diff --git a/CHANGELOG.md b/CHANGELOG.md index 1f5a1f75..c256b262 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ ### **HEAD -> main** 2021/09/11 mandic00@live.com +- simplify dependencies - fix file permissions - remove old build server - change build process diff --git a/build.json b/build.json index 47949424..6d7eb814 100644 --- a/build.json +++ b/build.json @@ -116,6 +116,7 @@ "input": "src/human.ts", "output": "dist/human.js", "minify": true, + "globalName": "Human", "external": ["fs", "os", "buffer", "util"] }, { diff --git a/demo/webgpu/index.js b/demo/webgpu/index.js index 3e7f0385..d4d7b53e 100644 --- a/demo/webgpu/index.js +++ b/demo/webgpu/index.js @@ -265,6 +265,7 @@ async function main() { } human = new Human(config.main); + // human.tf.env().set('WEBGPU_USE_GLSL', false); document.getElementById('log').innerText = `Human: version ${human.version}`; await startWorkers(); diff --git a/demo/webgpu/worker.js b/demo/webgpu/worker.js index 671c97b9..81195a43 100644 --- a/demo/webgpu/worker.js +++ b/demo/webgpu/worker.js @@ -1,11 +1,9 @@ -// load Human using IIFE script as Chome Mobile does not support Modules as Workers - /// -// import Human from '../dist/human.esm.js'; +// import Human from '../../dist/human.esm'; // load Human using IIFE script as Chome Mobile does not support Modules as Workers +self.importScripts('../../assets/tf.es2017.js'); +self.importScripts('../../assets/tf-backend-webgpu.es2017.js'); self.importScripts('../../dist/human.js'); -self.importScripts('../../node_modules/@tensorflow/tfjs-core/dist/tf-core.es2017.js'); -// self.importScripts('../../assets/tf-backend-webgpu.fesm.js'); let human; diff --git a/dist/human.js b/dist/human.js index d1e355e8..5915ff97 100644 --- a/dist/human.js +++ b/dist/human.js @@ -4,61 +4,61 @@ author: ' */ -(()=>{var c5=Object.defineProperty;var BI=e=>c5(e,"__esModule",{value:!0});var Dm=typeof require!="undefined"?require:e=>{throw new Error('Dynamic require of "'+e+'" is not 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Array.isArray(e)||typeof e=="object"}function Jm(e){return e.kernelName!=null}var U5=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()}},rc=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new U5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let 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i,l=Jm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Jm(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Dh(p,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,A,y);let x=y.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:S}=b;return this.makeTensorFromDataId(v,k,S)});if(s){let b=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:p}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>p(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,d=Jm(e)?null:e.backwardsFunc,h;return 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Om(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof sc||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Om(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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$D=W({stringToHashBucketFast_:DD}),FD={fft:lp,ifft:Ac,rfft:up,irfft:Xg},OD={hammingWindow:u_,hannWindow:Mb,frame:zb,stft:p_},_e={flipLeftRight:A_,grayscaleToRGB:x_,resizeNearestNeighbor:U_,resizeBilinear:W_,rotateWithOffset:v_,cropAndResize:m_,nonMaxSuppression:k_,nonMaxSuppressionAsync:__,nonMaxSuppressionWithScore:$_,nonMaxSuppressionWithScoreAsync:O_,nonMaxSuppressionPadded:M_,nonMaxSuppressionPaddedAsync:L_,threshold:j_,transform:X_},Hb={bandPart:Z_,gramSchmidt:J_,qr:eD},PD={absoluteDifference:sD,computeWeightedLoss:Ir,cosineDistance:aD,hingeLoss:iD,huberLoss:uD,logLoss:dD,meanSquaredError:pD,sigmoidCrossEntropy:gD,softmaxCrossEntropy:xD},xc={sparseFillEmptyRows:vD,sparseReshape:kD,sparseSegmentMean:SD,sparseSegmentSum:TD},mp={stringNGrams:ED,stringSplit:_D,stringToHashBucketFast:$D},Sr=class extends Tx{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return Z(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return cb(e,t)}dispose(){this.iterations_!=null&&Z(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Sr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var gp=class extends Sr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>Ke(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>Ke(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;H(()=>{let u=ae(L(i,this.rho),L(ct(o),1-this.rho)),c=L(de(hn(ae(l,this.epsilon)),hn(ae(i,this.epsilon))),o),d=ae(L(l,this.rho),L(ct(c),1-this.rho));i.assign(u),l.assign(d);let h=ae(L(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Z(this.accumulatedGrads.map(e=>e.variable)),Z(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};gp.className="Adadelta";Zr(gp);var Ap=class extends Sr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>Ml(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;H(()=>{let i=ae(o,ct(a));o.assign(i);let l=ae(L(de(a,hn(ae(i,z.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Z(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Ap.className="Adagrad";Zr(Ap);var yp=class extends Sr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let o=z.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:H(()=>Ke(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:H(()=>Ke(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=ae(L(u,this.beta1),L(l,1-this.beta1)),h=ae(L(c,this.beta2),L(ct(l),1-this.beta2)),p=de(d,n),f=de(h,s);u.assign(d),c.assign(h);let m=ae(L(de(p,ae(hn(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&&Z(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Z(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),H(()=>{this.accBeta1.assign(ea(this.beta1,this.iterations_+1)),this.accBeta2.assign(ea(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};yp.className="Adam";Zr(yp);var xp=class extends Sr{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ge(1,this.accBeta1),s=de(-this.learningRate,ae(L(this.iteration,this.decay),1));t.forEach((r,a)=>{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)}};xp.className="Adamax";Zr(xp);var bc=class extends Sr{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=z.registeredVariables[n];H(()=>{let o=ae(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Jt(Ie(-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)}};bp.className="Momentum";Zr(bp);var vp=class extends Sr{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=z.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=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:H(()=>Ke(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:H(()=>Ke(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:H(()=>Ke(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;H(()=>{let u=ae(L(i,this.decay),L(ct(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,d=ae(L(c,this.decay),L(o,1-this.decay)),h=de(L(o,this.learningRate),hn(ge(u,ae(ct(d),this.epsilon)))),p=ae(L(l,this.momentum),h);i.assign(u),c.assign(d),l.assign(p);let f=ge(r,p);r.assign(f)}else{let c=ae(L(i,this.decay),L(ct(o),1-this.decay)),d=ae(L(l,this.momentum),de(L(o,this.learningRate),hn(ae(c,this.epsilon))));i.assign(c),l.assign(d);let h=ge(r,d);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Z(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Z(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Z(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};vp.className="RMSProp";Zr(vp);var Lo=class{static sgd(e){return new bc(e)}static momentum(e,t,n=!1){return new bp(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new vp(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new yp(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new gp(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new xp(e,t,n,s,r)}static 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D={};Pe(D,{ERF_A1:()=>XD,ERF_A2:()=>KD,ERF_A3:()=>ZD,ERF_A4:()=>YD,ERF_A5:()=>JD,ERF_P:()=>qD,PARALLELIZE_THRESHOLD:()=>sA,SELU_SCALE:()=>jb,SELU_SCALEALPHA:()=>Gb,applyActivation:()=>pp,assertAndGetBroadcastShape:()=>At,assertAxesAreInnerMostDims:()=>KN,assertParamsConsistent:()=>zD,assignToTypedArray:()=>r$,axesAreInnerMostDims:()=>Fg,calculateShapes:()=>mx,checkEinsumDimSizes:()=>c$,combineLocations:()=>db,complexWithEvenIndex:()=>t$,complexWithOddIndex:()=>n$,computeConv2DInfo:()=>uc,computeConv3DInfo:()=>Vx,computeDefaultPad:()=>vg,computeDilation2DInfo:()=>fT,computeOptimalWindowSize:()=>BD,computeOutAndReduceShapes:()=>hb,computeOutShape:()=>LD,computePool2DInfo:()=>Wx,computePool3DInfo:()=>mT,convertConv2DDataFormat:()=>Ux,decodeEinsumEquation:()=>l$,eitherStridesOrDilationsAreOne:()=>tr,expandShapeToKeepDim:()=>zo,exponent:()=>o$,exponents:()=>a$,fromStringArrayToUint8:()=>x$,fromUint8ToStringArray:()=>y$,getAxesPermutation:()=>pb,getBroadcastDims:()=>oN,getComplexWithIndex:()=>s$,getEinsumComputePath:()=>d$,getEinsumPermutation:()=>u$,getFusedBiasGradient:()=>hp,getFusedDyActivation:()=>dp,getImageCenter:()=>WD,getInnerMostAxes:()=>ZN,getPermuted:()=>UD,getReductionAxes:()=>Gt,getReshaped:()=>VD,getReshapedPermuted:()=>HD,getSliceBeginCoords:()=>GD,getSliceSize:()=>jD,getUndoAxesPermutation:()=>Og,isIdentityPermutation:()=>h$,log:()=>zS,mergeRealAndImagArrays:()=>QD,prepareAndValidate:()=>fx,prepareSplitSize:()=>f$,segment_util:()=>Kb,shouldFuse:()=>fp,slice_util:()=>xn,splitRealAndImagArrays:()=>e$,tupleValuesAreOne:()=>Yr,upcastType:()=>Cs,validateInput:()=>pg,validateUpdateShape:()=>hg,warn:()=>Ys});function zD(e,t){let n=e[0].length;e.forEach((r,a)=>{M(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),M(t>=0&&t`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let o=0;o`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function LD(e,t){let n=e[0].slice();for(let s=1;s=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function HD(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a/g,qb=",",Xb="...";function l$(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace(i$,"").length)/rA.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${rA}").`);let[s,r]=e.split(rA);M(s.indexOf(Xb)===-1,()=>`The ellipsis notation ("${Xb}") is not supported yet.`);let 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For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(at(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Nr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Kl))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Us(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new G("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Kl.className="Sequential";oe.registerClass(Kl);function dM(e){return new Nr(e)}function hM(e){return new Kl(e)}function pM(e,t){return t==null&&(t={}),lM(e,t)}function tv(e){return T3(e)}function fM(e,t){Rs.registerCallbackConstructor(e,t)}var On=class extends oe.Serializable{getConfig(){return{}}},nv=class extends On{apply(e,t=1){return BO(e,t)}};nv.className="elu";oe.registerClass(nv);var sv=class extends On{apply(e){return Hg(e)}};sv.className="selu";oe.registerClass(sv);var rv=class extends On{apply(e){return sr(e)}};rv.className="relu";oe.registerClass(rv);var av=class extends On{apply(e){return H(()=>fc(6,sr(e)))}};av.className="relu6";oe.registerClass(av);var ov=class extends On{apply(e){return e}};ov.className="linear";oe.registerClass(ov);var iv=class extends On{apply(e){return Hn(e)}};iv.className="sigmoid";oe.registerClass(iv);var lv=class extends On{apply(e){return VO(e)}};lv.className="hardSigmoid";oe.registerClass(lv);var uv=class extends On{apply(e){return Ll(e)}};uv.className="softplus";oe.registerClass(uv);var cv=class extends On{apply(e){return WO(e)}};cv.className="softsign";oe.registerClass(cv);var dv=class extends On{apply(e){return Fl(e)}};dv.className="tanh";oe.registerClass(dv);var WA=class extends On{apply(e,t=-1){return ip(e,t)}};WA.className="softmax";oe.registerClass(WA);var hv=class extends On{apply(e,t=-1){return $g(e,t)}};hv.className="logSoftmax";oe.registerClass(hv);var pv=class extends On{apply(e,t=1){return H(()=>L(Hn(L(e,t)),e))}};pv.className="swish";oe.registerClass(pv);var fv=class extends On{apply(e){return H(()=>L(e,Fl(Ll(e))))}};fv.className="mish";oe.registerClass(fv);function oa(e){return e.getClassName()}function VA(e,t={}){return vc(e,oe.SerializationMap.getMap().classNameMap,t,"activation")}function ia(e){if(e==null){let t={};return t.className="linear",t.config={},VA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},VA(t)}else return e instanceof On?e:VA(e)}function UA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var mv=class extends oe.Serializable{},_c=class extends mv{constructor(e){super();UA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=Pt([1]);return this.hasL1&&(t=ae(t,ve(L(this.l1,Ht(e))))),this.hasL2&&(t=ae(t,ve(L(this.l2,Sc(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};_c.className="L1L2";oe.registerClass(_c);function mM(e){return UA(e),new _c({l1:e!=null?e.l1:null,l2:0})}function gM(e){return UA(e),new _c({l2:e!=null?e.l2:null,l1:0})}var gv={l1l2:"L1L2"};function dt(e){return oA(e)}function Av(e,t={}){return vc(e,oe.SerializationMap.getMap().classNameMap,t,"regularizer")}function kt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in gv?gv[e]:e,config:{}};return Av(n)}else return e instanceof mv?e:Av(e)}var HA=class extends Ze{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=sr(e);return this.maxValue!=null&&(n=Gn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};HA.className="ReLU";oe.registerClass(HA);var GA=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return Zh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};GA.className="LeakyReLU";oe.registerClass(GA);var jA=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=wt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=kt(e.alphaRegularizer),this.alphaConstraint=Xt(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 G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=at(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(Ft(t),t==="channelsFirst"?Xe(e,[0,2,3,1]):e))}function yv(e,t){return H(()=>(Ft(t),t==="channelsFirst"?Xe(e,[0,2,3,4,1]):e))}function AM(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Xe(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Sg(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ws(i,n)),i})}function xv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=ZA(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ta.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Xe(l,[0,3,1,2])),l})}function yM(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=yv(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Ng(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ws(i,n)),a==="channelsFirst"&&(i=Xe(i,[0,4,1,2,3])),i})}var YA=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",YA.verifyArgs(t),this.rank=e,Qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,gs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=ia(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Xt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ar("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!lA(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:oa(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Dc=class extends YA{constructor(e,t){super(e,t);this.kernel=null,Dc.verifyArgs(t),this.filters=t.filters,Qt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Xt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=at(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],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 H(()=>{e=ze(e);let n,s=this.bias==null?null:this.bias.read(),r=l3(this.activation.getClassName());if(r!=null&&this.rank===2)n=xv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=AM(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=xv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=yM(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=at(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},$c=class extends Dc{constructor(e){super(2,e);$c.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!lA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};$c.className="Conv2D";oe.registerClass($c);var Fc=class extends Dc{constructor(e){super(3,e);Fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Fc.className="Conv3D";oe.registerClass(Fc);var JA=class extends $c{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],h=this.strides[1],p=lr(i,d,u,this.padding),f=lr(l,h,c,this.padding),m=[r,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,1]));let g=Tg(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Xe(g,[0,3,1,2])),this.bias!=null&&(g=Ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=at(e);let t=e.slice(),n,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]=lr(t[s],i,a,this.padding),t[r]=lr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};JA.className="Conv2DTranspose";oe.registerClass(JA);var QA=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],u=s[a],c=s[o],d=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=lr(l,f,d,this.padding),y=lr(u,m,h,this.padding),x=lr(c,g,p,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,4,1]));let v=Qx(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Xe(v,[0,4,1,2,3])),this.bias!==null&&(v=Ws(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=at(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],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=lr(t[s],u,o,this.padding),t[r]=lr(t[r],c,i,this.padding),t[a]=lr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};QA.className="Conv3DTranspose";oe.registerClass(QA);var bv=class extends Dc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Xt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Xt(t.pointwiseConstraint)}build(e){if(e=at(e),e.length{e=ze(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Xe(e,[0,2,3,1])),n=vb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Xe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseConstraint),e.pointwiseConstraint=qt(this.pointwiseConstraint),e}};bv.className="SeparableConv";var e1=class extends bv{constructor(e){super(2,e)}};e1.className="SeparableConv2D";oe.registerClass(e1);var qp=class extends Dc{constructor(e){super(1,e);qp.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"&&!lA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};qp.className="Conv1D";oe.registerClass(qp);var t1=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=Sp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Sp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Sp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Sp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};t1.className="Cropping2D";oe.registerClass(t1);var n1=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,$O(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 H(()=>{let n=ze(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Xe(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 Xe(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}};n1.className="UpSampling2D";oe.registerClass(n1);function xM(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=zs()),Ft(r);let o=ZA(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=dc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Xe(o,[0,3,1,2])),o})}var s1=class extends YA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Xt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=at(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 H(()=>{e=ze(e);let n=xM(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Hs(t,this.kernelSize[0],this.padding,this.strides[0]),a=Hs(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=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseRegularizer),e}};s1.className="DepthwiseConv2D";oe.registerClass(s1);function vv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("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 wv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Bs(2,l));if(t=Xe(t,u),a!=null)throw new Oe("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=ce(ce(r,"bool"),"float32"),r.rank===l-1&&(r=Ot(r,-1)),r=Xe(r,u)),s&&(t=fs(t,0),r!=null&&(r=fs(r,0)));let c=[],d,h=n,p=t.shape[0],f=ms(t),m;r!=null&&(m=ms(r));for(let A=0;Ae(y,h));if(r==null)d=x[0],h=x[1];else{let b=H(()=>{let v=m[A],k=ge(ps(v),v),S=ae(L(x[0],v),L(h[0],k)),C=h.map((_,O)=>ae(L(x[1][O],v),L(_,k)));return{output:S,newStates:C}});d=b.output,h=b.newStates}i&&c.push(d)}let g;return i&&(g=Dn(c,1)),[d,g,h]})}var ur=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Bs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){SA(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 H(()=>{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 G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Cr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Pt([n,s])):this.states_=[Pt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Pt([n,s])):this.states_[0]=Pt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let s=0;sJt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=vv(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 zt({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 Vs){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=ze(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 G(`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=wv((p,f)=>{let m=this.cell.call([p].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?c:u;return this.returnState?[h].concat(d):h})}getInitialState(e){return H(()=>{let t=Pt(e.shape);return t=ve(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gA(t,[1,n]):t):this.cell.stateSize>1?[gA(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()===ur.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Us(s,n);return new e(Object.assign(t,{cell:r}))}};ur.className="RNN";oe.registerClass(ur);var Oc=class extends Ze{},Xp=class extends Oc{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,Qt(this.units,"units"),this.activation=ia(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0ps(e),rate:this.dropout,training:s})),0ps(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=or(L(e,a),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ws(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ae(r,or(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:oa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Xp.className="SimpleRNNCell";oe.registerClass(Xp);var r1=class extends ur{constructor(e){e.cell=new Xp(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};r1.className="SimpleRNN";oe.registerClass(r1);var Kp=class extends Oc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Qt(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0ps(e),rate:this.dropout,training:n,count:3})),0ps(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};a1.className="GRU";oe.registerClass(a1);var Pc=class extends Oc{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,Qt(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=at(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 Es{apply(i,l){let u=r.apply([a]),c=new Tp().apply([a]),d=r.apply([a*2]);return A3(A3(u,c),d)}},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 H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0ps(e),rate:this.dropout,training:n,count:4})),0ps(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};o1.className="LSTM";oe.registerClass(o1);var Zp=class extends Oc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{Ho(`RNNCell_${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 Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Us(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 CA(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;ax3(t(),n),o=()=>Cc(a,t,s);return!r||r<=1?Jt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Jt(l.clone()))}var bM=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("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 H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Pt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Cr("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 G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Pt(r)):this.states_=[Pt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Pt(r)):this.states_[0]=Pt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let o=0;oJt(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],u=e[i?4:3],c=Hs(l,s[0],r,a[0],o[0]),d=Hs(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};kv.className="ConvRNN2D";var Yp=class extends Pc{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,Qt(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(i=>Qt(i,"kernelSize")),this.strides=Zl(s||1,2,"strides"),this.strides.forEach(i=>Qt(i,"strides")),this.padding=r||"valid",gs(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=Zl(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Qt(i,"dilationRate"))}build(e){var t;e=at(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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,u=this.filters;i=new(t=class extends Es{apply(d,h){let p=l.apply([u]),f=qn([u]),m=l.apply([u*2]);return mA([p,f,m])}},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 H(()=>{if(e.length!==3)throw new G(`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;0ps(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,te,ne)=>!te||!te[ne]?ee:L(te[ne],ee),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),h=l(s,i,3);0ps(r),rate:this.recurrentDropout,training:n,count:o}));let p=this.recurrentDropoutMask,f=l(r,p,0),m=l(r,p,1),g=l(r,p,2),A=l(r,p,3),y=3,[x,b,v,k]=an(this.kernel.read(),o,y),[S,C,_,O]=this.useBias?an(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,_,this.padding),h=this.inputConv(h,k,O,this.padding);let[E,R,T,P]=an(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,E),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let V=this.recurrentActivation.apply(ae(u,f)),j=this.recurrentActivation.apply(ae(c,m)),q=ae(L(j,a),L(V,this.activation.apply(ae(d,g)))),X=L(this.recurrentActivation.apply(ae(h,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=bM(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Jr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ws(r,n,this.dataFormat):r}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yp.className="ConvLSTM2DCell";oe.registerClass(Yp);var i1=class extends kv{constructor(e){let t=new Yp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};i1.className="ConvLSTM2D";oe.registerClass(i1);var Jp=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s{this.invokeCallHook(e,t);let n=ze(e);if(0x3(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()}};Jp.className="Dropout";oe.registerClass(Jp);var l1=class extends Jp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};l1.className="SpatialDropout1D";oe.registerClass(l1);var u1=class extends Ze{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Qt(this.units,"units"),this.activation=ia(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Xt(e.kernelConstraint),this.biasConstraint=Xt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=at(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=at(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=l3(this.activation.getClassName()),r;return s!=null?r=or(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:oa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};u1.className="Dense";oe.registerClass(u1);var c1=class extends Ze{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=at(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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${e.shape.length}`),w.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 Oe("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 H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;us){o=r-s;let l=[];for(let u=0;u0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c"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 Oe("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 G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`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)=>Mc(r,e[a].shape.length)):s=[Mc(this.axes,t.shape.length),Mc(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,s[0]),n=Lp(n,s[1])),vM(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Mc(this.axes,e.length),Mc(this.axes,t.length)],n}computeOutputShape(e){w.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 Oe("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}};k1.className="Dot";oe.registerClass(k1);var I1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return Cc(()=>ae(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};I1.className="GaussianNoise";oe.registerClass(I1);var S1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?Cc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};S1.className="GaussianDropout";oe.registerClass(S1);var C1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(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 H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Cc(()=>{let r=ze(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Po(Wl(n),this.rate);l=Ip(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ae(L(r,l),L(ae(l,-1),i));return ae(L(d,u),c)},()=>ze(e),t.training||!1)}return e})}};C1.className="AlphaDropout";oe.registerClass(C1);function zc(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=Hx(e,t,n,s,r,a);else if(e.rank===3)o=Gx(e,t,n,s,r,a);else if(e.rank===4)o=jx(e,t,n,s,r,a);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function wM(e,t,n,s,r=.001){return H(()=>{let a=np(e,s),o=a.mean,i=a.variance;return[zc(e,o,i,n,t,r),o,i]})}function kM(e,t,n,s,r=.001){return H(()=>{let a=np(e,s),o=a.mean,i=a.variance,l=[];for(let f of Bs(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=U(o,l),c=U(i,l),d=t==null?null:U(t,l),h=n==null?null:U(n,l);return[zc(e,u,c,h,d,r),o,i]})}function IM(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),Bs(0,e.rank-1))?wM(e,t,n,s,r):kM(e,t,n,s,r)}var T1=class extends Ze{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Xt(e.betaConstraint),this.gammaConstraint=Xt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=at(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let 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 H(()=>{let n=t.training==null?!1:t.training,s=ze(e),r=s.shape,a=r.length,o=Bs(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Wo(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,Bs(0,a).slice(0,a-1)),d=()=>{if(c){let A=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),x=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return zc(s,A,y,x,b,this.epsilon)}else return zc(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[h,p,f]=IM(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=L(ge(v,y),b);A.write(ge(v,k))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:qt(this.betaConstraint),gammaConstraint:qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};T1.className="BatchNormalization";oe.registerClass(T1);var N1=class extends Ze{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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=at(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!==na(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=ze(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=np(n,this.axis,a),l=Wo(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?U(f,l):f,c=u(this.gamma.read()),d=u(this.beta.read()),h=[],p=[];for(let f=0;f{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=zs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. 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a==="max"?o=ep(e,t,n,i):o=jh(e,t,n,i),r==="channelsFirst"&&(o=Xe(o,[0,3,1,2])),o})}function Iv(e,t,n,s,r,a){return H(()=>{Ft(r),h3(a),gs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=zs()),a==null&&(a="max"),e=yv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Mg(e,t,n,i):o=kg(e,t,n,i),r==="channelsFirst"&&(o=Xe(o,[0,4,1,2,3])),o})}var Sv=class extends Ze{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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Qt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=Hs(t,this.poolSize[0],this.padding,this.strides[0]),n=Hs(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},D1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Qp(e,t,n,s,r,"max")}};D1.className="MaxPooling2D";oe.registerClass(D1);var $1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Qp(e,t,n,s,r,"avg")}};$1.className="AveragePooling2D";oe.registerClass($1);var Tv=class extends Ze{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 G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),gs(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=at(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=Hs(t,this.poolSize[0],this.padding,this.strides[0]),n=Hs(n,this.poolSize[1],this.padding,this.strides[1]),s=Hs(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},F1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Iv(e,t,n,s,r,"max")}};F1.className="MaxPooling3D";oe.registerClass(F1);var O1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Iv(e,t,n,s,r,"avg")}};O1.className="AveragePooling3D";oe.registerClass(O1);var Nv=class extends Ze{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e(a)}},B1=class extends Rv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=at(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=at(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return H(()=>(e=ze(e),wv((a,o)=>[ze(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};B1.className="TimeDistributed";oe.registerClass(B1);function CM(e){Uo(DO,"BidirectionalMergeMode",e)}var TM="concat",W1=class extends Rv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Us(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Us(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?TM:e.mergeMode,CM(this.mergeMode),e.weights)throw new Oe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):$n(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=vv(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new 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Add some layers first.");this.model=new Nr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ls("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Kl))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Us(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new G("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Kl.className="Sequential";oe.registerClass(Kl);function hM(e){return new Nr(e)}function pM(e){return new Kl(e)}function fM(e,t){return t==null&&(t={}),uM(e,t)}function tv(e){return T3(e)}function mM(e,t){Rs.registerCallbackConstructor(e,t)}var On=class extends oe.Serializable{getConfig(){return{}}},nv=class extends On{apply(e,t=1){return WO(e,t)}};nv.className="elu";oe.registerClass(nv);var sv=class extends On{apply(e){return Gg(e)}};sv.className="selu";oe.registerClass(sv);var rv=class extends On{apply(e){return sr(e)}};rv.className="relu";oe.registerClass(rv);var av=class extends On{apply(e){return H(()=>fc(6,sr(e)))}};av.className="relu6";oe.registerClass(av);var ov=class extends On{apply(e){return e}};ov.className="linear";oe.registerClass(ov);var iv=class extends On{apply(e){return Hn(e)}};iv.className="sigmoid";oe.registerClass(iv);var lv=class extends On{apply(e){return UO(e)}};lv.className="hardSigmoid";oe.registerClass(lv);var uv=class extends On{apply(e){return Ll(e)}};uv.className="softplus";oe.registerClass(uv);var cv=class extends On{apply(e){return VO(e)}};cv.className="softsign";oe.registerClass(cv);var dv=class extends On{apply(e){return Fl(e)}};dv.className="tanh";oe.registerClass(dv);var VA=class extends On{apply(e,t=-1){return ip(e,t)}};VA.className="softmax";oe.registerClass(VA);var hv=class extends On{apply(e,t=-1){return Fg(e,t)}};hv.className="logSoftmax";oe.registerClass(hv);var pv=class extends On{apply(e,t=1){return H(()=>L(Hn(L(e,t)),e))}};pv.className="swish";oe.registerClass(pv);var fv=class extends On{apply(e){return H(()=>L(e,Fl(Ll(e))))}};fv.className="mish";oe.registerClass(fv);function oa(e){return e.getClassName()}function UA(e,t={}){return vc(e,oe.SerializationMap.getMap().classNameMap,t,"activation")}function ia(e){if(e==null){let t={};return t.className="linear",t.config={},UA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},UA(t)}else return e instanceof On?e:UA(e)}function HA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var mv=class extends oe.Serializable{},_c=class extends mv{constructor(e){super();HA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=Pt([1]);return this.hasL1&&(t=ae(t,ve(L(this.l1,Ht(e))))),this.hasL2&&(t=ae(t,ve(L(this.l2,Sc(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};_c.className="L1L2";oe.registerClass(_c);function gM(e){return HA(e),new _c({l1:e!=null?e.l1:null,l2:0})}function AM(e){return HA(e),new _c({l2:e!=null?e.l2:null,l1:0})}var gv={l1l2:"L1L2"};function dt(e){return iA(e)}function Av(e,t={}){return vc(e,oe.SerializationMap.getMap().classNameMap,t,"regularizer")}function kt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in gv?gv[e]:e,config:{}};return Av(n)}else return e instanceof mv?e:Av(e)}var GA=class extends Ze{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=sr(e);return this.maxValue!=null&&(n=Gn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};GA.className="ReLU";oe.registerClass(GA);var jA=class extends Ze{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return Zh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jA.className="LeakyReLU";oe.registerClass(jA);var qA=class extends Ze{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=wt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=kt(e.alphaRegularizer),this.alphaConstraint=Xt(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 G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=at(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(Ft(t),t==="channelsFirst"?Xe(e,[0,2,3,1]):e))}function yv(e,t){return H(()=>(Ft(t),t==="channelsFirst"?Xe(e,[0,2,3,4,1]):e))}function yM(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Xe(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Cg(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ws(i,n)),i})}function xv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=YA(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ta.