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human
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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Jf.nextTensorId++}nextVariableId(){return Jf.nextVariableId++}clone(e){let t=M.runKernel(Ma,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return M.runKernel(Sa,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Yf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Df(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Df(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Yf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:C,dtype:T}=x;return this.makeTensorFromDataId(k,C,T)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Df(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Jv(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&ir(e[0])&&(r=e.map(o=>El(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n==="string"){let o=this.state.tensorInfo.get(a),u=Uw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new md(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*jf(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 md||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*jf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Jv(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Gd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Ag(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=P$(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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t=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)}};fb.className="Adam";Nr(fb);var mb=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=Tr(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};mb.className="Adamax";Nr(mb);var kp=class extends $r{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=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(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)}};gb.className="Momentum";Nr(gb);var bb=class extends $r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),ln(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),ln(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(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)}};bb.className="RMSProp";Nr(bb);var Vr=class{static sgd(e){return new kp(e)}static momentum(e,t,n=!1){return new gb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new bb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new fb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new pb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new mb(e,t,n,s,r)}static adagrad(e,t=.1){return new hb(e,t)}},Fi={sgd:Vr.sgd,momentum:Vr.momentum,adadelta:Vr.adadelta,adagrad:Vr.adagrad,rmsprop:Vr.rmsprop,adamax:Vr.adamax,adam:Vr.adam},EO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function RO(){return new Promise(e=>EO(()=>e()))}var 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DO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function FO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var yb=30;function OO(e){return e<=yb?e:pd(e,Math.floor(Math.sqrt(e)))}function PO(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function zO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function MO(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?a.push(i):r.push(i);s.push(...r),s.push(0),s.push(...a)}return s}function LO(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function BO(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function VO(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var _I=1.7580993408473768,AI=1.0507009873554805,WO=.3275911,UO=.254829592,GO=-.284496736,HO=1.421413741,qO=-1.453152027,jO=1.061405429;function KO(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function XO(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let 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this.assertNotDisposed(),wL(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function wL(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function bm(e){return e.map(t=>t.read())}function Fb(e){e.forEach(t=>{t[0].write(t[1])})}var 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},IL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=IL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Rp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ps(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return gn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return gn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Zr(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(gn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=gn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=SL(e),i=this.computeOutputShape(a),o,u=CL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ps(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Sd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return bm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function aB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Rp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Yo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let T=x.inboundNodes[k];if(v.indexOf(T)!==-1)throw new ps(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(T)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(T)===-1&&v.push(T);let E=T.inboundLayers.length;for(let A=0;A<E;A++){let P=T.inputTensors[A],R=T.inboundLayers[A],F=T.nodeIndices[A],$=T.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(T);v.indexOf(T)>=0;)v.splice(v.indexOf(T),1);i.push(T)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],T=k.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(y+1,E),n[T.id]=T}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Wc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Wc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new ps(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Fb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${t0}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=vm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Xr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return _u(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=pa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Id(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Wc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(gn(c)),d=Id(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return gn(r)}runInternalGraph(e,t){t==null&&(t=pa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Wc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let T=r[x];if(T.inboundNodes.length<=k){i(m,g);return}let E=T.inboundNodes[k];b.push(E.outputTensors[C])}b.length>0&&m.apply(gn(b),y)}function u(m){let g=m.name,b=fs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!AM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function n0(e,t){return iB(e,t,"classWeight")}async function s0(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,"float32")}else return null}function oB(e,t){return V(e,t)}var uB=32;function r0(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=Dx("input",e.inputNames,n),i=Dx("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Dx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function lB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function cB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Nd?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Px(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Px(t,this.feedOutputNames,r,!1,"target"),bB(e,t,null),yB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. 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a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=lm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-lm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Ws(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ws(Hc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(Hc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(Hc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Qu(e.optimizer_config),n=fs(t),s;if(typeof e.loss=="string")s=qr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>qr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=qr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>qr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=qr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=_n.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await _n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:xB,generatedBy:`TensorFlow.js tfjs-layers v${t0}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await _n.