Pse,Mse,Wse,Zse,rre,nre,lre,cre,pre,ere,fre,gre,bre,Sre,Nre,Ore,AQ,zre,Wre,Gre,qre,Uee,Zre,Jre,eae,sae,iae,fQ,uae,cae,Gee,_re,hae,vae,Aae,xQ,Iae,Nae,_ae,Fae,Lae,Wae,Gae,qae,Kae,Jae,toe,soe,ooe,uoe,poe,Fee,Pre,moe,Aoe,xoe,voe,koe,Ioe,Toe,Eoe,$oe,Poe,Ooe,zoe,Woe,Uoe,Hoe,qoe,Dre,CQ,Zoe,Qoe,nie,oie,uie,TQ,die,hie,gie,Yre];for(let e of Aie)rr(e);var Lr=K();Lr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Lr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Lr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Lr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Lr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Lr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Lr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Lr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Lr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Lr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function yie(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function vn(e){if(e<=1)return"i32";if(e===2)return"vec2";if(e===3)return"vec3";if(e===4)return"vec4";throw Error(`GPU for rank ${e} is not yet supported`)}function qm(e,t){return e==="float32"?t?"vec4":"f32":e==="int32"||e==="bool"?t?"vec4":"i32":e}function Xm(){return` [[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]] `}function hx(){return` ${Xm()} fn main([[builtin(local_invocation_id)]] localId : vec3, [[builtin(global_invocation_id)]] globalId : vec3, [[builtin(num_workgroups)]] numWorkgroups: vec3) `}function Go(){return` ${Xm()} fn main([[builtin(local_invocation_id)]] localId : vec3, [[builtin(global_invocation_id)]] globalId : vec3) `}function tt(){return` ${hx()} { let index = getGlobalIndex(globalId, localId, numWorkgroups); `}function xie(e,t,n,s=!1){let r=` let workGroupSizeX = ${n.workGroupSize[0]}u; let workGroupSizeY = ${n.workGroupSize[1]}u; let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=Q4(t.shape),f=` [[block]] struct Matrix0 { numbers: array<${qm(t.dtype,n.isVec4)}>; }; [[block]] struct Uniform { size : i32; numChannels : i32; outShapeStrides : vec2; dispatchSize : vec3; }; [[group(0), binding(0)]] var result : Matrix0; [[group(0), binding(2)]] var uniforms: Uniform; `;return[Y4,f,r,J4,h,n.getUserCode()].join(` `)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${vn(e[f].shape.length)}; `}),o+=`outShape : ${vn(t.shape.length)} ; `;let i=t.shape.length-1;o+=` outShapeStrides: ${vn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(` [[block]] struct Matrix0 { numbers: array>; }; [[group(0), binding(0)]] var result : Matrix0; `):a.push(` [[block]] struct Matrix0 { numbers: array<${qm(t.dtype,n.isVec4)}>; }; [[group(0), binding(0)]] var result : Matrix0; `),n.variableNames.forEach((h,f)=>{a.push(` [[block]] struct Matrix${1+f} { numbers: array<${qm(e[f].dtype,n.isVec4)}>; }; [[group(0), binding(${1+f})]] var ${h} : Matrix${1+f}; `)}),o!==""&&a.push(` [[group(0), binding(${1+n.variableNames.length})]] var uniforms : Uniforms; `),a.push(r);let[l,c]=Iie(t.shape,n.dispatchLayout),u=Q4(t.shape),d=[Y4,a.join(` `),J4,u,l,bie(t.shape.length)];if(n.atomic||d.push(vie(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>wie(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(` `);d.push(h)}return d.push(n.getUserCode()),d.join(` `)}var Y4=` fn idiv(a: i32, b: i32, sign: f32) -> i32 { var res: i32 = a / b; let mod: i32 = a % b; if (sign < 0. && mod != 0) { res = res - 1; } return res; } fn isNanCustom(val : f32) -> bool { if (val > 0.0) { return false; } if (val < 0.0) { return false; } if (val == 0.0) { return false; } return true; } fn isNanCustomVec4F32(val : vec4) -> vec4 { var res = vec4 (0.0); for (var i = 0u; i < 4u; i = i + 1u) { if (isNanCustom(val[i])) { res[i] = 1.0; } else { res[i] = 0.0; } } return res; } // Checks whether coordinates lie within the bounds of the shape. fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool { return all(coord >= vec4(0)) && all(coord < shape); } fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool { return all(coord >= vec3(0)) && all(coord < shape); } fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool { return all(coord >= vec2(0)) && all(coord < shape); } `,J4=` fn getFlatIndex1D(coord : i32, shape : i32) -> i32 { return coord; } fn getFlatIndex2D(coords : vec2, shape : vec2) -> i32 { return i32(dot(vec2(coords), vec2(f32(shape.y), 1.0))); } fn getFlatIndex3D(coords : vec3, shape : vec3) -> i32 { return i32(dot(vec3(coords), vec3(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0))); } fn getFlatIndex4D(coords : vec4, shape : vec4) -> i32 { return i32(dot(vec4(coords), vec4( f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0))); } // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex(globalId : vec3, localId : vec3, numWorkgroups: vec3) -> i32 { if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) { return i32(globalId.x); } let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY + localId.y * workGroupSizeX + localId.x; let workGroupID = (globalId - localId)/vec3( workGroupSizeX, workGroupSizeY, workGroupSizeZ); return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y + workGroupID.y * numWorkgroups.x + workGroupID.x) * (workGroupSizeX * workGroupSizeY * workGroupSizeZ) + localInvocationIndex); } `;function bie(e){let t="";switch(e){case 0:case 1:t+=` fn getOutputFlatIndex(coords : i32) -> i32 { return coords; } `;break;case 2:t+=` fn getOutputFlatIndex(coords : vec2) -> i32 { return i32(dot(vec2(coords), vec2(f32(uniforms.outShapeStrides), 1.0))); } `;break;case 3:t+=` fn getOutputFlatIndex(coords : vec3) -> i32 { return i32(dot(vec3(coords), vec3(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0))); } `;break;case 4:t+=` fn getOutputFlatIndex(coords : vec4) -> i32 { return i32(dot(vec4(coords), vec4( f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0))); } `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function vie(e,t,n){let s=e.length,r=qm(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4) { result.numbers[flatIndex] = ${r}(value); } fn setOutputFlatI32(flatIndex : i32, value : vec4) { result.numbers[flatIndex] = ${r}(value); }`:a=`fn setOutputFlat(flatIndex : i32, value : f32) { result.numbers[flatIndex] = ${r}(value); } fn setOutputFlatI32(flatIndex : i32, value : i32) { result.numbers[flatIndex] = ${r}(value); }`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=vn(s);n?a+=` fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlat(flatIndex / 4, value); } fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlatI32(flatIndex / 4, value); } `:a+=` fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlat(flatIndex, value); } fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) { let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")})); setOutputFlatI32(flatIndex, value); } `}return a}function wie(e,t,n,s){let r=kie(e,n);return e.shape.length<=t.length&&(r+=Sie(e,t,n,s)),r}function kie(e,t){let n=e.name,s=e.shape.length,r=vn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?` fn ${a}() -> vec4 { return vec4(${n}.numbers[0]); } `:` fn ${a}() ->f32 { return f32(${n}.numbers[0]); } `;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?` fn ${a}(${i}) -> vec4 { return vec4(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}), ${l}) / 4]); } `:` fn ${a}(${i}) -> f32 { return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}), ${l})]); } `}function Sie(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=vn(l);if(v.arraysEqual(e.shape,t)&&s)return n?` fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4 { return vec4(${r}.numbers[globalIndex]); } fn ${o}ByCoords(coords : ${c}) -> vec4 { return vec4(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]); } `:` fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 { return f32(${r}.numbers[globalIndex]); } fn ${o}ByCoords(coords : ${c}) -> f32 { return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]); } `;let u=E.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?` fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4 { return get${a}(); } fn ${o}ByCoords(coords : ${c}) -> vec4 { return get${a}(); } `:` fn ${o}ByGlobalIndex(globalIndex : i32) -> f32{ return get${a}(); } fn ${o}ByCoords(coords : ${c}) -> f32{ return get${a}(); } `;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(` `);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=vn(i),A=e.shape.map((x,y)=>`coords[${y+d}]`).join(", ");h=`${g}(${A})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?` fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4 { var coords = getCoordsFromFlatIndex(globalIndex); ${p} return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4]; } fn ${o}ByCoords(coordsIn : ${c}) -> vec4 { var coords = coordsIn; ${p} return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4]; } `:` fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 { var coords = getCoordsFromFlatIndex(globalIndex); ${p} return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]); } fn ${o}ByCoords(coordsIn : ${c}) -> f32 { var coords = coordsIn; ${p} return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]); } `}function Iie(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3, localId : vec3, numWorkgroups: vec3) -> ${vn(a)}{ let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups); return getCoordsFromFlatIndex(globalIndex); } `,a];let o="",i=[n,s,r],l=0;for(let p=0;p) -> ${u} { ${o} `;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function Q4(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=vn(t),r=[];for(let o=0;o vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); }`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return` fn getCoordsFromFlatIndex(index : i32) -> ${s} { ${a} return ${s}(${r.join(",")}); } `}var eC={};Oe(eC,{ArrayBufferToTypedArray:()=>tC,GPUBytesPerElement:()=>Ax,computeDispatch:()=>Fe,computeWorkGroupSizeForConv2d:()=>fx,computeWorkGroupSizeForMatMul:()=>mx,computeWorkPerThreadForConv2d:()=>gx,flatDispatchLayout:()=>je,isWebGPUSupported:()=>yx,tilesFitEvenlyIntoShape:()=>ra});var pc=65535,El=e=>{let t=1;for(let n=0;nn%e[s]==0)}function Fe(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(El(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(El(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(El(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=pc&&a<=pc&&o<=pc)return[r,a,o];v.assert(r>pc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>pc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=pc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function fx(e,t){let n=El(e.x.map(r=>t[r])),s=El(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function mx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function gx(e,t){let n=El(e.x.map(r=>t[r])),s=El(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function je(e){return{x:e.map((t,n)=>n)}}function Ax(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function tC(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,Hie=` if (isNanCustom(a)) { return a; } if (isNanCustom(b)) { return b; } `,nC=` if (isNaN.r > 0.) { resultTemp.r = uniforms.NAN; } if (isNaN.g > 0.) { resultTemp.g = uniforms.NAN; } if (isNaN.b > 0.) { resultTemp.b = uniforms.NAN; } if (isNaN.a > 0.) { resultTemp.a = uniforms.NAN; } `,jie=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `,qie=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); var resultTemp = vec4(0); let s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { resultTemp[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { resultTemp[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { resultTemp[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); `,Xie="return f32(a != b);",Kie="return vec4(a != b);",Zie=` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } if (b == 0.0) { return 1.0; } if (round(abs(b) % 2.0) != 1.0) { return pow(abs(a), b); } return sign(a) * pow(abs(a), b); `,Yie=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); var resultTemp = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS let isExpZero = b == vec4(0.0); if (isExpZero.r) { resultTemp.r = 1.0; } if (isExpZero.g) { resultTemp.g = 1.0; } if (isExpZero.b) { resultTemp.b = 1.0; } if (isExpZero.a) { resultTemp.a = 1.0; } let isNaN = vec4(a < vec4(0.0)) * vec4(floor(b) < b); ${nC} return resultTemp; `,Jie="if (a < 0.0) { return b * a; } return a;",Qie=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function sC(e,t){let n=t?nC:Hie;return t?` var resultTemp = vec4(${e}(a, b)); let isNaN = min(vec4(isNanCustomVec4F32(a)) + vec4(isNanCustomVec4F32(b)), vec4(1.0)); `+n+` return resultTemp; `:n+` return ${e}(a, b); `}function Ap(e,t){switch(e){case 0:return Rie;case 1:return Cie;case 2:return _ie;case 3:return Eie;case 4:return t?Pie:Die;case 5:return t?Oie:Fie;case 6:return t?zie:Mie;case 7:return t?Bie:Lie;case 8:return t?Vie:Wie;case 9:return t?Gie:Uie;case 10:return t?Kie:Xie;case 11:return $ie;case 12:return t?qie:jie;case 14:return t?Qie:Jie;case 15:return sC("max",t);case 16:return sC("min",t);case 13:return t?Yie:Zie;case 17:return Tie;case 18:return Nie;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var xt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(xt||(xt={}));var ele="return abs(a);",tle="return ceil(a);",nle="return cos(a);",sle=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,rle="return exp(a) - 1.0;",ale="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",ole=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; } if (a.g >= 0.0) { resFloat.g = a.g; } if (a.b >= 0.0) { resFloat.b = a.b; } if (a.a >= 0.0) { resFloat.a = a.a; } return resFloat; `,ile="return exp(a);",lle="return floor(a);",ule="return a;",cle=`if (a < 0.0) { return 1.0/0.0; } return log(a);`,dle="return f32(!(a >= 1.0));",ple="return -a;",hle="return (a < 0.0) ? b * a : a;",fle="return max(a, 0.0);",mle="return clamp(a, 0.0, 6.0);",gle="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",Ale=` var resFloat = a * vec4(a >= vec4(0.0)); let isNaN = isNan(a); if (isNaN.r) { resFloat.r = a.r; } if (isNaN.g) { resFloat.g = a.g; } if (isNaN.b) { resFloat.b = a.b; } if (isNaN.a) { resFloat.a = a.a; } return resFloat; `,yle="return 1.0/sqrt(a);",xle="return 1.0 / (1.0 + exp(-1.0 * a));",ble="return sin(a);",vle=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,wle="return sqrt(a);",kle="return a * a;",Sle=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,Ile="return f32(i32((a)));";function hc(e,t){switch(e){case 0:return ele;case 2:return nle;case 3:return sle;case 1:return tle;case 4:return t?ole:ale;case 5:return ile;case 6:return rle;case 7:return lle;case 8:return ule;case 9:return cle;case 10:return dle;case 11:return ple;case 12:return hle;case 13:return t?Ale:fle;case 14:return t?gle:mle;case 15:return yle;case 18:return xle;case 16:return ble;case 17:return vle;case 19:return wle;case 20:return kle;case 21:return Sle;case 22:return Ile;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function aa(e,t=!1){if(e===null)return null;if(e==="linear")return hc(xt.LINEAR);if(e==="relu")return hc(xt.RELU,t);if(e==="elu")return hc(xt.ELU,t);if(e==="relu6")return hc(xt.RELU6,t);if(e==="prelu")return Ap(Wt.PRELU,t);if(e==="sigmoid")return hc(xt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function rC(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return` var mm_Asub : array, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>; var mm_Bsub : array, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>; let RowPerThread = ${n.RowPerThread}; let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4 let TileAOuter = ${n.TileAOuter}; let TileBOuter = ${n.TileBOuter}; let TileInner = ${n.TileInner}; ${Go()} { let tileRow = i32(localId.y) * RowPerThread; let tileCol = i32(localId.x); let globalRow = i32(globalId.y) * RowPerThread; let globalCol = i32(globalId.x); let numTiles = (uniforms.dimInner - 1) / TileInner + 1; var acc: array, ${n.RowPerThread}>; var ACached : vec4; var BCached : array, 4>; // Loop over shared dimension. var globalColA = tileCol; let RowPerThreadB = TileInner / ${t[1]}; let tileRowB = i32(localId.y) * RowPerThreadB; for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { let inputRow = tileRow + innerRow; let inputCol = tileCol; mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId); } globalColA = globalColA + TileInner / ColPerThread; // Load one tile of B into local memory. for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId); } workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < TileInner / ColPerThread; k = k + 1) { BCached[0] = mm_Bsub[k * ColPerThread][tileCol]; BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol]; BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol]; BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol]; for (var i = 0; i < RowPerThread; i = i + 1) { ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached[0] * ACached.x + acc[i]; acc[i] = BCached[1] * ACached.y + acc[i]; acc[i] = BCached[2] * ACached.z + acc[i]; acc[i] = BCached[3] * ACached.w + acc[i]; } } workgroupBarrier(); } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { mm_write(globalRow + innerRow, globalCol, acc[innerRow], globalId); } }`}function Cle(e){return` var mm_Asub : array, ${e[0]}>; let tileSize = ${e[0]*4}; ${Go()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / tileSize + 1; // Without this initialization strange values show up in acc. var acc = vec4(0.0); // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. let colA = t * tileSize / 4 + tileCol; mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < tileSize / 4; k = k + 1) { let rowB = t * tileSize + k * 4; let BCached0 = mm_readB(rowB, globalCol, globalId); let BCached1 = mm_readB(rowB + 1, globalCol, globalId); let BCached2 = mm_readB(rowB + 2, globalCol, globalId); let BCached3 = mm_readB(rowB + 3, globalCol, globalId); let ACached = mm_Asub[k]; acc = acc + BCached0 * ACached.x; acc = acc + BCached1 * ACached.y; acc = acc + BCached2 * ACached.z; acc = acc + BCached3 * ACached.w; } workgroupBarrier(); } if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) { mm_write(globalRow, globalCol, acc, globalId); } } `}var Tle=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=mx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ra(o,this.aShape.slice(1)),ra(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]; } return vec4(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]; } return vec4(0.0)`,n="",s="";if(this.activation){let o=aa(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4, outCoord : vec3) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=` fn activation(a : vec4, outCoord : vec3) -> vec4 { ${o} }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize}; let batch = i32(globalId.z); ${e}; } fn mm_readB(row : i32, col : i32, globalId : vec3) -> vec4 { let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize}; let batch = i32(globalId.z); ${t}; } fn mm_write(row : i32, col : i32, valueIn : vec4, globalId : vec3) { if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2]) { var value = valueIn; let batch = i32(globalId.z); let outCoord = vec3(batch, row, col * 4); ${r} ${s} setOutput(outCoord[0], outCoord[1], outCoord[2], value); } } ${this.outputShape[1]>1?rC([this.vecSize,this.workPerThread,1],this.workGroupSize):Cle(this.workGroupSize)} `}};function xx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return` var mm_Asub : array, ${n}>; var mm_Bsub : array, ${r}>; ${Go()} { let tileRow = i32(localId.y) * ${e[1]}; let tileCol = i32(localId.x) * ${e[0]}; let globalRow = i32(globalId.y) * ${e[1]}; let globalCol = i32(globalId.x) * ${e[0]}; let numTiles = (uniforms.dimInner - 1) / ${r} + 1; var acc : array, ${e[1]}>; var ACached : f32; var BCached : array; // Without this initialization strange values show up in acc. for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) { acc[innerRow][innerCol] = 0.0; } } let ColPerThreadA = ${r} / ${t[0]}; let tileColA = i32(localId.x) * ColPerThreadA; let RowPerThreadB = ${r} / ${t[1]}; let tileRowB = i32(localId.y) * RowPerThreadB; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) { let inputRow = tileRow + innerRow; let inputCol = tileColA + innerCol; mm_Asub[inputRow][inputCol] = mm_readA( globalRow + innerRow, t * ${r} + inputCol, globalId); } } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; mm_Bsub[inputRow][inputCol] = mm_readB( t * ${r} + inputRow, globalCol + innerCol, globalId); } } workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < ${r}; k = k + 1) { for (var inner = 0; inner < ${e[0]}; inner = inner + 1) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) { ACached = mm_Asub[tileRow + innerRow][k]; for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) { if ((globalCol + innerCol) < uniforms.dimBOuter && (globalRow + innerRow) < uniforms.dimAOuter) { mm_write(globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol], globalId); } } } } `}function Nle(e){return` let TileSize = ${e[0]*4}; var mm_Asub : array, ${e[0]}>; ${Go()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / TileSize + 1; // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. let colA = t * TileSize + tileCol * 4; mm_Asub[tileCol] = vec4(mm_readA(globalRow, colA, globalId), mm_readA(globalRow, colA + 1, globalId), mm_readA(globalRow, colA + 2, globalId), mm_readA(globalRow, colA + 3, globalId)); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < TileSize / 4; k = k + 1) { let rowB = t * TileSize + k * 4; let BCached = vec4(mm_readB(rowB, globalCol, globalId), mm_readB(rowB + 1, globalCol, globalId), mm_readB(rowB + 2, globalCol, globalId), mm_readB(rowB + 3, globalCol, globalId)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) { mm_write(globalRow, globalCol, acc, globalId); } } `}var aC=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=mx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ra(r,this.aShape.slice(1)),ra(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch * batchASize + row * uniforms.dimInner + col]; } return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row]; } return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col]; } return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + col * uniforms.dimInner + row]; } return 0.0;`;let n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=` fn activation(a : f32, outCoord : vec3) -> f32 { ${o} } `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; let batch = i32(globalId.z); ${e} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { let batch = i32(globalId.z); let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; ${t} } fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3) { var value = valueIn; let batch = i32(globalId.z); let outCoord = vec3(batch, row, col); ${r} ${s} setOutput(batch, row, col, value); } ${this.outputShape[1]>1?xx([this.workPerThread,this.workPerThread,1],this.workGroupSize):Nle(this.workGroupSize)} `}};function Ele(){return` var sumValues : array; ${Go()} { let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId); let batch = coords[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) { let dataA = mm_readA(batch, row, k); let dataB = mm_readB(batch, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); for(var currentSize = workGroupSizeX / 2u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var Rle=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=` fn activation(a : f32, outCoord : vec3) -> f32 { ${o} } `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(batch: i32, row : i32, col : i32) -> f32 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; ${e} } fn mm_readB(batch: i32, row : i32, col : i32) -> f32 { let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; ${t} } fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) { var value = valueIn; let outCoord = vec3(batch, row, col); ${r} ${s} setOutput(batch, row, col, value); } ${Ele()} `}};function $le(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return` var mm_Asub1 : array, ${t}>; var mm_Bsub1 : array, ${s}>; var mm_Asub2 : array, ${t}>; var mm_Bsub2 : array, ${s}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. // Introduces two shared memory buffers, some logical threads could handle // arithmetic operations and others handle IO operations between barrier api, // makes ALUs and load/store units work simultaneously, could improves // the performance. ${Go()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${s} + 1; var acc = 0.0; var globalColA = tileCol; var globalRowB = tileRow; for (var t = 0; t < numTiles; t = t + 1) { if (t == 0) { if (tileRow < ${t}) { // Load one tile of A and B into local memory. // globalRow is always greater than or equal tileRow. mm_Asub1[tileRow][tileCol] = mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); globalColA = globalColA + ${s}; mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } } else { if (tileRow < ${t}) { // Load one tile of A and B into local memory. // globalRow is always greater than or equal tileRow. mm_Asub1[tileRow][tileCol] = mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); globalColA = globalColA + ${s}; mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } else { // Compute acc values for a single thread. for (var k = 0; k < ${s}; k = k + 1) { let subRow = tileRow - ${t}; if (subRow < 0) { continue; } acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol]; } } } workgroupBarrier(); if (t != 0) { t = t + 1; } if (t < numTiles) { if (tileRow < ${t}) { // Load one tile of A and B into local memory. // globalRow is always greater than or equal tileRow. mm_Asub2[tileRow][tileCol] = mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); globalColA = globalColA + ${s}; mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); globalRowB = globalRowB + ${s}; } else { // Compute acc values for a single thread. for (var k = 0; k < ${s}; k = k + 1) { let subRow = tileRow - ${t}; if (subRow < 0) { continue; } acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol]; } } } workgroupBarrier(); } let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t}; if (tileRow >= ${t} && writeCol >= 0) { mm_write(writeCol, globalCol, acc, globalId); } } `}var _le=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { return A.numbers[batch * batchASize + row * uniforms.dimInner + col]; } return 0.0;`,t=`if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col]; } return 0.0;`,n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${o} }`:n=`fn activation(a : f32, outCoord : vec3) -> f32 { ${o} }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${n} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; let batch = i32(globalId.z); ${e} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { let batch = i32(globalId.z); let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; ${t} } fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3) { if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimBOuter))) { let batch = i32(globalId.z); let outCoord = vec3(batch, row, col); var value = valueIn; ${r} ${s} setOutput(batch, row, col, value); } } ${$le(this.workGroupSize)} `}};function Ge(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Dle={kernelName:Fi,backendName:"webgpu",kernelFunc:Ge};function bx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=nl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=Ge({inputs:{x:e},backend:r,attrs:{shape:w}}),N=Ge({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,N],F=Math.max(A,x),_=d%4==0&&f%4==0&&!n&&!s&&f>=32,P;h*f<=32?P=new Rle([F,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?P=new _le(w,k,[F,h,f],a,l,o):_?P=new Tle(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):P=new aC(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[C,N];a&&T.push(a),o&&T.push(o);let M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=r.runWebGPUProgram(P,T,e.dtype,M),j=Ge({inputs:{x:U},backend:r,attrs:{shape:b}});R.push(U);for(let z of R)r.disposeData(z.dataId);return j}function Ple(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return bx({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Fle={kernelName:yo,backendName:"webgpu",kernelFunc:Ple},oC=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${Ap(this.op,!1)} } ${tt()} if(index < uniforms.size) { let areal = getARealAtOutCoordsByGlobalIndex(index); let aimag = getAImagAtOutCoordsByGlobalIndex(index); let breal = getBRealAtOutCoordsByGlobalIndex(index); let bimag = getBImagAtOutCoordsByGlobalIndex(index); setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}},Ole=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords); let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}]; let b = getBAtOutCoordsByCoords(coords);`;return` fn binaryOperation(a : f32, b : f32) -> f32 { ${Ap(this.op,!1)} } var sharedBuf : array; ${tt()} // Fill in the shared memory buffer. Here we need a loop to make sure // that all data in A|B are uploaded when |sharedMemorySize| is larger // than work group size. for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) { sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]); } workgroupBarrier(); for(var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if(flatIndex < uniforms.size) { let coords = getCoordsFromFlatIndex(flatIndex); ${t} setOutputFlat(flatIndex, binaryOperation(a, b)); } } } `}},Mle=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return` fn binaryOperation(a : vec4, b : vec4) -> vec4 { ${Ap(this.op,this.isVec4)} } ${tt()} if (index < uniforms.size) { let a = getAAtOutCoordsByGlobalIndex(index); let b = getBAtOutCoordsByGlobalIndex(index); setOutputFlat(index, binaryOperation(a, b)); } } `}},iC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return` fn binaryOperation(a : f32, b : f32) -> f32 { ${Ap(this.op,!1)} } ${tt()} if (index < uniforms.size) { let a = getAAtOutCoordsByGlobalIndex(index); let b = getBAtOutCoordsByGlobalIndex(index); setOutputFlat(index, binaryOperation(a, b)); } } `}};function lC(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Mle(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Ole(e,t,n,a):new iC(e,t,n)}function Js(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var zle={kernelName:Ga,backendName:"webgpu",kernelFunc:Js};function fc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Js({inputs:{x:s},backend:n}),l=Js({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Lle={kernelName:Jc,backendName:"webgpu",kernelFunc:fc},Km=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${hc(this.op,!1)} } ${tt()} if (index < uniforms.size) { let a = getAAtOutCoordsByGlobalIndex(index); setOutputFlat(index, unaryOperation(a)); } } `}};function Tn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new Km(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function jn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==Wt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[A,x]=g,y={dataId:A.dataId,dtype:A.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=lC(e,o.shape,i.shape);return l.runWebGPUProgram(w,[y,b],zn(A.dtype,x.dtype))});else{let g=new oC(Wt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new oC(Wt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(A,x,"float32")}let m=fc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||zn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=lC(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Ble,ceilImpl:Wle,concatImpl:Vle,equalImpl:Ule,expImpl:Gle,expm1Impl:Hle,floorImpl:jle,gatherNdImpl:qle,gatherV2Impl:Xle,greaterEqualImpl:Kle,greaterImpl:Zle,lessEqualImpl:Yle,lessImpl:Jle,logImpl:Qle,maxImpl:eue,maximumImpl:tue,minimumImpl:nue,multiplyImpl:sue,negImpl:rue,notEqualImpl:aue,prodImpl:oue,rangeImpl:iue,rsqrtImpl:lue,simpleAbsImpl:uue,sliceImpl:cue,stridedSliceImpl:due,stringNGramsImpl:pue,subImpl:hue,tileImpl:fue,topKImpl:mue,transposeImpl:gue,uniqueImpl:C2e}=bm,Aue=Tn({opType:xt.ABS,cpuKernelImpl:uue}),yue={kernelName:ci,backendName:"webgpu",kernelFunc:Aue},xue=jn({opSnippet:Wt.ADD,cpuKernelImpl:Ble,supportsComplex:!0}),bue={kernelName:Gr,backendName:"webgpu",kernelFunc:xue},vue=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return` ${tt()} for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromFlatIndex(flatIndex); ${e.join(` `)} setOutputFlat(flatIndex, ${t}); } } } `}};function wue(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Js({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>zn(i,l)),a=s.map(i=>i.shape),o=new vue(a);return n.runWebGPUProgram(o,s,r)}var kue={kernelName:Ca,backendName:"webgpu",kernelFunc:wue},uC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=` var xBestIndices : array; var xBestValues : array; `,n=` xBestIndices[localId.x] = bestIndex; xBestValues[localId.x] = bestValue; for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) { workgroupBarrier(); for (var w = 0; w < ${this.reductionFactor}; w = w + 1) { let i = i32(localId.x) * ${this.reductionFactor} + w; if (i < currentSize) { let candidateIndex = xBestIndices[i]; let candidate = xBestValues[i]; if(candidate ${this.op} bestValue && !isNanCustom(candidate)) { bestValue = candidate; bestIndex = candidateIndex; } } } xBestIndices[localId.x] = bestIndex; xBestValues[localId.x] = bestValue; } if (localId.x == 0u) { setOutputFlatI32(flatOutputIndex, i32(bestIndex)); } `,s=(o,i)=>this.outputShape.length===1?o:`${o}[${i}]`,r=o=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${o}]`;return` fn DIV_CEIL(a : i32, b : i32) -> i32 { return ((a - 1) / b + 1); } let WorkGroupSize = ${this.workGroupSize[0]}; ${e?t:""} // In order to get a flattened index into the input tensor, we need to // add back the index along the reduced dimension to |outputCoords|. // This function outputs the offset to the first value along // |axis| and the stride to get the next value of the input along |axis|. fn getInputCoordInfo(globalId : vec3) -> vec2{ let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId); var i = ${this.outputShape.length-1}; var stride = 1; var inputStride = 1; var offset = 0; for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) { let length = ${r(`${this.inputShape.length} - r`)}; if (${this.inputShape.length} - r == uniforms.axis) { inputStride = stride; } else { offset = offset + ${s("outputCoords","i")} * stride; i = i - 1; } stride = stride * length; } return vec2(offset, inputStride); } fn getInputIndex(coordInfo : vec2, index : i32) -> i32{ return coordInfo[0] + coordInfo[1] * index; } ${Go()} { let coordInfo = getInputCoordInfo(globalId); var bestIndex = 0; var bestValue = f32(x.numbers[getInputIndex(coordInfo, bestIndex)]); let Length = ${r("uniforms.axis")}; let WorkPerThread = DIV_CEIL(Length, WorkGroupSize); for (var w = 0; w < WorkPerThread; w = w + 1) { let i = i32(globalId.x) * WorkPerThread + w; if (i < Length) { let candidate = f32(x.numbers[getInputIndex(coordInfo, i)]); if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) { bestValue = candidate; bestIndex = i; } } } let flatOutputIndex = i32(globalId.y); ${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"} } `}},Sue=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s tile : array, ${this.workGroupSize[0]}>; ${Xm()} fn main([[builtin(local_invocation_id)]] localId : vec3, [[builtin(workgroup_id)]] workgroupId : vec3) { var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x); var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { tile[localId.y][localId.x] = A.numbers[y * width + x]; } workgroupBarrier(); x = i32(workgroupId.y) * TILE_DIM + i32(localId.x); y = i32(workgroupId.x) * TILE_DIM + i32(localId.y); if (x < height && y < width) { setOutputFlat((y * height + x), tile[localId.x] [localId.y]); } } `}},Iue=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;sn.disposeData(h.dataId)),p}var Eue={kernelName:Ta,backendName:"webgpu",kernelFunc:Nue};function Rue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Rl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new uC(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var $ue={kernelName:ru,backendName:"webgpu",kernelFunc:Rue},cC=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2; pad : vec2; dilation : vec2; convDims : vec2; filterDims : vec2;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let batch = coords[0]; let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"}; var count = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) { let xR = xRCorner + wR; if (xR < 0 || xR >= uniforms.convDims.x) { continue; } for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) { let xC = xCCorner + wC; if (xC < 0 || xC >= uniforms.convDims.y) { continue; } let value = getX(batch, xR, xC, coords[3]); ${e} } } setOutputFlat(index, ${t}); } } `}},dC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.stride; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputFlat(index, value); } } `}};function _ue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Js({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new dC(u):(d=new cC(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Due={kernelName:Na,backendName:"webgpu",kernelFunc:_ue};function Pue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return bx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Fue={kernelName:Ea,backendName:"webgpu",kernelFunc:Pue},Oue=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${vn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=vn(this.rank),t=Mue(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${vx[a]} = uniforms.start[${a}] + coords.${vx[a]};`),` ${tt()} if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromFlatIndex(index); ${n.join(` `)} setOutputFlat(index, getSource(${t})); } } `}},vx=["x","y","z","w","u","v"];function Mue(e){if(e===1)return"sourceLoc";if(e<=6)return vx.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function mc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Pt.parseSliceParams(r,a,o);if(Pt.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=cue(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new Oue(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var zue={kernelName:Bi,backendName:"webgpu",kernelFunc:mc},Lue=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=Ge({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Rl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=Ge({inputs:{x:m},backend:n,attrs:{shape:u}}),A=mc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),A},Bue={kernelName:di,backendName:"webgpu",kernelFunc:Lue},pC=jn({opSnippet:Wt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:aue}),Wue={kernelName:Ni,backendName:"webgpu",kernelFunc:pC};function yp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Js({inputs:{x:r.complexTensorInfos.real},backend:n})}var Vue={kernelName:id,backendName:"webgpu",kernelFunc:yp};function Uue(e,t){let n=new Km(e.shape,xt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function wx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Js({inputs:{x:r},backend:n});let o=Gt(r.shape),i=wx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=fc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=yp({inputs:{input:r},backend:n}),i=wx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Js({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Uue(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=pC({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Gue={kernelName:Ra,backendName:"webgpu",kernelFunc:wx},Hue=Tn({opType:xt.CEIL,cpuKernelImpl:Wle}),jue={kernelName:$a,backendName:"webgpu",kernelFunc:Hue},que=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${tt()} if(index < uniforms.size) { let value = getAAtOutCoordsByGlobalIndex(index); var clampedValue : vec4; for (var i = 0; i < 4; i = i + 1) { if (isNanCustom(value[i])) { clampedValue[i] = value[i]; } else { clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); } } setOutputFlat(index, clampedValue); } } `}},Xue=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return` ${tt()} if(index < uniforms.size) { let value = getAAtOutCoordsByGlobalIndex(index); if (isNanCustom(value)) { setOutputFlat(index, value); return; } setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } `}};function Kue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new que(r.shape):i=new Xue(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Zue={kernelName:Hr,backendName:"webgpu",kernelFunc:Kue},Yue=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;ayp({inputs:{input:m},backend:n})),d=e.map(m=>Zm({inputs:{input:m},backend:n})),p=kx(u,t,n),h=kx(d,t,n),f=fc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return Ge({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=Vle(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeData(A.dataId)),g}let{tensors2D:a,outShape:o}=Que(e,t,n),i=new Yue(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=Ge({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Que(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ge({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function hC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Js({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),kx(i,a,n)}var ece={kernelName:pi,backendName:"webgpu",kernelFunc:hC},tce=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2; stride : vec2; dilation : vec2; outWidth : i32; itemsPerBlockRow : i32; inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return` ${tt()} for(var i = 0; i<${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; let rc = getCoordsFromFlatIndex(flatIndex); if(flatIndex < uniforms.size) { let blockIndex = rc[0]; let pos = rc[1]; let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1]; let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow; var value = 0.0; if(d0 < uniforms.aShape[${e}] && d0 >= 0) { let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0]; let d1 = offsetX + uniforms.dilation[0] * ((pos % uniforms.itemsPerBlockRow) / uniforms.inChannels); let ch = pos % uniforms.inChannels; if(d1 < uniforms.aShape[${t}] && d1 >= 0) { value = getA(d0, d1, ch); } } setOutputFlat(flatIndex, value); } } } `}};function fC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=Ge({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=bx({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Ge({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function nce({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:A,dataFormat:x}=n,y=x==="channelsLast",b=l*c*u,w=m*f,k=[w,b],C=!1,N=!1,R=[],F=Ge({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),_=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(F),R.push(_);let P=new tce(k,y),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,A]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],M=s.runWebGPUProgram(P,[F],F.dtype,T),U=Ge({inputs:{x:M},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(M),R.push(U);let j=[1,k[0],k[1]],z=new aC(j,[1,w,n.outChannels],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),C,N),X=j[1],Z=j[2],J=n.outChannels,ee=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[Z]}],ne=s.runWebGPUProgram(z,[U,_],U.dtype,ee),Q=y?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],te=Ge({inputs:{x:ne},backend:s,attrs:{shape:Q}});R.push(ne);for(let oe of R)s.disposeData(oe.dataId);return te}var mC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ra(r,[o,l]),ra(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape); let divBy4Remainder${e} = flatIndex${e} % 4; let divBy4Index${e} = flatIndex${e} / 4; let curData${e} = x.numbers[divBy4Index${e}]; if (divBy4Remainder${e} == 0) { temp = curData${e}; } else { // TODO: This could end up being a redundant load with another one in // the same shader invocation. Perhaps there's an opportunity for // optimization let nextData${e} = x.numbers[divBy4Index${e} + 1]; if (divBy4Remainder${e} == 1) { temp = vec4(curData${e}.yzw, nextData${e}.x); } elseif (divBy4Remainder${e} == 2) { temp = vec4(curData${e}.zw, nextData${e}.xy); } elseif (divBy4Remainder${e} == 3) { temp = vec4(curData${e}.w, nextData${e}.xyz); } } `}getUserCode(){let t=rC([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2]; let outCol = r % uniforms.outShape[2]; let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]); let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1]; let inChCoord = c % uniforms.xShape[3]; var coord = vec4( batch, outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0], outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1], inChCoord); var resData = vec4(0.0); ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (coordsInBounds4D(coord, uniforms.xShape)) { resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4]; } else { resData = vec4(0.0); }`:`var temp = vec4(0.0); ${this.getSampleAWithRemainder(1)} resData = temp; if (WCol == (uniforms.filterDims[1] - 1)) { coord = vec4( coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0); ${this.getSampleAWithRemainder(2)} if (inChCoord == 0) { resData = vec4(resData.xyz, temp.x); } elseif (inChCoord == 1) { resData = vec4(resData.xy, temp.xy); } else { resData = vec4(resData.x, temp.xyz); } } `} return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) { ${r} } return vec4(0.0); `,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return W.numbers[row * uniforms.dimBOuter / 4 + col]; } return vec4(0.0); `,i="",l="";if(this.activation){let d=aa(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4, outCoord : vec4) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${d} }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4) -> vec4 { let b = getLeakyreluAlphaAtOutCoords(); ${d} }`,new Error("Leakyrelu is not supported.");i=` fn activation(a : vec4, outCoord : vec4) -> vec4 { ${d} }`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${i} fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { let r = row; let c = col * 4; var batch = i32(globalId.z); ${a} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> vec4 { ${o} } fn mm_write(row : i32, col : i32, valueInput : vec4, globalId : vec3) { var batch = i32(globalId.z); var value = valueInput; if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter) { let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col * 4); ${c} ${l} setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value); } } ${t} `}},gC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fx(this.dispatchLayout,this.outputShape),this.elementsPerThread=gx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ra(s,[a,i]),ra(r,[i,o])]}getUserCode(){let e=xx(this.elementsPerThread,this.workGroupSize),t=` let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]); let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1]; let coord = vec4( batch, outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0], outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1], col % uniforms.xShape[3]); // The bounds checking is always needed since we use it to pad zero for the // 'same' padding type. if(coordsInBounds4D(coord, uniforms.xShape)) { return x.numbers[getFlatIndex4D(coord, uniforms.xShape)]; } return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${t} } return 0.0; `,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { return W.numbers[row * uniforms.dimBOuter + col]; } return 0.0; `,r="",a="";if(this.activation){let l=aa(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${l} }`:r=` fn activation(a : f32, outCoord : vec4) -> f32 { ${l} } `,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${r} fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { var batch = i32(globalId.z); ${n} } fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { ${s} } fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3) { var batch = i32(globalId.z); var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); ${o} ${a} result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; } ${e} `}},AC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=aa(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4) -> f32{ let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${r} }`:e=` fn activation(a : f32, outCoord : vec4) -> f32{ ${r} } `,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return` ${e} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 { let coord = vec4(batch, row, col, chan); if(coordsInBounds4D(coord, uniforms.xShape)) { return getX(batch, row, col, chan); } return 0.0; } fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{ let coord = vec4(row, col, xChannel, outChannel); if(coordsInBounds4D(coord, uniforms.wShape)) { return getW(row, col, xChannel, outChannel); } return 0.0; } fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) { let coord = vec4(batch, row, col, chan); if (coordsInBounds4D(coord, uniforms.outShape)) { ${n} ${t} setOutput(batch, row, col, chan, value); } } ${hx()} { let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups); let batch = coords[0]; let outChannel = coords[3]; var acc = 0.0; for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) { for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) { for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) { let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0]; let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1]; let v = readInp(batch, coordRow, coordCol, xChannel); let f = readFilt(row, col, xChannel, outChannel); acc = acc + v * f; } } } writeResult(batch, coords[1], coords[2], outChannel, acc); } `}};function sce(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return fC({x:r,filter:a,convInfo:p,backend:s});if(K().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return nce({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=K().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new AC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new mC(p):h=new gC(p),!g){let A=p.outShape[1]*p.outShape[2],x=p.outShape[3],y=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[A]},{type:"int32",data:[x]},{type:"int32",data:[y]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var rce={kernelName:_a,backendName:"webgpu",kernelFunc:sce},ace=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fx(this.dispatchLayout,this.outputShape),this.elementsPerThread=gx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return` fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { var batch = i32(globalId.z); if (row < uniforms.dimAOuter && col < uniforms.dimInner) { let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { return 0.0; } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { return 0.0; } let coord = vec4( batch, i32(xR), i32(xC), col % uniforms.outBackprop[3]); return x.numbers[getFlatIndex4D(coord, uniforms.xShape)]; } return 0.0; } fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; if (row < uniforms.dimInner && col < uniforms.dimBOuter && coordX >= 0 && coordY >= 0) { let coord = vec4(coordX, coordY, col, row % uniforms.outBackprop[3]); return W.numbers[getFlatIndex4D(coord, uniforms.wShape)]; } return 0.0; } fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3) { var batch = i32(globalId.z); var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; } ${xx(this.elementsPerThread,this.workGroupSize)} `}},oce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return` ${tt()} { if(index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let batch = coords[0]; let d1 = coords[${n}]; let dyCorner = vec2(coords[${e}]), coords[${t}]) - uniforms.pads; let dyRCorner = dyCorner.x; let 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. var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); let wRPerm = uniforms.filterDims.x - 1 - wR; if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || wRPerm < 0) { continue; } let idyR = dyR; for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); let wCPerm = uniforms.filterDims.y - 1 - wC; if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || fract(dyC) > 0.0 || wCPerm < 0) { continue; } let idyC = dyC; for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { if (${this.isChannelsLast}) { let xValue = getDy(batch, idyR, idyC, d2); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } else { let xValue = getDy(batch, d2, idyR, idyC); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } } setOutputFlat(index, dotProd); } } `}};function ice(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new