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Xe(l,[0,3,1,2])),l})}function xM(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=zs()),Ft(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=yv(e,a);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Eg(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ws(i,n)),a==="channelsFirst"&&(i=Xe(i,[0,4,1,2,3])),i})}var JA=class extends Ze{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",JA.verifyArgs(t),this.rank=e,Qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,gs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=ia(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Xt(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ar("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!uA(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:oa(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Dc=class extends JA{constructor(e,t){super(e,t);this.kernel=null,Dc.verifyArgs(t),this.filters=t.filters,Qt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Xt(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=at(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],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 H(()=>{e=ze(e);let n,s=this.bias==null?null:this.bias.read(),r=l3(this.activation.getClassName());if(r!=null&&this.rank===2)n=xv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=yM(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=xv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=xM(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=at(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},$c=class extends Dc{constructor(e){super(2,e);$c.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!uA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};$c.className="Conv2D";oe.registerClass($c);var Fc=class extends Dc{constructor(e){super(3,e);Fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Fc.className="Conv3D";oe.registerClass(Fc);var QA=class extends $c{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],h=this.strides[1],p=lr(i,d,u,this.padding),f=lr(l,h,c,this.padding),m=[r,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,1]));let g=Ng(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Xe(g,[0,3,1,2])),this.bias!=null&&(g=Ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=at(e);let t=e.slice(),n,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]=lr(t[s],i,a,this.padding),t[r]=lr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};QA.className="Conv2DTranspose";oe.registerClass(QA);var e1=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=ze(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let 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],u=s[a],c=s[o],d=this.kernelSize[0],h=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=lr(l,f,d,this.padding),y=lr(u,m,h,this.padding),x=lr(c,g,p,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Xe(n,[0,2,3,4,1]));let v=Qx(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Xe(v,[0,4,1,2,3])),this.bias!==null&&(v=Ws(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=at(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],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=lr(t[s],u,o,this.padding),t[r]=lr(t[r],c,i,this.padding),t[a]=lr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};e1.className="Conv3DTranspose";oe.registerClass(e1);var bv=class extends Dc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Xt(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Xt(t.pointwiseConstraint)}build(e){if(e=at(e),e.length{e=ze(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Xe(e,[0,2,3,1])),n=vb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Xe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseConstraint),e.pointwiseConstraint=qt(this.pointwiseConstraint),e}};bv.className="SeparableConv";var t1=class extends bv{constructor(e){super(2,e)}};t1.className="SeparableConv2D";oe.registerClass(t1);var qp=class extends Dc{constructor(e){super(1,e);qp.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"&&!uA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};qp.className="Conv1D";oe.registerClass(qp);var n1=class extends Ze{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=Sp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Sp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Sp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Sp(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}};n1.className="Cropping2D";oe.registerClass(n1);var s1=class extends Ze{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,FO(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 H(()=>{let n=ze(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Xe(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 Xe(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}};s1.className="UpSampling2D";oe.registerClass(s1);function bM(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=zs()),Ft(r);let o=YA(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=dc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Xe(o,[0,3,1,2])),o})}var r1=class extends JA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Xt(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=at(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 H(()=>{e=ze(e);let n=bM(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Hs(t,this.kernelSize[0],this.padding,this.strides[0]),a=Hs(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=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseRegularizer),e}};r1.className="DepthwiseConv2D";oe.registerClass(r1);function vv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("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 wv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Bs(2,l));if(t=Xe(t,u),a!=null)throw new Oe("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=ce(ce(r,"bool"),"float32"),r.rank===l-1&&(r=Ot(r,-1)),r=Xe(r,u)),s&&(t=fs(t,0),r!=null&&(r=fs(r,0)));let c=[],d,h=n,p=t.shape[0],f=ms(t),m;r!=null&&(m=ms(r));for(let A=0;Ae(y,h));if(r==null)d=x[0],h=x[1];else{let b=H(()=>{let v=m[A],k=ge(ps(v),v),S=ae(L(x[0],v),L(h[0],k)),C=h.map((_,O)=>ae(L(x[1][O],v),L(_,k)));return{output:S,newStates:C}});d=b.output,h=b.newStates}i&&c.push(d)}let g;return i&&(g=Dn(c,1)),[d,g,h]})}var ur=class extends Ze{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Bs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){CA(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 H(()=>{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 G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Cr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Pt([n,s])):this.states_=[Pt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Pt([n,s])):this.states_[0]=Pt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let s=0;sJt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=vv(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 zt({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 Vs){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=ze(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 G(`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=wv((p,f)=>{let m=this.cell.call([p].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?c:u;return this.returnState?[h].concat(d):h})}getInitialState(e){return H(()=>{let t=Pt(e.shape);return t=ve(t,[1,2]),t=Ic(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?AA(t,[1,n]):t):this.cell.stateSize>1?[AA(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()===ur.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Us(s,n);return new e(Object.assign(t,{cell:r}))}};ur.className="RNN";oe.registerClass(ur);var Oc=class extends Ze{},Xp=class extends Oc{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,Qt(this.units,"units"),this.activation=ia(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0ps(e),rate:this.dropout,training:s})),0ps(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=or(L(e,a),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ws(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ae(r,or(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:oa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Xp.className="SimpleRNNCell";oe.registerClass(Xp);var a1=class extends ur{constructor(e){e.cell=new Xp(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};a1.className="SimpleRNN";oe.registerClass(a1);var Kp=class extends Oc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Qt(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0ps(e),rate:this.dropout,training:n,count:3})),0ps(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(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)}};o1.className="GRU";oe.registerClass(o1);var Pc=class extends Oc{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,Qt(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Xt(e.kernelConstraint),this.recurrentConstraint=Xt(e.recurrentConstraint),this.biasConstraint=Xt(e.biasConstraint),this.dropout=Gl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=at(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 Es{apply(i,l){let u=r.apply([a]),c=new Tp().apply([a]),d=r.apply([a*2]);return A3(A3(u,c),d)}},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 H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0ps(e),rate:this.dropout,training:n,count:4})),0ps(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};i1.className="LSTM";oe.registerClass(i1);var Zp=class extends Oc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{Ho(`RNNCell_${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 Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Us(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 TA(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;ax3(t(),n),o=()=>Cc(a,t,s);return!r||r<=1?Jt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Jt(l.clone()))}var vM=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("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 H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Pt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Cr("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 G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Pt(r)):this.states_=[Pt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Pt(r)):this.states_[0]=Pt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let o=0;oJt(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],u=e[i?4:3],c=Hs(l,s[0],r,a[0],o[0]),d=Hs(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};kv.className="ConvRNN2D";var Yp=class extends Pc{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,Qt(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(i=>Qt(i,"kernelSize")),this.strides=Zl(s||1,2,"strides"),this.strides.forEach(i=>Qt(i,"strides")),this.padding=r||"valid",gs(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=Zl(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Qt(i,"dilationRate"))}build(e){var t;e=at(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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,u=this.filters;i=new(t=class extends Es{apply(d,h){let p=l.apply([u]),f=qn([u]),m=l.apply([u*2]);return gA([p,f,m])}},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 H(()=>{if(e.length!==3)throw new G(`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;0ps(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,te,ne)=>!te||!te[ne]?ee:L(te[ne],ee),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),h=l(s,i,3);0ps(r),rate:this.recurrentDropout,training:n,count:o}));let p=this.recurrentDropoutMask,f=l(r,p,0),m=l(r,p,1),g=l(r,p,2),A=l(r,p,3),y=3,[x,b,v,k]=an(this.kernel.read(),o,y),[S,C,_,O]=this.useBias?an(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,_,this.padding),h=this.inputConv(h,k,O,this.padding);let[E,R,T,P]=an(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,E),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let V=this.recurrentActivation.apply(ae(u,f)),j=this.recurrentActivation.apply(ae(c,m)),q=ae(L(j,a),L(V,this.activation.apply(ae(d,g)))),X=L(this.recurrentActivation.apply(ae(h,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=vM(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Jr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ws(r,n,this.dataFormat):r}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yp.className="ConvLSTM2DCell";oe.registerClass(Yp);var l1=class extends kv{constructor(e){let t=new Yp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};l1.className="ConvLSTM2D";oe.registerClass(l1);var Jp=class extends Ze{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s{this.invokeCallHook(e,t);let n=ze(e);if(0x3(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()}};Jp.className="Dropout";oe.registerClass(Jp);var u1=class extends Jp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};u1.className="SpatialDropout1D";oe.registerClass(u1);var c1=class extends Ze{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Qt(this.units,"units"),this.activation=ia(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Xt(e.kernelConstraint),this.biasConstraint=Xt(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=at(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=at(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e),s=l3(this.activation.getClassName()),r;return s!=null?r=or(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:oa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};c1.className="Dense";oe.registerClass(c1);var d1=class extends Ze{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=at(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Array.isArray(this.axes)?s=this.axes.map((r,a)=>Mc(r,e[a].shape.length)):s=[Mc(this.axes,t.shape.length),Mc(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,s[0]),n=Lp(n,s[1])),wM(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Mc(this.axes,e.length),Mc(this.axes,t.length)],n}computeOutputShape(e){w.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 Oe("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}};I1.className="Dot";oe.registerClass(I1);var S1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return Cc(()=>ae(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};S1.className="GaussianNoise";oe.registerClass(S1);var C1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?Cc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};C1.className="GaussianDropout";oe.registerClass(C1);var T1=class extends Ze{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(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 H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Cc(()=>{let r=ze(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Po(Wl(n),this.rate);l=Ip(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ae(L(r,l),L(ae(l,-1),i));return ae(L(d,u),c)},()=>ze(e),t.training||!1)}return e})}};T1.className="AlphaDropout";oe.registerClass(T1);function zc(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=Hx(e,t,n,s,r,a);else if(e.rank===3)o=Gx(e,t,n,s,r,a);else if(e.rank===4)o=jx(e,t,n,s,r,a);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function kM(e,t,n,s,r=.001){return H(()=>{let a=np(e,s),o=a.mean,i=a.variance;return[zc(e,o,i,n,t,r),o,i]})}function IM(e,t,n,s,r=.001){return H(()=>{let a=np(e,s),o=a.mean,i=a.variance,l=[];for(let f of Bs(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=U(o,l),c=U(i,l),d=t==null?null:U(t,l),h=n==null?null:U(n,l);return[zc(e,u,c,h,d,r),o,i]})}function SM(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),Bs(0,e.rank-1))?kM(e,t,n,s,r):IM(e,t,n,s,r)}var N1=class extends Ze{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Xt(e.betaConstraint),this.gammaConstraint=Xt(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=at(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let 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 H(()=>{let n=t.training==null?!1:t.training,s=ze(e),r=s.shape,a=r.length,o=Bs(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Wo(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,Bs(0,a).slice(0,a-1)),d=()=>{if(c){let A=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),x=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return zc(s,A,y,x,b,this.epsilon)}else return zc(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[h,p,f]=SM(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=L(ge(v,y),b);A.write(ge(v,k))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:qt(this.betaConstraint),gammaConstraint:qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};N1.className="BatchNormalization";oe.registerClass(N1);var E1=class extends Ze{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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=at(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!==na(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=ze(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=np(n,this.axis,a),l=Wo(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?U(f,l):f,c=u(this.gamma.read()),d=u(this.beta.read()),h=[],p=[];for(let f=0;f{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=zs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. 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a==="max"?o=ep(e,t,n,i):o=jh(e,t,n,i),r==="channelsFirst"&&(o=Xe(o,[0,3,1,2])),o})}function Iv(e,t,n,s,r,a){return H(()=>{Ft(r),h3(a),gs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=zs()),a==null&&(a="max"),e=yv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=zg(e,t,n,i):o=Ig(e,t,n,i),r==="channelsFirst"&&(o=Xe(o,[0,4,1,2,3])),o})}var Sv=class extends Ze{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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Qt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=Hs(t,this.poolSize[0],this.padding,this.strides[0]),n=Hs(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},$1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Qp(e,t,n,s,r,"max")}};$1.className="MaxPooling2D";oe.registerClass($1);var F1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Qp(e,t,n,s,r,"avg")}};F1.className="AveragePooling2D";oe.registerClass(F1);var Tv=class extends Ze{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 G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),gs(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=at(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=Hs(t,this.poolSize[0],this.padding,this.strides[0]),n=Hs(n,this.poolSize[1],this.padding,this.strides[1]),s=Hs(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},O1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Iv(e,t,n,s,r,"max")}};O1.className="MaxPooling3D";oe.registerClass(O1);var P1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ft(r),gs(s),Iv(e,t,n,s,r,"avg")}};P1.className="AveragePooling3D";oe.registerClass(P1);var Nv=class extends Ze{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Us(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?NM:e.mergeMode,TM(this.mergeMode),e.weights)throw new Oe("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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s=I("image",e,t,n),r=I("boxes",e,t,n),a=I("boxInd",e,t,n),o=I("cropSize",e,t,n),i=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[_e.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},HL=(e,t,n)=>{switch(e.op){case"Equal":return[us(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[Bl(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[jn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Po(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Dg(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[Mo(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Ps(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Qh(I("a",e,t,n))];case"LogicalOr":return[Pg(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[bn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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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 g7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(h=>Xn(h)[0]),c=[];s!=null&&(c=s.map(h=>Xn(h.name)[0]));let d=[...t];for(;d.length>0;){let h=d.pop();if((A7(h)||sB(h)||rB(h))&&o==null&&(o=h,i=o.children.map(p=>p.name).filter(p=>r.has(p))),r.add(h.name),n[h.name]==null&&u.indexOf(h.name)===-1&&c.indexOf(h.name)===-1){if(h.inputs.length===0){a.push(h.name);continue}h.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),d.push(p))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function QL(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>Xn(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(h=>l.has(h.name))&&a.push(d)})}return u}var eB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],tB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],nB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function A7(e){return eB.indexOf(e.op)>=0}function sB(e){return tB.indexOf(e.op)>=0}function rB(e){return nB.indexOf(e.op)>=0}var a2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new a2(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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=g7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${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 QL(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(c=>this.graph.nodes[Xn(c)[0]]),r=t.map(c=>Xn(c)[0]),a=r.map(c=>this.graph.nodes[c]);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={},u={};return H(()=>{let c=new m7(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Xn(f),A=[];A[g]=e[f],d[m]=A});let h=this.getFrozenTensorIds(d),p={};for(let f=0;fwn(f,d,c))})}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=oL(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}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));let a=new m7(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>wn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(p=>{p&&!p.kept&&!p.isDisposed&&!c.has(p.id)&&p.dispose()})}),this.parent==null&&a.dispose(c),i}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(y=>this.graph.nodes[Xn(y)[0]]),o=n.map(y=>Xn(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=g7(e,i,this.weightMap,this._initNodes),h=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,b]=Xn(y),v=[];v[b]=e[y],p[x]=v});let f={},m=this.getFrozenTensorIds(p),g={};for(;h.length>0;){let y=this.processStack(a,h,t,p,g,m,o,f,l);await Promise.all(y)}c==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(y=>!A7(y)&&!wn(y.name,p,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`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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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=Xn(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},aB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new a2(l7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=l7.Instance.transformGraph(e.modelInitializer);this.initializer=new a2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ge)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function mt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${iB}${oB}`);let n=new y7(e,t);return await n.load(),n}var lB="3.9.0",x7={};Pe(x7,{CSVDataset:()=>_7,Dataset:()=>Jl,FileDataSource:()=>z7,TextLineDataset:()=>N7,URLDataSource:()=>L7,array:()=>_B,csv:()=>VB,func:()=>UB,generator:()=>HB,microphone:()=>jB,version_data:()=>qB,webcam:()=>GB,zip:()=>DB});var uB=Sa(A5()),cB=Sa(A5());function dB(e,t){return sf(e,t)}function sf(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Yl(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=sf(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function hB(e,t=v7){return b7(e,t)}function b7(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Yl(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=b7(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function v7(e){return e===null?null:Yl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function w7(e,t){let n=new Map;sf(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(w.isPromise(a)){let o=await a;n.set(r,o)}}return sf(e,t,n)}function Yl(e){let t=!1;if(Q().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=y5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ge)&&!(e instanceof Promise)&&!t)}function pB(e){return e==null||fB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ge||w.isTypedArray(e)}function fB(e){return e===null||typeof e!="object"&&typeof e!="function"}function mB(e){return dB(e,gB)}function gB(e){return e instanceof Ge?{value:e.clone(),recurse:!1}:Yl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var k7=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},o2=class extends k7{constructor(){super(o2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;st===!0)}rowMajorBatch(e,t=!0){return new IB(this,e,t)}columnMajorBatch(e,t=!0,n=v7){return this.rowMajorBatch(e,t).map(r=>hB(r,n))}concatenate(e,t){return new C7(I7([this,e]),t)}take(e){return e<0||e==null?this:new kB(this,e)}skip(e){return e<0||e==null?this:new wB(this,e)}prefetch(e){return new T7(this,e)}shuffle(e,t){return new RB(this,e,t)}serial(){return new vB(this)}},xB=class extends en{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:mB(e),done:!1}}},bB=class extends en{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},vB=class extends en{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},wB=class extends en{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},IB=class extends en{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},SB=class extends en{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Z(e.value)}}},CB=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=$s.getTensorsInContainer(e.value),n=this.transform(e.value),s=$s.getTensorsInContainer(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},TB=class extends en{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},S7=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=$s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=$s.getTensorsInContainer(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},l2=class extends en{constructor(){super();this.outputQueue=new o2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},NB=class extends l2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=$s.getTensorsInContainer(e.value),n=this.transform(e.value),s=$s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return!0}},C7=class extends en{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ua;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ua||(ua={}));var EB=class extends en{constructor(e,t=ua.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof en?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await w7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ua.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ua.SHORTEST:return{value:null,done:!0};case ua.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},T7=class extends en{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new k7(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},RB=class extends T7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=cB.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Jl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is - ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,$B),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(s=>H(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>H(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let s=i2(async()=>({value:await t.iterator(),done:!1}));return AB(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=uB.alea(t||w.now().toString());return Kn(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Jl.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends Jl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function _B(e){return Kn(async()=>I7(e),e.length)}function DB(e){if(!Yl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await w7(e,s=>{if(s instanceof Jl)return{value:s.iterator(),recurse:!1};if(Yl(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return yB(n,ua.SHORTEST)},t)}function $B(e){if(e===null)return null;let t=e[0];return pB(t)?{value:FB(e),recurse:!1}:{value:null,recurse:!0}}function FB(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ge?Dn(e):dn(e)}var N7=class extends Jl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},rf='"',Wc=Symbol("out"),E7=Symbol("field"),af=Symbol("quote"),u2=Symbol("quoteafterquote"),R7=Symbol("quoteinquote"),_7=class extends Jl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new N7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new D7(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(w.sizeFromShape(t));return n.set(e,n.length-e.length),dn(n,t)}},$7=class extends en{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=Mt([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=Ms([a,r,i,o],[1,4])}else this.cropBox=Ms([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().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 $7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=ls.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=Ot(ce(e,"float32"),0),n;n=_e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},F7=class{},O7=class extends en{split(e){return new OB(this,e)}},OB=class extends O7{constructor(e,t){super();this.upstream=e,this.impl=new PB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PB=class extends l2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},MB=class extends en{decodeUTF8(){return new zB(this)}},zB=class extends O7{constructor(e){super();this.upstream=e,this.impl=new LB(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LB=class extends l2{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=y5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},P7=class extends MB{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Q().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 BB(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=WB(e));let r=await w.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new P7(a,t)}else throw new Error(r.statusText)}var WB=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 M7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var z7=class extends F7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(M7(this.input)&&Q().get("IS_NODE")){let e=vi("fs");this.input=e.readFileSync(this.input.substr(7))}return new P7(this.input,this.options)}},L7=class extends F7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return M7(this.url)?new z7(this.url,this.fileOptions).iterator():BB(this.url,this.fileOptions)}};function VB(e,t={}){return new _7(new L7(e),t)}function UB(e){let t=i2(e);return Kn(async()=>t)}function HB(e){return Kn(async()=>{let t=await e();return i2(()=>t.next())})}async function GB(e,t){return $7.create(e,t)}async function jB(e){return D7.create(e)}var qB="3.9.0";function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var XB=rr.whereImpl,of=class extends $u{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Hd(this,wr())}nextDataId(){return of.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Q().get("IS_NODE")&&D.warn(` + ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=r2(a,n),i=s===0?0:e.size/s,l=H(()=>{let c=[];e=U(e,[1,s,i]);for(let 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o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[tp(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[ve(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[vg(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Hh(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[er(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[Ox(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Lg(I("x",e,t,n),o,i)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[_g(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Sg(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[eb(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},KL=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[ft(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[zl(s,ce(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[zl(a,ce(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=lt(r[0]).shape,i=r.map(l=>{let u=w.arraysEqual(l.shape,a);if(!u&&!w.arraysEqual(lt(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:U(l,a)});return[Dn(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return ms(r,s)}case"Tile":{let s=I("reps",e,t,n);return[Ts(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return an(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[_b(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Db(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[eA(s,a,r,a.dtype===o.dtype?o:ce(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZL=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=xc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=xc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[xc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[xc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},YL=(e,t,n)=>{switch(e.op){case"FFT":return[lp(I("x",e,t,n))];case"IFFT":return[Ac(I("x",e,t,n))];case"RFFT":return[up(I("x",e,t,n))];case"IRFFT":return[Kg(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JL=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=mp.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=mp.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[mp.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QL=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Ot(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[lt(I("x",e,t,n),s)]}case"Reshape":return[U(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[gb(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Qr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[sp(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[qh(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[tb(I("x",e,t,n),s,r)]}case"BroadcastTo":return[cc(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[qx(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function f7(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>EL(a,o,i));case"basic_math":return H(()=>RL(a,o,i));case"control":return PL(a,o,i);case"convolution":return H(()=>ML(a,o,i));case"creation":return H(()=>zL(a,o,i));case"dynamic":return LL(a,o,i);case"evaluation":return H(()=>BL(a,o,i));case"image":return H(()=>HL(a,o,i));case"graph":return H(()=>WL(a,o,i));case"logical":return H(()=>GL(a,o,i));case"matrices":return H(()=>jL(a,o,i));case"normalization":return H(()=>qL(a,o,i));case"reduction":return H(()=>XL(a,o,i));case"slice_join":return H(()=>KL(a,o,i));case"sparse":return H(()=>ZL(a,o,i));case"spectral":return H(()=>YL(a,o,i));case"string":return H(()=>JL(a,o,i));case"transformation":return H(()=>QL(a,o,i));case"hash_table":return UL(a,o,i,s);case"custom":let l=Vv(a.op);if(l&&l.customExecutor)return l.customExecutor(new NL(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 w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var m7=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 g7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(h=>Xn(h)[0]),c=[];s!=null&&(c=s.map(h=>Xn(h.name)[0]));let d=[...t];for(;d.length>0;){let h=d.pop();if((A7(h)||rB(h)||aB(h))&&o==null&&(o=h,i=o.children.map(p=>p.name).filter(p=>r.has(p))),r.add(h.name),n[h.name]==null&&u.indexOf(h.name)===-1&&c.indexOf(h.name)===-1){if(h.inputs.length===0){a.push(h.name);continue}h.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),d.push(p))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function eB(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>Xn(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(h=>l.has(h.name))&&a.push(d)})}return u}var tB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],nB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],sB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function A7(e){return tB.indexOf(e.op)>=0}function rB(e){return nB.indexOf(e.op)>=0}function aB(e){return sB.indexOf(e.op)>=0}var o2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new o2(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=g7(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 eB(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(c=>this.graph.nodes[Xn(c)[0]]),r=t.map(c=>Xn(c)[0]),a=r.map(c=>this.graph.nodes[c]);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={},u={};return H(()=>{let c=new m7(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Xn(f),A=[];A[g]=e[f],d[m]=A});let h=this.getFrozenTensorIds(d),p={};for(let f=0;fwn(f,d,c))})}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=iL(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}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));let a=new m7(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>wn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(p=>{p&&!p.kept&&!p.isDisposed&&!c.has(p.id)&&p.dispose()})}),this.parent==null&&a.dispose(c),i}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(y=>this.graph.nodes[Xn(y)[0]]),o=n.map(y=>Xn(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=g7(e,i,this.weightMap,this._initNodes),h=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,b]=Xn(y),v=[];v[b]=e[y],p[x]=v});let f={},m=this.getFrozenTensorIds(p),g={};for(;h.length>0;){let y=this.processStack(a,h,t,p,g,m,o,f,l);await Promise.all(y)}c==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(y=>!A7(y)&&!wn(y.name,p,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`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 [${r}]. Consider providing the following inputs: [${u}]. ${y}`)}return p}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([d]=Er(c.node.name,n)),s[c.node.name]==null){let h=f7(c.node,s,n,this._resourceManager);d||([d]=Er(c.node.name,n));let p=n.currentContext;w.isPromise(h)?u.push(h.then(f=>(s[d]=f,n.currentContext=p,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),f))):(s[d]=h,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Er(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!wn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!wn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=Xn(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);w.