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=_n.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Ax(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Ax(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var i0=class extends pr{};i0.className="Functional";ae.registerClass(i0);async function wB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=fs(s,t);if(e.weightsManifest!=null){let a=await _n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function kB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return IB(e,void 0,t)}async function IB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=fs(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=SB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function SB(e,t){let n=_n.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Im=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Rp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Im||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=HI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=GI(this.outputs[0])}this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[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(nt(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 pr({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 ps("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 ps("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 ps("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 ps("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 U("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 i=new e(a);if(!(i instanceof Im))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=fs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Vb=Im;Vb.className="Sequential";ae.registerClass(Vb);function cpe(e){return new pr(e)}function dpe(e){return new Vb(e)}function ppe(e,t){return t==null&&(t={}),kB(e,t)}function CB(e){return HI(e)}function hpe(e,t){Ob.registerCallbackConstructor(e,t)}var wn=class extends ae.Serializable{getConfig(){return{}}},o0=class extends wn{apply(e,t=1){return XM(e,t)}};o0.className="elu";ae.registerClass(o0);var u0=class extends wn{apply(e){return cI(e)}};u0.className="selu";ae.registerClass(u0);var l0=class extends wn{apply(e){return Xs(e)}};l0.className="relu";ae.registerClass(l0);var c0=class extends wn{apply(e){return j(()=>gp(6,Xs(e)))}};c0.className="relu6";ae.registerClass(c0);var d0=class extends wn{apply(e){return e}};d0.className="linear";ae.registerClass(d0);var p0=class extends wn{apply(e){return qs(e)}};p0.className="sigmoid";ae.registerClass(p0);var h0=class extends wn{apply(e){return QM(e)}};h0.className="hardSigmoid";ae.registerClass(h0);var f0=class extends wn{apply(e){return Ol(e)}};f0.className="softplus";ae.registerClass(f0);var m0=class extends wn{apply(e){return YM(e)}};m0.className="softsign";ae.registerClass(m0);var g0=class extends wn{apply(e){return Hu(e)}};g0.className="tanh";ae.registerClass(g0);var Wb=class extends wn{apply(e,t=-1){return ib(e,t)}};Wb.className="softmax";ae.registerClass(Wb);var b0=class extends wn{apply(e,t=-1){return eI(e,t)}};b0.className="logSoftmax";ae.registerClass(b0);var y0=class extends wn{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};y0.className="swish";ae.registerClass(y0);var v0=class extends wn{apply(e){return j(()=>V(e,Hu(Ol(e))))}};v0.className="mish";ae.registerClass(v0);function br(e){return e.getClassName()}function Wf(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Wf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Wf(t)}else return e instanceof wn?e:Wf(e)}function Ub(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x0=class extends ae.Serializable{},Wl=class extends x0{constructor(e){super();Ub(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ye(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ye(V(this.l2,Bl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wl.className="L1L2";ae.registerClass(Wl);function NB(e){return Ub(e),new Wl({l1:e!=null?e.l1:null,l2:0})}function TB(e){return Ub(e),new Wl({l2:e!=null?e.l2:null,l1:0})}var Mx={l1l2:"L1L2"};function at(e){return vb(e)}function Lx(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Mx?Mx[e]:e,config:{}};return Lx(n)}else return e instanceof x0?e:Lx(e)}var Gb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Gb.className="ReLU";ae.registerClass(Gb);var Hb=class extends Ge{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=Oe(e);return jg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Hb.className="LeakyReLU";ae.registerClass(Hb);var qb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),tb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:at(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};qb.className="PReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return hp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className="ELU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="ThresholdedReLU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="Softmax";ae.registerClass(Xb);function Ki(e,t,n){if(typeof e=="number")return pa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!HM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function ms(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+gr([n-t,0]);else if(s==="same")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Yb(e,t){return j(()=>(St(t),t==="channelsFirst"?qe(e,[0,2,3,1]):e))}function w0(e,t){return j(()=>(St(t),t==="channelsFirst"?qe(e,[0,2,3,4,1]):e))}function $B(e,t,n,s=1,r="valid",a,i=1){return j(()=>{if(a==null&&(a=bs()),St(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=qk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Bx(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Yb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=da.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function _B(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=w0(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Kk(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Qb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Qb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ki(t.kernelSize,e,"kernelSize"),this.strides=Ki(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Ki(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!xb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ul=class extends Qb{constructor(e,t){super(e,t);this.kernel=null,Ul.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=PI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$B(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_B(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=ms(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},k0=class extends Ul{constructor(e){super(2,e);k0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!xb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Pp=k0;Pp.className="Conv2D";ae.registerClass(Pp);var I0=class extends Ul{constructor(e){super(3,e);I0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},zp=I0;zp.className="Conv3D";ae.registerClass(zp);var Zb=class extends Pp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(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=nt(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],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Zb.className="Conv2DTranspose";ae.registerClass(Zb);var Jb=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=qE(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(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 i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jb.className="Conv3DTranspose";ae.registerClass(Jb);var S0=class extends Ul{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=JD(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=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};S0.className="SeparableConv";var ey=class extends S0{constructor(e){super(2,e)}};ey.className="SeparableConv2D";ae.registerClass(ey);var C0=class extends Ul{constructor(e){super(1,e);C0.