oce(p);else{f=new ace(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var lce={kernelName:Da,backendName:"webgpu",kernelFunc:ice},uce=Tn({opType:xt.COS}),cce={kernelName:Pa,backendName:"webgpu",kernelFunc:uce},dce=Tn({opType:xt.COSH}),pce={kernelName:Fa,backendName:"webgpu",kernelFunc:dce},hce=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let height_ratio = f32(${n}); let width_ratio = f32(${a}); let b = coords[0]; let y = coords[1]; let x = coords[2]; let d = coords[3]; // get box vals let y1 = getBoxes(b, 0); let x1 = getBoxes(b, 1); let y2 = getBoxes(b, 2); let x2 = getBoxes(b, 3); // get image in batch index let bInd = i32(round(getBoxInd(b))); if(bInd < 0 || bInd >= uniforms.outShape[0]) { return; } let height_scale = ${s}; let width_scale = ${o}; let in_y = ${r}; if( in_y < 0.0 || in_y > ${e} ) { setOutputFlat(index, uniforms.extrapolationValue); return; } let in_x = ${i}; if( in_x < 0.0 || in_x > ${t} ) { setOutputFlat(index, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${this.methodId} == 1) { // Compute the four integer indices. let sourceFloorCR = vec2(sourceFracIndexCR); let sourceCeilCR = vec2(ceil(sourceFracIndexCR)); let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d); let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d); let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d); let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d); let fracCR = sourceFracIndexCR - vec2(sourceFloorCR); let top = topLeft + (topRight - topLeft) * fracCR.x; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; let newValue = top + (bottom - top) * fracCR.y; setOutputFlat(index, newValue); } else { // Compute the coordinators of nearest neighbor point. let sourceNearestCR = vec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); let newValue = getImage( bInd, sourceNearestCR.y, sourceNearestCR.x, d); setOutputFlat(index, newValue); } } } `}},fce=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new hce(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},mce={kernelName:fi,backendName:"webgpu",kernelFunc:fce},gce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let b = coords[0]; let h = ${this.getHeightCoordString()}; let w = ${this.getWidthCoordString()}; let d = ${this.getDepthCoordString()}; let in_h = h / uniforms.blockSize; let offset_h = h % uniforms.blockSize; let in_w = w / uniforms.blockSize; let offset_w = w % uniforms.blockSize; let offset_d = (offset_h * uniforms.blockSize + offset_w) * ${this.getOutputDepthSize()}; let in_d = d + offset_d; let rlt = ${this.getInputSamplingString()}; setOutputFlat(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Ace(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new gce(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var yce={kernelName:mi,backendName:"webgpu",kernelFunc:Ace},yC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2; stride : vec2; dilation : vec2; inDims : vec2;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=aa(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4, outCoord : vec4) -> vec4 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${r} }`:e=` fn activation(a : vec4, outCoord : vec4) -> vec4 { ${r} } `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return` ${e} ${Xm()} fn main([[builtin(global_invocation_id)]] globalId: vec3) { let batch = 0; let r = i32(globalId.x); let c = i32(globalId.y) * 4; let d2 = i32(globalId.z) * 4; let xRCCorner = vec2(r, c) * uniforms.stride - uniforms.pad; let d1 = d2; let q = 0; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var wVals : array, 9>; wVals[0] = getW(0, 0, d1, q); wVals[1] = getW(0, 1, d1, q); wVals[2] = getW(0, 2, d1, q); wVals[3] = getW(1, 0, d1, q); wVals[4] = getW(1, 1, d1, q); wVals[5] = getW(1, 2, d1, q); wVals[6] = getW(2, 0, d1, q); wVals[7] = getW(2, 1, d1, q); wVals[8] = getW(2, 2, d1, q); var xVals : array, 6>, 3>; for (var wR = 0; wR < 3; wR = wR + 1) { let xR = xRCorner + wR * uniforms.dilation[0]; for (var wC = 0; wC < 6; wC = wC + 1) { let xC = xCCorner + wC * uniforms.dilation[1]; if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) { xVals[wR][wC] = vec4(0.0); } else { xVals[wR][wC] = getX(batch, xR, xC, d1); } } } var dotProd : array, 4>; dotProd[0] = vec4(0.0); dotProd[1] = vec4(0.0); dotProd[2] = vec4(0.0); dotProd[3] = vec4(0.0); for (var wR = 0; wR < 3; wR = wR + 1) { for (var wC = 0; wC < 3; wC = wC + 1) { let indexW = wR * 3 + wC; dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW]; dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW]; dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW]; dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW]; } } for (var i = 0; i < 4; i = i + 1) { let coords = vec4(batch, r, c + i, d2); if (coordsInBounds4D(coords, uniforms.outShape)) { ${n} ${t} setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]); } } } `}},xC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2; stride : vec2; dilation : vec2; inDims : vec2;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=aa(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, outCoord : vec4) -> f32 { let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); ${a} }`:t=` fn activation(a : f32, outCoord : vec4) -> f32 { ${a} } `,n="dotProd = activation(dotProd, coords);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);":"";return` ${t} fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) { let coord = vec4(batch, row, col, chan); if (coordsInBounds4D(coord, uniforms.outShape)) { setOutput(batch, row, col, chan, value); } } ${hx()} { let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups); let batch = coords[0]; let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; let d2 = coords[3]; let d1 = d2 / ${e}; let q = d2 - d1 * ${e}; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0]; let inputColEnd = inputColStart + ${this.convInfo.filterWidth} * uniforms.dilation[1]; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; // Extract if checking out of for loop for performance. if (inputRowStart >= 0 && inputColStart >= 0 && inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) { // Here using a constant value |this.convInfo.filterHeight| instead // of uniform value is in order to loop unrolling. for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; let xVal = getX(batch, xR, xC, d1); let wVal = getW(wR, wC, d1, q); dotProd = dotProd + xVal * wVal; } } } else { for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; if (xR < 0 || xR >= uniforms.inDims[0]) { continue; } for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; if (xC < 0 || xC >= uniforms.inDims[1]) { continue; } let xVal = getX(batch, xR, xC, d1); let wVal = getW(wR, wC, d1, q); dotProd = dotProd + xVal * wVal; } } } ${s} ${n} writeResult(batch, coords[1], coords[2], d2, dotProd); } `}};function xce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new yC(d):p=new xC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var bce={kernelName:Oa,backendName:"webgpu",kernelFunc:xce},bC=jn({opSnippet:Wt.MUL,cpuKernelImpl:sue,supportsComplex:!0}),vce={kernelName:Qa,backendName:"webgpu",kernelFunc:bC},wce=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=` if (isNanCustom(candidate)) { bestValue = uniforms.NAN; } elseif (candidate ${this.reduceType==="min"?"<":">"} bestValue) { bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=` var xBestValues : array; `,a=` xBestValues[localId.x] = bestValue; ${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "} var currentSize = WorkGroupSize; for(; currentSize > 1;) { workgroupBarrier(); for (var w = 0; w < ${this.reductionFactor}; w = w + 1) { let i = i32(localId.x) * ${this.reductionFactor} + w; if (i < currentSize) { let candidate = xBestValues[i]; ${t} } } workgroupBarrier(); xBestValues[localId.x] = bestValue; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor}); ${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""} } if (localId.x == 0u) { ${s} } `;return` fn DIV_CEIL(a : i32, b : i32) -> i32 { return ((a - 1) / b + 1); } let WorkGroupSize = ${this.workGroupSize[0]}; ${e?r:""} fn getOffset(globalId : vec3) -> i32 { let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } ${Go()} { let offset = getOffset(globalId); var bestValue = ${n}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(Length, WorkGroupSize); for (var w = 0; w < WorkPerThread; w = w + 1) { let i = i32(globalId.x) * WorkPerThread + w; if (i < Length) { let candidate = f32(x.numbers[offset + i]); ${t} } } let flatOutputIndex = i32(globalId.y); ${e?a:s} } `}};function xp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=Rl({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=eue(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:x,outDtype:y}=oue(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,y,A);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),A=v.sizeFromShape(u.shape)/m,x={windowSize:m,inSize:m,batchSize:A,outSize:1},y=s==="mean"?"float32":bd(e.dtype),b=[{type:"int32",data:[m]}],w=new wce(x,s,y),k=r.runWebGPUProgram(w,[u],y,b);o.push(k),f=Ge({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Sx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return xp(r,a,o,"sum",n)}var kce={kernelName:co,backendName:"webgpu",kernelFunc:Sx};function Sce(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m=0&&(p=Sx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Ice={kernelName:nd,backendName:"webgpu",kernelFunc:Sce},Cce=Tn({opType:xt.ELU}),Tce={kernelName:za,backendName:"webgpu",kernelFunc:Cce},Nce=jn({opSnippet:Wt.EQUAL,dtype:"bool",cpuKernelImpl:Ule}),Ece={kernelName:gi,backendName:"webgpu",kernelFunc:Nce},vC=Tn({opType:xt.EXP,cpuKernelImpl:Gle,dtype:"float32"}),Rce={kernelName:La,backendName:"webgpu",kernelFunc:vC};function Ix(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),Ge({inputs:{x:a},backend:s,attrs:{shape:i}})}var $ce={kernelName:Ai,backendName:"webgpu",kernelFunc:Ix},_ce=Tn({opType:xt.EXPM1,cpuKernelImpl:Hle}),Dce={kernelName:yi,backendName:"webgpu",kernelFunc:_ce},Pce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return` ${tt()} if (index < uniforms.size) { setOutputFlat(index, uniforms.value); } } `}};function gc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Pce(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var Fce={kernelName:du,backendName:"webgpu",kernelFunc:gc},Oce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputFlat(index, outputValue); } } `}},Mce={kernelName:xi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Oce(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},zce=Tn({opType:xt.FLOOR,cpuKernelImpl:jle}),Lce={kernelName:Ba,backendName:"webgpu",kernelFunc:zce},Bce=jn({opSnippet:Wt.INT_DIV,dtype:"int32"}),Wce={kernelName:Wa,backendName:"webgpu",kernelFunc:Bce},Vce=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},wC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=xie(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function kC(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function SC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=kC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>wC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let A=[i,o,...l,...u.dispatch];u.setUniform(n.device,A);let x;if(a){let y={source:t};x=n.device.importExternalTexture(y)}else x=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,x,c.dataId),c}var Uce={kernelName:dd,backendName:"webgpu",kernelFunc:Gce},Ac;function Gce(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(K().getBool("WEBGPU_USE_IMPORT")&&o)return SC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(Ac==null&&(Ac=document.createElement("canvas").getContext("2d")),Ac.canvas.width=u,Ac.canvas.height=d,Ac.drawImage(r,0,0,u,d),r=Ac.canvas),c||l||o||i)return SC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let A=h.length,x=0;for(let y=0;y(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } `}},jce={kernelName:Va,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new Hce(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function qce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A=o!=null,x=i!=null,y;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"))return fC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=K().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],C=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)y=new AC(g,A,h,x);else{w?y=new mC(g,A,h,x):y=new gC(g,A,h,x);let R=g.outShape[1]*g.outShape[2],F=g.outShape[3],_=g.filterHeight*g.filterWidth*g.inShape[3];C.push({type:"int32",data:[R]},{type:"int32",data:[F]},{type:"int32",data:[_]})}let N=[r,a];return A&&N.push(o),x&&N.push(i),n.runWebGPUProgram(y,N,r.dtype,C)}var Xce={kernelName:xo,backendName:"webgpu",kernelFunc:qce};function Kce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,A=i!=null;g&&m.push(o),A&&m.push(i);let x;f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?x=new yC(f,g,p,A):x=new xC(f,g,p,A);let y=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}];return n.runWebGPUProgram(x,m,"float32",y)}var Zce={kernelName:bo,backendName:"webgpu",kernelFunc:Kce},Yce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${vn(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); var flattenIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexTemp = i32(round(getIndices(coords[0], j))); let strideNum = ${e}; flattenIndex = flattenIndex + indexTemp * strideNum; } setOutputFlat(index, getA(flattenIndex, coords[1])); } } `}};function Jce(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=Ge({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=Ge({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),y=n.bufferSync(s),b=qle(x,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Yce(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),A=Ge({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),A}var Qce={kernelName:vi,backendName:"webgpu",kernelFunc:Jce},ede=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=tde(this.aShape,"i32");return` ${tt()} if (index < uniforms.size) { let resRC = getCoordsFromFlatIndex(index); setOutputFlat(index, getA(${e})); } } `}};function tde(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;rn.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,N.dtype,N.values)}let m=new ede(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let A=Ge({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(x=>n.disposeData(x.dataId)),A}var nde={kernelName:bi,backendName:"webgpu",kernelFunc:IC},sde=jn({opSnippet:Wt.GREATER,cpuKernelImpl:Zle,dtype:"bool"}),rde={kernelName:wi,backendName:"webgpu",kernelFunc:sde},ade=jn({opSnippet:Wt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Kle}),ode={kernelName:Ua,backendName:"webgpu",kernelFunc:ade},ide=jn({opSnippet:Wt.LESS,dtype:"bool",cpuKernelImpl:Jle}),lde={kernelName:Si,backendName:"webgpu",kernelFunc:ide},ude=jn({opSnippet:Wt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Yle}),cde={kernelName:Ii,backendName:"webgpu",kernelFunc:ude},dde=Tn({opType:xt.LOG,cpuKernelImpl:Qle}),pde={kernelName:Ha,backendName:"webgpu",kernelFunc:dde},hde=jn({opSnippet:Wt.LOGICAL_AND,dtype:"bool"}),fde={kernelName:Ci,backendName:"webgpu",kernelFunc:hde},mde=Tn({opType:xt.LOGICAL_NOT}),gde={kernelName:gu,backendName:"webgpu",kernelFunc:mde};function CC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return xp(r,a,o,"max",n)}var Ade={kernelName:ja,backendName:"webgpu",kernelFunc:CC},yde=jn({opSnippet:Wt.MAX,cpuKernelImpl:tue}),xde={kernelName:qa,backendName:"webgpu",kernelFunc:yde};function bde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return Js({inputs:{x:r},backend:n});d=new dC(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new cC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var vde={kernelName:Xa,backendName:"webgpu",kernelFunc:bde};function wde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return xp(r,o,a,"mean",n)}var kde={kernelName:Ka,backendName:"webgpu",kernelFunc:wde};function Sde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return xp(r,a,o,"min",n)}var Ide={kernelName:Za,backendName:"webgpu",kernelFunc:Sde},Cde=jn({opSnippet:Wt.MIN,cpuKernelImpl:nue}),Tde={kernelName:Ya,backendName:"webgpu",kernelFunc:Cde},Nde=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=vn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${tt()} if (index < uniforms.size) { let start = ${o}(${t}); let end = ${o}(${n}); var outC = getCoordsFromFlatIndex(index); for (var i = 0; i < ${e}; i = i + 1) { if (${a} < ${s}) { ${a} = ${s} * 2 - ${a} - ${this.offset}; } elseif(${a} >= ${r}) { ${a} = (${r} - 1) * 2 - ${a} + ${this.offset}; } } let coords = outC - start; setOutputFlat(index, getX(${i})); } } `}},Ede={kernelName:Ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Nde(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function Rde(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=rue(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Km(s.shape,xt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var $de={kernelName:Ti,backendName:"webgpu",kernelFunc:Rde};function _de(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Xs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Dde={kernelName:Ei,backendName:"webgpu",kernelFunc:_de};function Pde(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Xs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Fde={kernelName:Ri,backendName:"webgpu",kernelFunc:Pde};function