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=Xn(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},oB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},iB="?tfjs-format=file",lB="model.json",y7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new oB}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=_n.browserHTTPRequest(e,this.loadOptions);else{let t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new o2(l7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=l7.Instance.transformGraph(e.modelInitializer);this.initializer=new o2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ge)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function mt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${lB}${iB}`);let n=new y7(e,t);return await n.load(),n}var uB="3.9.0",x7={};Pe(x7,{CSVDataset:()=>_7,Dataset:()=>Jl,FileDataSource:()=>z7,TextLineDataset:()=>N7,URLDataSource:()=>L7,array:()=>DB,csv:()=>UB,func:()=>HB,generator:()=>GB,microphone:()=>qB,version_data:()=>XB,webcam:()=>jB,zip:()=>$B});var cB=Sa(A5()),dB=Sa(A5());function hB(e,t){return sf(e,t)}function sf(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Yl(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=sf(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function pB(e,t=v7){return b7(e,t)}function b7(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Yl(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=b7(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function v7(e){return e===null?null:Yl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function w7(e,t){let n=new Map;sf(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(w.isPromise(a)){let o=await a;n.set(r,o)}}return sf(e,t,n)}function Yl(e){let t=!1;if(Q().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=y5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ge)&&!(e instanceof Promise)&&!t)}function fB(e){return e==null||mB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ge||w.isTypedArray(e)}function mB(e){return e===null||typeof e!="object"&&typeof e!="function"}function gB(e){return hB(e,AB)}function AB(e){return e instanceof Ge?{value:e.clone(),recurse:!1}:Yl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var k7=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},i2=class extends k7{constructor(){super(i2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;st===!0)}rowMajorBatch(e,t=!0){return new SB(this,e,t)}columnMajorBatch(e,t=!0,n=v7){return this.rowMajorBatch(e,t).map(r=>pB(r,n))}concatenate(e,t){return new C7(I7([this,e]),t)}take(e){return e<0||e==null?this:new IB(this,e)}skip(e){return e<0||e==null?this:new kB(this,e)}prefetch(e){return new T7(this,e)}shuffle(e,t){return new _B(this,e,t)}serial(){return new wB(this)}},bB=class extends en{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:gB(e),done:!1}}},vB=class extends en{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},wB=class extends en{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},kB=class extends en{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},SB=class extends en{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},CB=class extends en{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Z(e.value)}}},TB=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=$s.getTensorsInContainer(e.value),n=this.transform(e.value),s=$s.getTensorsInContainer(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},NB=class extends en{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},S7=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=$s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=$s.getTensorsInContainer(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},u2=class extends en{constructor(){super();this.outputQueue=new i2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},EB=class extends u2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=$s.getTensorsInContainer(e.value),n=this.transform(e.value),s=$s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)$s.isTensorInList(r,s)||r.dispose();return!0}},C7=class extends en{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ua;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ua||(ua={}));var RB=class extends en{constructor(e,t=ua.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof en?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await w7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ua.FAIL:throw new Error(`Zipped streams should have the same length. 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If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=cB.alea(t||w.now().toString());return Kn(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Jl.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends Jl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function DB(e){return Kn(async()=>I7(e),e.length)}function $B(e){if(!Yl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await w7(e,s=>{if(s instanceof Jl)return{value:s.iterator(),recurse:!1};if(Yl(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return xB(n,ua.SHORTEST)},t)}function FB(e){if(e===null)return null;let t=e[0];return fB(t)?{value:OB(e),recurse:!1}:{value:null,recurse:!0}}function OB(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ge?Dn(e):dn(e)}var N7=class extends Jl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},rf='"',Wc=Symbol("out"),E7=Symbol("field"),af=Symbol("quote"),c2=Symbol("quoteafterquote"),R7=Symbol("quoteinquote"),_7=class extends Jl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new N7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new D7(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(w.sizeFromShape(t));return n.set(e,n.length-e.length),dn(n,t)}},$7=class extends en{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=Mt([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=Ms([a,r,i,o],[1,4])}else this.cropBox=Ms([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().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 $7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=ls.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=Ot(ce(e,"float32"),0),n;n=_e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},F7=class{},O7=class extends en{split(e){return new PB(this,e)}},PB=class extends O7{constructor(e,t){super();this.upstream=e,this.impl=new MB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MB=class extends u2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},zB=class extends en{decodeUTF8(){return new LB(this)}},LB=class extends O7{constructor(e){super();this.upstream=e,this.impl=new BB(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},BB=class extends u2{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=y5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},P7=class extends zB{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Q().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 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F7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return M7(this.url)?new z7(this.url,this.fileOptions).iterator():WB(this.url,this.fileOptions)}};function UB(e,t={}){return new _7(new L7(e),t)}function HB(e){let t=l2(e);return Kn(async()=>t)}function GB(e){return Kn(async()=>{let t=await e();return l2(()=>t.next())})}async function jB(e,t){return $7.create(e,t)}async function qB(e){return D7.create(e)}var XB="3.9.0";function ke(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var KB=rr.whereImpl,of=class extends $u{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Hd(this,wr())}nextDataId(){return of.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Q().get("IS_NODE")&&D.warn(` ============================ Hi there \u{1F44B}. 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Got input batch dimensions of (${f}) and (${m}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([h,p]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,h]:[g,h,c],k=i?[A,p,d]:[A,d,p],S=xt({inputs:{x:r},backend:n,attrs:{shape:v}}),C=xt({inputs:{x:a},backend:n,attrs:{shape:k}}),_=o?S.shape[1]:S.shape[2],O=o?S.shape[2]:S.shape[1],E=i?C.shape[1]:C.shape[2],R=Math.max(g,A),T=n.data.get(S.dataId).values,P=n.data.get(C.dataId).values,V=w.computeStrides(S.shape),j=w.computeStrides(C.shape),[q,X,ee]=o?[V[0],1,V[1]]:[V[0],V[1],1],[te,ne,se]=i?[1,j[1],j[0]]:[j[1],1,j[0]],J=O*E,ie=We([R,O,E],S.dtype),le=ie.values,he=n.blockSize;for(let Ae=0;AeMath.acos(e)),oV={kernelName:Si,backendName:"cpu",kernelFunc:aV},iV=ot(Ci,e=>Math.acosh(e)),lV={kernelName:Ci,backendName:"cpu",kernelFunc:iV};function uV(e){let{inputs:t,backend:n}=e,s=t;ke(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=We(s[0].shape,s[0].dtype),o=a.values;for(let i=0;iy&&(y=v,x=b)}p[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",p)}var gV={kernelName:Ea,backendName:"cpu",kernelFunc:mV};function AV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;ke(r,"argMin");let o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=As({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],D.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=D.computeOutAndReduceShapes(l.shape,o),h=w.sizeFromShape(c),p=w.makeZerosTypedArray(h,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;gn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",p)}var yV={kernelName:Pu,backendName:"cpu",kernelFunc:AV},xV=ot(Ei,e=>Math.asin(e)),bV={kernelName:Ei,backendName:"cpu",kernelFunc:xV},vV=ot(Ri,e=>Math.asinh(e)),wV={kernelName:Ri,backendName:"cpu",kernelFunc:vV},kV=ot(_i,e=>Math.atan(e)),IV={kernelName:_i,backendName:"cpu",kernelFunc:kV},SV=Lt((e,t)=>Math.atan2(e,t)),CV=tn($i,SV),TV={kernelName:$i,backendName:"cpu",kernelFunc:CV},NV=ot(Di,e=>Math.atanh(e)),EV={kernelName:Di,backendName:"cpu",kernelFunc:NV};function b2(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,d=r.effectiveFilterWidth,h=r.padInfo.top,p=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;bq?q=le:a==="avg"&&(X+=le,ee++)}if(isNaN(q))break}let te=R+T*x+S;g[te]=a==="avg"?X/ee:q}}}return m}function Rw(e,t,n,s,r=!1,a=!1){let o=We(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,d=s.effectiveFilterHeight,h=s.effectiveFilterWidth,p=s.padInfo.top,f=s.padInfo.left,m=We(t,n,e);for(let g=0;gO&&(O=j,r?E=a?((g*s.inHeight+R)*s.inWidth+P)*s.inChannels+A:(R*s.inWidth+P)*s.inChannels+A:E=T*h+V)}}o.set(E,g,y,k,A)}}return o}function _w(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,d=r.dilationWidth,h=r.effectiveFilterDepth,p=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,A=r.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let _=0;_Ce?Ce=He:a==="avg"&&(Te+=He,De++),isNaN(Ce))break}if(isNaN(Ce))break}if(isNaN(Ce))break}let Me=Ae+R;b[Me]=a==="avg"?Te/De:Ce}}}}return x}function RV(e,t){let n=We(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,h=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m=T&&(T=ne,P=j*c*d+X*c+te)}}}n.set(P,m,A,v,_,g)}}}return n}function _V(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ke(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=cr({inputs:{x:r},backend:n});else{let h=n.data.get(r.dataId).values,p=w.computeStrides(r.shape),f=b2(h,r.shape,r.dtype,p,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var DV={kernelName:Ra,backendName:"cpu",kernelFunc:_V};function $V(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;ke(r,"avgPool3d");let c=D.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,h=_w(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(h.shape,"float32",h.values)}var FV={kernelName:Mu,backendName:"cpu",kernelFunc:$V};function OV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;ke([r,a],"avgPool3DGrad");let c=D.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,h=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,_=v-1-c.padInfo.top,O=We(a.shape,"float32"),E=1/(f*m*g),R=n.bufferSync(r);for(let T=0;T=c.outDepth||Math.floor(J)!==J))for(let ie=0;ie=c.outHeight||Math.floor(le)!==le))for(let he=0;he=c.outWidth||Math.floor(Ae)!==Ae)continue;ne+=R.get(T,J,le,Ae,P)}}}O.set(ne*E,T,V,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var PV={kernelName:Zd,backendName:"cpu",kernelFunc:OV};function MV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ke([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,h=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=We(o.shape,"float32"),k=1/(p*f),S=n.data.get(r.dataId).values,C=We(r.shape,"float32",S);for(let _=0;_=c.outHeight||Math.floor(q)!==q))for(let X=0;X=c.outWidth||Math.floor(ee)!==ee)continue;V+=C.get(_,q,ee,O)}}v.set(V*k,_,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var zV={kernelName:Kd,backendName:"cpu",kernelFunc:MV};function LV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ke([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,h=n.data.get(l.dataId).values,p=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,A=p.length,y=h.length,x=d.length,b=0,v=0,k=0,S=0;for(let C=0;C=g&&(b=0),v>=x&&(v=0),k>=A&&(k=0),S>=y&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var BV={kernelName:Ga,backendName:"cpu",kernelFunc:LV};function WV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;ke([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),h=D.getSliceSize(c,o,a.length),p=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=As({inputs:{x:p},backend:n,attrs:{perm:u}}),m=xt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=Yo({inputs:{x:m},backend:n,attrs:{begin:d,size:h}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var VV={kernelName:Fi,backendName:"cpu",kernelFunc:WV};function UV(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,u=h2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var HV={kernelName:Yd,backendName:"cpu",kernelFunc:UV};function GV(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=D.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var jV={kernelName:Bm,backendName:"cpu",kernelFunc:GV},qV=ot(Ur,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.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 u=0;um.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return cr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(D.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>Zo({inputs:{input:b},backend:n})),g=i.map(b=>eu({inputs:{input:b},backend:n})),A=tu({inputs:m,backend:n,attrs:{axis:a}}),y=tu({inputs:g,backend:n,attrs:{axis:a}}),x=Zn({inputs:{real:A,imag:y},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return xt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=D.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,h=p2(c,o,t[0].dtype,d),p=D.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(p,t[0].dtype,h);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var JV={kernelName:Oi,backendName:"cpu",kernelFunc:tu};function Dw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;ke([r,a],"conv2d");let d=D.convertConv2DDataFormat(l),h=D.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),p=h.filterHeight,f=h.filterWidth,m=h.dilationHeight,g=h.dilationWidth,A=h.padInfo.left,y=h.padInfo.top,x=h.dataFormat==="channelsLast",b=new Ut(h.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],_=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],V=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X=h.inHeight)continue;let he=ie*k[0],Ae=ee+le*C;for(let Ce=0;Ce=h.inWidth)continue;let ut=he+Me*k[1],nt=Ae+Fe*_,st=ut;for(let et=0;et=u.inDepth)continue;let X=j*_[0],ee=E+q*C[1];for(let te=0;te=u.inHeight)continue;let le=X+J*_[1],he=ee+ie*C[2];for(let Ae=0;Ae=u.inWidth)continue;let Fe=le+De*_[2],ut=he+Me*u.inChannels,nt=Fe;for(let st=0;stMath.cos(e)),dU={kernelName:Pa,backendName:"cpu",kernelFunc:cU},hU=ot(Ma,e=>Math.cosh(e)),pU={kernelName:Ma,backendName:"cpu",kernelFunc:hU};function fU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,h,p]=r.shape,f=a.shape[0],[m,g]=i,A=We([f,m,g,p],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S=c)continue;let P=m>1?(E-_)*(d-1)/(m-1):0,V=g>1?(R-O)*(h-1)/(g-1):0;for(let j=0;j1?_*(d-1)+j*P:.5*(_+E)*(d-1);if(q<0||q>d-1){for(let X=0;X1?O*(h-1)+ne*V:.5*(O+R)*(h-1);if(se<0||se>h-1){for(let he=0;he1?O*(h-1)+X*V:.5*(O+R)*(h-1);if(ee<0||ee>h-1){for(let se=0;seA+f-y-1:(A,y)=>A+y;for(let A=0;A`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,h=u*a,p=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*h*p),g=0;for(let A=0;A`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${h}'`);let p=D.computeConv2DInfo(r.shape,a.shape,o,h,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:y}=p,x=y.left,b=y.top,v=p.outChannels/p.inChannels,k=new Ut(p.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,_=k.values;for(let O=0;O=p.inHeight)continue;let X=j*d[0],ee=E+q*c[1];for(let te=0;te=p.inWidth)continue;let le=X+J*d[1],he=ee+ie*p.inChannels,Ae=ne,Ce=le;for(let Te=0;Te{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=s.shape.length,d=l.data.get(r.dataId).values,h=r.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:g,outHeight:A,outWidth:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:_,outShape:O}=D.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),E=w.sizeFromShape(O),R=O.length,T=w.getArrayFromDType(s.dtype,E);for(let V=0;V=0&&ie=0&&hene&&(ne=Te)}}}let se=w.locToIndex([V,j,X,te],R,w.computeStrides(O));T[se]=ne}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},RU={kernelName:ih,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:_}=D.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===_.length,()=>`Error in ${ih}, dy must have the same rank as output ${_.length}, but got ${a.rank}`);let O=w.toNestedArray(_,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T=0&&J=0&&leee&&(ee=he,te=se,ne=ie)}}}E[te][ne][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},_U={kernelName:oh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:_}=D.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===_.length,()=>`Error in ${oh}, dy must have the same rank as output ${_.length}, but got ${a.rank}`);let O=w.toNestedArray(_,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T=0&&J=0&&leee&&(ee=he,te=J,ne=le)}}}E[T][te][ne][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Hc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"sum");let i;r.dtype==="bool"?i=ca({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=cr({inputs:{x:r},backend:n});let l=i.shape.length,u=w.parseAxisParam(a,i.shape),c=D.getAxesPermutation(u,l),d=u,h=i;c!=null&&(h=As({inputs:{x:i},backend:n,attrs:{perm:c}}),d=D.getInnerMostAxes(d.length,l)),D.assertAxesAreInnerMostDims("sum",d,h.shape.length);let[p,f]=D.computeOutAndReduceShapes(h.shape,d),m=D.upcastType(h.dtype,"int32"),g=lf(n,p,m),A=w.sizeFromShape(f),y=n.data.get(g.dataId).values,x=n.data.get(h.dataId).values;for(let b=0;b=0&&(h=Hc({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var FU={kernelName:lh,backendName:"cpu",kernelFunc:$U};function OU(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;ke([s,r],"eluGrad");let a=new Float32Array(w.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",a)}var PU={kernelName:uh,backendName:"cpu",kernelFunc:OU},MU=D.ERF_P,zU=D.ERF_A1,LU=D.ERF_A2,BU=D.ERF_A3,WU=D.ERF_A4,VU=D.ERF_A5,UU=ot(zi,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+MU*n);return t*(1-((((VU*s+WU)*s+BU)*s+LU)*s+zU)*s*Math.exp(-n*n))}),HU={kernelName:zi,backendName:"cpu",kernelFunc:UU};function df(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:i}})}var GU={kernelName:Bi,backendName:"cpu",kernelFunc:df},jU=Lt((e,t)=>e/t),v2=tn(Ba,jU),w2={kernelName:Ba,backendName:"cpu",kernelFunc:v2};function Fw(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,u=[r,a],c=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",c),h=w.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:s}=e,r=n,a=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[o,i,l,u]=s.shape,c=r.data.get(s.dataId).values;for(let h=0;h=0&&xMath.floor(e/t)),nH=tn(Ha,tH,null,"int32"),sH={kernelName:Ha,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=Dw({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=Vc({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=x2(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var aH={kernelName:Co,backendName:"cpu",kernelFunc:rH};function oH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=$w({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=Vc({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=x2(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var iH={kernelName:To,backendName:"cpu",kernelFunc:oH};function lH(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=w.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,u,c,d]=D.prepareAndValidate(s,r);if(u===0)return n.makeTensorInfo(l,s.dtype,[]);let h=n.data.get(r.dataId).values,p=n.bufferSync(s),f=Z7(h,p,s.dtype,u,i,c,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var uH={kernelName:Hi,backendName:"cpu",kernelFunc:lH};function cH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;ke([r,a],"gatherV2");let l=i;i==null&&(l=0);let u=w.sizeFromShape(a.shape),c=w.parseAxisParam(o,r.shape)[0],d=D.segment_util.collectGatherOpShapeInfo(r,a,c,l),h=xt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),p=xt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],m=n.bufferSync(p),g=n.bufferSync(h),A=Y7(g,m,f);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(d.outputShape,A.dtype,A.values)}var dH={kernelName:Ui,backendName:"cpu",kernelFunc:cH};function hH(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=xt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=Fw(i,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var pH={kernelName:dh,backendName:"cpu",kernelFunc:hH},fH=ot(ji,e=>Number.isFinite(e)?1:0,"bool"),mH={kernelName:ji,backendName:"cpu",kernelFunc:fH},gH=ot(qi,e=>Math.abs(e)===1/0?1:0,"bool"),AH={kernelName:qi,backendName:"cpu",kernelFunc:gH},yH=ot(Xi,e=>Number.isNaN(e)?1:0,"bool"),xH={kernelName:Xi,backendName:"cpu",kernelFunc:yH};function bH(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=nw(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var vH={kernelName:ph,backendName:"cpu",kernelFunc:bH},wH=ot(Yi,e=>Math.log1p(e)),kH={kernelName:Yi,backendName:"cpu",kernelFunc:wH},IH=Lt((e,t)=>e&&t),SH=tn(Ji,IH,null,"bool"),CH={kernelName:Ji,backendName:"cpu",kernelFunc:SH},TH=ot(Vu,e=>e?0:1,"bool"),NH={kernelName:Vu,backendName:"cpu",kernelFunc:TH},EH=Lt((e,t)=>e||t),RH=tn(Uu,EH,null,"bool"),_H={kernelName:Uu,backendName:"cpu",kernelFunc:RH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;ke(r,"LRN");let u=r.shape[3],c=u-1,d=n.data.get(r.dataId).values,h=w.sizeFromShape(r.shape),p=new Float32Array(h);function f(m){let g=m%u,A=m-g+Math.max(0,g-a),y=m-g+Math.min(g+a,c),x=0;for(;A<=y;A++){let b=d[A];x+=b*b}return x}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=cr({inputs:{x:r},backend:n});else{let h=n.data.get(r.dataId).values,p=w.computeStrides(r.shape),f=b2(h,r.shape,r.dtype,p,c,"max");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var zH={kernelName:Ja,backendName:"cpu",kernelFunc:MH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;ke(r,"maxPool3d");let c=D.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,h=_w(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"max");return n.makeTensorInfo(h.shape,"float32",h.values)}var BH={kernelName:Gu,backendName:"cpu",kernelFunc:LH};function WH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;ke([r,a],"maxPool3DGrad");let c=D.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),h=RV(d,c),p=c.strideDepth,f=c.strideHeight,m=c.strideWidth,g=c.dilationDepth,A=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,k=x-1-c.padInfo.front,S=v-1-c.padInfo.left,C=b-1-c.padInfo.top,_=We(a.shape,"float32"),O=n.bufferSync(r);for(let E=0;E=c.outDepth||Math.floor(ne)!==ne))for(let se=0;se=c.outHeight||Math.floor(J)!==J))for(let ie=0;ie=c.outWidth||Math.floor(le)!==le)continue;let he=x*b*v-1-h.get(E,ne,J,le,R),Ae=te*b*v+se*v+ie,Ce=he===Ae?1:0;if(Ce===0)continue;ee+=O.get(E,ne,J,le,R)*Ce}}}_.set(ee,E,T,P,V,R)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var VH={kernelName:gh,backendName:"cpu",kernelFunc:WH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ke([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,h=D.computePool2DInfo(i.shape,l,u,1,c,d),p=n.data.get(i.dataId).values,f=We(h.outShape,i.dtype,Rw(p,i.shape,i.dtype,h).values),m=h.strideHeight,g=h.strideWidth,A=h.dilationHeight,y=h.dilationWidth,x=h.effectiveFilterHeight,b=h.effectiveFilterWidth,v=b-1-h.padInfo.left,k=x-1-h.padInfo.top,S=We(i.shape,"float32"),C=n.data.get(r.dataId).values,_=We(r.shape,"float32",C);for(let O=0;O=h.outHeight||Math.floor(X)!==X))for(let ee=0;ee=h.outWidth||Math.floor(te)!==te)continue;let ne=x*b-1-f.get(O,X,te,E),se=q*b+ee,J=ne===se?1:0;if(J===0)continue;j+=_.get(O,X,te,E)*J}}S.set(j,O,R,T,E)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var HH={kernelName:mh,backendName:"cpu",kernelFunc:UH};function GH(e,t,n,s,r){let a=w.computeStrides(t),o=b2(e,t,n,a,r,"max"),i=Rw(e,t,n,r,!0,s);return[o.values,i.values]}var jH={kernelName:Ah,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;ke(s,"MaxPoolWithArgmax");let u=l.data.get(s.dataId).values,c=D.computePool2DInfo(s.shape,r,a,[1,1],o),[d,h]=GH(u,s.shape,s.dtype,i,c),p=l.write(d,c.outShape,s.dtype),f=l.write(h,c.outShape,s.dtype);return[{dataId:p,shape:c.outShape,dtype:s.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function qH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=w.parseAxisParam(a,r.shape),u=D.computeOutAndReduceShapes(r.shape,i)[1],c=w.sizeFromShape(u),d=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));d.push(h);let p=ca({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(p);let f=v2({inputs:{a:p,b:h},backend:n});d.push(f);let m=Hc({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),m}var XH={kernelName:Qa,backendName:"cpu",kernelFunc:qH};function KH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"min");let i=w.parseAxisParam(a,r.shape),l=i,u=D.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=As({inputs:{x:r},backend:n,attrs:{perm:u}}),l=D.getInnerMostAxes(l.length,r.shape.length)),D.assertAxesAreInnerMostDims("min",l,c.shape.length);let[d,h]=D.computeOutAndReduceShapes(c.shape,l),p=w.sizeFromShape(h),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;Ax[0]+r.shape[b]+x[1]),l=a.map(x=>x[0]),u=a.map((x,b)=>x[0]+r.shape[b]),c=o==="reflect"?0:1,d=n.data.get(r.dataId).values,h=r.shape.length,p=w.computeStrides(r.shape),f=w.sizeFromShape(i),m=i.length,g=w.computeStrides(i),A=w.getTypedArrayFromDType(r.dtype,f);for(let x=0;x=u[k]&&(b[k]=(u[k]-1)*2-b[k]+c);b=b.map((k,S)=>k-l[S]);let v=w.locToIndex(b,h,p);A[x]=d[v]}return{dataId:n.write(A,i,r.dtype),shape:i,dtype:r.dtype}}var JH={kernelName:no,backendName:"cpu",kernelFunc:YH},QH=Lt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),eG=tn(Qi,QH),tG={kernelName:Qi,backendName:"cpu",kernelFunc:eG},nG=Sa(g5());function Pw(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=r.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);let l=w.parseAxisParam([i],r.shape),u=Ow({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=D.expandShapeToKeepDim(u.shape,l),d=xt({inputs:{x:u},backend:n,attrs:{shape:c}}),h=y2({inputs:{a:r,b:d},backend:n}),p=q7({inputs:{x:h},backend:n}),f=Hc({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=xt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=v2({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var sG={kernelName:xo,backendName:"cpu",kernelFunc:Pw};function rG(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;ke(r,"multinomial");let l=i?r:Pw({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],d=n.data.get(l.dataId).values,h=[u,a],p=w.makeZerosTypedArray(w.sizeFromShape(h),"int32");for(let f=0;f=0&&c[d]{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=df({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=tu({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var xG={kernelName:ol,backendName:"cpu",kernelFunc:zw};function bG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;ke(r,"pad");let i=a.map((y,x)=>y[0]+r.shape[x]+y[1]),l=a.map(y=>y[0]),u=n.data.get(r.dataId).values,c=w.sizeFromShape(r.shape),d=r.shape.length,h=w.computeStrides(r.shape),p=w.sizeFromShape(i),f=i.length,m=w.computeStrides(i),g=w.getTypedArrayFromDType(r.dtype,p);o!==0&&g.fill(o);for(let 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Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=cr({inputs:{x:r},backend:n});else{let h=n.data.get(r.dataId).values,p=w.computeStrides(r.shape),f=v2(h,r.shape,r.dtype,p,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var $V={kernelName:Ra,backendName:"cpu",kernelFunc:DV};function FV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;ke(r,"avgPool3d");let c=D.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,h=_w(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(h.shape,"float32",h.values)}var OV={kernelName:Mu,backendName:"cpu",kernelFunc:FV};function PV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;ke([r,a],"avgPool3DGrad");let c=D.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,h=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,_=v-1-c.padInfo.top,O=We(a.shape,"float32"),E=1/(f*m*g),R=n.bufferSync(r);for(let T=0;T=c.outDepth||Math.floor(J)!==J))for(let ie=0;ie=c.outHeight||Math.floor(le)!==le))for(let he=0;he=c.outWidth||Math.floor(Ae)!==Ae)continue;ne+=R.get(T,J,le,Ae,P)}}}O.set(ne*E,T,V,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var MV={kernelName:Zd,backendName:"cpu",kernelFunc:PV};function zV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ke([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,h=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=We(o.shape,"float32"),k=1/(p*f),S=n.data.get(r.dataId).values,C=We(r.shape,"float32",S);for(let _=0;_=c.outHeight||Math.floor(q)!==q))for(let X=0;X=c.outWidth||Math.floor(ee)!==ee)continue;V+=C.get(_,q,ee,O)}}v.set(V*k,_,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var LV={kernelName:Kd,backendName:"cpu",kernelFunc:zV};function BV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ke([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,h=n.data.get(l.dataId).values,p=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,A=p.length,y=h.length,x=d.length,b=0,v=0,k=0,S=0;for(let C=0;C=g&&(b=0),v>=x&&(v=0),k>=A&&(k=0),S>=y&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var WV={kernelName:Ga,backendName:"cpu",kernelFunc:BV};function VV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;ke([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),h=D.getSliceSize(c,o,a.length),p=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=As({inputs:{x:p},backend:n,attrs:{perm:u}}),m=xt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=Yo({inputs:{x:m},backend:n,attrs:{begin:d,size:h}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var UV={kernelName:Fi,backendName:"cpu",kernelFunc:VV};function HV(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,u=p2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var GV={kernelName:Yd,backendName:"cpu",kernelFunc:HV};function jV(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=D.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var qV={kernelName:Wm,backendName:"cpu",kernelFunc:jV},XV=ot(Ur,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.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 u=0;um.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return cr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(D.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>Zo({inputs:{input:b},backend:n})),g=i.map(b=>eu({inputs:{input:b},backend:n})),A=tu({inputs:m,backend:n,attrs:{axis:a}}),y=tu({inputs:g,backend:n,attrs:{axis:a}}),x=Zn({inputs:{real:A,imag:y},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return xt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=D.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,h=f2(c,o,t[0].dtype,d),p=D.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(p,t[0].dtype,h);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var QV={kernelName:Oi,backendName:"cpu",kernelFunc:tu};function Dw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;ke([r,a],"conv2d");let d=D.convertConv2DDataFormat(l),h=D.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),p=h.filterHeight,f=h.filterWidth,m=h.dilationHeight,g=h.dilationWidth,A=h.padInfo.left,y=h.padInfo.top,x=h.dataFormat==="channelsLast",b=new Ut(h.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],_=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],V=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X=h.inHeight)continue;let he=ie*k[0],Ae=ee+le*C;for(let Ce=0;Ce=h.inWidth)continue;let ut=he+Me*k[1],nt=Ae+Fe*_,st=ut;for(let et=0;et=u.inDepth)continue;let X=j*_[0],ee=E+q*C[1];for(let te=0;te=u.inHeight)continue;let le=X+J*_[1],he=ee+ie*C[2];for(let Ae=0;Ae=u.inWidth)continue;let Fe=le+De*_[2],ut=he+Me*u.inChannels,nt=Fe;for(let st=0;stMath.cos(e)),hU={kernelName:Pa,backendName:"cpu",kernelFunc:dU},pU=ot(Ma,e=>Math.cosh(e)),fU={kernelName:Ma,backendName:"cpu",kernelFunc:pU};function mU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,h,p]=r.shape,f=a.shape[0],[m,g]=i,A=We([f,m,g,p],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S=c)continue;let P=m>1?(E-_)*(d-1)/(m-1):0,V=g>1?(R-O)*(h-1)/(g-1):0;for(let j=0;j1?_*(d-1)+j*P:.5*(_+E)*(d-1);if(q<0||q>d-1){for(let X=0;X1?O*(h-1)+ne*V:.5*(O+R)*(h-1);if(se<0||se>h-1){for(let he=0;he1?O*(h-1)+X*V:.5*(O+R)*(h-1);if(ee<0||ee>h-1){for(let se=0;seA+f-y-1:(A,y)=>A+y;for(let A=0;A`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,h=u*a,p=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*h*p),g=0;for(let A=0;A`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${h}'`);let p=D.computeConv2DInfo(r.shape,a.shape,o,h,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:y}=p,x=y.left,b=y.top,v=p.outChannels/p.inChannels,k=new Ut(p.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,_=k.values;for(let O=0;O=p.inHeight)continue;let X=j*d[0],ee=E+q*c[1];for(let te=0;te=p.inWidth)continue;let le=X+J*d[1],he=ee+ie*p.inChannels,Ae=ne,Ce=le;for(let Te=0;Te{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=s.shape.length,d=l.data.get(r.dataId).values,h=r.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:g,outHeight:A,outWidth:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:_,outShape:O}=D.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),E=w.sizeFromShape(O),R=O.length,T=w.getArrayFromDType(s.dtype,E);for(let V=0;V=0&&ie=0&&hene&&(ne=Te)}}}let se=w.locToIndex([V,j,X,te],R,w.computeStrides(O));T[se]=ne}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},_U={kernelName:ih,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:_}=D.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===_.length,()=>`Error in ${ih}, dy must have the same rank as output ${_.length}, but got ${a.rank}`);let O=w.toNestedArray(_,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T=0&&J=0&&leee&&(ee=he,te=se,ne=ie)}}}E[te][ne][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},DU={kernelName:oh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:h,inHeight:p,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:_}=D.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===_.length,()=>`Error in ${oh}, dy must have the same rank as output ${_.length}, but got ${a.rank}`);let O=w.toNestedArray(_,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T=0&&J=0&&leee&&(ee=he,te=J,ne=le)}}}E[T][te][ne][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Hc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;ke(r,"sum");let i;r.dtype==="bool"?i=ca({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=cr({inputs:{x:r},backend:n});let l=i.shape.length,u=w.parseAxisParam(a,i.shape),c=D.getAxesPermutation(u,l),d=u,h=i;c!=null&&(h=As({inputs:{x:i},backend:n,attrs:{perm:c}}),d=D.getInnerMostAxes(d.length,l)),D.assertAxesAreInnerMostDims("sum",d,h.shape.length);let[p,f]=D.computeOutAndReduceShapes(h.shape,d),m=D.upcastType(h.dtype,"int32"),g=lf(n,p,m),A=w.sizeFromShape(f),y=n.data.get(g.dataId).values,x=n.data.get(h.dataId).values;for(let b=0;b=0&&(h=Hc({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var OU={kernelName:lh,backendName:"cpu",kernelFunc:FU};function PU(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;ke([s,r],"eluGrad");let a=new Float32Array(w.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",a)}var MU={kernelName:uh,backendName:"cpu",kernelFunc:PU},zU=D.ERF_P,LU=D.ERF_A1,BU=D.ERF_A2,WU=D.ERF_A3,VU=D.ERF_A4,UU=D.ERF_A5,HU=ot(zi,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+zU*n);return t*(1-((((UU*s+VU)*s+WU)*s+BU)*s+LU)*s*Math.exp(-n*n))}),GU={kernelName:zi,backendName:"cpu",kernelFunc:HU};function df(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:i}})}var jU={kernelName:Bi,backendName:"cpu",kernelFunc:df},qU=Lt((e,t)=>e/t),w2=tn(Ba,qU),k2={kernelName:Ba,backendName:"cpu",kernelFunc:w2};function Fw(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,u=[r,a],c=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",c),h=w.