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"&&!xb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ty=C0;ty.className="Conv1D";ae.registerClass(ty);var ny=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Uc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uc(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}};ny.className="Cropping2D";ae.registerClass(ny);var sy=class extends Ge{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,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sy.className="UpSampling2D";ae.registerClass(sy);function AB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),St(r);let i=Yb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=pp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var ry=class extends Qb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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 j(()=>{e=Oe(e);let n=AB(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=nt(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=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(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=yt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};ry.className="DepthwiseConv2D";ae.registerClass(ry);function N0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("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 T0(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=On(r,-1)),r=qe(r,l)),s&&(t=Yn(t,0),r!=null&&(r=Yn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],C=ge(Xn(k),k),T=ie(V(v[0],k),V(d[0],C)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,C)));return{output:T,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Qn(c,1)),[p,g,d]})}var $0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Bp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({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 ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){gm(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 j(()=>{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;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");gm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(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 U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=T0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ye(t,[1,2]),t=Ll(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?fm(t,[1,n]):t):this.cell.stateSize>1?[fm(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()===$0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fs(s,n);return new e(Object.assign(t,{cell:r}))}},_r=$0;_r.className="RNN";ae.registerClass(_r);var Gl=class extends Ge{},Mp=class extends Gl{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,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Mp.className="SimpleRNNCell";ae.registerClass(Mp);var ay=class extends _r{constructor(e){e.cell=new Mp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(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)}};ay.className="SimpleRNN";ae.registerClass(ay);var Lp=class extends Gl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Ln(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Lp.className="GRUCell";ae.registerClass(Lp);var iy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(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)}};iy.className="GRU";ae.registerClass(iy);var Hl=class extends Gl{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,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(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,o){let u=r.apply([a]),l=new Cp().apply([a]),c=r.apply([a*2]);return Cx(Cx(u,l),c)}},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 j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Ln(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Hl.className="LSTMCell";ae.registerClass(Hl);var oy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Hl(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(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)}};oy.className="LSTM";ae.registerClass(oy);var Bp=class extends Gl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){gm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Zr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fs(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 bm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Fb(t)}};Bp.className="StackedRNNCells";ae.registerClass(Bp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):WI(t(),n),o=()=>Vl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _0=class extends _r{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,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 j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=ms(u,s[0],r,a[0],i[0]),p=ms(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};_0.className="ConvRNN2D";var Vp=class extends Hl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Ki(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Ki(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",St(this.dataFormat),this.dilationRate=Ki(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let 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 i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends es{apply(c,p){let d=u.apply([l]),h=zn([l]),f=u.apply([l*2]);return Nb([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,C]=Ln(this.kernel.read(),i,y),[T,E,A,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,T,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,C,P,this.padding);let[R,F,$,z]=Ln(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ua(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 ua(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Vp.className="ConvLSTM2DCell";ae.registerClass(Vp);var uy=class extends _0{constructor(e){let t=new Vp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};uy.className="ConvLSTM2D";ae.registerClass(uy);var Wp=class extends Ge{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.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Vl(()=>WI(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()}};Wp.className="Dropout";ae.registerClass(Wp);var ly=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ly.className="SpatialDropout1D";ae.registerClass(ly);var cy=class extends Ge{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,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(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=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=PI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(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:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Dense";ae.registerClass(cy);var dy=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return KM(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};dy.className="Flatten";ae.registerClass(dy);var py=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};py.className="Activation";ae.registerClass(py);var hy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),qM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hy.className="RepeatVector";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Reshape";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};my.className="Permute";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return cm(Ku(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=cm(Ku(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};gy.className="Masking";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Ip(n,"int32"));let s=VI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:at(this.embeddingsRegularizer),activityRegularizer:at(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};by.className="Embedding";ae.registerClass(by);var mi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Ll(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:On(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},yy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};yy.className="Add";ae.registerClass(yy);var vy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};vy.className="Multiply";ae.registerClass(vy);var xy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};xy.className="Average";ae.registerClass(xy);var wy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tr(t,e[n]);return t})}};wy.className="Maximum";ae.registerClass(wy);var ky=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=gp(t,e[n]);return t})}};ky.className="Minimum";ae.registerClass(ky);var Iy=class extends mi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>Nb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Xn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(On(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return Bk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Concatenate";ae.registerClass(Iy);function Su(e,t){for(;e<0;)e+=t;return e}function EB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${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 Fe("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 