Ym(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=yp({inputs:{input:s},backend:n}),a=Ym({inputs:{x:r},backend:n}),o=Zm({inputs:{input:s},backend:n}),i=Ym({inputs:{x:o},backend:n}),l=fc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return gc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Ode={kernelName:Ki,backendName:"webgpu",kernelFunc:Ym};function TC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=yp({inputs:{input:s},backend:n}),a=TC({inputs:{x:r},backend:n}),o=Zm({inputs:{input:s},backend:n}),i=Ym({inputs:{x:o},backend:n}),l=fc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return gc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Mde={kernelName:$i,backendName:"webgpu",kernelFunc:TC};function zde(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ix({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Ix({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=hC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Lde={kernelName:Di,backendName:"webgpu",kernelFunc:zde},Bde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=vn(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${tt()} if (index < uniforms.size) { let start = ${r}; let end = ${a}; let outC = getCoordsFromFlatIndex(index); if (${o} || ${i}) { setOutputFlat(index, uniforms.constantValue); } else { let coords = outC - start; setOutputFlat(index, getX(${l})); } } } `}},NC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return Js({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return gc({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Bde(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Wde={kernelName:eo,backendName:"webgpu",kernelFunc:NC},Vde=jn({opSnippet:Wt.POW}),Ude={kernelName:to,backendName:"webgpu",kernelFunc:Vde};function Gde(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new iC(Wt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Hde={kernelName:no,backendName:"webgpu",kernelFunc:Gde};function jde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return xp(r,a,o,"prod",n)}var qde={kernelName:Pi,backendName:"webgpu",kernelFunc:jde},Xde=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=iue(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Kde={kernelName:xu,backendName:"webgpu",kernelFunc:Xde},EC=jn({opSnippet:Wt.DIV}),Zde={kernelName:Ma,backendName:"webgpu",kernelFunc:EC},Yde=Tn({opType:xt.RELU}),Jde={kernelName:so,backendName:"webgpu",kernelFunc:Yde},Qde=Tn({opType:xt.RELU6}),epe={kernelName:ao,backendName:"webgpu",kernelFunc:Qde},tpe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( ${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"}, ${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"}); let effectiveOutSize = vec2( ${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"}, ${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"}); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(rc) * effectiveInputOverOutputRatioRC"}; // Compute the four integer indices. let sourceFloorRC = vec2(sourceFracIndexRC); let sourceCeilRC = vec2( min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); let top = topLeft + (topRight - topLeft) * fracRC.y; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; let newValue = top + (bottom - top) * fracRC.x; setOutputFlat(index, newValue); } } `}};function npe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new tpe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var spe={kernelName:ro,backendName:"webgpu",kernelFunc:npe},rpe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":t="vec2(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( ${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"}, ${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"}); let effectiveOutSize = vec2( ${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"}, ${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"}); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${t}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e}))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputFlat(index, newValue); } } `}};function ape(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new rpe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var ope={kernelName:vu,backendName:"webgpu",kernelFunc:ape},ipe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32; cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) * uniforms.sinRadians; let coordYFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) * uniforms.cosRadians; let coordX = i32(round(coordXFloat + uniforms.centerX)); let coordY = i32(round(coordYFloat + uniforms.centerY)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputFlat(index, outputValue); } } `}},lpe={kernelName:Zi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new ipe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},upe=Tn({opType:xt.RSQRT,cpuKernelImpl:lue}),cpe={kernelName:oo,backendName:"webgpu",kernelFunc:upe},dpe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=je(e),this.dispatch=Fe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=vn(r.length);this.uniforms=`sliceDim : i32; strides: ${i}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } `):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2(flattenedIndex, coords[1])",a=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 { let d0 = index / uniforms.updatesShape[1]; let d1 = index - d0 * uniforms.updatesShape[1]; return vec2(d0, d1); } `);let o=`getUpdates(${s})`,i=this.type==="int32"?"ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));":` var assumed = atomicLoad(&(result.numbers[flatIndex])); var success = 0; for (; success == 0;) { let new = bitcast(assumed) + updateValue; let newI32 = bitcast(new); let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32); assumed = resValue[0]; success = resValue[1]; } `;return` ${a} ${tt()} if (index < uniforms.size) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${t})); flattenedIndex = flattenedIndex + indexInside * ${n}; } let updateValue = ${o}; let flatIndex = getOutputFlatIndex(${r}); ${i} } }`}};function ppe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=Ge({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=Ge({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=gc({backend:n,attrs:{shape:p,value:0,dtype:m}}),A=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:u},{type:"int32",data:[A]}],y=new dpe(f.shape,i,h.shape.length,f.shape.length,u,p,m),b=n.runWebGPUProgram(y,[f,h],m,x,g),w=Ge({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var hpe={kernelName:zi,backendName:"webgpu",kernelFunc:ppe},fpe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o= 1.0) { setOutputFlat(index, getA(${t})); } else { setOutputFlat(index, getB(${t})); } } } `}};function mpe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new fpe(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var gpe={kernelName:Li,backendName:"webgpu",kernelFunc:mpe},Ape=Tn({opType:xt.SIGMOID}),ype={kernelName:lo,backendName:"webgpu",kernelFunc:Ape},xpe=Tn({opType:xt.SIN}),bpe={kernelName:io,backendName:"webgpu",kernelFunc:xpe},vpe=Tn({opType:xt.SINH}),wpe={kernelName:Wi,backendName:"webgpu",kernelFunc:vpe},RC=jn({opSnippet:Wt.SUB,cpuKernelImpl:hue,supportsComplex:!0}),kpe={kernelName:fo,backendName:"webgpu",kernelFunc:RC};function Spe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=CC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=Ge({inputs:{x:i},backend:n,attrs:{shape:l}}),u=RC({inputs:{a:r,b:c},backend:n}),d=vC({inputs:{x:u},backend:n}),p=Sx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=Ge({inputs:{x:p},backend:n,attrs:{shape:l}}),f=EC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Ipe={kernelName:po,backendName:"webgpu",kernelFunc:Spe},Cpe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;An.disposeData(A.dataId)),g},Tpe={kernelName:Vi,backendName:"webgpu",kernelFunc:Cpe},Npe=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let l=vn(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return` ${tt()} let globalIndex = index * ${this.workPerThread}; if (globalIndex < uniforms.size) { var sum = vec4(0.0); var found = vec4(false); for (var i = 0; i < uniforms.updateSize; i = i + 1) { var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${this.indicesSnippet})); flattenedIndex = flattenedIndex + indexInside * ${this.strideString}; } for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) { let curIndex = globalIndex + innerIndex; let coords = getCoordsFromFlatIndex(curIndex); if (flattenedIndex == coords[0]) { sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet}; found[innerIndex] = true; } } } for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) { let curIndex = globalIndex + innerIndex; if (curIndex < uniforms.size) { setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex]))); } } } }`}};function Epe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new Npe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=Ge({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var Rpe={kernelName:ld,backendName:"webgpu",kernelFunc:Epe};function $pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=mc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var _pe={kernelName:Ui,backendName:"webgpu",kernelFunc:$pe},Dpe=Tn({opType:xt.SQRT}),Ppe={kernelName:uo,backendName:"webgpu",kernelFunc:Dpe},Fpe={kernelName:Iu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Km(n.shape,xt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},Ope=jn({opSnippet:Wt.SQUARED_DIFFERENCE}),Mpe={kernelName:ho,backendName:"webgpu",kernelFunc:Ope},zpe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=vn(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); setOutputFlat(index, getX(${t})); } } `}};function Lpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Pt.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=Ge({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Pt.computeOutShape(x,y,b),C=mc({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=Ge({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),N=ze(r.shape,r.dtype,C),R=due(h,N,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let C=new zpe(h),N=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(C,[r],r.dtype,N);w=Ge({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var Bpe={kernelName:Gi,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=pue(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Vpe={kernelName:ud,backendName:"webgpu",kernelFunc:Wpe},Upe=Tn({opType:xt.TANH}),Gpe={kernelName:mo,backendName:"webgpu",kernelFunc:Upe},Hpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=ze(r.shape,r.dtype,c),d=fue(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Hpe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Xpe={kernelName:jr,backendName:"webgpu",kernelFunc:qpe},Kpe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32; dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return` ${tt()} if (index < uniforms.size) { let outC = getCoordsFromFlatIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced // above, Figure5(a) shows that element[1] is in the second half of // the group when group size is 2, but it is in the first half of // the group when group size is 4. let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; var i = 0; if (isFirstInPair) { i = elemIdx; } else { i = elemIdx - uniforms.inc; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.inc; } else { i1 = i32(getIndices(batch, i + uniforms.inc)); } var x0 = f32(0.0); var x1 = f32(0.0); if (i0 < uniforms.inputSize) { x0 = getX(batch, i0); } else { x0 = uniforms.negativeInf; } if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = uniforms.negativeInf; } let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction let iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutputFlat(index, f32(i0)); } else { setOutputFlat(index, f32(i1)); } } } `}},Zpe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return` ${tt()} if (index < uniforms.size) { let outC = getCoordsFromFlatIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ // (k=4), we only need to output the indices at positions |, the // indices at positions _ can be thrown away, see Figure5(b) After // Phase 2 (Merge phase) in the Bitonic Top K paper referenced // above. // For example, the paper shows we only need to output the orange // bars. The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back to // the previous sequence to find the corresponding value, we need // to double the index. When we double the index, we basically // interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k // position of each 2k positions by - elemIdx % k. E.g. for output // at index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. var i = 0; if (elemIdx < uniforms.k) { i = elemIdx; } else { i = elemIdx * 2 - elemIdx % uniforms.k; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.k; } else { i1 = i32(getIndices(batch, i + uniforms.k)); } let x0 = getX(batch, i0); var x1 = f32(0.0); if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = x0; } if (x0 >= x1) { setOutputFlat(index, f32(i0)); } else { setOutputFlat(index, f32(i1)); } } } `}};function yc(e,t){t!==null&&e.disposeData(t.dataId)}function $C(e){let t=1;for(;tf===null?[d,d]:[d,f],g=(w,k,C)=>{let N=m(),R=new Kpe(C),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram(R,N,"int32",_),yc(n,P)};for(let w=1;w=1;C/=2)g(k,C,[u,h])}for(let w=h;w>p;w/=2){let k=m(),C=new Zpe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],F=f;f=n.runWebGPUProgram(C,k,"int32",R),yc(n,F);let _=p/2,P=_*2;for(let T=_;T>=1;T/=2)g(P,T,f.shape)}let A=f;f=mc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),yc(n,A);let x=IC({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});yc(n,d);let y=i.slice(0,-1);y.push(a),A=f,f=Ge({inputs:{x:f},attrs:{shape:y},backend:n}),yc(n,A);let b=x;return x=Ge({inputs:{x},attrs:{shape:y},backend:n}),yc(n,b),[x,f]}var Jpe={kernelName:ji,backendName:"webgpu",kernelFunc:Ype},Qpe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) + inCoord; } if (inCoord < -len) { inCoord = inCoord + sz2; } else { inCoord = -inCoord - 1.0; } } } elseif (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } elseif (uniforms.fillModeId == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0); } } elseif (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord - len * f32(i32(f32(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } elseif (uniforms.fillModeId == 4) { return clamp(outCoord, 0.0, len - 1.0); } return outCoord; } fn readWithFillValue(batch : i32, coordY : i32, coordX : i32, channel : i32) -> f32 { var outputValue : f32; if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = uniforms.fillValue; } return outputValue; } ${tt()} if (index < uniforms.size) { let coords = getCoordsFromFlatIndex(index); var outputValue : f32; let batch = coords[0]; let x = coords[2]; let y = coords[1]; let channel = coords[3]; let xf = f32(x); let yf = f32(y); let a1 = getTransforms(batch, 0); let a2 = getTransforms(batch, 1); let a3 = getTransforms(batch, 2); let b1 = getTransforms(batch, 3); let b2 = getTransforms(batch, 4); let b3 = getTransforms(batch, 5); let c1 = getTransforms(batch, 6); let c2 = getTransforms(batch, 7); let projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = uniforms.fillValue; } else { let inX = (a1 * xf + a2 * yf + a3) / projection; let inY = (b1 * xf + b2 * yf + b3) / projection; let mapX = mapCoord(inX, f32(uniforms.imageShape[2])); let mapY = mapCoord(inY, f32(uniforms.imageShape[1])); if (uniforms.interpolationModeId == 1) { let coordY = i32(round(mapY)); let coordX = i32(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { let yFloor = floor(mapY); let xFloor = floor(mapX); let yCeil = yFloor + 1.0; let xCeil = xFloor + 1.0; let valueYFloor = (xCeil - mapX) * readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yFloor), i32(xCeil), channel); let valueYCeil = (xCeil - mapX) * readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yCeil), i32(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutputFlat(index, outputValue); } } `}};function ehe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Qpe(g),x=o==="nearest"?1:2,y;switch(i){case"constant":y=1;break;case"reflect":y=2;break;case"wrap":y=3;break;case"nearest":y=4;break;default:y=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[y]},{type:"float32",data:[l]}];return n.runWebGPUProgram(A,[r,a],"float32",b)}var the={kernelName:qi,backendName:"webgpu",kernelFunc:ehe};function nhe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;mn.disposeData(m.dataId)),f}var she={kernelName:Xi,backendName:"webgpu",kernelFunc:nhe},rhe=[Fle,yue,bue,kue,Eue,$ue,Due,Fue,Bue,Gue,jue,Zue,Lle,ece,rce,lce,cce,pce,mce,yce,bce,Ice,Tce,Ece,$ce,Rce,Dce,Fce,Mce,Uce,Lce,Wce,jce,Xce,Zce,Qce,nde,rde,ode,zle,Jue,lde,cde,pde,fde,gde,Ade,xde,vde,kde,Ide,Tde,Ede,vce,$de,Dde,Fde,Wue,Mde,Lde,Wde,Hde,qde,Ude,Kde,Vue,Zde,Jde,epe,Dle,spe,ope,lpe,cpe,hpe,gpe,ype,bpe,wpe,zue,Bpe,Vpe,Ipe,Tpe,_pe,Rpe,Ppe,Fpe,Mpe,kpe,kce,Gpe,Xpe,Jpe,the,Tue,she,Ode];for(let e of rhe)rr(e);var ahe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=_C(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=_C(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function _C(e,t){return`${e}_${t}`}var DC=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` [[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d"}; ${tt()} let flatIndexBase = index * uniforms.numChannels; for (var i = 0; i < uniforms.numChannels; i = i + 1) { let flatIndex = flatIndexBase + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromFlatIndex(flatIndexBase); let values = ${e}; result.numbers[flatIndex] = i32(floor(255.0 * values[i])); } } } `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},ohe=class extends DC{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},ihe=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),PC=class extends Yl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!yx())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new ahe(this.device),this.tensorMap=new Xc(this,ns()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return PC.