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:s}=e,r=n,a=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[o,i,l,u]=s.shape,c=r.data.get(s.dataId).values;for(let h=0;h=0&&xMath.floor(e/t)),sH=tn(Ha,nH,null,"int32"),rH={kernelName:Ha,backendName:"cpu",kernelFunc:sH};function aH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=Dw({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=Vc({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=b2(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var oH={kernelName:Co,backendName:"cpu",kernelFunc:aH};function iH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=$w({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h}});if(o){let g=m;m=Vc({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(p){let g=m;m=b2(n,m,p,i,f),n.disposeIntermediateTensorInfo(g)}return m}var lH={kernelName:To,backendName:"cpu",kernelFunc:iH};function uH(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=w.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,u,c,d]=D.prepareAndValidate(s,r);if(u===0)return n.makeTensorInfo(l,s.dtype,[]);let h=n.data.get(r.dataId).values,p=n.bufferSync(s),f=Z7(h,p,s.dtype,u,i,c,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var cH={kernelName:Hi,backendName:"cpu",kernelFunc:uH};function dH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;ke([r,a],"gatherV2");let l=i;i==null&&(l=0);let u=w.sizeFromShape(a.shape),c=w.parseAxisParam(o,r.shape)[0],d=D.segment_util.collectGatherOpShapeInfo(r,a,c,l),h=xt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),p=xt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],m=n.bufferSync(p),g=n.bufferSync(h),A=Y7(g,m,f);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(d.outputShape,A.dtype,A.values)}var hH={kernelName:Ui,backendName:"cpu",kernelFunc:dH};function pH(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=xt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=Fw(i,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var fH={kernelName:dh,backendName:"cpu",kernelFunc:pH},mH=ot(ji,e=>Number.isFinite(e)?1:0,"bool"),gH={kernelName:ji,backendName:"cpu",kernelFunc:mH},AH=ot(qi,e=>Math.abs(e)===1/0?1:0,"bool"),yH={kernelName:qi,backendName:"cpu",kernelFunc:AH},xH=ot(Xi,e=>Number.isNaN(e)?1:0,"bool"),bH={kernelName:Xi,backendName:"cpu",kernelFunc:xH};function vH(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=nw(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var wH={kernelName:ph,backendName:"cpu",kernelFunc:vH},kH=ot(Yi,e=>Math.log1p(e)),IH={kernelName:Yi,backendName:"cpu",kernelFunc:kH},SH=Lt((e,t)=>e&&t),CH=tn(Ji,SH,null,"bool"),TH={kernelName:Ji,backendName:"cpu",kernelFunc:CH},NH=ot(Vu,e=>e?0:1,"bool"),EH={kernelName:Vu,backendName:"cpu",kernelFunc:NH},RH=Lt((e,t)=>e||t),_H=tn(Uu,RH,null,"bool"),DH={kernelName:Uu,backendName:"cpu",kernelFunc:_H};function $H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;ke(r,"LRN");let u=r.shape[3],c=u-1,d=n.data.get(r.dataId).values,h=w.sizeFromShape(r.shape),p=new Float32Array(h);function f(m){let g=m%u,A=m-g+Math.max(0,g-a),y=m-g+Math.min(g+a,c),x=0;for(;A<=y;A++){let b=d[A];x+=b*b}return x}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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zG={kernelName:ho,backendName:"cpu",kernelFunc:MG},LG={kernelName:Il,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[u,c,d,h]=s.shape,[p,f]=D.getImageCenter(o,c,d),m=255,g=Math.sin(r),A=Math.cos(r),y=i.data.get(s.dataId).values;for(let b=0;b=0&&P=0&&V{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),WG={kernelName:po,backendName:"cpu",kernelFunc:BG};function Bw(e,t,n,s,r,a,o,i,l,u){let c=[s/r,r],d=e.values,h=t.values;if(s===0)return We(n,t.dtype);let p=We(c,t.dtype);p.values.fill(l);for(let f=0;f=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let A=0;A1||r.shape.length===1?1:w.sizeFromShape(r.shape.slice(1));for(let 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o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=A2(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var dj={kernelName:Ih,backendName:"cpu",kernelFunc:cj};function hj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=y2(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var hj={kernelName:Ih,backendName:"cpu",kernelFunc:dj};function pj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape 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ei(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function Af(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Qo(e),...ei(e)]),t}function i6(e,t=!1){let n=Q().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?w.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=w.squeezeShape(e).newShape);let s=w.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=Qo(e),a=2,o=2;return e.length&&([a,o]=ei(e)),s=r*(a/2)*(o/2),w.sizeToSquarishShape(s).map(i=>i*2)}return w.sizeToSquarishShape(s)}function yf(e){return e%2==0}function Zc(e,t){if(e=e.slice(-2),t=t.slice(-2),w.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s||yf(n)&&yf(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&yf(e[0])&&yf(t[0])}var xf,bf;function l6(e){if(xf==null){let t=dr(e);xf=t.getParameter(t.MAX_TEXTURE_SIZE)}return xf}function dq(){xf=null}function hq(){bf=null}function u6(e){if(bf==null){let t=dr(e);bf=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,bf)}function c6(e){if(e===0)return 0;let t,n=dr(e);return xs(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:xs(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function xs(e,t){return e.getExtension(t)!=null}function R2(e){try{if(dr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function d6(e){if(e===0)return!1;let t=dr(e);if(e===1){if(!xs(t,"OES_texture_float"))return!1}else 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r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function p6(e){return e!==2?!1:dr(e).fenceSync!=null}function su(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=Q();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>R2(2)?2:R2(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>l6(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>u6(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:c6(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ac.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>d6(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>h6(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>p6(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>ac.isMobile()&&Ne.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function kn(){let e,t,n,s,r,a,o,i,l,u;return Q().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=` bool isnan_custom(float val) { return (val > 0.0 || val < 0.0) ? false : val != 0.0; } @@ -103,11 +103,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function ti(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function vf(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function pq(e,t){let n=e.length,s=e.map(a=>`${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 fq(e,t,n="index"){let s=e.map((a,o)=>o),r=pq(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function _2(e){let t=w.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function ti(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function vf(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function fq(e,t){let n=e.length,s=e.map(a=>`${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 mq(e,t,n="index"){let s=e.map((a,o)=>o),r=fq(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function D2(e){let t=w.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function D2(){return` +`}function $2(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } @@ -150,22 +150,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return c / 255.0; } -`,{getBroadcastDims:m6}=D;function mq(e,t,n){let s=[];if(e.forEach(p=>{let f=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?s.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${p.name};`),s.push(`uniform int offset${p.name};`)),n.enableShapeUniforms){let{uniformShape:m}=$2(n.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${p.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${p.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(p=>{s.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let r=s.join(` -`),a=e.map(p=>gq(p,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),o=t.texShape,i=kn(),l=xq(i),u,c,d=wq(i);return t.isPacked?(u=Aq(t.logicalShape,o,n.enableShapeUniforms),c=vq(i)):(u=yq(t.logicalShape,o,n.enableShapeUniforms),c=bq(i)),n.packedInputs&&(d+=Cq),[d,l,c,r,u,a,n.userCode].join(` -`)}function ru(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return zq(e,t);case 1:return Bq(e,t);case 2:return Vq(e,t);case 3:return Hq(e,t);case 4:return jq(e,t);case 5:return qq(e);case 6:return Xq(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function g6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Mq(e);case 1:return Lq(e,t);case 2:return Wq(e,t);case 3:return Uq(e,t);default:return Gq(e,t)}}function gq(e,t,n=!1,s){let r="";n?r+=g6(e,s):r+=ru(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=Kq(e,t):r+=Zq(e,t)),r}function Aq(e,t,n){switch(e.length){case 0:return A6();case 1:return Tq(e,t,n);case 2:return Oq(e,t,n);case 3:return Eq(e,t,n);default:return _q(e,t,n)}}function yq(e,t,n){switch(e.length){case 0:return A6();case 1:return Nq(e,t,n);case 2:return Pq(e,t,n);case 3:return Rq(e,t,n);case 4:return Dq(e,t,n);case 5:return $q(e,t);case 6:return Fq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function xq(e){return` +`,{getBroadcastDims:m6}=D;function gq(e,t,n){let s=[];if(e.forEach(p=>{let f=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?s.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${p.name};`),s.push(`uniform int offset${p.name};`)),n.enableShapeUniforms){let{uniformShape:m}=F2(n.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${p.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${p.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(p=>{s.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let r=s.join(` +`),a=e.map(p=>Aq(p,t,n.packedInputs,n.enableShapeUniforms)).join(` +`),o=t.texShape,i=kn(),l=bq(i),u,c,d=kq(i);return t.isPacked?(u=yq(t.logicalShape,o,n.enableShapeUniforms),c=wq(i)):(u=xq(t.logicalShape,o,n.enableShapeUniforms),c=vq(i)),n.packedInputs&&(d+=Tq),[d,l,c,r,u,a,n.userCode].join(` +`)}function ru(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Lq(e,t);case 1:return Wq(e,t);case 2:return Uq(e,t);case 3:return Gq(e,t);case 4:return qq(e,t);case 5:return Xq(e);case 6:return Kq(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function g6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return zq(e);case 1:return Bq(e,t);case 2:return Vq(e,t);case 3:return Hq(e,t);default:return jq(e,t)}}function Aq(e,t,n=!1,s){let r="";n?r+=g6(e,s):r+=ru(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=Zq(e,t):r+=Yq(e,t)),r}function yq(e,t,n){switch(e.length){case 0:return A6();case 1:return Nq(e,t,n);case 2:return Pq(e,t,n);case 3:return Rq(e,t,n);default:return Dq(e,t,n)}}function xq(e,t,n){switch(e.length){case 0:return A6();case 1:return Eq(e,t,n);case 2:return Mq(e,t,n);case 3:return _q(e,t,n);case 4:return $q(e,t,n);case 5:return Fq(e,t);case 6:return Oq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function bq(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function bq(e){return` + `}function vq(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function vq(e){return` + `}function wq(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function wq(e){return`${e.version} + `}function kq(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -220,10 +220,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee return fract((p3.x + p3.y) * p3.z); } - ${kq} ${Iq} ${Sq} - `}var kq=` + ${Cq} + `}var Iq=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -235,7 +235,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Iq=` +`,Sq=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -243,7 +243,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Sq=` +`,Cq=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -252,7 +252,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Cq=` +`,Tq=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -267,7 +267,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int getOutputCoords() { return 0; } - `}function Tq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?` + `}function Nq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } @@ -296,7 +296,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${s[0]}, ${s[1]})); return 2 * (resTexRC.x * ${s[1]} + resTexRC.y); } - `}function Nq(e,t,n){return t[0]===1?n?` + `}function Eq(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -324,7 +324,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function Eq(e,t,n){if(n)return` + `}function Rq(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -355,7 +355,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function Rq(e,t,n){if(n)return` + `}function _q(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -371,7 +371,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${s} return ivec3(r, c, d); } - `}function _q(e,t,n){if(n)return` + `}function Dq(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -412,7 +412,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec${e.length}(${l}); } - `}function Dq(e,t,n){if(n)return` + `}function $q(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); @@ -428,7 +428,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${s} return ivec4(r, c, d, d2); } - `}function $q(e,t){let n=ti(["r","c","d","d2","d3"],e);return` + `}function Fq(e,t){let n=ti(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -440,7 +440,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function Fq(e,t){let n=ti(["r","c","d","d2","d3","d4"],e);return` + `}function Oq(e,t){let n=ti(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function Oq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` + `}function Pq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -484,7 +484,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function Pq(e,t,n){return w.arraysEqual(e,t)?n?` + `}function Mq(e,t,n){return w.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -538,11 +538,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function ni(e){return`offset${e}`}function Mq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=kn();return` + `}function ni(e){return`offset${e}`}function zq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=kn();return` vec4 ${n}() { return ${s.texture2D}(${t}, halfCR); } - `}function zq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return` + `}function Lq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return` float ${s}() { return sampleTexture(${n}, halfCR); } @@ -556,7 +556,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${i}, ${l}, ${o}); return sampleTexture(${n}, uv); } - `}function Lq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=kn();if(t)return` + `}function Bq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=kn();if(t)return` vec4 ${s}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -569,7 +569,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${o[0]}, ${o[1]}, index); return ${a.texture2D}(${n}, uv); } - `}function Bq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` + `}function Wq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${s}(int index) { ${au(e)} } @@ -607,7 +607,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${a}, ${o}, index + ${i}); return sampleTexture(${n}, uv); } - `}function Wq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=kn();if(a!=null&&w.arraysEqual(n,a))return t?` + `}function Vq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=kn();if(a!=null&&w.arraysEqual(n,a))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]); @@ -631,7 +631,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${s}, uv); } - `}function Vq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return` + `}function Uq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]); return sampleTexture(${s}, uv); @@ -689,7 +689,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${u}, ${c}, index); return sampleTexture(${s}, uv); } -`}function Uq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let h=n.slice(1),p=[1,2],f=ou(e,h),m=["b","row","col"];return` +`}function Hq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let h=n.slice(1),p=[1,2],f=ou(e,h),m=["b","row","col"];return` ${g6(f,t)} vec4 ${r}(int b, int row, int col) { return ${r}(${iu(m,p)}); @@ -709,7 +709,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${l}, ${u}, ${d}, ${c}, b, row, col); return ${i.texture2D}(${s}, uv); } - `}function Hq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=w.squeezeShape(n),u=i;if(u.length=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(` + `}function Zq(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=m6(e.shapeInfo.logicalShape,t.logicalShape),l=ht(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(` `);let h="";o<2&&a>0?h="coords":h=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+u]}`).join(", ");let p="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,A=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)p=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!A)o===1?p=` @@ -968,7 +968,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 outputValue = get${s}(${h}); ${p} } - `}function Zq(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return` + `}function Yq(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return` float ${r}() { return sampleTexture(${n}, resultUV); } @@ -979,7 +979,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${h} return get${s}(${f}); } - `}function ht(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function $2(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!w.arraysEqual(t,n)&&s.lengthe[n]).join(", ")}function Yq(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=mq(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);Q().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},p={},f={};for(let x=0;x{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:p,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function y6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function Jq(e,t,n,s,r){t.program.enableShapeUniforms||(y6(t.inShapeInfos,n),y6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`],p=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(p){let{uniformShape:m}=$2(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(p,new Int32Array(m));break;case 2:e.gl.uniform2iv(p,new Int32Array(m));break;case 3:e.gl.uniform3iv(p,new Int32Array(m));break;case 4:e.gl.uniform4iv(p,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Qq(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:d}=$2(e.packedInputs,o.shape,l),h="",p="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];h=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)p=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),A=w.sizeFromShape(o.shape)===1,y=D.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${u?d:""}_${c.length}_${A}_${y}_${g}_${h}_${p}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Q().getNumber("WEBGL_VERSION")}`,a}function bs(e){return Q().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var eX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=jc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` + `}function ht(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function F2(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!w.arraysEqual(t,n)&&s.lengthe[n]).join(", ")}function Jq(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=gq(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);Q().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},p={},f={};for(let x=0;x{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:p,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function y6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was 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Shape ${i} and ${l} must match`)})}function Qq(e,t,n,s,r){t.program.enableShapeUniforms||(y6(t.inShapeInfos,n),y6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`],p=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(p){let{uniformShape:m}=F2(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(p,new Int32Array(m));break;case 2:e.gl.uniform2iv(p,new Int32Array(m));break;case 3:e.gl.uniform3iv(p,new Int32Array(m));break;case 4:e.gl.uniform4iv(p,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function eX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:d}=F2(e.packedInputs,o.shape,l),h="",p="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];h=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)p=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),A=w.sizeFromShape(o.shape)===1,y=D.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${u?d:""}_${c.length}_${A}_${y}_${g}_${h}_${p}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Q().getNumber("WEBGL_VERSION")}`,a}function bs(e){return Q().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var tX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=jc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${this.enableShapeUniforms?vf(["r","c","d"],e):ti(["r","c","d"],e)} return ivec3(r, c, d); @@ -999,7 +999,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},tX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=jc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` + `}},nX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=jc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${this.enableShapeUniforms?vf(["r","c","d"],e):ti(["r","c","d"],e)} return ivec3(r, c, d); @@ -1019,14 +1019,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},nX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ys.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=` + `}},sX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ys.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=` ${f6} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},sX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ys.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=` + `}},rX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ys.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=` ${f6} void main() { @@ -1034,8 +1034,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},rX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=` - ${this.enableShapeUniforms?D2():_2(e)} + `}},aX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=` + ${this.enableShapeUniforms?$2():D2(e)} void main() { ivec3 coords = getOutputCoords(); @@ -1064,7 +1064,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n.output} = vec4(${s}, 0., 0., 0.); } - `}},aX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=bs(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+=` + `}},oX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=bs(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}; @@ -1093,7 +1093,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } } `}this.userCode=` - ${this.enableShapeUniforms?D2():_2(e)} + ${this.enableShapeUniforms?$2():D2(e)} void main() { ivec3 coords = getOutputCoords(); 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be(t,()=>t.attachShader(s,this.vertexShader)),be(t,()=>t.attachShader(s,n)),Kw(t,s),this.debug&&mf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=N6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&mf(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?n6(this.gl,e,t):s6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(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(),r6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=nu(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&&mf(this.gl,this.program),Kc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xc(this.gl,Q().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(Q().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(Q().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 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s=this.gl;gf(s,e,this.framebuffer),this.debug&&Kc(s),this.outputTexture=e,be(s,()=>s.viewport(0,0,t,n)),be(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),be(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 oX(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:L6(e,t)}function GX(e,t){if(e===1)return"rc";let n="";for(let s=0;se.bindTexture(i,o)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),be(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),be(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function O2(e){return e.internalFormatFloat}function k6(e,t,n,s){let[r,a]=qc(t,n);return Yc(e,r,a,O2(s),s.textureFormatFloat,e.FLOAT)}function P2(e){return e.internalFormatHalfFloat}function I6(e,t,n,s){let[r,a]=qc(t,n);return Yc(e,r,a,P2(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function M2(e){return e.downloadTextureFormat}function S6(e,t,n,s){let[r,a]=qc(t,n);return Yc(e,r,a,M2(s),e.RGBA,e.UNSIGNED_BYTE)}function z2(e){return e.internalFormatPackedFloat}function C6(e,t,n,s){let[r,a]=nu(t,n);return Yc(e,r,a,z2(s),e.RGBA,e.FLOAT)}function L2(e){return e.internalFormatPackedHalfFloat}function T6(e,t,n,s){let[r,a]=nu(t,n);return Yc(e,r,a,L2(s),e.RGBA,s.textureTypeHalfFloat)}function N6(e,t,n){let s=0,r=3*4,a=3*4+2*4;return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),N2(e,t,"clipSpacePos",n,3,a,s)&&N2(e,t,"uv",n,2,a,r)}function E6(e,t,n,s,r,a){be(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),be(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function R6(e,t,n){be(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _6(e,t,n,s){let r=e.createBuffer();be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return be(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function D6(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 $6(e,t,n,s){let[r,a]=qc(t,n),o=4,i=new Uint8Array(tq(t*n,o));return be(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function F6(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(nq(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function O6(e,t,n){let s=new Float32Array(t*n*4);return be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var wf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Q().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,pf(t,e)):this.gl=dr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Q().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Xc(this.gl,r),xs(this.gl,a))this.textureHalfFloatExtension=Xc(this.gl,a);else if(Q().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),xs(this.gl,s))this.colorBufferHalfFloatExtension=Xc(this.gl,s);else if(Q().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",xs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(xs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=v6(this.gl),this.indexBuffer=w6(this.gl),this.framebuffer=e6(this.gl),this.textureConfig=T2(this.gl,this.textureHalfFloatExtension)}get debug(){return Q().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;be(e,()=>e.finish()),be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.deleteFramebuffer(this.framebuffer)),be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),be(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),k6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),I6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),S6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),R6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),E6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),T6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(E2(this.gl,this.framebuffer),this.outputTexture=null),be(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$6(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return F6(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return D6(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=_6(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(Q().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>O6(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=qw(t,e);this.vertexShader==null&&(this.vertexShader=b6(t));let s=Xw(t);return be(t,()=>t.attachShader(s,this.vertexShader)),be(t,()=>t.attachShader(s,n)),Kw(t,s),this.debug&&mf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=N6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&mf(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?n6(this.gl,e,t):s6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(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(),r6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=nu(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&&mf(this.gl,this.program),Kc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xc(this.gl,Q().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(Q().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(Q().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 w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Q().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=iX(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)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),gf(this.gl,e,this.framebuffer),this.debug&&Kc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(gf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Kc(this.gl)):E2(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;gf(s,e,this.framebuffer),this.debug&&Kc(s),this.outputTexture=e,be(s,()=>s.viewport(0,0,t,n)),be(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),be(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 iX(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:L6(e,t)}function jX(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[0]}`;let s="";for(let r=e-2;r= ${t[r]}`,r= ${t}; bool rEdge = rp1 >= ${n}; - `}function ZX(e,t){let n=e.length,s=qX(n,t);return n===1?`getA(rc), + `}function YX(e,t){let n=e.length,s=XX(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]}), @@ -1158,8 +1158,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${s>0?"}":""} `}this.userCode=` - ${YX(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?D2():_2(e)} + ${JX(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?$2():D2(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1174,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function YX(e,t){return` + `}};function JX(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?fq(["r","c","d"],"inputShape"):ti(["r","c","d"],e)} + ${t?mq(["r","c","d"],"inputShape"):ti(["r","c","d"],e)} return ivec3(r, c, d); } - `}var JX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=V6(t,n),r=U6(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=W6(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===on.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===on.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===on.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===on.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===on.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=V6(n,s),a=U6(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=W6(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Q().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 QX(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 W6(e,t,n,s,r){let a=eK(t,s),o;if(r){let[l,u]=nu(e[0],e[1]);o=l*u}else{let[l,u]=qc(e[0],e[1]);o=l*u}let i=QX(n,a);return o*i}function eK(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return M2(t);case on.PACKED_2X2_FLOAT16:return z2(t);case on.UNPACKED_FLOAT32:return F2(t);case on.UNPACKED_FLOAT16:return O2(t);case on.PACKED_4X1_UNSIGNED_BYTE:return P2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function tK(e){return Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function V6(e,t){if(e===ys.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===ys.RENDER||e==null)return tK(t);if(e===ys.DOWNLOAD||e===ys.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function U6(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ha=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` + `}var QX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=V6(t,n),r=U6(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=W6(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===on.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===on.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===on.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===on.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===on.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=V6(n,s),a=U6(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=W6(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Q().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 eK(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 W6(e,t,n,s,r){let a=tK(t,s),o;if(r){let[l,u]=nu(e[0],e[1]);o=l*u}else{let[l,u]=qc(e[0],e[1]);o=l*u}let i=eK(n,a);return o*i}function tK(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return z2(t);case on.PACKED_2X2_FLOAT16:return L2(t);case on.UNPACKED_FLOAT32:return O2(t);case on.UNPACKED_FLOAT16:return P2(t);case on.PACKED_4X1_UNSIGNED_BYTE:return M2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function nK(e){return Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function V6(e,t){if(e===ys.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===ys.RENDER||e==null)return nK(t);if(e===ys.DOWNLOAD||e===ys.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function U6(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ha=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1190,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},js="if (isnan(x)) return x;",nK="return x;",H6="return abs(x);",sK="return (x >= 0.0) ? x : (exp(x) - 1.0);",rK=js+` + `}},js="if (isnan(x)) return x;",sK="return x;",H6="return abs(x);",rK="return (x >= 0.0) ? x : (exp(x) - 1.0);",aK=js+` return (x < 0.0) ? 0.0 : x; -`,aK=js+` +`,oK=js+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,kf="return x;",oK="return 1.0 / (1.0 + exp(-1.0 * x));",iK="return x;",lK=` +`,kf="return x;",iK="return 1.0 / (1.0 + exp(-1.0 * x));",lK="return x;",uK=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,uK=` +`,cK=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,cK=` +`,dK=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,dK="return 1.0 / (1.0 + exp(-1.0 * x));",lu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` +`,hK="return 1.0 / (1.0 + exp(-1.0 * x));",lu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1234,14 +1234,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},hK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=In("rc",t),s=ht(t),r=GX(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` + `}},pK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=In("rc",t),s=ht(t),r=jX(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})); } - `}},pK=rr.whereImpl,fK=1e-7,mK=1e-4,If={};function gK(e){return e in If||(If[e]={}),If[e]}var AK=Q().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yK=600;function xK(){return Q().global.screen==null?1024:Q().global.screen.height*Q().global.screen.width*window.devicePixelRatio*yK/1024/1024}var uu=class extends $u{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,!Q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=dr(Q().getNumber("WEBGL_VERSION"));this.binaryCache=gK(Q().getNumber("WEBGL_VERSION")),this.gpgpu=new wf(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 JX(this.gpgpu),this.numMBBeforeWarning=xK(),this.texData=new Hd(this,wr())}nextDataId(){return uu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Q().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:ys.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(Q().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:ys.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 lu(o,kf):d=new ha(o,kf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),p=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),h=this.readSync(r.imag.dataId);c=D.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let p;i?p=new lu(s,kf):p=new ha(s,kf);let f=this.runWebGLProgram(p,[{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(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...ff(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let p=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=p[0],m=p[1];c=D.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let p=this.gpgpu.gl;be(p,()=>p.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(p=>p(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&wr().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=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(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=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.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(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],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 Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Q().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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=AK){return Q().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)0&&w.isString(n[0])){let r=n.map(a=>w.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 wr().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new hK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new jX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Qo(e.shape),...ei(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Qo(t),...ei(t)],a=new B6(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=Af(s),o,i=ff(a);n?o=new tX(a):o=new eX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.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===jc.DENSE){let m=ff(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.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&&w.sizeFromShape(m.shape)<=Q().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&&!Zc(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=Qq(e,l,u),d=this.getAndSaveBinary(c,()=>Yq(this.gpgpu,e,l,u)),h=this.activeTimers!=null,p;h&&(p=this.startTimer()),Jq(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=Q().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Q().