j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ye(V(e,t),a[0]):o=ye(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=On(o,1)),o})}var Sy=class extends mi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(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],n=e[1];if(t.length>3||n.length>3)throw new Fe("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 U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Su(r,e[a].shape.length)):s=[Su(this.axes,t.shape.length),Su(this.axes,n.shape.length)],this.normalize&&(t=Cd(t,s[0]),n=Cd(n,s[1])),EB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Su(this.axes,e.length),Su(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 Fe("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}};Sy.className="Dot";ae.registerClass(Sy);var Cy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Vl(()=>ie(Sp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Cy.className="GaussianNoise";ae.registerClass(Cy);var Ny=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Vl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Sp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ny.className="GaussianDropout";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Vl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=jo(Pl(n),this.rate);u=Ip(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Ty.className="AlphaDropout";ae.registerClass(Ty);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=vE(e,t,n,s,r,a);else if(e.rank===3)i=wE(e,t,n,s,r,a);else if(e.rank===4)i=IE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function DB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Zu(e,l,c,d,p,r),i,o]})}function FB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?RB(e,t,n,s,r):DB(e,t,n,s,r)}var $y=class extends Ge{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=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=pa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),C=V(ge(k,y),x);b.write(ge(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="BatchNormalization";ae.registerClass($y);var _y=class extends Ge{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=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(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=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=Jg(n,this.axis,!0),u=pa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=cs(i,d),o=cs(o,d),c=cs(c,h),p=cs(p,h),Zu(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};_y.className="LayerNormalization";ae.registerClass(_y);function OB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],pi(e,s)})}var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>OB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="ZeroPadding2D";ae.registerClass(Ay);function Up(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Yb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=Zg(e,t,n,o):i=Wg(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function A0(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=w0(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=aI(e,t,n,o):i=Gk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var E0=class extends Ge{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 U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ll(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ey=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"max")}};Ey.className="MaxPooling1D";ae.registerClass(Ey);var Ry=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"avg")}};Ry.className="AveragePooling1D";ae.registerClass(Ry);var R0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Dy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"max")}};Dy.className="MaxPooling2D";ae.registerClass(Dy);var Fy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"avg")}};Fy.className="AveragePooling2D";ae.registerClass(Fy);var D0=class extends Ge{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 U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(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=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),s=ms(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 j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Oy=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"max")}};Oy.className="MaxPooling3D";ae.registerClass(Oy);var Py=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"avg")}};Py.className="AveragePooling3D";ae.registerClass(Py);var F0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},zy=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return It(n,1)})}};zy.className="GlobalAveragePooling1D";ae.registerClass(zy);var My=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};My.className="GlobalMaxPooling1D";ae.registerClass(My);var O0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};Ly.className="GlobalAveragePooling2D";ae.registerClass(Ly);var By=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};By.className="GlobalMaxPooling2D";ae.registerClass(By);var P0=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=fs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Vy=class extends P0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(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 j(()=>(e=Oe(e),T0((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Vy.className="TimeDistributed";ae.registerClass(Vy);function PB(e){hi(VM,"BidirectionalMergeMode",e)}var zB="concat",Wy=class extends P0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=fs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=fs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zB:e.mergeMode,PB(this.mergeMode),e.weights)throw new Fe("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()):gn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(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=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new 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implemented`)}},r4=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[dR(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[qe(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",i=r==="prelu",o=I("numArgs",e,t,n),u=I("leakyreluAlpha",e,t,n);if(a){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[l,c]=I("args",e,t,n);return[da.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:l,activation:r,preluActivationWeights:c,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a4=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[qu(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[qu(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[OR(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[ib(I("x",e,t,n))];case"LogSoftmax":return[eI(I("x",e,t,n))];case"SparseToDense":return[xI(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i4=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[As(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[It(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[pm(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ye(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Bk(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[cm(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Gu(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[GA(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[iI(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[Qk(I("x",e,t,n),i,o,u)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Hk(s,r,a)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),u=I("size",e,t,n),l=I("binaryOutput",e,t,n);return[QE(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},o4=(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[ju(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),i=I("indices",e,t,n);return[ju(a,ce(i,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I("x",e,t,n);return[Yn(a,r)]}case"ReverseV2":{let s=I("axis",e,t,n),r=I("x",e,t,n);return[Yn(r,s)]}case"Slice":{let s=I("begin",e,t,n),r=I("size",e,t,n);return[He(I("x",e,t,n),s,r)]}case"StridedSlice":{let s=I("begin",e,t,n),r=I("end",e,t,n),a=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),u=I("ellipsisMask",e,t,n),l=I("newAxisMask",e,t,n),c=I("shrinkAxisMask",e,t,n),p=I("x",e,t,n);return[k3(p,s,r,a,i,o,u,l,c)]}case"Pack":return j(()=>{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:G(u,a)});return[Qn(o,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Fs(r,s)}case"Tile":{let s=I("reps",e,t,n);return[cs(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 Ln(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[M3(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[W3(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[xI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},u4=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Vc.