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*Ax(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*Ax(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new DC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new ohe),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=tC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;lN.shape),i="int32";o.map(N=>{a.push({type:i,data:N})});let l=v.computeStrides(r.shape);if(a.push({type:i,data:l}),e.size){let N=v.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((N,R)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[R]}}),h=p.map(N=>N.dtype).concat(r.dtype),f=p.map(N=>E.getBroadcastDims(N.shape,r.shape)),m=p.map(N=>v.arraysEqual(N.shape,r.shape)).join("_"),g=f.map(N=>N.join("_")).join(";"),A=kC(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(A,()=>wC(this.device,e,y,p,r)),w=this.activeTimers!=null,k=Vce(this.device,x,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let C=this.getComputePass();if(w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,0),C.setPipeline(b),C.setBindGroup(0,k),C.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let N={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(N)}return K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),w&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=ihe){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)Cx,webgpu_util:()=>eC});Nu.isBrowser()&&yx()&&al("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new Cx(r,s)},3);var Yt;(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"})(Yt||(Yt={}));var bp;(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",e[e.elu=6]="elu"})(bp||(bp={}));var OC;function lhe(e){OC=e.wasm.cwrap(yo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function uhe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=bp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],x=c?a.shape[1]:a.shape[2],y=nl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...y,A,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(a.shape).buffer);return OC(p,k,r.shape.length,h,C,a.shape.length,l,c,g,f,m,d||0,w),b}var che={kernelName:yo,backendName:"wasm",setupFunc:lhe,kernelFunc:uhe};function Nn(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,Yt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var dhe=Nn(ci);function qn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=E.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),A=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id,y=()=>s(d,g,c.shape.length,p,A,u.shape.length,Yt[c.dtype],x);if(t&&c.dtype==="float32")return y(),m;let b=E.getBroadcastDims(c.shape,f),w=E.getBroadcastDims(u.shape,f),k=b.every((N,R)=>N===R),C=w.every((N,R)=>N===R);if(k&&C)return y(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var phe=!0,hhe=qn(Gr,phe),MC;function fhe(e){MC=e.wasm.cwrap(Ca,null,["array","number","number","number"])}function mhe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return MC(a,r.length,Yt[s.dtype],o),s}var ghe={kernelName:Ca,backendName:"wasm",setupFunc:fhe,kernelFunc:mhe};function Jm(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Ahe={kernelName:Ga,backendName:"wasm",kernelFunc:Jm},zC;function yhe(e){zC=e.wasm.cwrap(go,null,["number","array","number","number","number","array","number"])}function xc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=bhe(t.x.shape,s.perm),o=!0;for(let f=0;f=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var vhe={kernelName:go,backendName:"wasm",kernelFunc:xc,setupFunc:yhe};function Ho(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Phe={kernelName:Fi,backendName:"wasm",kernelFunc:is},UC;function Fhe(e){UC=e.wasm.cwrap(Ea,null,["number","array","number","number","array","number","number","number","number"])}function Ohe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=nl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[A,h,d]:[A,d,h],k=is({inputs:{x:r},backend:n,attrs:{shape:b}}),C=is({inputs:{x:a},backend:n,attrs:{shape:w}}),N=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(C.dataId).id,F=o?k.shape[2]:k.shape[1],_=i?C.shape[1]:C.shape[2],P=Math.max(g,A),T=n.makeOutput([P,F,_],k.dtype),M=n.dataIdMap.get(T.dataId).id,U=new Uint8Array(new Int32Array(k.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return UC(N,U,k.shape.length,R,j,C.shape.length,o,i,M),n.disposeData(k.dataId),n.disposeData(C.dataId),T.shape=y,T}var Mhe={kernelName:Ea,backendName:"wasm",setupFunc:Fhe,kernelFunc:Ohe};function vp(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Pt.parseSliceParams(t,n,s),i=Pt.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=Pt.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=km(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)zhe(l,u[0],p,a,o);else if(h===3)Lhe(l,u[0],u[1],p,a,o);else if(h===4)Bhe(l,u[0],u[1],u[2],p,a,o);else{let f=km(l,a,o,t.shape,t.dtype);p.set(f)}return c}function zhe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;cA*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=is({inputs:{x:r},backend:n,attrs:{shape:l}}),f=xc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=is({inputs:{x:f},backend:n,attrs:{shape:u}}),g=vp({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Uhe={kernelName:di,backendName:"wasm",kernelFunc:Vhe};function wp(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Ghe={kernelName:Ra,backendName:"wasm",kernelFunc:wp},Hhe=Nn($a),GC;function jhe(e){GC=e.wasm.cwrap(Hr,null,["number","number","number","number"])}function qhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return GC(i,a,o,c),l}var Xhe={kernelName:Hr,backendName:"wasm",setupFunc:jhe,kernelFunc:qhe};function HC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Jm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(y=>{let b=v.sizeFromShape(y.shape.slice(s));return is({inputs:{x:y},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(y=>({vals:n.readSync(y.dataId),shape:y.shape}));r=E.computeOutShape(h.map(y=>y.shape),1);let m=h[0].shape[0]===1,g=Fy(f,r,t[0].dtype,m),A=E.computeOutShape(a.map(y=>y.shape),s);o.shape=A;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=E.fromStringArrayToUint8(g),h.forEach(y=>n.disposeData(y.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([a],l),u=r;c!==null&&(u=xc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;KC(f,o?1:0,i?1:0,h,m,Yt[r.dtype]);let g=p;if(c!==null){let A=E.getUndoAxesPermutation(c);g=xc({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var ufe={kernelName:hi,backendName:"wasm",setupFunc:ife,kernelFunc:lfe},ZC;function cfe(e){ZC=e.wasm.cwrap(mi,null,["number","number","number","array","number","array","array","number","number"])}function dfe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return ZC(A,a,o==="NHWC"?1:0,x,r.shape.length-1,y,b,f.length,w),m}var pfe={kernelName:mi,backendName:"wasm",setupFunc:cfe,kernelFunc:dfe},YC;function hfe(e){YC=e.wasm.cwrap(Oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ffe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d}=n,p=c==null?[1,1]:c,h=E.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,x=h.padInfo.bottom,y=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,C=h.strideWidth,N=h.inChannels,R=h.outChannels,F=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let _=s.makeOutput(h.outShape,"float32"),P=s.dataIdMap.get(_.dataId).id;return YC(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,x,y,F,b,w,k,C,N,R,P),_}var mfe={kernelName:Oa,backendName:"wasm",setupFunc:hfe,kernelFunc:ffe},gfe=Nn(za),Afe=!1,yfe=qn(gi,Afe,"bool"),xfe=Nn(La,"float32");function Nx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),is({inputs:{x:r},backend:s,attrs:{shape:i}})}var bfe={kernelName:Ai,backendName:"wasm",kernelFunc:Nx};function JC(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var vfe={kernelName:du,backendName:"wasm",kernelFunc:JC},QC;function wfe(e){QC=e.wasm.cwrap(xi,null,["number","number","number","number","number","number"])}function kfe(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return QC(a,i,l,c,u,o),r}var Sfe={kernelName:xi,backendName:"wasm",kernelFunc:kfe,setupFunc:wfe},Ife=Nn(Ba),Cfe=!1,Tfe=qn(Wa,Cfe),e6;function Nfe(e){e6=e.wasm.cwrap(Va,null,["number","number","number","number","number","number","number"])}function Efe(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return e6(u,d,p,h,f,r,g),m}var Rfe={kernelName:Va,backendName:"wasm",setupFunc:Nfe,kernelFunc:Efe},t6;function $fe(e){t6=e.wasm.cwrap(xo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _fe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=bp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,ne=i==null?0:s.dataIdMap.get(i.dataId).id;return t6(A,z,X,Z,x,w,k,b,C,N,R,F,j,_,P,T,M,U,y,g,ne,f||0,ee),J}var Dfe={kernelName:xo,backendName:"wasm",setupFunc:$fe,kernelFunc:_fe},n6;function Pfe(e){n6=e.wasm.cwrap(bo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ffe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=bp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,ne=i==null?0:s.dataIdMap.get(i.dataId).id;return n6(A,z,X,Z,x,w,k,b,C,N,R,F,j,_,P,T,M,U,y,g,ne,f||0,ee),J}var Ofe={kernelName:bo,backendName:"wasm",setupFunc:Pfe,kernelFunc:Ffe},s6;function Mfe(e){s6=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","array","number"])}function zfe(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=R2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(c.dataId).id;return s6(h,Yt[s.dtype],m,o,d,i,g,A),c}var Lfe={kernelName:vi,backendName:"wasm",setupFunc:Mfe,kernelFunc:zfe},r6;function Bfe(e){r6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Wfe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=t.readSync(a.dataId),u=r.shape[l];for(let R=0;R=0,()=>`GatherV2: the index value ${F} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=is({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=is({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let A=p.shape.length-1,y=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,C=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),N=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return r6(y,Yt[r.dtype],C,A,w,d.batchSize,N,k),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var Vfe={kernelName:bi,backendName:"wasm",setupFunc:Bfe,kernelFunc:Wfe},Ufe=!1,Gfe=qn(wi,Ufe,"bool"),Hfe=!1,jfe=qn(Ua,Hfe,"bool"),a6;function qfe(e){a6=e.wasm.cwrap(ki,null,["number","number","number","number"])}function Xfe(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;a6(r,Yt[t.dtype],n,o)}return a}var Kfe={kernelName:ki,backendName:"wasm",setupFunc:qfe,kernelFunc:Xfe},Zfe=!1,Yfe=qn(Si,Zfe,"bool"),Jfe=!1,Qfe=qn(Ii,Jfe,"bool"),eme=Nn(Ha),tme=!1,nme=qn(Ci,tme,"bool"),o6;function sme(e){o6=e.wasm.cwrap(ja,null,["number","number","number","number"])}function rme(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ho(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("max",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;o6(l,Yt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var ame={kernelName:ja,backendName:"wasm",setupFunc:sme,kernelFunc:rme},ome=!1,ime=qn(qa,ome),i6;function lme(e){i6=e.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ume(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.dilationHeight,x=u.dilationWidth,y=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=s.makeOutput(u.outShape,"float32"),N=s.dataIdMap.get(C.dataId).id;return i6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,b,w,k,N),C}var cme={kernelName:Xa,backendName:"wasm",setupFunc:lme,kernelFunc:ume},l6;function dme(e){l6=e.wasm.cwrap(Ka,null,["number, number, number"])}function pme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ho(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=c;c.dtype!=="float32"&&(x=wp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let y=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;l6(l,A,b)}if(h&&t.disposeData(u.dataId),a){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return c.dtype!=="float32"&&t.disposeData(x.dataId),y}var hme={kernelName:Ka,backendName:"wasm",setupFunc:dme,kernelFunc:pme},u6;function fme(e){u6=e.wasm.cwrap(Za,null,["number","number","number","number"])}function mme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Ho(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y)}let f=c.shape.length;E.assertAxesAreInnerMostDims("min",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;u6(l,Yt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var gme={kernelName:Za,backendName:"wasm",setupFunc:fme,kernelFunc:mme},Ame=!1,yme=qn(Ya,Ame),Ex;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Ex||(Ex={}));var c6;function xme(e){c6=e.wasm.cwrap(Ja,null,["number","array","number","number","array","array","number","number"])}function bme(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return c6(o,c,t.shape.length,Yt[t.dtype],p,h,Ex[r],l),i}var vme={kernelName:Ja,backendName:"wasm",kernelFunc:bme,setupFunc:xme},wme=!0,kme=qn(Qa,wme),Sme=Nn(Ti);function Rx(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var d6;function Ime(e){d6=e.wasm.cwrap(Ei,"number",["number","number","number","number","number"])}function Cme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=d6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Tme={kernelName:Ei,backendName:"wasm",setupFunc:Ime,kernelFunc:Cme},p6;function Nme(e){p6=e.wasm.cwrap(yu,"number",["number","number","number","number","number","bool"])}function Eme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=p6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rx(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[A,x]}var Rme={kernelName:yu,backendName:"wasm",setupFunc:Nme,kernelFunc:Eme},h6;function $me(e){h6=e.wasm.cwrap(Ri,"number",["number","number","number","number","number","number"])}function _me(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=h6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rx(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[A,x]}var Dme={kernelName:Ri,backendName:"wasm",setupFunc:$me,kernelFunc:_me},Pme=!1,Fme=qn(Ni,Pme,"bool"),f6;function Ome(e){f6=e.wasm.cwrap(_i,null,["number","number","number","number","number"])}function Mme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return f6(d,a,o,i,c),l}var zme={kernelName:_i,backendName:"wasm",setupFunc:Ome,kernelFunc:Mme};function Lme(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Bme={kernelName:$i,backendName:"wasm",kernelFunc:Lme};function Wme(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Nx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching 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y=new Float32Array(x),b=1/l.width,w=1/l.height,k=g(U6);d.uniform1fv(k.uniform.m,y),d.uniform2f(k.uniform.px,b,w),m()},detectEdges:()=>{A.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{A.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{A.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:x=>{let y=x||1;A.convolution.call(this,[0,-1*y,0,-1*y,1+4*y,-1*y,0,-1*y,0])},emboss:x=>{let y=x||1;A.convolution.call(this,[-2*y,-1*y,0,-1*y,1,1*y,0,1*y,2*y])},blur:x=>{let y=x/7/l.width,b=x/7/l.height,w=g(V6);d.uniform2f(w.uniform.px,0,b),m(u.INTERMEDIATE),d.uniform2f(w.uniform.px,y,0),m()},pixelate:x=>{let y=x/l.width,b=x/l.height,w=g(W6);d.uniform2f(w.uniform.size,y,b),m()}};this.add=function(x){let y=Array.prototype.slice.call(arguments,1),b=A[x];a.push({func:b,args:y})},this.reset=function(){a=[]},this.get=function(){return 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t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Dl(t[0],n),-Dl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},Ege=(e,t)=>[Dl(e,t[0]),Dl(e,t[1])];function s8(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s[a[0]/r*(p[0]-r/2),a[1]/r*(p[1]-r/2),p[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?n8(n,[0,0]):Gx,c=i?o.map(p=>[...Ege(p,l),p[2]]):o,u=i?Nge(s):Gx,d=[...s0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+Dl(d,u[0])),Math.round(p[1]+Dl(d,u[1])),Math.round(p[2]||0)])}function Hx(e,t,n,s){let r=t.landmarks.length>=Lx.count?Lx.symmetryLine:Tp.symmetryLine,a=0,o=Gx,i;if(e&&Ae.kernels.includes("rotatewithoffset"))if(a=Cge(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=s0({startPoint:t.startPoint,endPoint:t.endPoint}),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=$e.rotateWithOffset(n,a,0,u);o=n8(-a,c),i=Ux(t,d,[s,s]),se(d)}else i=Ux(t,n,[s,s]);else i=Ux(t,n,[s,s]);return[a,o,i]}var a8=6,Os,o8=[],i8=null,Ms=0,jx=()=>Ms;async function l8(e){var t,n;return Ae.initial&&(Os=null),Os?e.debug&&re("cached model:",Os.modelUrl):(Os=await et(nt(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Os||!Os.modelUrl?re("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&re("load model:",Os.modelUrl)),Ms=Os.inputs[0].shape?Os.inputs[0].shape[2]:0,Ms===-1&&(Ms=64),o8=s8(Ms),i8=lr(o8),Os}function Rge(e){let t={};t.boxStarts=De(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,i8),t.boxSizes=De(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Ms),t.centersNormalized=pe(t.centers,Ms),t.halfBoxSize=pe(t.boxSizesNormalized,2),t.starts=me(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Ms),t.endNormalized=L(t.ends,Ms);let n=Pu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>se(t[s])),n}async function u8(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let n={};n.resized=$e.resizeBilinear(e,[Ms,Ms]),n.div=pe(n.resized,127.5),n.normalized=me(n.div,.5);let s=Os==null?void 0:Os.