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||(Q().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=H(()=>{if(!Q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Q().getBool("DEBUG");Q().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(Q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?fK:mK}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,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=i6(n,i),t.texShape=c),r!=null){let d=Af(n),h,p=c[1],f=c[0],m=r instanceof Uint8Array;i?([p,f]=nu(c[0],c[1]),h=new aX(d,m)):h=new rX(d,m);let g=this.makeTensorInfo([f,p],s);m?this.texData.get(g.dataId).usage=ys.PIXELS:this.texData.get(g.dataId).usage=ys.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),p,f,r);let A=[[f,p]],y=!0,x=this.runWebGLProgram(h,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(c,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=bK(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]*w.bytesPerElement(t)}};uu.nextDataId=0;function bK(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 uu,2);var wK={forceHalfFloat:G6},j6=` + `}},fK=rr.whereImpl,mK=1e-7,gK=1e-4,If={};function AK(e){return e in If||(If[e]={}),If[e]}var yK=Q().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),xK=600;function bK(){return Q().global.screen==null?1024:Q().global.screen.height*Q().global.screen.width*window.devicePixelRatio*xK/1024/1024}var uu=class extends $u{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,!Q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=dr(Q().getNumber("WEBGL_VERSION"));this.binaryCache=AK(Q().getNumber("WEBGL_VERSION")),this.gpgpu=new wf(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 QX(this.gpgpu),this.numMBBeforeWarning=bK(),this.texData=new Hd(this,wr())}nextDataId(){return uu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Q().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:ys.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(Q().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:ys.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 lu(o,kf):d=new ha(o,kf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),p=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),h=this.readSync(r.imag.dataId);c=D.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let p;i?p=new lu(s,kf):p=new ha(s,kf);let f=this.runWebGLProgram(p,[{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(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...ff(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let p=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=p[0],m=p[1];c=D.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let p=this.gpgpu.gl;be(p,()=>p.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(p=>p(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&wr().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=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(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=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.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(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],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 Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Q().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 u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=yK){return Q().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)0&&w.isString(n[0])){let r=n.map(a=>w.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 wr().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new pK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new qX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Qo(e.shape),...ei(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Qo(t),...ei(t)],a=new B6(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=Af(s),o,i=ff(a);n?o=new nX(a):o=new tX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.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===jc.DENSE){let m=ff(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.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&&w.sizeFromShape(m.shape)<=Q().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&&!Zc(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=eX(e,l,u),d=this.getAndSaveBinary(c,()=>Jq(this.gpgpu,e,l,u)),h=this.activeTimers!=null,p;h&&(p=this.startTimer()),Qq(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=Q().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Q().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||(Q().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=H(()=>{if(!Q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Q().getBool("DEBUG");Q().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(Q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?mK:gK}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,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=i6(n,i),t.texShape=c),r!=null){let d=Af(n),h,p=c[1],f=c[0],m=r instanceof Uint8Array;i?([p,f]=nu(c[0],c[1]),h=new oX(d,m)):h=new aX(d,m);let g=this.makeTensorInfo([f,p],s);m?this.texData.get(g.dataId).usage=ys.PIXELS:this.texData.get(g.dataId).usage=ys.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),p,f,r);let A=[[f,p]],y=!0,x=this.runWebGLProgram(h,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(c,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=vK(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]*w.bytesPerElement(t)}};uu.nextDataId=0;function vK(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 uu,2);var kK={forceHalfFloat:G6},j6=` if (isnan(a)) return a; if (isnan(b)) return b; `,cu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=D.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=bs(this.outputShape.length),this.userCode=` @@ -1303,21 +1303,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function Yn(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 kK={kernelName:qa,backendName:"webgl",kernelFunc:Yn};function pa(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=Yn({inputs:{x:s},backend:n}),l=Yn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var IK={kernelName:Jd,backendName:"webgl",kernelFunc:pa},q6="return (a < 0.) ? b * a : a;",X6=` + `}};function Yn(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 IK={kernelName:qa,backendName:"webgl",kernelFunc:Yn};function pa(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=Yn({inputs:{x:s},backend:n}),l=Yn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var SK={kernelName:Jd,backendName:"webgl",kernelFunc:pa},q6="return (a < 0.) ? b * a : a;",X6=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function SK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(X6,r.shape,o.shape):new cu(q6,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var CK={kernelName:Xa,backendName:"webgl",kernelFunc:SK},K6="return (a < 0.) ? b * a : a;",Z6=` +`;function CK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(X6,r.shape,o.shape):new cu(q6,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var TK={kernelName:Xa,backendName:"webgl",kernelFunc:CK},K6="return (a < 0.) ? b * a : a;",Z6=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function TK(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(Z6,s.shape,r.shape):new cu(K6,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var NK={kernelName:io,backendName:"webgl",kernelFunc:TK},Y6="if (isnan(x)) return x;",EK=` +`;function NK(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(Z6,s.shape,r.shape):new cu(K6,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var EK={kernelName:io,backendName:"webgl",kernelFunc:NK},Y6="if (isnan(x)) return x;",RK=` if (isnan(a)) return a; if (isnan(b)) return b; -`,RK=` +`,_K=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; -`;function Je({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),h=n(d.values,l);return i.makeTensorInfo(o.shape,l,h)}let u=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new lu(o.shape,t):c=new ha(o.shape,e),i.runWebGLProgram(c,[o],l)}}function ln({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new cu(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,S],Cs(b.dtype,v.dtype))}),y=pa({inputs:{real:g,imag:A},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(A),y}let d=a||Cs(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?D.fromUint8ToStringArray(f):f,A=l.dtype==="string"?D.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,g,A,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let h=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return h?p=new Jc(t,l.shape,u.shape,n):p=new cu(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],d)}}function Cf(e,t=!1){if(e==="linear")return t?iK:nK;if(e==="relu")return t?uK:rK;if(e==="elu")return t?lK:sK;if(e==="relu6")return t?cK:aK;if(e==="prelu")return t?Z6:K6;if(e==="leakyrelu")return t?X6:q6;if(e==="sigmoid")return t?dK:oK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var J6=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=bs(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),d=s?"i * 2, rc.y":"rc.y, i * 2",h=r?"rc.z, i * 2":"i * 2, rc.z",p=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) { +`;function Je({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),h=n(d.values,l);return i.makeTensorInfo(o.shape,l,h)}let u=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new lu(o.shape,t):c=new ha(o.shape,e),i.runWebGLProgram(c,[o],l)}}function ln({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new cu(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,S],Cs(b.dtype,v.dtype))}),y=pa({inputs:{real:g,imag:A},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(A),y}let d=a||Cs(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?D.fromUint8ToStringArray(f):f,A=l.dtype==="string"?D.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,g,A,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let h=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return h?p=new Jc(t,l.shape,u.shape,n):p=new cu(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],d)}}function Cf(e,t=!1){if(e==="linear")return t?lK:sK;if(e==="relu")return t?cK:aK;if(e==="elu")return t?uK:rK;if(e==="relu6")return t?dK:oK;if(e==="prelu")return t?Z6:K6;if(e==="leakyrelu")return t?X6:q6;if(e==="sigmoid")return t?hK:iK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var J6=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=bs(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),d=s?"i * 2, rc.y":"rc.y, i * 2",h=r?"rc.z, i * 2":"i * 2, rc.z",p=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(); ${o} }`:l?m=`vec4 activation(vec4 a) { @@ -1369,7 +1369,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},t4="return a * b;";function B2(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=D.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new e4(Q6.REAL,s.shape,r.shape),c=new e4(Q6.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=pa({inputs:{real:h,imag:p},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=CX(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),h=n.texData.get(d.dataId);return h.values=u,d}let o;return Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Jc(t4,s.shape,r.shape):o=new cu(t4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var _K={kernelName:so,backendName:"webgl",kernelFunc:B2};function DK(e,t,n){let s=[Qo(e.shape),...ei(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Qo(t),...ei(t)],o=new B6(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Zc(r.shape,l)&&!(c.texture!==null&&Zc(c.shape,l))?DK(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var $K={kernelName:ul,backendName:"webgl",kernelFunc:ye},n4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=` + `}},t4="return a * b;";function W2(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=D.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new e4(Q6.REAL,s.shape,r.shape),c=new e4(Q6.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=pa({inputs:{real:h,imag:p},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=TX(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),h=n.texData.get(d.dataId);return h.values=u,d}let o;return Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Jc(t4,s.shape,r.shape):o=new cu(t4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var DK={kernelName:so,backendName:"webgl",kernelFunc:W2};function $K(e,t,n){let s=[Qo(e.shape),...ei(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Qo(t),...ei(t)],o=new B6(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Zc(r.shape,l)&&!(c.texture!==null&&Zc(c.shape,l))?$K(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var FK={kernelName:ul,backendName:"webgl",kernelFunc:ye},n4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1422,7 +1422,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}},FK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,d=` + `}},OK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,d=` if (${t==="sum"}) { sumValue += dot(values, ones); } else if (${t==="prod"}) { @@ -1514,12 +1514,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function OK(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=D.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function si(e,t,n,s){let r=OK(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=ht(this.rank),r=L6("rc",this.rank),a=new Array(this.rank);for(let u=0;u6)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 ${this.rank} is not yet supported.`);let s=ht(this.rank),r=L6("rc",this.rank),a=new Array(this.rank);for(let u=0;u=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,p]:[A,p,d],S=s?[y,f,h]:[y,h,f],C=ye({inputs:{x:e},backend:r,attrs:{shape:k}}),_=ye({inputs:{x:t},backend:r,attrs:{shape:S}}),O=[C,_],E=Math.max(A,y),R=n?C.shape[1]:C.shape[2],T=a!=null,P=o!=null,V=l==="leakyrelu",j=l!=null?Cf(l,!0):null,q=T||P||V||j!=null,X;if((p===1||f===1)&&R>s4&&q===!1){let te=C,ne=_;n&&(te=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),O.push(te)),s&&(ne=Sn({inputs:{x:_},backend:r,attrs:{perm:[0,2,1]}}),O.push(ne));let se=f!==1,J=f===1,ie=te;se&&(ie=ye({inputs:{x:te},backend:r,attrs:{shape:[E,R,1]}}),O.push(ie));let le=f===1?2:1,he=ne;J&&(he=ye({inputs:{x:ne},backend:r,attrs:{shape:[E,1,R]}}),O.push(he));let Ae=B2({inputs:{a:ie,b:he},backend:r});X=Nf({inputs:{x:Ae},backend:r,attrs:{axis:le,keepDims:!0}}),O.push(Ae)}else{let te=Cs(e.dtype,t.dtype),ne=new J6(k,S,[E,p,f],n,s,T,j,P,V),se=[C,_];if(a!=null&&se.push(a),P&&se.push(o),V){let J=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));se.push(J),O.push(J)}X=r.runWebGLProgram(ne,se,te)}let ee=ye({inputs:{x:X},backend:r,attrs:{shape:v}});O.push(X);for(let te of O)r.disposeIntermediateTensorInfo(te);return ee}function VK(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return Ef({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var UK={kernelName:So,backendName:"webgl",kernelFunc:VK},r4="return abs(x);";function HK(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=M6(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(s.shape,r4):r=new ha(s.shape,r4),n.runWebGLProgram(r,[s],s.dtype)}var GK={kernelName:Ii,backendName:"webgl",kernelFunc:HK},jK=js+` + `}};function Tf(e,t,n){let s=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LK(e.shape,t):new MK(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function BK(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,l=D.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=Tf(e,l,s),i=D.getInnerMostAxes(i.length,a)),D.assertAxesAreInnerMostDims("sum",i,a);let[d,h]=D.computeOutAndReduceShapes(c.shape,i),p=d;n&&(p=D.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(h),g=w.sizeFromShape(e.shape)/f,A=ye({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=Mh(e.dtype),x=si(A,y,"sum",s),b=ye({inputs:{x},attrs:{shape:p},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),u&&s.disposeIntermediateTensorInfo(c),b}function Nf(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return BK(r,a,o,n)}var WK={kernelName:yo,backendName:"webgl",kernelFunc:Nf};function Sn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,p]:[A,p,d],S=s?[y,f,h]:[y,h,f],C=ye({inputs:{x:e},backend:r,attrs:{shape:k}}),_=ye({inputs:{x:t},backend:r,attrs:{shape:S}}),O=[C,_],E=Math.max(A,y),R=n?C.shape[1]:C.shape[2],T=a!=null,P=o!=null,V=l==="leakyrelu",j=l!=null?Cf(l,!0):null,q=T||P||V||j!=null,X;if((p===1||f===1)&&R>s4&&q===!1){let te=C,ne=_;n&&(te=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),O.push(te)),s&&(ne=Sn({inputs:{x:_},backend:r,attrs:{perm:[0,2,1]}}),O.push(ne));let se=f!==1,J=f===1,ie=te;se&&(ie=ye({inputs:{x:te},backend:r,attrs:{shape:[E,R,1]}}),O.push(ie));let le=f===1?2:1,he=ne;J&&(he=ye({inputs:{x:ne},backend:r,attrs:{shape:[E,1,R]}}),O.push(he));let Ae=W2({inputs:{a:ie,b:he},backend:r});X=Nf({inputs:{x:Ae},backend:r,attrs:{axis:le,keepDims:!0}}),O.push(Ae)}else{let te=Cs(e.dtype,t.dtype),ne=new J6(k,S,[E,p,f],n,s,T,j,P,V),se=[C,_];if(a!=null&&se.push(a),P&&se.push(o),V){let J=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));se.push(J),O.push(J)}X=r.runWebGLProgram(ne,se,te)}let ee=ye({inputs:{x:X},backend:r,attrs:{shape:v}});O.push(X);for(let te of O)r.disposeIntermediateTensorInfo(te);return ee}function UK(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return Ef({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var HK={kernelName:So,backendName:"webgl",kernelFunc:UK},r4="return abs(x);";function GK(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=M6(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(s.shape,r4):r=new ha(s.shape,r4),n.runWebGLProgram(r,[s],s.dtype)}var jK={kernelName:Ii,backendName:"webgl",kernelFunc:GK},qK=js+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,qK=Je({opSnippet:jK}),XK={kernelName:Si,backendName:"webgl",kernelFunc:qK},KK=js+` +`,XK=Je({opSnippet:qK}),KK={kernelName:Si,backendName:"webgl",kernelFunc:XK},ZK=js+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:Ci,backendName:"webgl",kernelFunc:ZK},a4="return a + b;",JK=ln({opSnippet:a4,packedOpSnippet:a4,supportsComplex:!0,cpuKernelImpl:iX}),QK={kernelName:Vr,backendName:"webgl",kernelFunc:JK},eZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,YK=Je({opSnippet:ZK}),JK={kernelName:Ci,backendName:"webgl",kernelFunc:YK},a4="return a + b;",QK=ln({opSnippet:a4,packedOpSnippet:a4,supportsComplex:!0,cpuKernelImpl:lX}),eZ={kernelName:Vr,backendName:"webgl",kernelFunc:QK},tZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1551,7 +1551,7 @@ return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:Ci,back float result = ${s}; setOutput(result); } - `}},tZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `}},nZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1559,7 +1559,7 @@ return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:Ci,back vec4 result = ${s}; setOutput(result); } - `}};function Rf(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Yn({inputs:{x:s[0]},backend:n});if(s.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=Rf({inputs:s.slice(0,l),backend:n}),c=Rf({inputs:s.slice(l),backend:n});return Rf({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Cs(l,u)),a=s.map(l=>l.shape),i=Q().getBool("WEBGL_PACK")?new tZ(s[0].shape,a):new eZ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var nZ={kernelName:Na,backendName:"webgl",kernelFunc:Rf};function sZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("all",u,i);let[h,p]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"all",n),A;if(o){let y=D.expandShapeToKeepDim(h,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var rZ={kernelName:Ti,backendName:"webgl",kernelFunc:sZ};function aZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("any",u,i);let[h,p]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"any",n),A;if(o){let y=D.expandShapeToKeepDim(h,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var oZ={kernelName:Ni,backendName:"webgl",kernelFunc:aZ},iZ=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=` + `}};function Rf(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Yn({inputs:{x:s[0]},backend:n});if(s.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=Rf({inputs:s.slice(0,l),backend:n}),c=Rf({inputs:s.slice(l),backend:n});return Rf({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Cs(l,u)),a=s.map(l=>l.shape),i=Q().getBool("WEBGL_PACK")?new nZ(s[0].shape,a):new tZ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var sZ={kernelName:Na,backendName:"webgl",kernelFunc:Rf};function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("all",u,i);let[h,p]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"all",n),A;if(o){let y=D.expandShapeToKeepDim(h,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var aZ={kernelName:Ti,backendName:"webgl",kernelFunc:rZ};function oZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("any",u,i);let[h,p]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(p),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"any",n),A;if(o){let y=D.expandShapeToKeepDim(h,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var iZ={kernelName:Ni,backendName:"webgl",kernelFunc:oZ},lZ=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]; @@ -1579,7 +1579,7 @@ return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:Ci,back } setOutput(float(bestIndex)); } - `}},lZ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.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=ht(i),u=In("coords",i),c,d;if(a===1){d=i+1;let S=ht(d);c=` + `}},uZ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.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=ht(i),u=In("coords",i),c,d;if(a===1){d=i+1;let S=ht(d);c=` ${S} sourceLocR = ${S}(${u.join()}, 0); ++${u[i-1]}; ${S} sourceLocG = ${S}(${u.join()}, 0); @@ -1641,23 +1641,23 @@ return log(x + sqrt(x * x - 1.0));`,ZK=Je({opSnippet:KK}),YK={kernelName:Ci,back } setOutput(bestIndex); } - `}};function o4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=D.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new iZ(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=o4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function i4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=D.computeOptimalWindowSize(a),i=new lZ(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=i4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function l4(e,t,n,s){let r=[n];if(D.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=D.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(c),h=ye({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(h);let p=o4(e,h,s);a.push(p);let f=ye({inputs:{x:p},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return i4(e,t,s)}function uZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=l4(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var cZ={kernelName:Ea,backendName:"webgl",kernelFunc:uZ};function dZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=l4(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var hZ={kernelName:Pu,backendName:"webgl",kernelFunc:dZ},pZ=js+` + `}};function o4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=D.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new lZ(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=o4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function i4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=D.computeOptimalWindowSize(a),i=new uZ(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=i4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function l4(e,t,n,s){let r=[n];if(D.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=D.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(c),h=ye({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(h);let p=o4(e,h,s);a.push(p);let f=ye({inputs:{x:p},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return i4(e,t,s)}function cZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=l4(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var dZ={kernelName:Ea,backendName:"webgl",kernelFunc:cZ};function hZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=l4(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var pZ={kernelName:Pu,backendName:"webgl",kernelFunc:hZ},fZ=js+` if (abs(x) > 1.) { return NAN; } return asin(x); 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NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelName:Di,backendName:"webgl",kernelFunc:TZ},Qc=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,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NZ=Je({opSnippet:TZ}),EZ={kernelName:Di,backendName:"webgl",kernelFunc:NZ},Qc=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,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=` const ivec2 strides = ivec2(${o}, ${i}); const ivec2 pads = ivec2(${h}, ${p}); @@ -1798,7 +1798,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(${x}); } - `}},W2=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,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,h=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let _=">=";this.userCode=` + `}},V2=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,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,h=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let _=">=";this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${A}); @@ -1961,7 +1961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput(${v}); } } - `}};function EZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Yn({inputs:{x:r},backend:n});let d=new Qc(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var RZ={kernelName:Ra,backendName:"webgl",kernelFunc:EZ};function _Z(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,l,u),h=new W2(d,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}var DZ={kernelName:Mu,backendName:"webgl",kernelFunc:_Z},$Z=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,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` + `}};function RZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Yn({inputs:{x:r},backend:n});let d=new Qc(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var _Z={kernelName:Ra,backendName:"webgl",kernelFunc:RZ};function DZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,l,u),h=new V2(d,"avg",!1);return n.runWebGLProgram(h,[r],"float32")}var $Z={kernelName:Mu,backendName:"webgl",kernelFunc:DZ},FZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${d}); @@ -2003,7 +2003,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},FZ=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,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*n*s);this.userCode=` + `}},OZ=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,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*n*s);this.userCode=` const ivec3 pads = ivec3(${p}, ${f}, ${m}); const float avgMultiplier = float(${g}); @@ -2059,7 +2059,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}};function OZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],h=D.computePool3DInfo(o.shape,i,l,d,u,c),p=new FZ(h);return n.runWebGLProgram(p,[r],o.dtype)}var PZ={kernelName:Zd,backendName:"webgl",kernelFunc:OZ};function MZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;su([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=new $Z(c);return n.runWebGLProgram(d,[r],o.dtype)}var zZ={kernelName:Kd,backendName:"webgl",kernelFunc:MZ};function LZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Ef({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var BZ={kernelName:_a,backendName:"webgl",kernelFunc:LZ},WZ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function PZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],h=D.computePool3DInfo(o.shape,i,l,d,u,c),p=new OZ(h);return n.runWebGLProgram(p,[r],o.dtype)}var MZ={kernelName:Zd,backendName:"webgl",kernelFunc:PZ};function zZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;su([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=new FZ(c);return n.runWebGLProgram(d,[r],o.dtype)}var LZ={kernelName:Kd,backendName:"webgl",kernelFunc:zZ};function BZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Ef({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var WZ={kernelName:_a,backendName:"webgl",kernelFunc:BZ},VZ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2069,7 +2069,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},VZ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},UZ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${o}; vec4 scale = ${i}; @@ -2082,7 +2082,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput((x - mean) * inv + offset); } - `}},UZ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let h=Q().getBool("WEBGL_PACK_NORMALIZATION")?new VZ(s.shape,r.shape,a.shape,c,d,l):new WZ(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(h,u,u[0].dtype)},HZ={kernelName:Ga,backendName:"webgl",kernelFunc:UZ},GZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=jZ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${V2[o]} = start[${o}] + coords.${V2[o]};`);s=` + `}},HZ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.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 u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let h=Q().getBool("WEBGL_PACK_NORMALIZATION")?new UZ(s.shape,r.shape,a.shape,c,d,l):new VZ(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(h,u,u[0].dtype)},GZ={kernelName:Ga,backendName:"webgl",kernelFunc:HZ},jZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=qZ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${U2[o]} = start[${o}] + coords.${U2[o]};`);s=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2092,7 +2092,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${s} setOutput(getSource(${n})); } - `}},V2=["x","y","z","w","u","v"];function jZ(e){if(e===1)return"sourceLoc";if(e<=6)return V2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var qZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=In("coords",this.rank),s=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=` + `}},U2=["x","y","z","w","u","v"];function qZ(e){if(e===1)return"sourceLoc";if(e<=6)return U2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var XZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=In("coords",this.rank),s=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; @@ -2121,7 +2121,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${i} setOutput(result); } - `}};function XZ(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=xn.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function du(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=xn.parseSliceParams(r,a,o);if(xn.assertParamsValid(r,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),h=$X(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,h)}let{isPacked:u}=n.texData.get(r.dataId),c=xn.isSliceContinous(r.shape,i,l);if(u||!c){let d=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qZ(l):new GZ(l),h=[i];return n.runWebGLProgram(d,[r],r.dtype,h)}return n.uploadToGPU(r.dataId),XZ(r,i,l,n)}var KZ={kernelName:pl,backendName:"webgl",kernelFunc:du},ZZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),h=D.getSliceSize(c,o,a.length),p=[],f=ye({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ye({inputs:{x:m},backend:n,attrs:{shape:c}}),A=du({inputs:{x:g},backend:n,attrs:{begin:d,size:h}});return p.push(f),p.push(m),p.push(g),p.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},YZ={kernelName:Fi,backendName:"webgl",kernelFunc:ZZ};function JZ(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),u=P6(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var QZ={kernelName:Yd,backendName:"webgl",kernelFunc:JZ},eY="return float(a != b);",u4=ln({opSnippet:eY,cpuKernelImpl:NX,dtype:"bool"}),tY={kernelName:tl,backendName:"webgl",kernelFunc:u4};function ed(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Yn({inputs:{x:r.complexTensorInfos.real},backend:n})}var nY={kernelName:xh,backendName:"webgl",kernelFunc:ed},sY="return float(int(x));";function rY(e,t){let n=new ha(e.shape,sY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function U2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Yn({inputs:{x:r},backend:n});let o=Pt(r.shape),i=U2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=pa({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=ed({inputs:{input:r},backend:n}),i=U2({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Yn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return rY(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=u4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var aY={kernelName:Da,backendName:"webgl",kernelFunc:U2},c4="return ceil(x);",oY=Je({opSnippet:c4,packedOpSnippet:c4,cpuKernelImpl:uX}),iY={kernelName:$a,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function KZ(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=xn.computeFlatOffset(t,w.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 du(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=xn.parseSliceParams(r,a,o);if(xn.assertParamsValid(r,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),h=FX(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,h)}let{isPacked:u}=n.texData.get(r.dataId),c=xn.isSliceContinous(r.shape,i,l);if(u||!c){let d=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XZ(l):new jZ(l),h=[i];return n.runWebGLProgram(d,[r],r.dtype,h)}return n.uploadToGPU(r.dataId),KZ(r,i,l,n)}var ZZ={kernelName:pl,backendName:"webgl",kernelFunc:du},YZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),h=D.getSliceSize(c,o,a.length),p=[],f=ye({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ye({inputs:{x:m},backend:n,attrs:{shape:c}}),A=du({inputs:{x:g},backend:n,attrs:{begin:d,size:h}});return p.push(f),p.push(m),p.push(g),p.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},JZ={kernelName:Fi,backendName:"webgl",kernelFunc:YZ};function QZ(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),u=P6(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var eY={kernelName:Yd,backendName:"webgl",kernelFunc:QZ},tY="return float(a != b);",u4=ln({opSnippet:tY,cpuKernelImpl:EX,dtype:"bool"}),nY={kernelName:tl,backendName:"webgl",kernelFunc:u4};function ed(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Yn({inputs:{x:r.complexTensorInfos.real},backend:n})}var sY={kernelName:xh,backendName:"webgl",kernelFunc:ed},rY="return float(int(x));";function aY(e,t){let n=new ha(e.shape,rY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function H2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Yn({inputs:{x:r},backend:n});let o=Pt(r.shape),i=H2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=pa({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=ed({inputs:{input:r},backend:n}),i=H2({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Yn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return aY(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=u4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var oY={kernelName:Da,backendName:"webgl",kernelFunc:H2},c4="return ceil(x);",iY=Je({opSnippet:c4,packedOpSnippet:c4,cpuKernelImpl:cX}),lY={kernelName:$a,backendName:"webgl",kernelFunc:iY},uY=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(); @@ -2132,7 +2132,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput(clamp(value, minVal, maxVal)); } - `}},uY=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=` + `}},cY=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(); @@ -2143,7 +2143,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function cY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Q().getBool("WEBGL_PACK_CLIP")?i=new uY(r.shape):i=new lY(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var dY={kernelName:Ur,backendName:"webgl",kernelFunc:cY},hY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Q().getBool("WEBGL_PACK_CLIP")?i=new cY(r.shape):i=new uY(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var hY={kernelName:Ur,backendName:"webgl",kernelFunc:dY},pY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2156,7 +2156,7 @@ return (log(1.0 + x) - log(1.0 - 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n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),p}if(Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new AY(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=xY(e,t,n),i=new gY(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ye({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function xY(e,t,n){let s=D.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function h4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=D.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return Yn({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return 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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,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,h=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` + `}},vY=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,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,h=e.filterWidth,p=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}); @@ -2424,7 +2424,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},vY=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=bs(this.outputShape.length);let{dataFormat:n}=t,s=kn(),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 u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=` + `}},wY=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=bs(this.outputShape.length);let{dataFormat:n}=t,s=kn(),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 u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=` blockIndex = rc.y + ${c}; pos = rc.x + ${u}; @@ -2471,7 +2471,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${s.output} = result; } - `}};function f4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],h=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||h===1)&&c>s4)&&u.isPacked&&p&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Zc(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let C=Ef({a:v,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),_=s.texData.get(C.dataId);w.