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,i]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Vc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Vc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Vc.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`)}},l4=(e,t,n)=>{switch(e.op){case"FFT":return[ob(I("x",e,t,n))];case"IFFT":return[kd(I("x",e,t,n))];case"RFFT":return[ub(I("x",e,t,n))];case"IRFFT":return[fI(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},c4=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Pf.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}=Pf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Pf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d4=(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[On(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[mr(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[lD(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[pi(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[eb(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Ug(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[JE(I("x",e,t,n),s,r)]}case"BroadcastTo":return[td(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[NE(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case"arithmetic":return j(()=>VW(a,i,o));case"basic_math":return j(()=>WW(a,i,o));case"control":return KW(a,i,o);case"convolution":return j(()=>XW(a,i,o));case"creation":return j(()=>YW(a,i,o));case"dynamic":return QW(a,i,o);case"evaluation":return j(()=>ZW(a,i,o));case"image":return j(()=>n4(a,i,o));case"graph":return j(()=>JW(a,i,o));case"logical":return j(()=>s4(a,i,o));case"matrices":return j(()=>r4(a,i,o));case"normalization":return j(()=>a4(a,i,o));case"reduction":return j(()=>i4(a,i,o));case"slice_join":return j(()=>o4(a,i,o));case"sparse":return j(()=>u4(a,i,o));case"spectral":return j(()=>l4(a,i,o));case"string":return j(()=>c4(a,i,o));case"transformation":return j(()=>d4(a,i,o));case"hash_table":return t4(a,i,o,s);case"custom":let u=W0(a.op);if(u&&u.customExecutor)return u.customExecutor(new BW(a,i,o));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 Kx=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;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Xx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>$n(d)[0]),c=[];s!=null&&(c=s.map(d=>$n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((cS(d)||g4(d)||b4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function p4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>$n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var h4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],f4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],m4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function cS(e){return h4.indexOf(e.op)>=0}function g4(e){return f4.indexOf(e.op)>=0}function b4(e){return m4.indexOf(e.op)>=0}var Om=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Om(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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Xx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${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 i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return p4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[$n(c)[0]]),r=t.map(c=>$n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Kx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=$n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=jx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>on(f,p,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,i){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let u=vW(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Kx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>on(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].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[$n(y)[0]]),i=n.map(y=>$n(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Xx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=$n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);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 b=o.filter(y=>!cS(y)&&!on(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. 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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=$n(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=$n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},y4=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Om(Ux.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Ux.Instance.transformGraph(e.modelInitializer);this.initializer=new Om(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 et)&&!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]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}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 bpe(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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TextDecoder;else{let{StringDecoder:n}=Pw();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof et)&&!(e instanceof Promise)&&!t)}function T4(e){return e==null||$4(e)||Array.isArray(e)||typeof e=="object"&&e instanceof et||w.isTypedArray(e)}function $4(e){return e===null||typeof e!="object"&&typeof e!="function"}function _4(e){return C4(e,A4)}function A4(e){return e instanceof et?{value:e.clone(),recurse:!1}:Ji(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var fS=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 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this.set(t,this.pop()),n}},mS=class extends fS{constructor(){super(mS.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;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},gS=mS;gS.INITIAL_CAPACITY=32;function bS(e){return new D4(e)}function Ky(e){return new F4(e)}function E4(e,t){return new yS(e,t)}function R4(e,t=vS.FAIL){return new U4(e,t)}var Ut=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await 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e=this.items[this.trav];return this.trav++,{value:_4(e),done:!1}}},F4=class extends Ut{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: ${e.message}`,e}}},O4=class extends Ut{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},P4=class extends Ut{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Re(e.value)}return this.upstream.next()}},z4=class extends Ut{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},M4=class extends Ut{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},L4=class extends Ut{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Re(e.value)}}},B4=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},V4=class extends Ut{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Yx=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Xy=class extends Ut{constructor(){super();this.outputQueue=new gS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},W4=class extends Xy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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}},yS=class extends Ut{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}},vS=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(vS||{}),U4=class extends Ut{constructor(e,t=0){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 Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await hS(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},xS=class extends Ut{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new fS(t)}summary(){return`${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()}},G4=class extends xS{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=S4.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}}},Zo=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),Tn(async()=>(await n.iterator()).columnMajorBatch(e,t,j4),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Tn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Tn(async()=>(await t.iterator()).filter(s=>j(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Tn(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return Tn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Tn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Tn(async()=>{let s=Ky(async()=>({value:await t.iterator(),done:!1}));return E4(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Tn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=I4.alea(t||w.now().toString());return Tn(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,Tn(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()}};Zo.MAX_BUFFER_SIZE=1e4;function Tn(e,t=null){return new class extends Zo{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function H4(e){return Tn(async()=>bS(e),e.length)}function q4(e){if(!Ji(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Tn(async()=>{let n=await hS(e,s=>{if(s instanceof Zo)return{value:s.iterator(),recurse:!1};if(Ji(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return R4(n,1)},t)}function j4(e){if(e===null)return null;let t=e[0];return T4(t)?