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=vt([d[0],d[2]],2),n.concat512=vt([d[1],d[3]],2),n.concat=vt([n.concat512,n.concat384],1),n.batch=ot(n.concat,0)}else n.batch=ot(s);se(s),n.boxes=Rge(n.batch),n.logits=De(n.batch,[0,0],[-1,1]),n.sigmoid=cs(n.logits),n.scores=ot(n.sigmoid),n.nms=await $e.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let d=0;d(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let h={};h.bbox=De(n.boxes,[r[d],0],[1,-1]),h.slice=De(n.batch,[r[d],a8-1],[1,-1]),h.squeeze=ot(h.slice),h.landmarks=G(h.squeeze,[a8,-1]),h.startPoint=De(h.bbox,[0,0],[-1,2]),h.endPoint=De(h.bbox,[0,2],[-1,2]),a.push({box:{startPoint:await h.startPoint.data(),endPoint:await h.endPoint.data()},landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(f=>se(h[f]))}}return Object.keys(n).forEach(d=>se(n[d])),{boxes:a,scaleFactor:[e.shape[2]/Ms,e.shape[1]/Ms]}}var Kx={};Gc(Kx,{connected:()=>Xx,kpt:()=>qx});var qx=["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","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],Xx={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var c8={initial:!0},un=[null,null],Xo=[[0,0],[0,0]],Zx=Number.MAX_SAFE_INTEGER,Yx,a0=null,Ko=[[0,0],[0,0],[0,0],[0,0]],d8=0;async function p8(e){var t,n,s;if(c8.initial&&(un[0]=null),!un[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){un[0]=await et(nt(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(un[0].modelSignature.inputs);Xo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Xo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0,!un[0]||!un[0].modelUrl?re("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&re("load model:",un[0].modelUrl)}else e.debug&&un[0]&&re("cached model:",un[0].modelUrl);return un[0]}async function h8(e){var t;if(c8.initial&&(un[1]=null),un[1])e.debug&&re("cached model:",un[1].modelUrl);else{un[1]=await et(nt(e.modelBasePath,e.body.modelPath||""));let n=Object.values(un[1].modelSignature.inputs);Xo[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xo[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 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Object.keys(t).forEach(s=>se(t[s])),n}function Dge(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+Ko[2][0]+Ko[2][1])/t[0]-Ko[2][0],n.position[1]*(t[1]+Ko[1][0]+Ko[1][1])/t[1]-Ko[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}var f8=e=>1-1/(1+Math.exp(e));async function Pge(e,t,n){var h;let s={};s.input=await _ge(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=un[1])==null?void 0:h.execute(s.input,Yx);let r=(await s.poseflag.data())[0],a=Math.max(0,(r-.8)/(1-.8)),o=await s.ld.data(),i=[],l=5;for(let f=0;fse(s[f]));let d={};for(let[f,m]of Object.entries(Xx)){let g=[];for(let A=0;Ab.part===m[A]),y=c.find(b=>b.part===m[A+1]);x&&y&&x.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&g.push([x.position,y.position])}d[f]=g}return{id:0,score:Math.trunc(100*a)/100,box:u.keypointsBox,boxRaw:u.keypointsBoxRaw,keypoints:c,annotations:d}}async function Jx(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>he()-d8,r=Zx<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&a0!==null?Zx++:(a0=await Pge(e,t,n),d8=he(),Zx=0),a0?[a0]:[]}var wc=[{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 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drier"},{class:80,label:"toothbrush"}];var Qs,Pl=0,Qx=[],m8=0,eb=Number.MAX_SAFE_INTEGER;async function g8(e){if(Ae.initial&&(Qs=null),Qs)e.debug&&re("cached model:",Qs.modelUrl);else{Qs=await et(nt(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Qs.modelSignature.inputs);Pl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!Qs||!Qs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Qs.modelUrl)}return Qs}async function Fge(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=ot(e);se(e);let o=nn(a,6,1);se(a);let i=An([o[1],o[0],o[3],o[2]],1),l=ot(i);se(i);let c=ot(o[4]),u=ot(o[5]);o.forEach(f=>se(f));let d=await $e.nonMaxSuppressionAsync(l,c,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);se(l),se(c),se(u);let p=await d.data();se(d);let h=0;for(let f of p){let m=Math.trunc(100*r[0][f][4])/100,g=r[0][f][5],A=wc[g].label,[x,y]=[r[0][f][0]/Pl,r[0][f][1]/Pl],b=[x,y,r[0][f][2]/Pl-x,r[0][f][3]/Pl-y],w=[Math.trunc(b[0]*t[0]),Math.trunc(b[1]*t[1]),Math.trunc(b[2]*t[0]),Math.trunc(b[3]*t[1])];s.push({id:h++,score:m,class:g,label:A,box:w,boxRaw:b})}return s}async function tb(e,t){let n=(t.object.skipTime||0)>he()-m8,s=eb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&Qx.length>0?(eb++,Qx):(eb=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=$e.resizeBilinear(e,[Pl,Pl]),i=t.object.enabled?Qs==null?void 0:Qs.execute(o,["tower_0/detections"]):null;m8=he(),se(o);let l=await Fge(i,a,t);Qx=l,r(l)}))}var rb={};Gc(rb,{connected:()=>sb,kpt:()=>nb});var nb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],sb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var cn,A8=0,Kn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ab=Number.MAX_SAFE_INTEGER;async function ob(e){return Ae.initial&&(cn=null),cn?e.debug&&re("cached model:",cn.modelUrl):(cn=await et(nt(e.modelBasePath,e.body.modelPath||"")),!cn||!cn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",cn.modelUrl)),cn}function Oge(e,t){let[n,s]=e.shape;return q(()=>{let r=G(e,[s*n]),a=gn(r,0).dataSync()[0];if(a>t){let o=Hs(r,0),i=Rd(o,n).dataSync()[0],l=pe(o,Re(n,"int32")).dataSync()[0];return[i,l,a]}return[0,0,a]})}async function ib(e,t){let n=(t.body.skipTime||0)>he()-A8,s=ab<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(Kn.keypoints).length>0?(ab++,[Kn]):(ab=0,new Promise(async r=>{var d;let a=q(()=>{if(!(cn==null?void 0:cn.inputs[0].shape))return null;let p=$e.resizeBilinear(e,[cn.inputs[0].shape[2],cn.inputs[0].shape[1]],!1);return L(p,2).sub(1)}),o;if(t.body.enabled&&(o=cn==null?void 0:cn.execute(a)),A8=he(),se(a),o){Kn.keypoints.length=0;let p=o.squeeze();se(o);let h=p.unstack(2);se(p);for(let f=0;f(((d=t.body)==null?void 0:d.minConfidence)||0)&&Kn.keypoints.push({score:Math.round(100*A)/100,part:nb[f],positionRaw:[m/cn.inputs[0].shape[2],g/cn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/cn.inputs[0].shape[2]),Math.round(e.shape[1]*g/cn.inputs[0].shape[1])]})}h.forEach(f=>se(f))}Kn.score=Kn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Kn.keypoints.map(p=>p.position[0]),l=Kn.keypoints.map(p=>p.position[1]);Kn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Kn.keypoints.map(p=>p.positionRaw[0]),u=Kn.keypoints.map(p=>p.positionRaw[1]);Kn.boxRaw=[Math.min(...c),Math.min(...u),Math.max(...c)-Math.min(...c),Math.max(...u)-Math.min(...u)];for(let[p,h]of Object.entries(sb)){let f=[];for(let m=0;mx.part===h[m]),A=Kn.keypoints.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}Kn.annotations[p]=f}r([Kn])}))}var Mge=["angry","disgust","fear","happy","sad","surprise","neutral"],Zn,o0=[],y8=0,x8=0,lb=Number.MAX_SAFE_INTEGER,ub=[.2989,.587,.114];async function b8(e){var t,n;return Ae.initial&&(Zn=null),Zn?e.debug&&re("cached model:",Zn.modelUrl):(Zn=await et(nt(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Zn||!Zn.modelUrl?re("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&re("load model:",Zn.modelUrl)),Zn}async function cb(e,t,n,s){var o,i;if(!Zn)return null;let r=lb<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>he()-x8;return t.skipAllowed&&a&&r&&y8===s&&o0[n]&&o0[n].length>0?(lb++,o0[n]):(lb=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Zn==null?void 0:Zn.inputs[0].shape)?Zn.inputs[0].shape[2]:0;p.resize=$e.resizeBilinear(e,[h,h],!1),[p.red,p.green,p.blue]=nn(p.resize,3,3),p.redNorm=L(p.red,ub[0]),p.greenNorm=L(p.green,ub[1]),p.blueNorm=L(p.blue,ub[2]),p.grayscale=sf([p.redNorm,p.greenNorm,p.blueNorm]),p.grayscaleSub=me(p.grayscale,.5),p.grayscaleMul=L(p.grayscaleSub,2),p.emotion=Zn==null?void 0:Zn.execute(p.grayscaleMul),x8=he();let f=await p.emotion.data();for(let m=0;m(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:Mge[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>se(p[m]))}o0[n]=c,y8=s,l(c)}))}var er,Zo=0,zge=2.3,db=Br.leftEyeLower0,pb=Br.rightEyeLower0,kc={leftBounds:[db[0],db[db.length-1]],rightBounds:[pb[0],pb[pb.length-1]]},Sc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function v8(e){var t,n;return Ae.initial&&(er=null),er?e.debug&&re("cached model:",er.modelUrl):(er=await et(nt(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!er||!er.modelUrl?re("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&re("load model:",er.modelUrl)),Zo=er.inputs[0].shape?er.inputs[0].shape[2]:0,Zo===-1&&(Zo=64),er}function i0(e,t,n,s){for(let r=0;r{let t=e[kc.leftBounds[0]][2],n=e[kc.rightBounds[0]][2];return t-n},w8=(e,t,n,s,r=!1,a)=>{let o=$p(Rp(r0([e[n],e[s]]),zge)),i=Ep(o),l=$e.cropAndResize(t,[[o.startPoint[1]/a,o.startPoint[0]/a,o.endPoint[1]/a,o.endPoint[0]/a]],[0],[Zo,Zo]);if(r&&Ae.kernels.includes("flipleftright")){let c=$e.flipLeftRight(l);se(l),l=c}return{box:o,boxSize:i,crop:l}},k8=(e,t,n,s=!1)=>{let r=[];for(let a=0;a{let s=e[Br[`${n}EyeUpper0`][Sc.upperCenter]][2],r=e[Br[`${n}EyeLower0`][Sc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function I8(e,t,n,s){if(!er)return n.debug&&re("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=w8(e,t,kc.leftBounds[0],kc.leftBounds[1],!0,s),{box:i,boxSize:l,crop:c}=w8(e,t,kc.rightBounds[0],kc.rightBounds[1],!0,s),u=vt([o,c]);se(o),se(c);let d=er.execute(u);se(u);let p=await d.data();se(d);let h=p.slice(0,Sc.numCoordinates*3),{rawCoords:f,iris:m}=k8(h,r,a,!0),g=p.slice(Sc.numCoordinates*3),{rawCoords:A,iris:x}=k8(g,i,l),y=Lge(e);Math.abs(y)<30?(i0(e,f,"left",null),i0(e,A,"right",null)):y<1?i0(e,f,"left",["EyeUpper0","EyeLower0"]):i0(e,A,"right",["EyeUpper0","EyeLower0"]);let b=S8(e,m,"left"),w=S8(e,x,"right");return e.concat(b).concat(w)}var Ic=[],tr=null,ia=0,hb=Number.MAX_SAFE_INTEGER,C8=0,T8=1.6;async function N8(e,t){var i,l,c,u,d,p,h,f;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>he()-C8,s=hb<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||Ic.length===0){let m=await u8(e,t);C8=he(),Ic=[];for(let g of m.boxes){let A={startPoint:g.box.startPoint,endPoint:g.box.endPoint,landmarks:g.landmarks,confidence:g.confidence};Ic.push($p(Rp(Q6(A,m.scaleFactor),Math.sqrt(T8))))}hb=0}else hb++;let r=[],a=[],o=0;for(let m=0;m[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/ia]);for(let F of Object.keys(Br))y.annotations[F]=Br[F].map(_=>y.mesh[_]);g=$p({...Rp(r0(y.mesh),T8),confidence:g.confidence}),y.box=Wx(g,e),y.boxRaw=Vx(g,e),y.score=y.faceScore,a.push(g),se(y.tensor),[A,x,y.tensor]=Hx((f=t.face.detector)==null?void 0:f.rotation,g,e,ia)}}else{y.box=Wx(g,e),y.boxRaw=Vx(g,e),y.score=y.boxScore,y.mesh=g.landmarks.map(b=>[(g.startPoint[0]+g.endPoint[0])/2+(g.endPoint[0]+g.startPoint[0])*b[0]/jx(),(g.startPoint[1]+g.endPoint[1])/2+(g.endPoint[1]+g.startPoint[1])*b[1]/jx()]),y.meshRaw=y.mesh.map(b=>[b[0]/(e.shape[2]||0),b[1]/(e.shape[1]||0),(b[2]||0)/ia]);for(let b of Object.keys(Tp))y.annotations[b]=[y.mesh[Tp[b]]]}r.push(y)}return Ic=[...a],r}async function E8(e){var t,n;return Ae.initial&&(tr=null),tr?e.debug&&re("cached model:",tr.modelUrl):(tr=await et(nt(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!tr||!tr.modelUrl?re("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&re("load model:",tr.modelUrl)),ia=tr.inputs[0].shape?tr.inputs[0].shape[2]:0,ia===-1&&(ia=64),tr}var R8=_l,$8=Np;var ys,l0=[],_8=0,D8=0,fb=Number.MAX_SAFE_INTEGER;async function P8(e){var n,s;let t=nt(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return Ae.initial&&(ys=null),ys?e.debug&&re("cached model:",t):(ys=await et(t),ys?e.debug&&re("load model:",t):re("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),ys}function mb(e){let t=e.image||e.tensor||e;if(!(ys==null?void 0:ys.inputs[0].shape))return t;let n=$e.resizeBilinear(t,[ys.inputs[0].shape[2],ys.inputs[0].shape[1]],!1),s=L(n,255);return se(n),s}async function gb(e,t,n,s){var o,i,l,c;if(!ys)return null;let r=fb<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>he()-_8;return t.skipAllowed&&r&&a&&D8===s&&((l=l0[n])==null?void 0:l.age)&&((c=l0[n])==null?void 0:c.age)>0?(fb++,l0[n]):(fb=0,new Promise(async u=>{var p,h;let d={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=mb(e),m=ys==null?void 0:ys.execute(f);_8=he(),se(f);let A=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(A[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(d.gender=A[0]<=.5?"female":"male",d.genderScore=Math.min(.99,x));let y=Hs(m.find(R=>R.shape[1]===100),1),b=(await y.data())[0];se(y);let k=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let C=m.find(R=>R.shape[1]===1024),N=C?await C.data():[];d.descriptor=Array.from(N),m.forEach(R=>se(R))}l0[n]=d,D8=s,u(d)}))}function u0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function _p(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function F8(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return $e.cropAndResize(t,a,[0],n)}function O8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function c0(e,t=1.5){let n=_p(e),s=u0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function d0(e){let t=_p(e),n=u0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Bge(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function M8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Bge(n)}var z8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Yo(e,t){let n=0;for(let s=0;s[n.x,n.y]),this.anchorsTensor=lr(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=Xt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Xt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=De(t,[0,0],[-1,2]),n.boxSizes=De(t,[0,2],[-1,2]),n.div=pe(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=le(n.div,this.anchorsTensor),n.halfBoxSizes=pe(n.boxSizes,this.doubleInputSizeTensor),n.sub=me(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=L(n.sub,this.inputSizeTensor),n.add=le(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=L(n.add,this.inputSizeTensor);let s=Pu([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>se(n[r])),s}normalizeLandmarks(t,n){let s={};s.reshape=G(t,[-1,7,2]),s.div=pe(s.reshape,this.inputSizeTensor),s.landmarks=le(s.div,this.anchors[n]);let r=L(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>se(s[a])),r}async predict(t,n){let s={};s.resize=$e.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=pe(s.resize,127.5),s.image=me(s.div,1),s.batched=this.model.execute(s.image),s.predictions=ot(s.batched),s.slice=De(s.predictions,[0,0],[-1,1]),s.sigmoid=cs(s.slice),s.scores=ot(s.sigmoid);let r=await s.scores.data();s.boxes=De(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l={};l.box=De(s.norm,[i,0],[1,-1]),l.slice=De(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=G(l.norm,[-1,2]);let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array(),h={startPoint:u,endPoint:d,palmLandmarks:p,confidence:r[i]},f=O8(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>se(l[m]))}return Object.keys(s).forEach(i=>se(s[i])),o}};var Vge=5,V8=1.65,U8=[0,5,9,13,17,1,2],Uge=0,Gge=2,G8=0,bb=class{constructor(t,n){ce(this,"handDetector");ce(this,"handPoseModel");ce(this,"inputSize");ce(this,"storedBoxes");ce(this,"skipped");ce(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>yb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return c0(d0(r),Vge)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=c0(d0(n),V8);s.palmLandmarks=[];for(let r=0;r[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=Ab(s,[0,0]),c=i.map(h=>[...yb(h,l),h[2]]),u=B8(r),d=[..._p(n),1],p=[Yo(d,u[0]),Yo(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>he()-G8,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&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&&(s=!0));let i=[];for(let l=0;l=n.hand.minConfidence/4){let w=G(y,[-1,3]),k=await w.array();se(y),se(w);let C=this.transformRawCoords(k,m,u,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let R={landmarks:C,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};i.push(R)}else this.storedBoxes[l]=null;se(y)}else{let u=c0(d0(c),V8),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var Yn={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Yn.nameMapping[e],getPoints:e=>Yn.pointsMapping[e]},Jo={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Jo.nameMapping[e]},Mt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Mt.nameMapping[e]},Fl=class{constructor(t){ce(this,"name");ce(this,"curls");ce(this,"directions");ce(this,"weights");ce(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}direction(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}weight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var{thumb:yr,index:la,middle:ua,ring:Ol,pinky:Ml}=Yn,{none:xr,half:Hge,full:br}=Jo,{verticalUp:Cc,verticalDown:uAe,horizontalLeft:vb,horizontalRight:jge,diagonalUpRight:qge,diagonalUpLeft:Tc,diagonalDownRight:cAe,diagonalDownLeft:dAe}=Mt,Qo=new Fl("thumbs up");Qo.curl(yr,xr,1);Qo.direction(yr,Cc,1);Qo.direction(yr,Tc,.25);Qo.direction(yr,qge,.25);for(let e of[Yn.index,Yn.middle,Yn.ring,Yn.pinky])Qo.curl(e,br,1),Qo.direction(e,vb,1),Qo.direction(e,jge,1);var Jt=new Fl("victory");Jt.curl(yr,Hge,.5);Jt.curl(yr,xr,.5);Jt.direction(yr,Cc,1);Jt.direction(yr,Tc,1);Jt.curl(la,xr,1);Jt.direction(la,Cc,.75);Jt.direction(la,Tc,1);Jt.curl(ua,xr,1);Jt.direction(ua,Cc,1);Jt.direction(ua,Tc,.75);Jt.curl(Ol,br,1);Jt.direction(Ol,Cc,.2);Jt.direction(Ol,Tc,1);Jt.direction(Ol,vb,.2);Jt.curl(Ml,br,1);Jt.direction(Ml,Cc,.2);Jt.direction(Ml,Tc,1);Jt.direction(Ml,vb,.2);Jt.weight(la,2);Jt.weight(ua,2);var ei=new Fl("point");ei.curl(yr,br,1);ei.curl(la,xr,.5);ei.curl(ua,br,.5);ei.curl(Ol,br,.5);ei.curl(Ml,br,.5);ei.weight(la,2);ei.weight(ua,2);var ti=new Fl("middle finger");ti.curl(yr,xr,1);ti.curl(la,br,.5);ti.curl(ua,br,.5);ti.curl(Ol,br,.5);ti.curl(Ml,br,.5);ti.weight(la,2);ti.weight(ua,2);var Nc=new Fl("open palm");Nc.curl(yr,xr,.75);Nc.curl(la,xr,.75);Nc.curl(ua,xr,.75);Nc.curl(Ol,xr,.75);Nc.curl(Ml,xr,.75);var H8=[Qo,Jt,ei,ti,Nc];var Xge=.7,zl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function j8(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function q8(e,t){if(!e||!t)return[0,0];let n=j8(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=j8(e[1],e[2],t[1],t[2]);return[n,s]}function X8(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function Kge(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let A;return g>zl.NO_CURL_START_LIMIT?A=Jo.none:g>zl.HALF_CURL_START_LIMIT?A=Jo.half:A=Jo.full,A}function K8(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Mt.horizontalLeft:r=Mt.horizontalRight:s===Math.abs(t)?t>0?r=Mt.horizontalLeft:r=Mt.horizontalRight:n>0?r=Mt.horizontalLeft:r=Mt.horizontalRight,r}function Z8(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Mt.verticalDown:r=Mt.verticalUp:s===Math.abs(t)?t<0?r=Mt.verticalDown:r=Mt.verticalUp:n<0?r=Mt.verticalDown:r=Mt.verticalUp,r}function Zge(e,t,n,s,r,a,o,i){let l,c=Z8(e,t,n,s),u=K8(r,a,o,i);return c===Mt.verticalUp?u===Mt.horizontalLeft?l=Mt.diagonalUpLeft:l=Mt.diagonalUpRight:u===Mt.horizontalLeft?l=Mt.diagonalDownLeft:l=Mt.diagonalDownRight,l}function Yge(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=zl.DISTANCE_VOTE_POWER:m>.66?h+=zl.DISTANCE_VOTE_POWER:f+=zl.