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,_.shape=n.outShape,g=Yn({inputs:{x:C},backend:s}),g.shape=n.outShape,A.push(C)}else{let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ye({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Ef({a:v,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ye({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(v),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function m4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:h,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,g=h*d,A=[m,g],y=!0,x=!1,b=[],v=ye({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let S=new vY(A,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=s.runWebGLProgram(S,[v],"float32",C),O=ye({inputs:{x:_},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(_),b.push(O);let E=r!=null,R=a!=null,T=i==="leakyrelu",P=i?Cf(i,!0):null,V=new J6(O.shape,k.shape,[1,g,n.outChannels],y,x,E,P,R,T),j=[O,k];if(r&&j.push(r),R&&j.push(a),T){let te=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(te),b.push(te)}let q=s.runWebGLProgram(V,j,"float32"),X=f?[1,h,d,n.outChannels]:[1,n.outChannels,h,d],ee=ye({inputs:{x:q},backend:s,attrs:{shape:X}});b.push(q);for(let te of b)s.disposeIntermediateTensorInfo(te);return ee}function wY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=D.convertConv2DDataFormat(l),h=D.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),p;if(h.filterHeight===1&&h.filterWidth===1&&h.dilationHeight===1&&h.dilationWidth===1&&h.strideHeight===1&&h.strideWidth===1&&(h.padInfo.type==="SAME"||h.padInfo.type==="VALID"))p=f4({x:r,filter:a,convInfo:h,backend:n});else if(Q().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)p=m4({x:r,filter:a,convInfo:h,backend:n});else{let m=new p4(h);p=n.runWebGLProgram(m,[r,a],"float32")}let f=ye({inputs:{x:p},backend:n,attrs:{shape:h.outShape}});return n.disposeIntermediateTensorInfo(p),f}var kY={kernelName:Fa,backendName:"webgl",kernelFunc:wY},IY=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=` + `}};function 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= coords.x; @@ -2513,7 +2513,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},SY=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,u=a?2:3,c=a?3:1;this.userCode=` + `}},CY=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,u=a?2:3,c=a?3:1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); void main() { @@ -2566,7 +2566,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},CY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=` + `}},TY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2608,7 +2608,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},TY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=` + `}},NY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${i}, ${l}, ${u}); void main() { @@ -2665,12 +2665,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}};function NY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=D.convertConv2DDataFormat(l),h=D.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),p=new IY(h);return n.runWebGLProgram(p,[r,a],"float32")}var EY={kernelName:Qd,backendName:"webgl",kernelFunc:NY};function RY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=D.convertConv2DDataFormat(u),h=D.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),p=new SY(h);return n.runWebGLProgram(p,[r,a],"float32")}var 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OY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=D.computeConv3DInfo(r.shape,l,o,1,i),c=new TY(u);return n.runWebGLProgram(c,[r,a],"float32")}var PY={kernelName:eh,backendName:"webgl",kernelFunc:OY};function MY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=D.computeConv3DInfo(l,a.shape,i,1,o),c=new NY(u);return n.runWebGLProgram(c,[r,a],"float32")}var zY={kernelName:th,backendName:"webgl",kernelFunc:MY},LY=Y6+` return cos(x); -`,LY=Je({opSnippet:zY}),BY={kernelName:Pa,backendName:"webgl",kernelFunc:LY},WY=` +`,BY=Je({opSnippet:LY}),WY={kernelName:Pa,backendName:"webgl",kernelFunc:BY},VY=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,VY=Je({opSnippet:WY}),UY={kernelName:Ma,backendName:"webgl",kernelFunc:VY},HY=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let 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return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput(newValue); } } - `}},GY=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new HY(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},jY={kernelName:Pi,backendName:"webgl",kernelFunc:GY},g4=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(${A4(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 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=` + `}},jY=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new GY(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},qY={kernelName:Pi,backendName:"webgl",kernelFunc:jY},g4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${A4(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() { ${ht(s)} coords = getOutputCoords(); int end = ${y4(s,"coords")}; @@ -2744,7 +2744,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(val); } - `}};function A4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function y4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw 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KY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=P6(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=lX(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var ZY={kernelName:nh,backendName:"webgl",kernelFunc:KY},YY=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function A4(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 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QY={kernelName:Mi,backendName:"webgl",kernelFunc:JY},x4=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=bs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function QY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=new JY(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var eJ={kernelName:Mi,backendName:"webgl",kernelFunc:QY},x4=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=bs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -2999,7 +2999,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${f} setOutput(result); } - `}};function eJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=D.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),h;Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?h=new b4(d):h=new x4(d);let p=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(h,[r,a],"float32",p)}var tJ={kernelName:La,backendName:"webgl",kernelFunc:eJ},nJ=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=` + `}};function tJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=D.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),h;Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?h=new b4(d):h=new x4(d);let p=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(h,[r,a],"float32",p)}var nJ={kernelName:La,backendName:"webgl",kernelFunc:tJ},sJ=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; @@ -3034,7 +3034,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},sJ=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=` + `}},rJ=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() { @@ -3079,13 +3079,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}};function rJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=D.computeConv2DInfo(r.shape,c,o,i,l,u,!0),h=new nJ(d);return n.runWebGLProgram(h,[r,a],"float32")}var aJ={kernelName:sh,backendName:"webgl",kernelFunc:rJ};function oJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=D.computeConv2DInfo(c,a.shape,o,i,l,u,!0),h=new sJ(d);return n.runWebGLProgram(h,[r,a],"float32")}var iJ={kernelName:rh,backendName:"webgl",kernelFunc:oJ},lJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function aJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=D.computeConv2DInfo(r.shape,c,o,i,l,u,!0),h=new sJ(d);return n.runWebGLProgram(h,[r,a],"float32")}var oJ={kernelName:sh,backendName:"webgl",kernelFunc:aJ};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=D.computeConv2DInfo(c,a.shape,o,i,l,u,!0),h=new rJ(d);return n.runWebGLProgram(h,[r,a],"float32")}var lJ={kernelName:rh,backendName:"webgl",kernelFunc:iJ},uJ=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 uJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=ye({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new lJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var cJ={kernelName:ah,backendName:"webgl",kernelFunc:uJ},dJ=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:u}=e,{top:c,left:d}=s;this.userCode=` + `}};function cJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=ye({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new uJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var dJ={kernelName:ah,backendName:"webgl",kernelFunc:cJ},hJ=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:u}=e,{top:c,left:d}=s;this.userCode=` const ivec2 strides = ivec2(${r}, ${a}); const ivec2 pads = ivec2(${c}, ${d}); const float neg_infinity = -3.4e38; @@ -3123,7 +3123,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam float result = curVal; setOutput(result); } - `}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=D.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new dJ(u);c=n.runWebGLProgram(d,[r,a],"float32");let h=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),h}var pJ={kernelName:Bu,backendName:"webgl",kernelFunc:hJ};function fJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=D.decodeEinsumEquation(r,a.length);D.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=D.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m=0&&(h=Nf({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var mJ={kernelName:lh,backendName:"webgl",kernelFunc:fJ},gJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",AJ=` + `}};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=D.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new hJ(u);c=n.runWebGLProgram(d,[r,a],"float32");let h=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),h}var fJ={kernelName:Bu,backendName:"webgl",kernelFunc:pJ};function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=D.decodeEinsumEquation(r,a.length);D.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=D.getEinsumComputePath(i,l),d=c.length,h=null,p=o.length,f=[];for(let m=0;m=0&&(h=Nf({inputs:{x:h},backend:n,attrs:{axis:u[m]-(o.length-p),keepDims:!1}}),f.push(h)),p--)}for(let m of f)m!==h&&n.disposeIntermediateTensorInfo(m);return h}var gJ={kernelName:lh,backendName:"webgl",kernelFunc:mJ},AJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",yJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3132,12 +3132,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,yJ=Je({opSnippet:gJ,packedOpSnippet:AJ}),xJ={kernelName:Wa,backendName:"webgl",kernelFunc:yJ},bJ="return (b >= 1.0) ? a : a * (b + 1.0);",vJ=` +`,xJ=Je({opSnippet:AJ,packedOpSnippet:yJ}),bJ={kernelName:Wa,backendName:"webgl",kernelFunc:xJ},vJ="return (b >= 1.0) ? a : a * (b + 1.0);",wJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,wJ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(vJ,s.shape,r.shape):new cu(bJ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},kJ={kernelName:uh,backendName:"webgl",kernelFunc:wJ},IJ=` +`,kJ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jc(wJ,s.shape,r.shape):new cu(vJ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},IJ={kernelName:uh,backendName:"webgl",kernelFunc:kJ},SJ=` return vec4(equal(a, b)); -`,SJ="return float(a == b);",CJ=ln({opSnippet:SJ,packedOpSnippet:IJ,dtype:"bool",cpuKernelImpl:dX}),TJ={kernelName:Li,backendName:"webgl",kernelFunc:CJ},NJ=` +`,CJ="return float(a == b);",TJ=ln({opSnippet:CJ,packedOpSnippet:SJ,dtype:"bool",cpuKernelImpl:hX}),NJ={kernelName:Li,backendName:"webgl",kernelFunc:TJ},EJ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3152,7 +3152,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam 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)); -`,EJ=Je({opSnippet:NJ}),RJ={kernelName:zi,backendName:"webgl",kernelFunc:EJ},v4="return exp(x);",w4=Je({opSnippet:v4,packedOpSnippet:v4,cpuKernelImpl:hX}),_J={kernelName:Va,backendName:"webgl",kernelFunc:w4};function H2(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&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ye({inputs:{x:a},backend:s,attrs:{shape:i}})}var DJ={kernelName:Bi,backendName:"webgl",kernelFunc:H2},k4="return exp(x) - 1.0;",$J=Je({opSnippet:k4,packedOpSnippet:k4,cpuKernelImpl:pX}),FJ={kernelName:Wi,backendName:"webgl",kernelFunc:$J},I4=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 ${e}.`);this.userCode=` +`,RJ=Je({opSnippet:EJ}),_J={kernelName:zi,backendName:"webgl",kernelFunc:RJ},v4="return exp(x);",w4=Je({opSnippet:v4,packedOpSnippet:v4,cpuKernelImpl:pX}),DJ={kernelName:Va,backendName:"webgl",kernelFunc:w4};function G2(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ye({inputs:{x:a},backend:s,attrs:{shape:i}})}var $J={kernelName:Bi,backendName:"webgl",kernelFunc:G2},k4="return exp(x) - 1.0;",FJ=Je({opSnippet:k4,packedOpSnippet:k4,cpuKernelImpl:fX}),OJ={kernelName:Wi,backendName:"webgl",kernelFunc:FJ},I4=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) { @@ -3185,12 +3185,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function S4(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ye({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new I4("real",l,t),c=new I4("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}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=pa({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function OJ(e){let{inputs:t,backend:n}=e,{input:s}=t;return S4(s,!1,n)}var PJ={kernelName:ch,backendName:"webgl",kernelFunc:OJ},MJ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function S4(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ye({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new I4("real",l,t),c=new I4("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}],h=n.runWebGLProgram(u,d,"float32"),p=n.runWebGLProgram(c,d,"float32"),f=pa({inputs:{real:h,imag:p},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function PJ(e){let{inputs:t,backend:n}=e,{input:s}=t;return S4(s,!1,n)}var MJ={kernelName:ch,backendName:"webgl",kernelFunc:PJ},zJ=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 td(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new MJ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var zJ={kernelName:Wu,backendName:"webgl",kernelFunc:td},LJ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function td(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new zJ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var LJ={kernelName:Wu,backendName:"webgl",kernelFunc:td},BJ=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]; @@ -3204,7 +3204,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(outputValue); } - `}},BJ={kernelName:Vi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new LJ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},C4="return floor(x);",WJ=Je({opSnippet:C4,packedOpSnippet:C4,cpuKernelImpl:fX}),VJ={kernelName:Ua,backendName:"webgl",kernelFunc:WJ},UJ=` + `}},WJ={kernelName:Vi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new BJ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},C4="return floor(x);",VJ=Je({opSnippet:C4,packedOpSnippet:C4,cpuKernelImpl:mX}),UJ={kernelName:Ua,backendName:"webgl",kernelFunc:VJ},HJ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3214,7 +3214,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } else { return NAN; } -`,HJ=` +`,GJ=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3235,7 +3235,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,GJ=ln({opSnippet:UJ,packedOpSnippet:HJ,dtype:"int32"}),jJ={kernelName:Ha,backendName:"webgl",kernelFunc:GJ},qJ=class{constructor(e){this.variableNames=["A"];let t=kn(),[n,s]=e;this.outputShape=e,this.userCode=` +`,jJ=ln({opSnippet:HJ,packedOpSnippet:GJ,dtype:"int32"}),qJ={kernelName:Ha,backendName:"webgl",kernelFunc:jJ},XJ=class{constructor(e){this.variableNames=["A"];let t=kn(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3257,7 +3257,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},XJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=kn(),[n,s]=e;this.outputShape=e,this.userCode=` + `}},KJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=kn(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3291,7 +3291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${t.output} = result; } - `}},KJ={kernelName:_h,backendName:"webgl",kernelFunc:ZJ},pu;function ZJ(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,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=l,pu.canvas.height=u,pu.drawImage(r,0,0,l,u),r=pu.canvas);let h=n.makeTensorInfo(c,"int32");n.texData.get(h.dataId).usage=ys.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let p=Q().getBool("WEBGL_PACK")?new XJ(d):new qJ(d),f=n.runWebGLProgram(p,[h],"int32");return n.disposeData(h.dataId),f}function YJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=D.convertConv2DDataFormat(c),g=D.computeConv2DInfo(r.shape,a.shape,l,d,u,h,!1,m),A,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"))A=f4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=m4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=p==="leakyrelu",S=p?Cf(p,!1):null,C=new p4(g,b,S,v,k),_=[r,a];if(o&&_.push(o),i&&_.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));_.push(O),y.push(O)}A=n.runWebGLProgram(C,_,"float32")}let x=ye({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var JJ={kernelName:Co,backendName:"webgl",kernelFunc:YJ};function QJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:p}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=D.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),A=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=h?Cf(h,A):null,x=[r,a],b=o!=null,v=i!=null,k=h==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(p,"float32"));x.push(O),f.push(O)}let S;A?S=new b4(g,b,y,v,k):S=new x4(g,b,y,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(S,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),_}var eQ={kernelName:To,backendName:"webgl",kernelFunc:QJ},tQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ht(t.length),r=ht(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=` + `}},ZJ={kernelName:_h,backendName:"webgl",kernelFunc:YJ},pu;function YJ(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,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=l,pu.canvas.height=u,pu.drawImage(r,0,0,l,u),r=pu.canvas);let h=n.makeTensorInfo(c,"int32");n.texData.get(h.dataId).usage=ys.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let p=Q().getBool("WEBGL_PACK")?new KJ(d):new XJ(d),f=n.runWebGLProgram(p,[h],"int32");return n.disposeData(h.dataId),f}function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=s,m=D.convertConv2DDataFormat(c),g=D.computeConv2DInfo(r.shape,a.shape,l,d,u,h,!1,m),A,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"))A=f4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=m4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:p,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=p==="leakyrelu",S=p?Cf(p,!1):null,C=new p4(g,b,S,v,k),_=[r,a];if(o&&_.push(o),i&&_.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));_.push(O),y.push(O)}A=n.runWebGLProgram(C,_,"float32")}let x=ye({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var QJ={kernelName:Co,backendName:"webgl",kernelFunc:JJ};function eQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:p}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=D.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),A=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=h?Cf(h,A):null,x=[r,a],b=o!=null,v=i!=null,k=h==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(p,"float32"));x.push(O),f.push(O)}let S;A?S=new b4(g,b,y,v,k):S=new x4(g,b,y,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(S,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),_}var tQ={kernelName:To,backendName:"webgl",kernelFunc:eQ},nQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ht(t.length),r=ht(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${s} strides = ${s}(${this.strides}); void main() { ${r} coords = getOutputCoords(); @@ -3302,21 +3302,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(getX(flattenIndex, coords[1])); } - `}};function nQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[l,u,c,d]=D.prepareAndValidate(s,r),h=ye({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),p=ye({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(s),x=mX(A,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new tQ(o,d,[u,c]),m=n.runWebGLProgram(f,[p,h],p.dtype),g=ye({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),g}var sQ={kernelName:Hi,backendName:"webgl",kernelFunc:nQ},rQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),s=aQ(e,2);this.userCode=` + `}};function sQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[l,u,c,d]=D.prepareAndValidate(s,r),h=ye({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),p=ye({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(s),x=gX(A,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new nQ(o,d,[u,c]),m=n.runWebGLProgram(f,[p,h],p.dtype),g=ye({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),g}var rQ={kernelName:Hi,backendName:"webgl",kernelFunc:sQ},aQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),s=oQ(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${s})); } - `}};function aQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;rn.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new rQ(h.shape,f),g=n.runWebGLProgram(m,[h,p],h.dtype);d.push(g);let A=ye({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var oQ={kernelName:Ui,backendName:"webgl",kernelFunc:T4},iQ="return float(a > b);",lQ=` + `}};function oQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;rn.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new aQ(h.shape,f),g=n.runWebGLProgram(m,[h,p],h.dtype);d.push(g);let A=ye({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var iQ={kernelName:Ui,backendName:"webgl",kernelFunc:T4},lQ="return float(a > b);",uQ=` return vec4(greaterThan(a, b)); -`,uQ=ln({opSnippet:iQ,packedOpSnippet:lQ,cpuKernelImpl:AX,dtype:"bool"}),cQ={kernelName:Gi,backendName:"webgl",kernelFunc:uQ},dQ="return float(a >= b);",hQ=` +`,cQ=ln({opSnippet:lQ,packedOpSnippet:uQ,cpuKernelImpl:yX,dtype:"bool"}),dQ={kernelName:Gi,backendName:"webgl",kernelFunc:cQ},hQ="return float(a >= b);",pQ=` return vec4(greaterThanEqual(a, b)); -`,pQ=ln({opSnippet:dQ,packedOpSnippet:hQ,dtype:"bool",cpuKernelImpl:yX}),fQ={kernelName:ja,backendName:"webgl",kernelFunc:pQ};function mQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return S4(s,!0,n)}var gQ={kernelName:dh,backendName:"webgl",kernelFunc:mQ},AQ="return float(!isnan(x) && !isinf(x));",yQ=Je({opSnippet:AQ,dtype:"bool"}),xQ={kernelName:ji,backendName:"webgl",kernelFunc:yQ},bQ="return float(isinf(x));",vQ=Je({opSnippet:bQ,dtype:"bool"}),wQ={kernelName:qi,backendName:"webgl",kernelFunc:vQ},kQ="return float(isnan(x));",IQ=Je({opSnippet:kQ,dtype:"bool"}),SQ={kernelName:Xi,backendName:"webgl",kernelFunc:IQ},CQ="return float(a < b);",TQ=` +`,fQ=ln({opSnippet:hQ,packedOpSnippet:pQ,dtype:"bool",cpuKernelImpl:xX}),mQ={kernelName:ja,backendName:"webgl",kernelFunc:fQ};function gQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return S4(s,!0,n)}var AQ={kernelName:dh,backendName:"webgl",kernelFunc:gQ},yQ="return float(!isnan(x) && !isinf(x));",xQ=Je({opSnippet:yQ,dtype:"bool"}),bQ={kernelName:ji,backendName:"webgl",kernelFunc:xQ},vQ="return float(isinf(x));",wQ=Je({opSnippet:vQ,dtype:"bool"}),kQ={kernelName:qi,backendName:"webgl",kernelFunc:wQ},IQ="return float(isnan(x));",SQ=Je({opSnippet:IQ,dtype:"bool"}),CQ={kernelName:Xi,backendName:"webgl",kernelFunc:SQ},TQ="return float(a < b);",NQ=` return vec4(lessThan(a, b)); -`,NQ=ln({opSnippet:CQ,packedOpSnippet:TQ,cpuKernelImpl:xX,dtype:"bool"}),EQ={kernelName:Ki,backendName:"webgl",kernelFunc:NQ},RQ="return float(a <= b);",_Q=` +`,EQ=ln({opSnippet:TQ,packedOpSnippet:NQ,cpuKernelImpl:bX,dtype:"bool"}),RQ={kernelName:Ki,backendName:"webgl",kernelFunc:EQ},_Q="return float(a <= b);",DQ=` return vec4(lessThanEqual(a, b)); -`,DQ=ln({opSnippet:RQ,packedOpSnippet:_Q,cpuKernelImpl:bX,dtype:"bool"}),$Q={kernelName:Zi,backendName:"webgl",kernelFunc:DQ};function FQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=vX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var OQ={kernelName:ph,backendName:"webgl",kernelFunc:FQ},PQ=`if (x < 0.0) return NAN; - return log(x);`,MQ=` +`,$Q=ln({opSnippet:_Q,packedOpSnippet:DQ,cpuKernelImpl:vX,dtype:"bool"}),FQ={kernelName:Zi,backendName:"webgl",kernelFunc:$Q};function OQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=wX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var PQ={kernelName:ph,backendName:"webgl",kernelFunc:OQ},MQ=`if (x < 0.0) return NAN; + return log(x);`,zQ=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; @@ -3325,16 +3325,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam result.a = isNaN.a == 1.0 ? NAN : result.a; return result; -`,zQ=Je({opSnippet:PQ,packedOpSnippet:MQ,cpuKernelImpl:wX}),LQ={kernelName:Ka,backendName:"webgl",kernelFunc:zQ},BQ="return log(1.0 + x);",WQ=Je({opSnippet:BQ}),VQ={kernelName:Yi,backendName:"webgl",kernelFunc:WQ},UQ="return float(a >= 1.0 && b >= 1.0);",HQ=` +`,LQ=Je({opSnippet:MQ,packedOpSnippet:zQ,cpuKernelImpl:kX}),BQ={kernelName:Ka,backendName:"webgl",kernelFunc:LQ},WQ="return log(1.0 + x);",VQ=Je({opSnippet:WQ}),UQ={kernelName:Yi,backendName:"webgl",kernelFunc:VQ},HQ="return float(a >= 1.0 && b >= 1.0);",GQ=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,GQ=ln({opSnippet:UQ,packedOpSnippet:HQ,dtype:"bool"}),jQ={kernelName:Ji,backendName:"webgl",kernelFunc:GQ},qQ="return float(!(x >= 1.0));",XQ=Je({opSnippet:qQ}),KQ={kernelName:Vu,backendName:"webgl",kernelFunc:XQ},ZQ="return float(a >= 1.0 || b >= 1.0);",YQ=` +`,jQ=ln({opSnippet:HQ,packedOpSnippet:GQ,dtype:"bool"}),qQ={kernelName:Ji,backendName:"webgl",kernelFunc:jQ},XQ="return float(!(x >= 1.0));",KQ=Je({opSnippet:XQ}),ZQ={kernelName:Vu,backendName:"webgl",kernelFunc:KQ},YQ="return float(a >= 1.0 || b >= 1.0);",JQ=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,JQ=ln({opSnippet:ZQ,packedOpSnippet:YQ,dtype:"bool"}),QQ={kernelName:Uu,backendName:"webgl",kernelFunc:JQ},eee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,QQ=ln({opSnippet:YQ,packedOpSnippet:JQ,dtype:"bool"}),eee={kernelName:Uu,backendName:"webgl",kernelFunc:QQ},tee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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]; @@ -3353,7 +3353,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam float val = x * ${i}; setOutput(val); } - `}},tee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},nee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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; @@ -3415,7 +3415,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam vec4 result = xAtOutputCoords * ${i}; setOutput(result); } - `}},nee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Q().getBool("WEBGL_PACK_NORMALIZATION")?new tee(r.shape,a,o,i,l):new eee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},see={kernelName:Hu,backendName:"webgl",kernelFunc:nee},ree=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=` + `}},see=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Q().getBool("WEBGL_PACK_NORMALIZATION")?new nee(r.shape,a,o,i,l):new tee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},ree={kernelName:Hu,backendName:"webgl",kernelFunc:see},aee=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]; @@ -3470,14 +3470,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(result); } - `}},aee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new ree(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},oee={kernelName:fh,backendName:"webgl",kernelFunc:aee};function iee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=si(i,e.dtype,"max",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function N4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=c!=null,h=n.shouldExecuteOnCPU([r]),p=r;if(d){if(h){let x=n.texData.get(p.dataId).values,b=new Array(i);for(let S=0;S{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new aee(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},iee={kernelName:fh,backendName:"webgl",kernelFunc:oee};function lee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=si(i,e.dtype,"max",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function N4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=c!=null,h=n.shouldExecuteOnCPU([r]),p=r;if(d){if(h){let x=n.texData.get(p.dataId).values,b=new Array(i);for(let S=0;S`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Yn({inputs:{x:r},backend:n});let d=new Qc(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var fee={kernelName:Ja,backendName:"webgl",kernelFunc:pee};function mee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,u,l),h=new W2(d,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}var gee={kernelName:Gu,backendName:"webgl",kernelFunc:mee},Aee=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=` +`,hee=ln({opSnippet:cee,packedOpSnippet:dee,cpuKernelImpl:SX}),pee={kernelName:Ya,backendName:"webgl",kernelFunc:hee};function fee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Yn({inputs:{x:r},backend:n});let d=new Qc(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var mee={kernelName:Ja,backendName:"webgl",kernelFunc:fee};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,u,l),h=new V2(d,"max",!1);return n.runWebGLProgram(h,[r],r.dtype)}var Aee={kernelName:Gu,backendName:"webgl",kernelFunc:gee},yee=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}, ${i}); void main() { @@ -3523,7 +3523,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}},yee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,h=u-1-e.padInfo.left,p=i*l*u-1;this.userCode=` + `}},xee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,h=u-1-e.padInfo.left,p=i*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${d}, ${h}); void main() { @@ -3587,14 +3587,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam } setOutput(dotProd); } - `}};function xee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],h=D.computePool3DInfo(o.shape,i,l,d,u,c),p=new W2(h,"max",!0),f=n.runWebGLProgram(p,[o],o.dtype),m=new yee(h),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var bee={kernelName:gh,backendName:"webgl",kernelFunc:xee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;su([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,h=D.computePool2DInfo(i.shape,l,u,1,c,d),p=!0,f=new Qc(h,"max",p),m=n.runWebGLProgram(f,[i],i.dtype),g=new Aee(h),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var wee={kernelName:mh,backendName:"webgl",kernelFunc:vee};function kee(e,t,n,s){let r=new Qc(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Qc(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Iee={kernelName:Ah,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];w.assert(D.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${o}; @@ -3623,7 +3623,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${r} coords = outC - start; setOutput(getX(${i})); } - `}},Fee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let s=e.length,r=ht(s),a=t.map(p=>p[0]).join(","),o=t.map((p,f)=>p[0]+e[f]).join(","),i=In("rc",s),l=In("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,h="";if(s===1){let p=` + `}},Oee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let s=e.length,r=ht(s),a=t.map(p=>p[0]).join(","),o=t.map((p,f)=>p[0]+e[f]).join(","),i=In("rc",s),l=In("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,h="";if(s===1){let p=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; @@ -3679,13 +3679,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam ${h} setOutput(result); } - `}},Oee=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Fee(s.shape,r,a):new $ee(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Pee={kernelName:no,backendName:"webgl",kernelFunc:Oee},Mee=`if (b == 0.0) return NAN; - return mod(a, b);`,zee=` + `}},Pee=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Oee(s.shape,r,a):new Fee(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Mee={kernelName:no,backendName:"webgl",kernelFunc:Pee},zee=`if (b == 0.0) return NAN; + return mod(a, b);`,Lee=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Sf+` return result; -`,Lee=ln({opSnippet:Mee,packedOpSnippet:zee}),Bee={kernelName:Qi,backendName:"webgl",kernelFunc:Lee},Wee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Bee=ln({opSnippet:zee,packedOpSnippet:Lee}),Wee={kernelName:Qi,backendName:"webgl",kernelFunc:Bee},Vee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3705,11 +3705,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,TZ=Je({opSnippet:CZ}),NZ={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},Vee=` + `}},Uee=` if (a == b) { return 1.0; }; -return a / b;`,Uee=` +return a / b;`,Hee=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3727,14 +3727,14 @@ return a / b;`,Uee=` } return result; -`,E4=ln({opSnippet:Vee,packedOpSnippet:Uee,checkOutOfBounds:!0}),Hee={kernelName:Ba,backendName:"webgl",kernelFunc:E4},R4="return a - b;",_4=ln({opSnippet:R4,packedOpSnippet:R4,supportsComplex:!0,cpuKernelImpl:WX}),Gee={kernelName:vo,backendName:"webgl",kernelFunc:_4};function D4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=N4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=D.expandShapeToKeepDim(i.shape,o),u=ye({inputs:{x:i},backend:n,attrs:{shape:l}}),c=_4({inputs:{a:r,b:u},backend:n}),d=w4({inputs:{x:c},backend:n}),h=Nf({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),p=ye({inputs:{x:h},backend:n,attrs:{shape:l}}),f=E4({inputs:{a:d,b:p},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}var jee={kernelName:xo,backendName:"webgl",kernelFunc:D4};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:D4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new Wee(u,c,a),h=[[o]],p=n.runWebGLProgram(d,[l],"int32",h);return i||n.disposeIntermediateTensorInfo(l),p}var Xee={kernelName:yh,backendName:"webgl",kernelFunc:qee},$4="return -x;";function Kee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=TX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(s.shape,$4):r=new ha(s.shape,$4),n.runWebGLProgram(r,[s],s.dtype)}var Zee={kernelName:el,backendName:"webgl",kernelFunc:Kee},Yee=rr.nonMaxSuppressionV3Impl;function Jee(e){D.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,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=Yee(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Qee={kernelName:nl,backendName:"webgl",kernelFunc:Jee},ete=rr.nonMaxSuppressionV4Impl;function tte(e){D.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:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:h,validOutputs:p}=ete(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var nte={kernelName:sl,backendName:"webgl",kernelFunc:tte},ste=rr.nonMaxSuppressionV5Impl;function rte(e){D.