{value:K4(e),recurse:!1}:{value:null,recurse:!0}}function K4(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?Qn(e):hs(e)}var wS=class extends Zo{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))}},jc='"',Nu=Symbol("out"),Qx=Symbol("field"),Kc=Symbol("quote"),Gf=Symbol("quoteafterquote"),Zx=Symbol("quoteinquote"),kS=class extends Zo{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 wS(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;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Nu;for(let i=0;i<r;i++)switch(a){case Nu:switch(e.charAt(i)){case jc:s=i+1,a=Kc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Nu;break;default:a=Qx,s=i;break}break;case Qx:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=Nu,s=i+1;break;default:}break;case Kc:switch(e.charAt(i)){case jc:a=Gf;break;default:}break;case Gf:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=Nu,s=i+1;break;case jc:a=Kc;break;default:a=Zx;break}break;case Zx:switch(e.charAt(i)){case jc:a=Kc;break;default:}break;default:}if(a===Gf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},IS=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(X().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new IS(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),hs(n,t)}},SS=class extends Ut{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=Qt([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,i=r+n,o=s+a;this.cropBox=ji([a,r,o,i],[1,4])}else this.cropBox=ji([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(X().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 SS(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=xk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=On(ce(e,"float32"),0),n;n=ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},CS=class{},NS=class extends Ut{split(e){return new X4(this,e)}},X4=class extends NS{constructor(e,t){super();this.upstream=e,this.impl=new Y4(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y4=class extends Xy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Q4=class extends Ut{decodeUTF8(){return new Z4(this)}},Z4=class extends NS{constructor(e){super();this.upstream=e,this.impl=new J4(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},J4=class extends Xy{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Pw();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},TS=class extends Q4{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().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=i=>{let o=r.result;if(o instanceof ArrayBuffer&&(o=new Uint8Array(o)),!(o instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(o)},r.onabort=i=>n(new Error("Aborted")),r.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function eU(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=tU(e));let a=await(n||w.fetch)(s,r);if(a.ok){let i=new Uint8Array(await a.arrayBuffer());return new TS(i,t)}else throw new Error(a.statusText)}var tU=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 $S(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var _S=class extends CS{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if($S(this.input)&&X().get("IS_NODE")){let e=Jm();this.input=e.readFileSync(this.input.substr(7))}return new TS(this.input,this.options)}},AS=class extends CS{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return $S(this.url)?new _S(this.url,this.fileOptions).iterator():eU(this.url,this.fileOptions)}};function nU(e,t={}){return new kS(new AS(e),t)}function sU(e){let t=Ky(e);return Tn(async()=>t)}function rU(e){return Tn(async()=>{let t=await e();return Ky(()=>t.next())})}async function aU(e,t){return SS.create(e,t)}async function iU(e){return IS.create(e)}var oU="0.0.0";function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var uU=xs.whereImpl,ES=class extends tl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Wd(this,Ss())}nextDataId(){return ES.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>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{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return N.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}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 De(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return Ss().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[c,p]=N.computeOutAndReduceShapes(u.shape,i),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var EG={kernelName:il,backendName:"cpu",kernelFunc:AG},RG=st(ol,e=>Math.asin(e)),DG={kernelName:ol,backendName:"cpu",kernelFunc:RG},FG=st(ul,e=>Math.asinh(e)),OG={kernelName:ul,backendName:"cpu",kernelFunc:FG},PG=st(ll,e=>Math.atan(e)),zG={kernelName:ll,backendName:"cpu",kernelFunc:PG},MG=At((e,t)=>Math.atan2(e,t)),LG=Gt(dl,MG),BG={kernelName:dl,backendName:"cpu",kernelFunc:LG},VG=st(cl,e=>Math.atanh(e)),WG={kernelName:cl,backendName:"cpu",kernelFunc:VG};function iv(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=De(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,C=x*s[0];for(let T=0;T<r.inChannels;++T)for(let E=0;E<r.outHeight;++E){let 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oe=ne*u-b,re=oe;for(;re<0;)re+=p;let le=Math.min(r.inWidth,f+oe),me=se+ne*E,we=y,Se=0,Ee=0;for(let Xe=W;Xe<q;Xe+=l){let Je=R+Xe*s[1];for(let Ye=te;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=re;Ce<le;Ce+=p){let ut=tt+Ce*s[3],rt=e[ut+F];if(a==="max"&&rt>we?we=rt:a==="avg"&&(Se+=rt,Ee++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Pe=me+F;x[Pe]=a==="avg"?Se/Ee:we}}}}return v}function UG(e,t){let n=De(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let C=k*r-h,T=C;for(;T<0;)T+=o;let E=Math.min(t.inHeight,c+C);for(let A=0;A<t.outWidth;++A){let P=A*a-f,R=P;for(;R<0;)R+=u;let 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c=N.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,C=c.effectiveFilterWidth,T=x-1-c.padInfo.front,E=C-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,"float32"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-T,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<C;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var XG={kernelName:ag,backendName:"cpu",kernelFunc:KG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var JG={kernelName:Pa,backendName:"cpu",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;ve([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tH={kernelName:io,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=Zy(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var sH={kernelName:ig,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var aH={kernelName:og,backendName:"cpu",kernelFunc:rH},iH=st(Ir,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oH={kernelName:Ir,backendName:"cpu",kernelFunc:iH},uH=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,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,"float32")},lH={kernelName:jd,backendName:"cpu",kernelFunc:uH};function eo(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var cH={kernelName:Qd,backendName:"cpu",kernelFunc:eo};function to(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return Os({inputs:{x:o[0]},backend:n});let u=o.map(m=>m.shape);if(N.assertParamsConsistent(u,a),o[0].dtype==="complex64"){let m=o.map(x=>ha({inputs:{input:x},backend:n})),g=o.map(x=>eo({inputs:{input:x},backend:n})),b=to({inputs:m,backend:n,attrs:{axis:a}}),y=to({inputs:g,backend:n,attrs:{axis:a}}),v=An({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=o.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return mt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=N.computeOutShape(l.map(m=>m.shape),1);let p=l[0].shape[0]===1,d=Jy(c,i,t[0].dtype,p),h=N.computeOutShape(o.