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+c*c),y=Math.max(g,A,x),b=e[0],w=e[1],k=n[0],C=n[1];y===g?(k=n[0],C=n[1]):y===x&&(b=t[0],w=t[1]);let F=q8([b,w],[k,C]),_=X8(F,zl.TOTAL_ANGLE_VOTE_POWER);p+=_[0],h+=_[1],f+=_[2];for(let T of s){let M=X8(T,zl.SINGLE_ANGLE_VOTE_POWER);p+=M[0],h+=M[1],f+=M[2]}let P;return p===Math.max(p,h,f)?P=Z8(l,i,c,d):f===Math.max(h,f)?P=K8(a,r,o,u):P=Zge(l,i,c,d,a,r,o,u),P}function Y8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Yn.all){let o=Yn.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=q8(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Yn.all){let o=a===Yn.thumb?1:0,i=Yn.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=Kge(l,c,u),p=Yge(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function p0(e){if(!e||e.length===0)return null;let t=Y8(e),n={};for(let s of Yn.all)n[Yn.getName(s)]={curl:Jo.getName(t.curls[s]),direction:Mt.getName(t.directions[s])};return n}function J8(e){let t=[];if(!e||e.length===0)return t;let n=Y8(e);for(let s of H8){let r=s.matchAgainst(n.curls,n.directions);r>=Xge&&t.push({name:s.name,confidence:r})}return t}var Q8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ca,da,eT;async function wb(e,t){let n=await eT.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;rn[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=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)];let c=p0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function kb(e){var n,s,r,a,o,i;Ae.initial&&(ca=null,da=null),!ca||!da?([ca,da]=await Promise.all([e.hand.enabled?et(nt(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?et(nt(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!ca||!ca.modelUrl?re("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&re("load model:",ca.modelUrl),!da||!da.modelUrl?re("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&re("load model:",da.modelUrl))):(e.debug&&re("cached model:",ca.modelUrl),e.debug&&re("cached model:",da.modelUrl));let t=new xb(ca);return eT=new bb(t,da),[ca,da]}function Ll(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function tT(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function h0(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function Sb(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var St=[null,null],Jge=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ni=[[0,0],[0,0]],Qge=["hand","fist","pinch","point","face","tip","pinchtip"],nT=4,sT=1.6,e2e=512,t2e=1.4,f0=Number.MAX_SAFE_INTEGER,Ib=0,pa=[0,0],jt={boxes:[],hands:[]},rT={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function aT(e){var t,n;if(Ae.initial&&(St[0]=null),St[0])e.debug&&re("cached model:",St[0].modelUrl);else{m0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),St[0]=await et(nt(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(St[0].modelSignature.inputs);ni[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ni[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!St[0]||!St[0].modelUrl?re("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&re("load model:",St[0].modelUrl)}return St[0]}async function oT(e){var t,n;if(Ae.initial&&(St[1]=null),St[1])e.debug&&re("cached model:",St[1].modelUrl);else{St[1]=await et(nt(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(St[1].modelSignature.inputs);ni[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ni[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!St[1]||!St[1].modelUrl?re("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&re("load model:",St[1].modelUrl)}return St[1]}async function n2e(e,t){let n=[];if(!e||!St[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,e2e),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=de(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await St[0].executeAsync(s.cast,Jge),s.boxes=ot(s.rawBoxes,[0,2]),s.scores=ot(s.rawScores,[0]);let i=ss(s.scores,1);se(i[nT]),i.splice(nT,1),s.filtered=An(i,1),se(i),s.max=gn(s.filtered,1),s.argmax=Hs(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=De(s.boxes,p,1),f=await h.data();se(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=h0(m,t2e),A=Sb(g),x=[Math.trunc(m[0]*pa[0]),Math.trunc(m[1]*pa[1]),Math.trunc(m[2]*pa[0]),Math.trunc(m[3]*pa[1])],y=u[p],b=Qge[d[p]],w={id:l++,score:y,box:x,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>se(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Cb(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&St[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[ni[1][0],ni[1][1]],"bilinear"),r.cast=de(r.crop,"float32"),r.div=pe(r.cast,255),[r.score,r.keypoints]=St[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/ni[1][1],u[1]/ni[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[pa[0]*(u[0]+t.boxRaw[0]),pa[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=p0(s.keypoints);for(let u of Object.keys(rT))s.annotations[u]=rT[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>se(r[i]))}return s}async function Tb(e,t){var r,a;if(!St[0]||!St[1]||!((r=St[0])==null?void 0:r.inputs[0].shape)||!((a=St[1])==null?void 0:a.inputs[0].shape))return[];pa=[e.shape[2]||0,e.shape[1]||0],f0++;let n=(t.hand.skipTime||0)>he()-Ib,s=f0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?jt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>he()-Ib,l=f0<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(u=>Cb(e,u,t))):t.skipAllowed&&i&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(u=>Cb(e,u,t))):(jt.boxes=await n2e(e,t),Ib=he(),jt.hands=await Promise.all(jt.boxes.map(u=>Cb(e,u,t))),f0=0);let c=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u.05&&d.box[3]/(e.shape[1]||1)>.05&&jt.hands[u].fingerScore&&jt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=h0(d.box,sT),h=h0(d.boxRaw,sT),f=Sb(h);jt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;uA0,horizontal:()=>Nb,kpt:()=>g0,relative:()=>Rb,vertical:()=>Eb});var g0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Nb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Eb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Rb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],A0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var iT=.005,xs={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function _b(e){for(let t of Nb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function lT(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=qs(e,xs.padding),n.resize=$e.resizeBilinear(n.pad,[t,t]);let s=de(n.resize,"int32");return Object.keys(n).forEach(r=>se(n[r])),s}function cT(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+xs.padding[2][0]+xs.padding[2][1])/t[0]-xs.padding[2][0],s.position[1]*(t[1]+xs.padding[1][0]+xs.padding[1][1])/t[1]-xs.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Ll(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Dn,y0=0,Db=Number.MAX_SAFE_INTEGER,Bl={boxes:[],bodies:[],last:0};async function dT(e){return Ae.initial&&(Dn=null),Dn?e.debug&&re("cached model:",Dn.modelUrl):(m0(["size"],e),Dn=await et(nt(e.modelBasePath,e.body.modelPath||"")),!Dn||!Dn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",Dn.modelUrl)),y0=Dn.inputs[0].shape?Dn.inputs[0].shape[2]:0,y0===-1&&(y0=256),Dn}async function s2e(e,t,n,s){let r=e[0][0],a=[],o=0;for(let d=0;dt.body.minConfidence){let p=[(s[3]-s[1])*r[d][1]+s[1],(s[2]-s[0])*r[d][0]+s[0]];a.push({score:Math.round(100*o)/100,part:g0[d],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}o=a.reduce((d,p)=>p.score>d?p.score:d,0);let i=[],l=Ll(a.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(A0)){let h=[];for(let f=0;fA.part===p[f]),g=a.find(A=>A.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:0,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:a,annotations:c};return _b(u),i.push(u),i}async function r2e(e,t,n,s){let r=[];for(let a=0;at.body.minConfidence){let 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Hb=class{constructor(){ce(this,"age",null);ce(this,"agegenderrace",null);ce(this,"blazeposedetect",null);ce(this,"blazepose",null);ce(this,"centernet",null);ce(this,"efficientpose",null);ce(this,"embedding",null);ce(this,"emotion",null);ce(this,"facedetect",null);ce(this,"faceiris",null);ce(this,"facemesh",null);ce(this,"faceres",null);ce(this,"gender",null);ce(this,"handpose",null);ce(this,"handskeleton",null);ce(this,"handtrack",null);ce(this,"movenet",null);ce(this,"nanodet",null);ce(this,"posenet",null);ce(this,"segmentation",null);ce(this,"antispoof",null)}};function jb(e){for(let t of Object.keys(e.models))e.models[t]=null}async function wT(e){var t,n,s,r,a,o,i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k,C,N,R,F,_,P,T,M,U;Ae.initial&&jb(e),e.config.hand.enabled&&(!e.models.handpose&&((n=(t=e.config.hand.detector)==null?void 0:t.modelPath)==null?void 0:n.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await 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p=[_l[d*3+0],_l[d*3+1],_l[d*3+2]].map(h=>u.mesh[h]);IT(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)){r.strokeStyle="pink";let d=u.box[0]+u.box[2]/2-u.box[3]*Rc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*Rc(u.rotation.angle.pitch)/90,h=new Path2D(` M ${u.box[0]+u.box[2]/2} ${u.box[1]} C ${d} ${u.box[1]}, ${d} ${u.box[1]+u.box[3]}, ${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]} `),f=new Path2D(` M ${u.box[0]} ${u.box[1]+u.box[3]/2} C ${u.box[0]} ${p}, ${u.box[0]+u.box[2]} ${p}, ${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2} `);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 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o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function TT(e,t,n){let s=Rn(ha,n);if(!t||!e)return;let r=Wl(e);r.lineJoin="round",r.font=s.font;for(let a=0;a{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=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],i=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=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return 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l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?y2e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var e5=async(e,t)=>{var p,h,f,m;let n,s,r,a,o,i,l,c,u=[];e.state="run:face",n=he();let d=await N8(t,e.config);if(e.performance.face=Ae.perfadd?(e.performance.face||0)+Math.trunc(he()-n):Math.trunc(he()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let g=0;g0&&d[g].annotations.rightEyeIris.length>0&&d[g].annotations.leftEyeIris[0]!==null&&d[g].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[g].annotations.leftEyeIris[3][0]-d[g].annotations.leftEyeIris[1][0]),Math.abs(d[g].annotations.rightEyeIris[4][1]-d[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y=e.config.face.detector.return?ot(d[g].tensor):null;se(d[g].tensor),d[g].tensor&&delete d[g].tensor,u.push({...d[g],id:g,age:c==null?void 0:c.age,gender:c==null?void 0:c.gender,genderScore:c==null?void 0:c.genderScore,embedding:c==null?void 0:c.descriptor,emotion:o,real:l,iris:x!==0?Math.trunc(500/x/11.7)/100:0,rotation:A,tensor:y}),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),u};var $T=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},_T=e=>{if(!e)return[];let t=[];for(let n=0;n450){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},DT=e=>{if(!e)return[];let t=[];for(let n=0;n.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},PT=e=>{if(!e)return[];let t=[];for(let n=0;n0){let 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s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,x,y,b,w,k,C,N,R,F,_,P,T,M,U,j,z,X,Z,J,ee;this.state="config";let r;this.config=Rn(this.config,n),this.state="check";let a=Hc(this,C0).call(this,t);a&&(re(a,t),s({error:a}));let o=he();await w0(this),await this.load(),r=he(),this.state="image";let i=await vc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(he()-r):Math.trunc(he()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&re("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=he(),this.config.skipAllowed=await j6(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(he()-r):Math.trunc(he()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?e5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=he(),l=this.config.face.enabled?await e5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Rn(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?Vb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?c=this.config.body.enabled?Jx(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?ib(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("movenet"))&&(c=this.config.body.enabled?Pb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=he(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await Vb(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await Jx(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await ib(i.tensor,p):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await Pb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Rn(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(N=this.config.hand.detector)==null?void 0:N.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?wb(i.tensor,h):[]:((_=(F=this.config.hand.detector)==null?void 0:F.modelPath)==null?void 0:_.includes("handtrack"))&&(u=this.config.hand.enabled?Tb(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=he(),((T=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await wb(i.tensor,h):[]:((U=(M=this.config.hand.detector)==null?void 0:M.modelPath)==null?void 0:U.includes("handtrack"))&&(u=this.config.hand.enabled?await Tb(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((j=this.config.object.modelPath)==null?void 0:j.includes("nanodet"))?d=this.config.object.enabled?Ob(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?tb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=he(),((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?d=this.config.object.enabled?await Ob(i.tensor,this.config):[]:((Z=this.config.object.modelPath)==null?void 0:Z.includes("centernet"))&&(d=this.config.object.enabled?await tb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,u,d]=await Promise.all([l,c,u,d])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=he(),f=[..._T(l),...$T(c),...PT(u),...DT(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(he()-o):Math.trunc(he()-o);let m=((ee=(J=this.process)==null?void 0:J.tensor)==null?void 0:ee.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return zT(l,c,u,f,m)}},se(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};$c=new WeakMap,Op=new WeakMap,Mp=new WeakMap,C0=new WeakMap;return w2e;})(); /** * @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 backend 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 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 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. * ============================================================================= */ /** * Human main module * @default Human Library * @summary * @author * @copyright * @license 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 See the LICENSE file. */