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:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),h=o,p=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=ste(c,d,h,p,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var ate={kernelName:rl,backendName:"webgl",kernelFunc:rte},ote=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`,E4=ln({opSnippet:Uee,packedOpSnippet:Hee,checkOutOfBounds:!0}),Gee={kernelName:Ba,backendName:"webgl",kernelFunc:E4},R4="return a - b;",_4=ln({opSnippet:R4,packedOpSnippet:R4,supportsComplex:!0,cpuKernelImpl:VX}),jee={kernelName:vo,backendName:"webgl",kernelFunc:_4};function D4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=N4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=D.expandShapeToKeepDim(i.shape,o),u=ye({inputs:{x:i},backend:n,attrs:{shape:l}}),c=_4({inputs:{a:r,b:u},backend:n}),d=w4({inputs:{x:c},backend:n}),h=Nf({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),p=ye({inputs:{x:h},backend:n,attrs:{shape:l}}),f=E4({inputs:{a:d,b:p},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),f}var qee={kernelName:xo,backendName:"webgl",kernelFunc:D4};function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:D4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new Vee(u,c,a),h=[[o]],p=n.runWebGLProgram(d,[l],"int32",h);return i||n.disposeIntermediateTensorInfo(l),p}var Kee={kernelName:yh,backendName:"webgl",kernelFunc:Xee},$4="return -x;";function Zee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=NX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new lu(s.shape,$4):r=new ha(s.shape,$4),n.runWebGLProgram(r,[s],s.dtype)}var Yee={kernelName:el,backendName:"webgl",kernelFunc:Zee},Jee=rr.nonMaxSuppressionV3Impl;function Qee(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=Jee(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var ete={kernelName:nl,backendName:"webgl",kernelFunc:Qee},tte=rr.nonMaxSuppressionV4Impl;function nte(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:h,validOutputs:p}=tte(c,d,o,i,l,u);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var ste={kernelName:sl,backendName:"webgl",kernelFunc:nte},rte=rr.nonMaxSuppressionV5Impl;function ate(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),h=o,p=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=rte(c,d,h,p,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var ote={kernelName:rl,backendName:"webgl",kernelFunc:ate},ite=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${s}), float(${n}), float(index == coords.y))); } - `}},ite=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new ote(l,a,o,i),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let h=[...r.shape,a],p=ye({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),p},lte={kernelName:ro,backendName:"webgl",kernelFunc:ite};function $f(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ed({inputs:{input:s},backend:n}),a=$f({inputs:{x:r},backend:n}),o=Df({inputs:{input:s},backend:n}),i=$f({inputs:{x:o},backend:n}),l=pa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return td({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var ute={kernelName:kl,backendName:"webgl",kernelFunc:$f};function F4(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=ed({inputs:{input:s},backend:n}),a=F4({inputs:{x:r},backend:n}),o=Df({inputs:{input:s},backend:n}),i=$f({inputs:{x:o},backend:n}),l=pa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return td({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var cte={kernelName:al,backendName:"webgl",kernelFunc:F4};function dte(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return H2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=H2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=h4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var hte={kernelName:ol,backendName:"webgl",kernelFunc:dte},pte=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=ht(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=` + `}},lte=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new ite(l,a,o,i),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let h=[...r.shape,a],p=ye({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),p},ute={kernelName:ro,backendName:"webgl",kernelFunc:lte};function $f(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ed({inputs:{input:s},backend:n}),a=$f({inputs:{x:r},backend:n}),o=Df({inputs:{input:s},backend:n}),i=$f({inputs:{x:o},backend:n}),l=pa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return td({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var cte={kernelName:kl,backendName:"webgl",kernelFunc:$f};function F4(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=ed({inputs:{input:s},backend:n}),a=F4({inputs:{x:r},backend:n}),o=Df({inputs:{input:s},backend:n}),i=$f({inputs:{x:o},backend:n}),l=pa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return td({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var dte={kernelName:al,backendName:"webgl",kernelFunc:F4};function hte(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return G2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=G2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=h4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var pte={kernelName:ol,backendName:"webgl",kernelFunc:hte},fte=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=ht(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=` int start = ${a}; int end = ${o}; @@ -3759,7 +3759,7 @@ return a / b;`,Uee=` setOutput(getX(${i})); } } - `}},fte=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=ht(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=In("rc",s),l=In("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1; + `}},mte=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=ht(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=In("rc",s),l=In("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1; if(${u}) { `,s===1?"":`} rc = outputLoc; @@ -3783,7 +3783,7 @@ return a / b;`,Uee=` ${p} setOutput(result); } - `}},O4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return td({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fte(r.shape,a,o):new pte(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},mte={kernelName:ao,backendName:"webgl",kernelFunc:O4},gte=` + `}},O4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return td({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mte(r.shape,a,o):new fte(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},gte={kernelName:ao,backendName:"webgl",kernelFunc:O4},Ate=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -3792,7 +3792,7 @@ return a / b;`,Uee=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Ate=` +`,yte=` // 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); @@ -3808,9 +3808,9 @@ return a / b;`,Uee=` vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); `+Sf+` return result; -`,yte=ln({opSnippet:gte,packedOpSnippet:Ate}),xte={kernelName:oo,backendName:"webgl",kernelFunc:yte};function bte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=D.getAxesPermutation(c,i),h=r;d!=null&&(h=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=D.getInnerMostAxes(c.length,i),l.push(h)),D.assertAxesAreInnerMostDims("prod",c,i);let p;if(n.shouldExecuteOnCPU([h])){let f=n.texData.get(h.dataId).values,{outVals:m,outShape:g,outDtype:A}=EX(h.shape,h.dtype,f,c);p=n.makeTensorInfo(g,A,m)}else{let[f,m]=D.computeOutAndReduceShapes(h.shape,c),g=w.sizeFromShape(m),A=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,g]}}),y=Mh(r.dtype),x=si(A,y,"prod",n);p=ye({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(p);let f=D.expandShapeToKeepDim(p.shape,u);p=ye({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var vte={kernelName:il,backendName:"webgl",kernelFunc:bte},P4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=RX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},wte={kernelName:ju,backendName:"webgl",kernelFunc:P4},kte="return 1.0 / x;",Ite=Je({opSnippet:kte}),Ste={kernelName:ll,backendName:"webgl",kernelFunc:Ite},Cte=js+` +`,xte=ln({opSnippet:Ate,packedOpSnippet:yte}),bte={kernelName:oo,backendName:"webgl",kernelFunc:xte};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=D.getAxesPermutation(c,i),h=r;d!=null&&(h=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=D.getInnerMostAxes(c.length,i),l.push(h)),D.assertAxesAreInnerMostDims("prod",c,i);let p;if(n.shouldExecuteOnCPU([h])){let f=n.texData.get(h.dataId).values,{outVals:m,outShape:g,outDtype:A}=RX(h.shape,h.dtype,f,c);p=n.makeTensorInfo(g,A,m)}else{let[f,m]=D.computeOutAndReduceShapes(h.shape,c),g=w.sizeFromShape(m),A=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,g]}}),y=Mh(r.dtype),x=si(A,y,"prod",n);p=ye({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(p);let f=D.expandShapeToKeepDim(p.shape,u);p=ye({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var wte={kernelName:il,backendName:"webgl",kernelFunc:vte},P4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=_X(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},kte={kernelName:ju,backendName:"webgl",kernelFunc:P4},Ite="return 1.0 / x;",Ste=Je({opSnippet:Ite}),Cte={kernelName:ll,backendName:"webgl",kernelFunc:Ste},Tte=js+` return (x < 0.0) ? 0.0 : x; -`,Tte=` +`,Nte=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3820,9 +3820,9 @@ return a / b;`,Uee=` result.a = isNaN.a ? x.a : result.a; return result; -`,Nte=Je({opSnippet:Cte,packedOpSnippet:Tte}),Ete={kernelName:lo,backendName:"webgl",kernelFunc:Nte},Rte=js+` +`,Ete=Je({opSnippet:Tte,packedOpSnippet:Nte}),Rte={kernelName:lo,backendName:"webgl",kernelFunc:Ete},_te=js+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,_te=` +`,Dte=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -3832,7 +3832,7 @@ return a / b;`,Uee=` result.a = isNaN.a ? x.a : result.a; return result; -`,Dte=Je({opSnippet:Rte,packedOpSnippet:_te}),$te={kernelName:co,backendName:"webgl",kernelFunc:Dte},Fte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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=` +`,$te=Je({opSnippet:_te,packedOpSnippet:Dte}),Fte={kernelName:co,backendName:"webgl",kernelFunc:$te},Ote=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -3865,7 +3865,7 @@ return a / b;`,Uee=` setOutput(newValue); } - `}},Ote=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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=` + `}},Pte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[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( ${u[0]/c[0]}, ${u[1]/c[1]}, @@ -3942,7 +3942,7 @@ return a / b;`,Uee=` setOutput(newValue); } - `}};function Pte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ote(r.shape,l,u,a,o):new Fte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var Mte={kernelName:uo,backendName:"webgl",kernelFunc:Pte},zte=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],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=` + `}};function Mte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Pte(r.shape,l,u,a,o):new Ote(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var zte={kernelName:uo,backendName:"webgl",kernelFunc:Mte},Lte=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],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4023,7 +4023,7 @@ return a / b;`,Uee=` setOutput(accumulator); } - `}};function Lte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new zte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Bte={kernelName:vh,backendName:"webgl",kernelFunc:Lte},Wte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function Bte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Lte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Wte={kernelName:vh,backendName:"webgl",kernelFunc:Bte},Vte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); @@ -4045,7 +4045,7 @@ return a / b;`,Uee=` setOutput(newValue); } - `}},Vte=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},Ute=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 u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",h;r?h="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, @@ -4086,7 +4086,7 @@ return a / b;`,Uee=` setOutput(newValue); } - `}};function Ute(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vte(r.shape,l,u,a,o):new Wte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var Hte={kernelName:qu,backendName:"webgl",kernelFunc:Ute},Gte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=` + `}};function Hte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ute(r.shape,l,u,a,o):new Vte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var Gte={kernelName:qu,backendName:"webgl",kernelFunc:Hte},jte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,h=1/c,p=Math.ceil(d)*2+2,f=Math.ceil(h)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4156,7 +4156,7 @@ return a / b;`,Uee=` setOutput(accumulator); } - `}};function jte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Gte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var qte={kernelName:bh,backendName:"webgl",kernelFunc:jte},Xte=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function qte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new jte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Xte={kernelName:bh,backendName:"webgl",kernelFunc:qte},Kte=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)); @@ -4166,7 +4166,7 @@ return a / b;`,Uee=` ${a} coords = getOutputCoords(); setOutput(getX(${r})); } - `}},Kte=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-${n} tensor is not yet supported`);this.outputShape=e;let s=In("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ht(n);n===1?this.userCode=` + `}},Zte=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=In("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ht(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4194,7 +4194,7 @@ return a / b;`,Uee=` } setOutput(result); } - `;function i(p){return d(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",d(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",d(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",d(p)}function d(p){let f=e.map((A,y)=>h(y,p)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function h(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Zte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Yn({inputs:{x:r},backend:n});let l=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kte(r.shape,i):new Xte(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Yte={kernelName:ho,backendName:"webgl",kernelFunc:Zte},Jte=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 = ${t.toFixed(2)};`:r=` + `;function i(p){return d(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",d(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",d(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",d(p)}function d(p){let f=e.map((A,y)=>h(y,p)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function h(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Yte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Yn({inputs:{x:r},backend:n});let l=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zte(r.shape,i):new Kte(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Jte={kernelName:ho,backendName:"webgl",kernelFunc:Yte},Qte=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() { @@ -4213,7 +4213,7 @@ return a / b;`,Uee=` } setOutput(outputValue); } - `}},Qte={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Jte(s.shape,a),[u,c]=D.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},ene=` + `}},ene={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Qte(s.shape,a),[u,c]=D.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},tne=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4228,7 +4228,7 @@ return a / b;`,Uee=` return base + 1.0; } } -`,tne=Je({opSnippet:ene}),nne={kernelName:po,backendName:"webgl",kernelFunc:tne},sne="return inversesqrt(x);",rne=Je({opSnippet:sne,cpuKernelImpl:_X}),ane={kernelName:fo,backendName:"webgl",kernelFunc:rne},M4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=ht(r.length),l=ht(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,p=t>1?"strides[j]":"strides";this.userCode=` +`,nne=Je({opSnippet:tne}),sne={kernelName:po,backendName:"webgl",kernelFunc:nne},rne="return inversesqrt(x);",ane=Je({opSnippet:rne,cpuKernelImpl:DX}),one={kernelName:fo,backendName:"webgl",kernelFunc:ane},M4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=ht(r.length),l=ht(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,p=t>1?"strides[j]":"strides";this.userCode=` ${i} strides = ${i}(${r}); void main() { @@ -4248,7 +4248,7 @@ return a / b;`,Uee=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function one(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=D.calculateShapes(a,r,o),h=[d/u,u];if(d===0)return n.makeTensorInfo(o,r.dtype);let p=ye({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ye({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new M4(l,i,p.shape.length,f.shape.length,c,h),A=n.runWebGLProgram(g,[f,p,m],f.dtype),y=ye({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),y}var ine={kernelName:cl,backendName:"webgl",kernelFunc:one},lne=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 u=0;u4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,hne=Je({opSnippet:dne}),pne={kernelName:hl,backendName:"webgl",kernelFunc:hne},z4="return 1.0 / (1.0 + exp(-1.0 * x));",fne=Je({opSnippet:z4,packedOpSnippet:z4,cpuKernelImpl:DX}),mne={kernelName:go,backendName:"webgl",kernelFunc:fne},gne=` +`,pne=Je({opSnippet:hne}),fne={kernelName:hl,backendName:"webgl",kernelFunc:pne},z4="return 1.0 / (1.0 + exp(-1.0 * x));",mne=Je({opSnippet:z4,packedOpSnippet:z4,cpuKernelImpl:$X}),gne={kernelName:go,backendName:"webgl",kernelFunc:mne},Ane=` if (isnan(x)) { return 0.0; } return sign(x); -`,Ane=Je({opSnippet:gne}),yne={kernelName:ml,backendName:"webgl",kernelFunc:Ane},xne=Y6+` +`,yne=Je({opSnippet:Ane}),xne={kernelName:ml,backendName:"webgl",kernelFunc:yne},bne=Y6+` return sin(x); -`,bne=Je({opSnippet:xne}),vne={kernelName:mo,backendName:"webgl",kernelFunc:bne},wne=` +`,vne=Je({opSnippet:bne}),wne={kernelName:mo,backendName:"webgl",kernelFunc:vne},kne=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,kne=Je({opSnippet:wne}),Ine={kernelName:fl,backendName:"webgl",kernelFunc:kne},Sne=` +`,Ine=Je({opSnippet:kne}),Sne={kernelName:fl,backendName:"webgl",kernelFunc:Ine},Cne=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4292,17 +4292,17 @@ return a / b;`,Uee=` result = log(exp_x + 1.0); } return result; -`,Cne=Je({opSnippet:Sne}),Tne={kernelName:gl,backendName:"webgl",kernelFunc:Cne},Nne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;An.disposeIntermediateTensorInfo(A)),g},Ene={kernelName:Al,backendName:"webgl",kernelFunc:Nne};function Rne(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: +`,Tne=Je({opSnippet:Cne}),Nne={kernelName:gl,backendName:"webgl",kernelFunc:Tne},Ene=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;An.disposeIntermediateTensorInfo(A)),g},Rne={kernelName:Al,backendName:"webgl",kernelFunc:Ene};function _ne(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,h,p,f,m]=FX(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(h,s.dtype,d),n.makeTensorInfo([h[0]],r.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var _ne={kernelName:wh,backendName:"webgl",kernelFunc:Rne};function Dne(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=OX(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var $ne={kernelName:kh,backendName:"webgl",kernelFunc:Dne};function Fne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,h,p,f,m]=OX(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(h,s.dtype,d),n.makeTensorInfo([h[0]],r.dtype,p),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Dne={kernelName:wh,backendName:"webgl",kernelFunc:_ne};function $ne(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=PX(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Fne={kernelName:kh,backendName:"webgl",kernelFunc:$ne};function One(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z6(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var One={kernelName:Ih,backendName:"webgl",kernelFunc:Fne};function Pne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z6(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Pne={kernelName:Ih,backendName:"webgl",kernelFunc:One};function Mne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z6(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Mne={kernelName:Sh,backendName:"webgl",kernelFunc:Pne};function zne(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=D.calculateShapes(a,r,i),h=!1,p=new M4(u,l,r.shape.length,a.shape.length,c,[d,1],h),f=n.runWebGLProgram(p,[a,r,o],a.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Lne={kernelName:Ch,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=D.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(h=>{let p=[...d];p[i]=h;let f=du({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=h,f})}var Wne={kernelName:yl,backendName:"webgl",kernelFunc:Bne},L4="return sqrt(x);",Vne=Je({opSnippet:L4,packedOpSnippet:L4,cpuKernelImpl:PX}),Une={kernelName:Ao,backendName:"webgl",kernelFunc:Vne},Hne="return x * x;",Gne=Je({opSnippet:Hne}),jne={kernelName:Xu,backendName:"webgl",kernelFunc:Gne},B4="return (a - b) * (a - b);",qne=ln({opSnippet:B4,packedOpSnippet:B4}),Xne={kernelName:bo,backendName:"webgl",kernelFunc:qne};function Kne({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=js+` + ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=z6(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var zne={kernelName:Sh,backendName:"webgl",kernelFunc:Mne};function Lne(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=D.calculateShapes(a,r,i),h=!1,p=new M4(u,l,r.shape.length,a.shape.length,c,[d,1],h),f=n.runWebGLProgram(p,[a,r,o],a.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Bne={kernelName:Ch,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=D.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(h=>{let p=[...d];p[i]=h;let f=du({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=h,f})}var Vne={kernelName:yl,backendName:"webgl",kernelFunc:Wne},L4="return sqrt(x);",Une=Je({opSnippet:L4,packedOpSnippet:L4,cpuKernelImpl:MX}),Hne={kernelName:Ao,backendName:"webgl",kernelFunc:Une},Gne="return x * x;",jne=Je({opSnippet:Gne}),qne={kernelName:Xu,backendName:"webgl",kernelFunc:jne},B4="return (a - b) * (a - b);",Xne=ln({opSnippet:B4,packedOpSnippet:B4}),Kne={kernelName:bo,backendName:"webgl",kernelFunc:Xne};function Zne({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=js+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,a=new ha(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Zne={kernelName:Gr,backendName:"webgl",kernelFunc:Kne},Yne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ht(n.length),a=ht(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,a=new ha(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Yne={kernelName:Gr,backendName:"webgl",kernelFunc:Zne},Jne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ht(n.length),a=ht(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); @@ -4310,15 +4310,15 @@ return a / b;`,Uee=` ${a} coords = getOutputCoords(); setOutput(getX(${o})); } - `}};function Jne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,{nonStrided:p,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=xn.sliceInfo(r.shape,a,o,i,l,u,c,d,h),x=ye({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(p){let k=du({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ye({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,_=We(x.shape,x.dtype,C),O=MX(y,_,m,f);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new Yne(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ye({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Qne={kernelName:xl,backendName:"webgl",kernelFunc:Jne};function ese(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,h=n.readSync(c.dataId),p=n.readSync(d.dataId),[f,m]=zX(h,p,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var tse={kernelName:Th,backendName:"webgl",kernelFunc:ese};function nse(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=LX(i,l,r),h=c.length;return[n.makeTensorInfo([h,2],"int32",u),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var sse={kernelName:Nh,backendName:"webgl",kernelFunc:nse};function rse(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=BX(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var ase={kernelName:Eh,backendName:"webgl",kernelFunc:rse},ose="return tan(x);",ise=Je({opSnippet:ose}),lse={kernelName:wo,backendName:"webgl",kernelFunc:ise},use=` + `}};function Qne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,{nonStrided:p,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=xn.sliceInfo(r.shape,a,o,i,l,u,c,d,h),x=ye({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(p){let k=du({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ye({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,_=We(x.shape,x.dtype,C),O=zX(y,_,m,f);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new Jne(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ye({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var ese={kernelName:xl,backendName:"webgl",kernelFunc:Qne};function tse(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,h=n.readSync(c.dataId),p=n.readSync(d.dataId),[f,m]=LX(h,p,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var nse={kernelName:Th,backendName:"webgl",kernelFunc:tse};function sse(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype 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-`,cse=Je({opSnippet:use}),dse={kernelName:ko,backendName:"webgl",kernelFunc:cse},hse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(h=>w.decodeString(h)):l,c=We(r.shape,r.dtype,u),d=VX(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new hse(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var fse={kernelName:Hr,backendName:"webgl",kernelFunc:W4},mse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function fse(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(h=>w.decodeString(h)):l,c=We(r.shape,r.dtype,u),d=UX(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new pse(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var mse={kernelName:Hr,backendName:"webgl",kernelFunc:W4},gse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4358,7 +4358,7 @@ return a / b;`,Uee=` setOutput(float(i1)); } } - `}},gse=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=` + `}},Ase=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(); @@ -4392,7 +4392,7 @@ return a / b;`,Uee=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function ri(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function V4(e){let t=1;for(;tl){let O=n.readSync(r.dataId),[E,R]=UX(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,td({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),h=d!==null&&d.isPacked,p=h?n.unpackTensor(r):r,m=w.sizeFromShape(u)/c,g=ye({inputs:{x:p},attrs:{shape:[m,c]},backend:n});h&&ri(n,p);let A=V4(a),y=V4(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new mse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),ri(n,q)};for(let O=1;O=1;R/=2)v(E,R,[m,y])}for(let O=y;O>A;O/=2){let E=b(),R=new gse([m,O/2]),P=[[c],[x===null?1:0],[A]],V=x;x=n.runWebGLProgram(R,E,"int32",P),ri(n,V);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=du({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),ri(n,k);let S=T4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});ri(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=ye({inputs:{x},attrs:{shape:C},backend:n}),ri(n,k);let _=S;return S=ye({inputs:{x:S},attrs:{shape:C},backend:n}),ri(n,_),[S,x]}var yse={kernelName:bl,backendName:"webgl",kernelFunc:Ase},xse=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=` + `}};function ri(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function V4(e){let t=1;for(;tl){let O=n.readSync(r.dataId),[E,R]=HX(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,td({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),h=d!==null&&d.isPacked,p=h?n.unpackTensor(r):r,m=w.sizeFromShape(u)/c,g=ye({inputs:{x:p},attrs:{shape:[m,c]},backend:n});h&&ri(n,p);let A=V4(a),y=V4(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new gse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),ri(n,q)};for(let O=1;O=1;R/=2)v(E,R,[m,y])}for(let O=y;O>A;O/=2){let E=b(),R=new Ase([m,O/2]),P=[[c],[x===null?1:0],[A]],V=x;x=n.runWebGLProgram(R,E,"int32",P),ri(n,V);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=du({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),ri(n,k);let S=T4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});ri(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=ye({inputs:{x},attrs:{shape:C},backend:n}),ri(n,k);let _=S;return S=ye({inputs:{x:S},attrs:{shape:C},backend:n}),ri(n,_),[S,x]}var xse={kernelName:bl,backendName:"webgl",kernelFunc:yse},bse=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) { @@ -4504,7 +4504,7 @@ return a / b;`,Uee=` } setOutput(outputValue); } - `}};function bse(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,h,p]=r.shape,[f,m]=u!=null?u:[d,h],g=[c,f,m,p],A=new xse(d,h,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var vse={kernelName:vl,backendName:"webgl",kernelFunc:bse};function wse(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;su(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:u}=HX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var kse={kernelName:Rh,backendName:"webgl",kernelFunc:wse};function Ise(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],u=new Array(i-1),c=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var Sse={kernelName:wl,backendName:"webgl",kernelFunc:Ise},Cse=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",u=Math.floor(n/4)*4,c=n%4,d=` + `}};function vse(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,h,p]=r.shape,[f,m]=u!=null?u:[d,h],g=[c,f,m,p],A=new bse(d,h,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var wse={kernelName:vl,backendName:"webgl",kernelFunc:vse};function kse(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;su(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:u}=GX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Ise={kernelName:Rh,backendName:"webgl",kernelFunc:kse};function Sse(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],u=new Array(i-1),c=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var Cse={kernelName:wl,backendName:"webgl",kernelFunc:Sse},Tse=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",u=Math.floor(n/4)*4,c=n%4,d=` sumValue += dot(values, segFilter); `,h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { @@ -4610,14 +4610,14 @@ return a / b;`,Uee=` } setOutput(${l}); } - `}};function Tse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=D.getAxesPermutation([u],i),d=r;c!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=D.getInnerMostAxes(1,i)[0]);let h=D.segment_util.computeOutShape(d.shape,u,o),p=w.sizeFromShape([d.shape[u]]),f=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=Mh(r.dtype),g=(b,v,k,S,C)=>{let _=b.shape[0],O=b.shape[1],E=D.segment_util.segOpComputeOptimalWindowSize(O,C),R={windowSize:E,inSize:O,batchSize:_,numSegments:C},T=new Cse(R,v),P=n.compileAndRun(T,[b,k],S);if(l.push(P),P.shape[1]===C)return P;let V=P4({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=W4({inputs:{x:V},backend:n,attrs:{reps:[O/E]}});return l.push(V),l.push(j),g(P,v,j,S,C)},A=g(f,"unsortedSegmentSum",a,m,o),y=ye({inputs:{x:A},backend:n,attrs:{shape:h}}),x=y;if(c!=null){l.push(y);let b=D.getUndoAxesPermutation(c);x=Sn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Nse={kernelName:Ku,backendName:"webgl",kernelFunc:Tse},Ese=[see,oee,UK,GK,XK,YK,QK,nZ,rZ,oZ,cZ,hZ,mZ,yZ,SZ,vZ,NZ,DZ,RZ,PZ,zZ,BZ,HZ,YZ,QZ,aY,iY,dY,fY,IK,xY,EY,_Y,kY,OY,MY,$Y,BY,UY,jY,XY,ZY,QY,aJ,iJ,tJ,cJ,pJ,mJ,xJ,kJ,TJ,RJ,_J,DJ,FJ,PJ,zJ,BJ,VJ,jJ,KJ,JJ,eQ,sQ,oQ,cQ,fQ,kK,gQ,AY,xQ,wQ,SQ,CK,EQ,$Q,OQ,VQ,LQ,jQ,KQ,QQ,lee,gee,fee,bee,wee,Iee,hee,Cee,Nee,Dee,Pee,Bee,Xee,_K,Zee,Qee,nte,ate,tY,lte,cte,hte,mte,xte,NK,vte,wte,nY,Hee,Ste,$te,Ete,$K,Mte,Bte,Hte,qte,Yte,Qte,nne,ane,ine,cne,pne,mne,yne,vne,Ine,KZ,jee,Tne,Ene,_ne,$ne,One,Mne,Lne,Wne,Une,jne,Xne,Zne,Qne,tse,sse,ase,Gee,BK,lse,dse,fse,yse,vse,WK,kse,Sse,Nse,ute];for(let e of Ese)No(e);var Pn;(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"})(Pn||(Pn={}));var nd;(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"})(nd||(nd={}));var U4;function Rse(e){U4=e.wasm.cwrap(So,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function _se(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:u,activation:c,leakyreluAlpha:d}=s,h=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=nd[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return U4(h,k,r.shape.length,p,S,a.shape.length,l,u,g,f,m,d||0,v),b}var Dse={kernelName:So,backendName:"wasm",setupFunc:Rse,kernelFunc:_se};function un(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var $se=un(Ii);function Cn(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:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,h=i.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,f=D.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,p);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,u.shape.length,h,A,c.shape.length,Pn[u.dtype],y);if(t&&u.dtype==="float32")return x(),m;let b=D.getBroadcastDims(u.shape,f),v=D.getBroadcastDims(c.shape,f),k=b.every((C,_)=>C===_),S=v.every((C,_)=>C===_);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Fse=!0,Ose=Cn(Vr,Fse),H4;function Pse(e){H4=e.wasm.cwrap(Na,null,["array","number","number","number"])}function Mse(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.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 H4(a,r.length,Pn[s.dtype],o),s}var zse={kernelName:Na,backendName:"wasm",setupFunc:Pse,kernelFunc:Mse};function Ff(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 Lse={kernelName:qa,backendName:"wasm",kernelFunc:Ff},G4;function Bse(e){G4=e.wasm.cwrap(Io,null,["number","array","number","number","number","array","number"])}function fu(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Vse(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 Use={kernelName:Io,backendName:"wasm",kernelFunc:fu,setupFunc:Bse};function fa(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=D.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let p=0;p`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 nre={kernelName:ul,backendName:"wasm",kernelFunc:Mn},Z4;function sre(e){Z4=e.wasm.cwrap(_a,null,["number","array","number","number","array","number","number","number","number"])}function rre(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,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],h=o?r.shape[l-1]:r.shape[l-2],p=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),A=w.sizeFromShape(m),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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sd(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=xn.parseSliceParams(t,n,s),i=xn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let f=xn.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=cf(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let h=r.typedArrayFromHeap(u),p=t.shape.length;if(p===2)ore(l,c[0],h,a,o);else if(p===3)ire(l,c[0],c[1],h,a,o);else if(p===4)lre(l,c[0],c[1],c[2],h,a,o);else{let f=cf(l,a,o,t.shape,t.dtype);h.set(f)}return u}function ore(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let 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mre(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),u=n.dataIdMap.get(l.dataId).id;return Y4(i,a,o,u),l}var gre={kernelName:Ur,backendName:"wasm",setupFunc:fre,kernelFunc:mre};function J4(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=D.computeOutShape(t.map(p=>p.shape),s),a=t.filter(p=>w.sizeFromShape(p.shape)>0);if(a.length===1)return Ff({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(p=>p.shape);if(D.assertParamsConsistent(i,s),a[0].dtype==="string"){let p=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return Mn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=D.computeOutShape(p.map(x=>x.shape),1);let m=p[0].shape[0]===1,g=p2(f,r,t[0].dtype,m),A=D.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=D.fromStringArrayToUint8(g),p.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(p=>{let f=w.sizeFromShape(p.shape.slice(s));return u+=f,f}),d=a.map(p=>n.typedArrayFromHeap(p)),h=n.typedArrayFromHeap(o);for(let p=0;p`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=D.getAxesPermutation([a],l),c=r;u!==null&&(c=fu({inputs:{x:r},attrs:{perm:u},backend:n}));let d=D.getInnerMostAxes(1,l)[0];D.assertAxesAreInnerMostDims("cumsum",[d],l);let h=n.makeOutput(c.shape,c.dtype),p=c.shape[d],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(h.dataId).id;nk(f,o?1:0,i?1:0,p,m,Pn[r.dtype]);let g=h;if(u!==null){let A=D.getUndoAxesPermutation(u);g=fu({inputs:{x:h},attrs:{perm:A},backend:n}),n.disposeData(c.dataId),n.disposeData(h.dataId)}return g}var _re={kernelName:za,backendName:"wasm",setupFunc:Ere,kernelFunc:Rre},sk;function Dre(e){sk=e.wasm.cwrap(Mi,null,["number","number","number","array","number","array","array","number","number"])}function $re(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return sk(A,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,f.length,v),m}var Fre={kernelName:Mi,backendName:"wasm",setupFunc:Dre,kernelFunc:$re},rk;function Ore(e){rk=e.wasm.cwrap(La,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pre(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:u,pad:c,dimRoundingMode:d}=n,h=u==null?[1,1]:u,p=D.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),f=p.filterHeight,m=p.filterWidth,g=p.padInfo.top,A=p.padInfo.right,y=p.padInfo.bottom,x=p.padInfo.left,b=p.dilationHeight,v=p.dilationWidth,k=p.strideHeight,S=p.strideWidth,C=p.inChannels,_=p.outChannels,O=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. 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Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(te.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return lk(A,q,X,ee,y,v,k,b,S,C,_,O,j,E,R,T,P,V,x,g,se,f||0,ne),te}var tae={kernelName:Co,backendName:"wasm",setupFunc:Qre,kernelFunc:eae},uk;function nae(e){uk=e.wasm.cwrap(To,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:h,activation:p,leakyreluAlpha:f}=n,m=D.computeConv2DInfo(r.shape,a.shape,l,c,u,h,!0),g=nd[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${x})`);b=J.id}let v=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,C=m.padInfo.right,_=m.padInfo.bottom,O=m.padInfo.left,E=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,P=m.strideWidth,V=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. 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b=D.expandShapeToKeepDim(x.shape,h);x.shape=b}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Oae={kernelName:Qa,backendName:"wasm",setupFunc:$ae,kernelFunc:Fae},gk;function Pae(e){gk=e.wasm.cwrap(eo,null,["number, number, number"])}function Mae(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,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=fa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;D.assertAxesAreInnerMostDims("min",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;gk(l,A,x)}if(p&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var zae={kernelName:eo,backendName:"wasm",setupFunc:Pae,kernelFunc:Mae},Lae=!1,Bae=Cn(to,Lae),q2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(q2||(q2={}));var Ak;function Wae(e){Ak=e.wasm.cwrap(no,null,["number","array","number","number","array","array","number","number"])}function Vae(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,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return Ak(o,u,t.shape.length,Pn[t.dtype],h,p,q2[r],l),i}var Uae={kernelName:no,backendName:"wasm",kernelFunc:Vae,setupFunc:Wae},Hae=!0,Gae=Cn(so,Hae),jae=un(el);function X2(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 yk;function qae(e){yk=e.wasm.cwrap(nl,"number",["number","number","number","number","number"])}function 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Qae(e){bk=e.wasm.cwrap(rl,"number",["number","number","number","number","number","number"])}function eoe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,h=bk(c,d,a,r,o,i),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=X2(t,h);t.wasm._free(g);let A=t.makeOutput([f],"int32",p),y=t.makeOutput([f],"float32",m);return[A,y]}var toe={kernelName:rl,backendName:"wasm",setupFunc:Qae,kernelFunc:eoe},noe=!1,soe=Cn(tl,noe,"bool"),vk;function roe(e){vk=e.wasm.cwrap(ro,null,["number","number","number","number","number"])}function aoe(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"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return vk(d,a,o,i,u),l}var ooe={kernelName:ro,backendName:"wasm",setupFunc:roe,kernelFunc:aoe};function 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hoe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return ak({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),h=s.map(m=>m[1]),p=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(h).buffer);return wk(o,c,t.shape.length,Pn[t.dtype],p,f,r,u),i}var kk={kernelName:ao,backendName:"wasm",kernelFunc:hoe,setupFunc:doe},poe=!1,foe=Cn(oo,poe),Ik;function moe(e){Ik=e.wasm.cwrap(io,null,["number","number","number"])}function goe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=n.makeOutput(s.