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var dH={kernelName:oo,backendName:"cpu",kernelFunc:to};function CC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s;ve([r,a],"conv2d");let p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new 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hH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"conv2dBackpropFilter");let p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,C);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=T.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=T.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var fH={kernelName:ug,backendName:"cpu",kernelFunc:hH};function mH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;ve([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=N.convertConv2DDataFormat(l),f=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:C,filterHeight:T,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=T-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let we=0;we<C;++we)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(T+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),rt=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let Jt=Et*q-tt,Sn=oe*we+re*Nt+le*Et,Cn=v*(T-1-In)+x*(E-1-Jt)+k*Se;for(let Xt=0;Xt<F;++Xt){let Rn=b[Sn+me*Xt],en=y[Cn+Xt];rt+=Rn*en}}}let Zt=te*we+ee*Ee+se*Ye+ne*Se;g[Zt]=rt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var gH={kernelName:Ta,backendName:"cpu",kernelFunc:mH};function bH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;ve([r,a],"conv3d");let l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let we=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)T[we+ut]+=Ce*C[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var yH={kernelName:Kd,backendName:"cpu",kernelFunc:bH};function vH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;ve([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,C,T]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),we=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*C+we;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*T+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let rt=ut*z,Zt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+rt,Jt=Nt*P+Zt;for(let Sn=le;Sn<me;++Sn){let Xt=(re+Sn*h-te)*q+Et,Rn=Sn*R+Jt;for(let en=Ee;en<Pe;++en){let Ls=(Se+en*f-Z)*K+Xt,xi=en*F+Rn;Ce+=$[Ls+Je]*E[xi+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var xH={kernelName:lg,backendName:"cpu",kernelFunc:vH};function wH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;ve([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,C]=l,T=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,we=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let 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TH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),C=w.computeStrides(b.shape);for(let T=0;T<f;T++){let E=T*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[T];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*C[2]+q*C[1]+T*C[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*C[2]+q*C[1]+T*C[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],we=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=we+(Se-we)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*C[2]+q*C[1]+T*C[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*C[2]+q*C[1]+T*C[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*C[2]+q*C[1]+T*C[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var $H={kernelName:lo,backendName:"cpu",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;ve(r,"cumsum");let u=N.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most 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new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:dg,backendName:"cpu",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"depthwiseConv2dNativeBackpropFilter");let p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),C=n.data.get(a.dataId).values,T=new Vt(a.shape,a.dtype,C);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*T.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:pg,backendName:"cpu",kernelFunc:PH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;ve([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,C]=p,T=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let we=me-se,Se=Math.max(0,Math.ceil(we/te)),Ee=Math.min(Y,(F+we)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-we;for(let rt=Je;rt<Ye;++rt){let 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s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),i}function T5(e,t){let n=lv(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(i),o}function s1(e){return e!==2?!1:vs(e).fenceSync!=null}function tu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=X();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>Bm(2)?2:Bm(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",()=>ZC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>JC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:e1(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!cp.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>t1(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",()=>n1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>s1(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",()=>cp.isMobile()?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 hn(){let e,t,n,s,r,a,i,o,u,l;return X().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function gi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function Hp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function $5(e,t){let n=e.length,s=e.map(a=>`${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 _5(e,t,n="index"){let s=e.map((a,i)=>i),r=$5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function cv(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 dv(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var r1=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:a1}=N;function A5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=pv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.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(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>E5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=hn(),u=F5(o),l,c,p=z5(o);return t.isPacked?(l=R5(t.logicalShape,i,n.enableShapeUniforms),c=P5(o)):(l=D5(t.logicalShape,i,n.enableShapeUniforms),c=O5(o)),n.packedInputs&&(p+=V5),[p,u,c,r,l,a,n.userCode].join(`
`)}function nu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return J5(e,t);case 1:return tK(e,t);case 2:return sK(e,t);case 3:return aK(e,t);case 4:return oK(e,t);case 5:return uK(e);case 6:return lK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function i1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Z5(e);case 1:return eK(e,t);case 2:return nK(e,t);case 3:return rK(e,t);default:return iK(e,t)}}function E5(e,t,n=!1,s){let r="";n?r+=i1(e,s):r+=nu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=cK(e,t):r+=dK(e,t)),r}function R5(e,t,n){switch(e.length){case 0:return o1();case 1:return W5(e,t,n);case 2:return Y5(e,t,n);case 3:return G5(e,t,n);default:return q5(e,t,n)}}function D5(e,t,n){switch(e.length){case 0:return o1();case 1:return U5(e,t,n);case 2:return Q5(e,t,n);case 3:return H5(e,t,n);case 4:return j5(e,t,n);case 5:return K5(e,t);case 6:return X5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function F5(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function O5(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function P5(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function z5(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${M5}
${L5}
${B5}
`}var M5=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,L5=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,B5=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,V5=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function o1(){return`
int getOutputCoords() {
return 0;
}
`}function W5(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));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function U5(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function G5(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function H5(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Hp(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=gi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function q5(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o="",u="b, r, c";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`
int b${l} = index / ${i};
index -= b${l} * ${i};
`+o,u=`b${l}, `+u;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${o}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${u});
}