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return Ik(a,o,l),i}var Aoe={kernelName:io,backendName:"wasm",setupFunc:moe,kernelFunc:goe},Sk;function 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iie={kernelName:Gr,backendName:"wasm",setupFunc:aie,kernelFunc:oie},Fk;function lie(e){Fk=e.wasm.cwrap(xl,null,["number","array","number","array","array","array","array","array","number","number"])}function uie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,p=D.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=D.slice_util.maskToAxes(d),g=r.shape.slice();m.forEach(E=>{a[E]=0,o[E]=1,g.splice(E,0,1)});let A=Mn({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:y,end:x,strides:b}=D.slice_util.getNormalizedAxes(A.shape,p,f,a,o,i,l,u,c);a=y,o=x,i=b;let 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qie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Kie=[33,133,362,263,1,78,308],jle=qie.map(e=>dd[e]),qle=Xie.map(e=>dd[e]),Xle=Kie.map(e=>dd[e]);var ey=hr.leftEyeLower0,ty=hr.rightEyeLower0,mu={leftBounds:[ey[0],ey[ey.length-1]],rightBounds:[ty[0],ty[ty.length-1]]},e8={count:468,mouth:13,symmetryLine:[13,hr.midwayBetweenEyes[0]]},Zie={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},gu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Bf(e,t,n,s){for(let r=0;r[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?J2(s,[0,0]):Lf,l=s!==0?o.map(d=>[...Kk(d,i),d[2]]):o,u=s!==0?Xk(r):Lf,c=[...ud({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+ma(c,u[0])),Math.round(d[1]+ma(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[mu.leftBounds[0]][2],s=t[mu.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let o=zf(Mf(Y2([t[s],t[r]]),this.irisEnlarge)),i=ld(o),l=_e.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(a&&os.flags.IS_BROWSER){let u=_e.flipLeftRight(l);Z(l),l=u}return{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,s,r=!1){let a=[];for(let o=0;o{let u=o;return l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=e8.count?e8.symmetryLine:Zie.symmetryLine,o=Gk(n.landmarks[r],n.landmarks[a]),i=ud({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=_e.rotateWithOffset(s,o,0,l),c=J2(-o,i),d=t.face.mesh.enabled?cd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):cd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),h=de(d,255);return Z(d),Z(u),[o,c,h]}async augmentIris(t,n){let{box:s,boxSize:r,crop:a}=this.getEyeBox(t,n,mu.leftBounds[0],mu.leftBounds[1],!0),{box:o,boxSize:i,crop:l}=this.getEyeBox(t,n,mu.rightBounds[0],mu.rightBounds[1]),u=ft([a,l]);Z(a),Z(l);let c=this.irisModel.predict(u);Z(u);let d=await c.data();Z(c);let 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n)Z(o);let r=await f8(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Jn.inputs[0].shape?c8(r,[e.shape[1],e.shape[2]],[Jn.inputs[0].shape[2],Jn.inputs[0].shape[1]]):[]}async function by(e){return Jn?e.debug&&ue("cached model:",Jn.modelUrl):(Jn=await mt(gt(e.modelBasePath,e.body.modelPath)),!Jn||!Jn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Jn.modelUrl)),Jn}function Hf(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function fd(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function m8(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 g8(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 Gf(e,t=1.5){let n=fd(e),s=Hf(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 jf(e){let t=fd(e),n=Hf(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}}var 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_e.nonMaxSuppressionAsync(s.norm,s.scores,10*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=Re(s.norm,[i,0],[1,-1]),u=H(()=>U(this.normalizeLandmarks(Re(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:u,confidence:r[i]})}for(let i of Object.keys(s))Z(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>ge(de(_e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Z(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),h=await l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(g8({startPoint:c,endPoint:d,palmLandmarks:h,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function ole(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function y8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ole(n)}var x8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ga(e,t){let n=0;for(let s=0;so[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=>ky([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Gf(jf(r),lle)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Gf(jf(n),w8);s.palmLandmarks=[];for(let r=0;r[o[0]*(p[0]-this.inputSize/2),o[1]*(p[1]-this.inputSize/2),o[2]*p[2]]),l=wy(s,[0,0]),u=i.map(p=>[...ky(p,l),p[2]]),c=v8(r),d=[...fd(n),1],h=[ga(d,c[0]),ga(d,c[1])];return u.map(p=>[Math.trunc(p[0]+h[0]),Math.trunc(p[1]+h[1]),Math.trunc(p[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await 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gre(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),u=n.dataIdMap.get(l.dataId).id;return Y4(i,a,o,u),l}var Are={kernelName:Ur,backendName:"wasm",setupFunc:mre,kernelFunc:gre};function J4(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=D.computeOutShape(t.map(p=>p.shape),s),a=t.filter(p=>w.sizeFromShape(p.shape)>0);if(a.length===1)return Ff({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(p=>p.shape);if(D.assertParamsConsistent(i,s),a[0].dtype==="string"){let p=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return Mn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=D.computeOutShape(p.map(x=>x.shape),1);let m=p[0].shape[0]===1,g=f2(f,r,t[0].dtype,m),A=D.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=D.fromStringArrayToUint8(g),p.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(p=>{let f=w.sizeFromShape(p.shape.slice(s));return u+=f,f}),d=a.map(p=>n.typedArrayFromHeap(p)),h=n.typedArrayFromHeap(o);for(let p=0;p`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=D.getAxesPermutation([a],l),c=r;u!==null&&(c=fu({inputs:{x:r},attrs:{perm:u},backend:n}));let d=D.getInnerMostAxes(1,l)[0];D.assertAxesAreInnerMostDims("cumsum",[d],l);let h=n.makeOutput(c.shape,c.dtype),p=c.shape[d],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(h.dataId).id;nk(f,o?1:0,i?1:0,p,m,Pn[r.dtype]);let g=h;if(u!==null){let A=D.getUndoAxesPermutation(u);g=fu({inputs:{x:h},attrs:{perm:A},backend:n}),n.disposeData(c.dataId),n.disposeData(h.dataId)}return g}var Dre={kernelName:za,backendName:"wasm",setupFunc:Rre,kernelFunc:_re},sk;function $re(e){sk=e.wasm.cwrap(Mi,null,["number","number","number","array","number","array","array","number","number"])}function Fre(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,h=u*a,p=c/(a*a),f=o==="NHWC"?[i,d,h,p]:[i,p,d,h],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return sk(A,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,f.length,v),m}var Ore={kernelName:Mi,backendName:"wasm",setupFunc:$re,kernelFunc:Fre},rk;function Pre(e){rk=e.wasm.cwrap(La,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mre(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:u,pad:c,dimRoundingMode:d}=n,h=u==null?[1,1]:u,p=D.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),f=p.filterHeight,m=p.filterWidth,g=p.padInfo.top,A=p.padInfo.right,y=p.padInfo.bottom,x=p.padInfo.left,b=p.dilationHeight,v=p.dilationWidth,k=p.strideHeight,S=p.strideWidth,C=p.inChannels,_=p.outChannels,O=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. 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b=D.expandShapeToKeepDim(x.shape,h);x.shape=b}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Pae={kernelName:Qa,backendName:"wasm",setupFunc:Fae,kernelFunc:Oae},gk;function Mae(e){gk=e.wasm.cwrap(eo,null,["number, number, number"])}function zae(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,u=o,{transposed:c,axes:d,originalAxes:h,inputWasTransposed:p}=fa(o,r,t);if(p){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;D.assertAxesAreInnerMostDims("min",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;gk(l,A,x)}if(p&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,h);y.shape=x}return y}var Lae={kernelName:eo,backendName:"wasm",setupFunc:Mae,kernelFunc:zae},Bae=!1,Wae=Cn(to,Bae),X2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(X2||(X2={}));var Ak;function Vae(e){Ak=e.wasm.cwrap(no,null,["number","array","number","number","array","array","number","number"])}function Uae(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,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),h=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(d).buffer);return Ak(o,u,t.shape.length,Pn[t.dtype],h,p,X2[r],l),i}var Hae={kernelName:no,backendName:"wasm",kernelFunc:Uae,setupFunc:Vae},Gae=!0,jae=Cn(so,Gae),qae=un(el);function K2(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 yk;function Xae(e){yk=e.wasm.cwrap(nl,"number",["number","number","number","number","number"])}function 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lie={kernelName:Gr,backendName:"wasm",setupFunc:oie,kernelFunc:iie},Fk;function uie(e){Fk=e.wasm.cwrap(xl,null,["number","array","number","array","array","array","array","array","number","number"])}function cie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=s,p=D.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=D.slice_util.maskToAxes(d),g=r.shape.slice();m.forEach(E=>{a[E]=0,o[E]=1,g.splice(E,0,1)});let A=Mn({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:y,end:x,strides:b}=D.slice_util.getNormalizedAxes(A.shape,p,f,a,o,i,l,u,c);a=y,o=x,i=b;let 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Xie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Kie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Zie=[33,133,362,263,1,78,308],qle=Xie.map(e=>dd[e]),Xle=Kie.map(e=>dd[e]),Kle=Zie.map(e=>dd[e]);var ty=hr.leftEyeLower0,ny=hr.rightEyeLower0,mu={leftBounds:[ty[0],ty[ty.length-1]],rightBounds:[ny[0],ny[ny.length-1]]},e8={count:468,mouth:13,symmetryLine:[13,hr.midwayBetweenEyes[0]]},Yie={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},gu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Bf(e,t,n,s){for(let r=0;r[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?Q2(s,[0,0]):Lf,l=s!==0?o.map(d=>[...Kk(d,i),d[2]]):o,u=s!==0?Xk(r):Lf,c=[...ud({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+ma(c,u[0])),Math.round(d[1]+ma(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[mu.leftBounds[0]][2],s=t[mu.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let o=zf(Mf(J2([t[s],t[r]]),this.irisEnlarge)),i=ld(o),l=_e.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(a&&os.flags.IS_BROWSER){let u=_e.flipLeftRight(l);Z(l),l=u}return{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,s,r=!1){let a=[];for(let o=0;o{let u=o;return l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=e8.count?e8.symmetryLine:Yie.symmetryLine,o=Gk(n.landmarks[r],n.landmarks[a]),i=ud({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=_e.rotateWithOffset(s,o,0,l),c=Q2(-o,i),d=t.face.mesh.enabled?cd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):cd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),h=de(d,255);return Z(d),Z(u),[o,c,h]}async augmentIris(t,n){let{box:s,boxSize:r,crop:a}=this.getEyeBox(t,n,mu.leftBounds[0],mu.leftBounds[1],!0),{box:o,boxSize:i,crop:l}=this.getEyeBox(t,n,mu.rightBounds[0],mu.rightBounds[1]),u=ft([a,l]);Z(a),Z(l);let c=this.irisModel.predict(u);Z(u);let d=await c.data();Z(c);let 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n)Z(o);let r=await f8(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Jn.inputs[0].shape?c8(r,[e.shape[1],e.shape[2]],[Jn.inputs[0].shape[2],Jn.inputs[0].shape[1]]):[]}async function vy(e){return Jn?e.debug&&ue("cached model:",Jn.modelUrl):(Jn=await mt(gt(e.modelBasePath,e.body.modelPath)),!Jn||!Jn.modelUrl?ue("load model failed:",e.body.modelPath):e.debug&&ue("load model:",Jn.modelUrl)),Jn}function Hf(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function fd(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function m8(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 g8(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 Gf(e,t=1.5){let n=fd(e),s=Hf(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 jf(e){let t=fd(e),n=Hf(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}}var 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_e.nonMaxSuppressionAsync(s.norm,s.scores,10*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=Re(s.norm,[i,0],[1,-1]),u=H(()=>U(this.normalizeLandmarks(Re(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:u,confidence:r[i]})}for(let i of Object.keys(s))Z(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>ge(de(_e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Z(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),h=await l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(g8({startPoint:c,endPoint:d,palmLandmarks:h,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function ile(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function y8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ile(n)}var x8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ga(e,t){let n=0;for(let s=0;so[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=>Iy([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Gf(jf(r),ule)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Gf(jf(n),w8);s.palmLandmarks=[];for(let r=0;r[o[0]*(p[0]-this.inputSize/2),o[1]*(p[1]-this.inputSize/2),o[2]*p[2]]),l=ky(s,[0,0]),u=i.map(p=>[...Iy(p,l),p[2]]),c=v8(r),d=[...fd(n),1],h=[ga(d,c[0]),ga(d,c[1])];return u.map(p=>[Math.trunc(p[0]+h[0]),Math.trunc(p[1]+h[1]),Math.trunc(p[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await 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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 ${i<0?"up":"down"}`})}return t},U8=e=>{if(!e)return[];let t=[];for(let n=0;n.06||d>.06)&&(u=!1),h>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let p=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||p<.01||f>.022||p>.022)&&(u=!1),(f<.01||p<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||p>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},H8=e=>{if(!e)return[];let t=[];for(let n=0;n0){let a=s.reduce((i,l)=>i.position[2]i.position[1]Cle,body:()=>q8,canvas:()=>Sle,face:()=>j8,gesture:()=>G8,hand:()=>X8,object:()=>K8,options:()=>ba,person:()=>Ile});var ba={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},va=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("Human: Invalid Canvas")},Jf=e=>Math.round(e*180/Math.PI);function Xy(e,t,n,s=0,r){e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function gd(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else 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${Jf(u.rotation.gaze.bearing)}\xB0`)),c.length===0&&c.push("face"),r.fillStyle=s.color;for(let d=c.length-1;d>=0;d--){let h=Math.max(u.box[0],0),p=d*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c[d],h+5,p+16)),r.fillStyle=s.labelColor,r.fillText(c[d],h+4,p+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)Xy(r,d[0],d[1],d[2],s);if(s.drawPolygons){r.lineWidth=1;for(let d=0;du.mesh[p]);Ky(r,h,s)}if(u.annotations&&u.annotations.leftEyeIris){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,h=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,h,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){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,h=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,h,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&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(d[0],d[1]);let h=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(h[0],h[1]),r.stroke()}}}}}async function q8(e,t,n){var a;let s=gn(ba,n);if(!t||!e)return;let r=va(e);r.lineJoin="round";for(let o=0;ou.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&Ky(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s)}}}async function X8(e,t,n){let s=gn(ba,n);if(!t||!e)return;let r=va(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,gd(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText("hand",a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText("hand",a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:s.color,Xy(r,o[0],o[1],0,s);if(s.drawLabels){let o=(i,l)=>{!i||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 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$e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Y8(e){var s,r,a,o,i,l,u,c,d,h,p,f,m,g,A,y,x,b,v,k,S;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if($e.canvas=e.canvas,!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C((n-1)*$e.body[C].box[T]+R)/n),O=e.body[C].boxRaw.map((R,T)=>((n-1)*$e.body[C].boxRaw[T]+R)/n),E=e.body[C].keypoints.map((R,T)=>({score:R.score,part:R.part,position:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[0]+R.position[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[1]+R.position[1])/n:R.position[1]],positionRaw:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[0]+R.positionRaw[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[1]+R.positionRaw[1])/n:R.position[1]]}));$e.body[C]={...e.body[C],box:_,boxRaw:O,keypoints:E}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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z8({canvas:It}):null),!Wt)return{tensor:null,canvas:Ee};Wt.reset(),Wt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Wt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Wt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Wt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Wt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Wt.addFilter("hue",t.filter.hue),t.filter.negative&&Wt.addFilter("negative"),t.filter.sepia&&Wt.addFilter("sepia"),t.filter.vintage&&Wt.addFilter("brownie"),t.filter.sepia&&Wt.addFilter("sepia"),t.filter.kodachrome&&Wt.addFilter("kodachrome"),t.filter.technicolor&&Wt.addFilter("technicolor"),t.filter.polaroid&&Wt.addFilter("polaroid"),t.filter.pixelate!==0&&Wt.addFilter("pixelate",t.filter.pixelate),Wt.apply(Ee)}else It=Ee,Wt&&(Wt=null);if(!n){let u;if(It.data){let c=[It.height,It.width,3];u=Bh(It.data,c,"int32")}else if(It instanceof ImageData)u=ls?ls.fromPixels(It):null;else 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d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(d[0],d[1]);let h=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(h[0],h[1]),r.stroke()}}}}}async function q8(e,t,n){var a;let s=gn(ba,n);if(!t||!e)return;let r=va(e);r.lineJoin="round";for(let o=0;ou.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&Zy(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),Ad(r,l,s)}}}async function X8(e,t,n){let s=gn(ba,n);if(!t||!e)return;let r=va(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,gd(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText("hand",a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText("hand",a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*o[2]}, ${127.5-2*o[2]}, 255, 0.5)`:s.color,Ky(r,o[0],o[1],0,s);if(s.drawLabels){let o=(i,l)=>{!i||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 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$e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function Y8(e){var s,r,a,o,i,l,u,c,d,h,p,f,m,g,A,y,x,b,v,k,S;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if($e.canvas=e.canvas,!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C((n-1)*$e.body[C].box[T]+R)/n),O=e.body[C].boxRaw.map((R,T)=>((n-1)*$e.body[C].boxRaw[T]+R)/n),E=e.body[C].keypoints.map((R,T)=>({score:R.score,part:R.part,position:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[0]+R.position[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[1]+R.position[1])/n:R.position[1]],positionRaw:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[0]+R.positionRaw[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[1]+R.positionRaw[1])/n:R.position[1]]}));$e.body[C]={...e.body[C],box:_,boxRaw:O,keypoints:E}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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Yy="2.1.5";var xu,yd,xd,li,ui,bu,t0,bd,n0,s0,r0,a0,Nle=class{constructor(t){as(this,xu,void 0);as(this,yd,void 0);as(this,xd,void 0);as(this,li,void 0);as(this,ui,void 0);as(this,bu,void 0);this.analyze=(...t)=>{if(!mn(this,yd))return;let n=this.tf.engine().state.numTensors,s=mn(this,xu);Ds(this,xu,n);let r=n-s;r!==0&&ue(...t,r)};as(this,t0,t=>{if(!mn(this,xd))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ge))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});as(this,bd,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let s=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&ue("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(ue("override: backend set to 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n,s=0;switch(this.config.warmup){case"face":s=256,n="data:image/jpeg;base64,"+Qf;break;case"full":case"body":s=1200,n="data:image/jpeg;base64,"+e0;break;default:n=null}let r=new Image;r.onload=async()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,s):document.createElement("canvas");a.width=r.naturalWidth,a.height=r.naturalHeight;let o=a.getContext("2d");o==null||o.drawImage(r,0,0);let i=await this.detect(a,this.config);t(i)},n?r.src=n:t(null)}));as(this,a0,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(Qf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(e0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r),s=await this.detect(a,this.config),this.tf.dispose(a)}else this.config.debug&&ue("Warmup tfjs-node not loaded");return s});this.version=Yy,Object.defineProperty(this,"version",{value:Yy}),$m.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${_x}/dist/`,this.config=gn($m,t||{}),this.tf=id,this.draw=Zy,this.state="idle",Ds(this,xu,0),Ds(this,yd,!1),Ds(this,xd,!1),Ds(this,li,!0),Ds(this,bu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.image=n=>ii(n,this.config),this.faceTriangulation=n8,this.faceUVMap=s8,this.sysinfo=p5(),Ds(this,ui,1)}similarity(t,n){return iy(t,n)}segmentation(t,n){return L8(t,n,this.config)}enhance(t){return ly(t)}match(t,n,s=0){return a8(t,n,s)}async load(t){this.state="load";let n=Ye();t&&(this.config=gn(this.config,t)),mn(this,li)&&(this.config.debug&&ue(`version: ${this.version}`),this.config.debug&&ue(`tfjs version: ${this.tf.version_core}`),this.config.debug&&ue("platform:",this.sysinfo.platform),this.config.debug&&ue("agent:",this.sysinfo.agent),await mn(this,bd).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&ue("configuration:",this.config),this.config.debug&&ue("tf flags:",this.tf.ENV.flags))),await B8(this),mn(this,li)&&(this.config.debug&&ue("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ds(this,li,!1));let s=Math.trunc(Ye()-n);s>(this.performance.load||0)&&(this.performance.load=s)}async detect(t,n){return new Promise(async s=>{this.state="config";let r,a;this.config=gn(this.config,n),this.state="check";let o=mn(this,t0).call(this,t);o&&(ue(o,t),s({error:o}));let i=Ye();await mn(this,bd).call(this),await this.load(),r=Ye();let l=ii(t,this.config);if(this.performance.image=Math.trunc(Ye()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ye(),await jy(l),a=Math.trunc(Ye()-r),a>0&&(this.performance.segmentation=a),l.canvas&&(Z(l.tensor),l=ii(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){ue("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}r=Ye(),this.config.skipFrame=await mn(this,n0).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ye()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],h=[];this.config.async?(u=this.config.face.enabled?qy(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ye(),u=this.config.face.enabled?await qy(this,l.tensor):[],a=Math.trunc(Ye()-r),a>0&&(this.performance.face=a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?xy(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Ny(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?Dy(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?Py(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await xy(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Ny(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?await Dy(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?await Py(l.tensor,this.config):[]),a=Math.trunc(Ye()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?Cy(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ye(),d=this.config.hand.enabled?await Cy(l.tensor,this.config):[],a=Math.trunc(Ye()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?By(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?Hy(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ye(),this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?await By(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?await Hy(l.tensor,this.config):[]),a=Math.trunc(Ye()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.config.async&&([u,c,d,h]=await Promise.all([u,c,d,h]));let p=[];this.config.gesture.enabled&&(r=Ye(),p=[...V8(u),...W8(c),...H8(d),...U8(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ye()-r)),this.performance.total=Math.trunc(Ye()-i),this.state="idle",this.result={face:u,body:c,hand:d,gesture:p,object:h,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var f;return Z8(u,c,d,p,(f=l==null?void 0:l.tensor)==null?void 0:f.shape)}},Z(l.tensor),s(this.result)})}async warmup(t){let n=Ye();if(t&&(this.config=gn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let s;typeof createImageBitmap=="function"?s=await mn(this,s0).call(this):typeof Image!="undefined"?s=await mn(this,r0).call(this):s=await mn(this,a0).call(this);let r=Ye();return this.config.debug&&ue("Warmup",this.config.warmup,Math.round(r-n),"ms",s),s}};xu=new WeakMap,yd=new WeakMap,xd=new WeakMap,li=new WeakMap,ui=new WeakMap,bu=new WeakMap,t0=new WeakMap,bd=new WeakMap,n0=new WeakMap,s0=new WeakMap,r0=new WeakMap,a0=new WeakMap;})(); +2Q==`;var Jy="2.1.5";var xu,yd,xd,li,ui,bu,t0,bd,n0,s0,r0,a0,J8=class{constructor(t){as(this,xu,void 0);as(this,yd,void 0);as(this,xd,void 0);as(this,li,void 0);as(this,ui,void 0);as(this,bu,void 0);this.analyze=(...t)=>{if(!mn(this,yd))return;let n=this.tf.engine().state.numTensors,s=mn(this,xu);Ds(this,xu,n);let r=n-s;r!==0&&ue(...t,r)};as(this,t0,t=>{if(!mn(this,xd))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ge))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});as(this,bd,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let s=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&ue("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(ue("override: backend set to tensorflow while running in browser"),this.config.backend="humangl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(ue("override: backend set to webgl while running in nodejs"),this.config.backend="tensorflow"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")ue("override: backend set to webgpu but browser does not support webgpu"),this.config.backend="humangl";else{let a=await navigator.gpu.requestAdapter();this.config.debug&&ue("enumerated webgpu adapter:",a)}this.config.backend==="humangl"&&Vk();let r=Object.keys(this.tf.engine().registryFactory);if(this.config.debug&&ue("available backends:",r),r.includes(this.config.backend)||(ue(`error: backend ${this.config.backend} not found in registry`),this.config.backend=this.tf.ENV.flags.IS_NODE?"tensorflow":"humangl",ue(`override: using backend ${this.config.backend} instead`)),this.config.debug&&ue("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&ue("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),o=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&ue(`wasm execution: ${a?"SIMD":"no SIMD"} ${o?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&ue("warning: wasm simd support is not enabled")}try{await this.tf.setBackend(this.config.backend)}catch(a){ue("error: cannot set backend:",this.config.backend,a)}}if(this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!1),this.tf.ENV.set("WEBGL_USE_SHAPES_UNIFORMS",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(ue("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&ue(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}this.tf.enableProdMode(),await this.tf.ready(),this.performance.backend=Math.trunc(Ye()-s)}});this.next=t=>Y8(t||this.result);as(this,n0,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let s=_e.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await s.data(),a=0;for(let l=0;l10*this.config.cacheSensitivity?0:o),i});as(this,s0,async()=>{let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(this.config.warmup){case"face":n=await t(Qf);break;case"full":n=await t(e0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await this.detect(r,this.config),r.close()}return s});as(this,r0,async()=>new Promise(t=>{let n,s=0;switch(this.config.warmup){case"face":s=256,n="data:image/jpeg;base64,"+Qf;break;case"full":case"body":s=1200,n="data:image/jpeg;base64,"+e0;break;default:n=null}let r=new Image;r.onload=async()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,s):document.createElement("canvas");a.width=r.naturalWidth,a.height=r.naturalHeight;let o=a.getContext("2d");o==null||o.drawImage(r,0,0);let i=await this.detect(a,this.config);t(i)},n?r.src=n:t(null)}));as(this,a0,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(Qf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(e0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r),s=await this.detect(a,this.config),this.tf.dispose(a)}else this.config.debug&&ue("Warmup tfjs-node not loaded");return s});this.version=Jy,Object.defineProperty(this,"version",{value:Jy}),Fm.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${_x}/dist/`,this.config=gn(Fm,t||{}),this.tf=id,this.draw=Yy,this.state="idle",Ds(this,xu,0),Ds(this,yd,!1),Ds(this,xd,!1),Ds(this,li,!0),Ds(this,bu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.image=n=>ii(n,this.config),this.faceTriangulation=n8,this.faceUVMap=s8,this.sysinfo=p5(),Ds(this,ui,1)}similarity(t,n){return ly(t,n)}segmentation(t,n){return L8(t,n,this.config)}enhance(t){return uy(t)}match(t,n,s=0){return a8(t,n,s)}async load(t){this.state="load";let n=Ye();t&&(this.config=gn(this.config,t)),mn(this,li)&&(this.config.debug&&ue(`version: ${this.version}`),this.config.debug&&ue(`tfjs version: ${this.tf.version_core}`),this.config.debug&&ue("platform:",this.sysinfo.platform),this.config.debug&&ue("agent:",this.sysinfo.agent),await mn(this,bd).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&ue("configuration:",this.config),this.config.debug&&ue("tf flags:",this.tf.ENV.flags))),await B8(this),mn(this,li)&&(this.config.debug&&ue("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ds(this,li,!1));let s=Math.trunc(Ye()-n);s>(this.performance.load||0)&&(this.performance.load=s)}async detect(t,n){return new Promise(async s=>{this.state="config";let r,a;this.config=gn(this.config,n),this.state="check";let o=mn(this,t0).call(this,t);o&&(ue(o,t),s({error:o}));let i=Ye();await mn(this,bd).call(this),await this.load(),r=Ye();let l=ii(t,this.config);if(this.performance.image=Math.trunc(Ye()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&l&&l.tensor&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ye(),await qy(l),a=Math.trunc(Ye()-r),a>0&&(this.performance.segmentation=a),l.canvas&&(Z(l.tensor),l=ii(l.canvas,this.config)),this.analyze("End Segmentation:")),!l||!l.tensor){ue("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}r=Ye(),this.config.skipFrame=await mn(this,n0).call(this,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ye()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],h=[];this.config.async?(u=this.config.face.enabled?Xy(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ye(),u=this.config.face.enabled?await Xy(this,l.tensor):[],a=Math.trunc(Ye()-r),a>0&&(this.performance.face=a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?by(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Ey(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?$y(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?My(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await by(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Ey(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?c=this.config.body.enabled?await $y(l.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(c=this.config.body.enabled?await My(l.tensor,this.config):[]),a=Math.trunc(Ye()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?Ty(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ye(),d=this.config.hand.enabled?await Ty(l.tensor,this.config):[],a=Math.trunc(Ye()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?Wy(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?Gy(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ye(),this.config.object.modelPath.includes("nanodet")?h=this.config.object.enabled?await Wy(l.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(h=this.config.object.enabled?await Gy(l.tensor,this.config):[]),a=Math.trunc(Ye()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.config.async&&([u,c,d,h]=await Promise.all([u,c,d,h]));let p=[];this.config.gesture.enabled&&(r=Ye(),p=[...V8(u),...W8(c),...H8(d),...U8(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ye()-r)),this.performance.total=Math.trunc(Ye()-i),this.state="idle",this.result={face:u,body:c,hand:d,gesture:p,object:h,performance:this.performance,canvas:l.canvas,timestamp:Date.now(),get persons(){var f;return Z8(u,c,d,p,(f=l==null?void 0:l.tensor)==null?void 0:f.shape)}},Z(l.tensor),s(this.result)})}async warmup(t){let n=Ye();if(t&&(this.config=gn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let s;typeof createImageBitmap=="function"?s=await mn(this,s0).call(this):typeof Image!="undefined"?s=await mn(this,r0).call(this):s=await mn(this,a0).call(this);let r=Ye();return this.config.debug&&ue("Warmup",this.config.warmup,Math.round(r-n),"ms",s),s}};xu=new WeakMap,yd=new WeakMap,xd=new WeakMap,li=new WeakMap,ui=new WeakMap,bu=new WeakMap,t0=new WeakMap,bd=new WeakMap,n0=new WeakMap,s0=new WeakMap,r0=new WeakMap,a0=new WeakMap;return Ele;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/test/build.log b/test/build.log index 2546d23f..d40d1652 100644 --- a/test/build.log +++ b/test/build.log @@ -211,3 +211,288 @@ 2021-09-11 11:16:40 STATE: Lint: {"locations":["src/**/*.ts","test/*.js","demo/**/*.js"],"files":75,"errors":0,"warnings":0} 2021-09-11 11:16:41 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 2021-09-11 11:16:41 INFO:  Done... +2021-09-11 11:22:21 INFO:  @vladmandic/human version 2.1.5 +2021-09-11 11:22:21 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.5.0 +2021-09-11 11:22:21 INFO:  Application: {"name":"@vladmandic/human","version":"2.1.5"} +2021-09-11 11:22:21 INFO:  Environment: {"profile":"development","config":"build.json","tsconfig":true,"eslintrc":true,"git":true} +2021-09-11 11:22:21 INFO:  Toolchain: {"build":"0.4.1","esbuild":"0.12.26","typescript":"4.4.3","typedoc":"0.21.9","eslint":"7.32.0"} +2021-09-11 11:22:21 INFO:  Build: {"profile":"development","steps":["serve","watch","compile"]} +2021-09-11 11:22:21 STATE: WebServer: {"ssl":false,"port":10030,"root":"."} +2021-09-11 11:22:21 STATE: WebServer: {"ssl":true,"port":10031,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"} +2021-09-11 11:22:21 STATE: Watch: {"locations":["src/**","src/**","tfjs/*"]} +2021-09-11 11:22:21 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1416} +2021-09-11 11:22:21 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":47,"inputBytes":456782,"outputBytes":396579} +2021-09-11 11:22:21 STATE: Compile: 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{"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1416} +2021-09-11 11:41:26 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":47,"inputBytes":456782,"outputBytes":396579} +2021-09-11 11:41:26 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1424} +2021-09-11 11:41:27 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":47,"inputBytes":456790,"outputBytes":396583} +2021-09-11 11:41:27 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1491} +2021-09-11 11:41:27 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":47,"inputBytes":456857,"outputBytes":396655} +2021-09-11 11:41:27 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":2168,"outputBytes":1590} +2021-09-11 11:41:27 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":47,"inputBytes":456956,"outputBytes":398559} +2021-09-11 11:41:27 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":7,"inputBytes":2168,"outputBytes":2343983} +2021-09-11 11:41:27 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":47,"inputBytes":2799349,"outputBytes":1391563} +2021-09-11 11:41:28 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":47,"inputBytes":2799349,"outputBytes":2583497} +2021-09-11 11:41:44 STATE: Typings: {"input":"src/human.ts","output":"types","files":47} +2021-09-11 11:41:50 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":14,"index":true} +2021-09-11 11:42:16 STATE: Lint: {"locations":["src/**/*.ts","test/*.js","demo/**/*.js"],"files":75,"errors":0,"warnings":0} +2021-09-11 11:42:16 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2021-09-11 11:42:16 INFO:  Done... diff --git a/wiki b/wiki index 3bf213c1..ec49beb9 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 3bf213c17d3f25368dc161ad33df8195b60dacb2 +Subproject commit ec49beb9f19c0abde3e62b24ba2c7749ef54c9aa