`}function j5(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Hp(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=gi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function K5(e,t){let n=gi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function X5(e,t){let n=gi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function Y5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function Q5(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function bi(e){return`offset${e}`}function Z5(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=hn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function J5(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);
}
`;let i=bi(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
return sampleTexture(${n}, uv);
}
`;let[o,u]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${o}, ${u}, ${i});
return sampleTexture(${n}, uv);
}
`}function eK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=hn();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(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function tK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${su(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let o=bi(n);return i===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
return sampleTexture(${n}, uv);
}
`}function nK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=hn();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]);
return ${u.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${u.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${u.texture2D}(${s}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${u.texture2D}(${s}, uv);
}
`}function sK(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);
}
`;let d=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ru(e,u),h=["row","col"];return`
${nu(d,t)}
float ${r}(int row, int col) {
return ${r}(${au(h,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${su(e)}
}
`;let l=a[0],c=a[1],p=bi(s);return c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${s}, uv);
}
`:l===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${p};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${s}, uv);
}
`}function rK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ru(e,d),m=["b","row","col"];return`
${i1(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${au(m,h)});
}
`}let o=hn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${s}, uv);
}
`;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${u}, ${l}, ${p}, ${c}, b, row, col);
return ${o.texture2D}(${s}, uv);
}
`}function aK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ru(e,l),g=["row","col","depth"];return`
${nu(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${au(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${i}, 1)));
${su(e)}
}
`;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(d===i&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let f=bi(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${p}, ${d}, index);
return sampleTexture(${s}, uv);
}
`}function iK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=hn();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
return ${r.texture2D}(${n}, uv);
}
`}function oK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ru(e,u),v=["row","col","depth","depth2"];return`
${nu(y,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${au(v,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${a}, 1)));
${su(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&c==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let b=bi(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
return sampleTexture(${s}, uv);
}
`}function uK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ru(e,u),g=["row","col","depth","depth2","depth3"];return`
${nu(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${au(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${a}, ${r})) +
depth3;
${su(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&c==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let f=bi(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function lK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ru(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
${nu(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${au(b,a)});
}
`}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${l}, ${u}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${su(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${l}, ${u}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===i&&p==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=bi(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${l} + depth * ${u} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function cK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=a1(e.shapeInfo.logicalShape,t.logicalShape),u=ot(i),l=i-a,c,p=["x","y","z","w","u","v"];a===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`
`);let d="";i<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!b)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h="return vec4(outputValue.x);":o.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${u} coords = getOutputCoords();
${c}
vec4 outputValue = get${s}(${d});
${h}
}
`}function dK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let l=ot(u),c=a1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=["x","y","z","w","u","v"];o===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
`);let f="";return u<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${l} coords = getOutputCoords();
${d}
return get${s}(${f});
}
`}function ot(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 ${e} is not yet supported`)}function pv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ru(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function au(e,t){return t.map(n=>e[n]).join(", ")}function pK(e,t,n,s){let r=n.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:C}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=A5(r,i,t),u=zC(e.gl,o),l=e.createProgram(u),c=null,p=e.getUniformLocation(l,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,d),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,d),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,d),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,d))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,"outShape",d),y=e.getUniformLocation(l,"outShapeStrides",d),b=e.getUniformLocation(l,"outTexShape",d));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,d)}),{program:t,fragmentShader:u,source:o,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function tw(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],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function hK(e,t,n,s,r){t.program.enableShapeUniforms||(tw(t.inShapeInfos,n),tw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=pv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));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((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function fK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=pv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function kn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var mK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Hp(["r","c","d"],e):gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},gK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Hp(["r","c","d"],e):gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},bK=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`
${r1}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},yK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`
${r1}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},vK=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?dv():cv(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},xK=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${o}] = values[0];
} else if (offset == 1) {
result[${o}] = values[1];
} else if (offset == 2) {
result[${o}] = values[2];
} else {
result[${o}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?dv():cv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},wK={};Ae(wK,{bindVertexProgramAttributeStreams:()=>g1,createBufferFromOutputTexture:()=>v1,createFloat16MatrixTexture:()=>p1,createFloat16PackedMatrixTexture:()=>m1,createFloat32MatrixTexture:()=>d1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>f1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>l1,createVertexShader:()=>u1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>w1,downloadFloat32MatrixFromBuffer:()=>x1,downloadMatrixFromPackedOutputTexture:()=>I1,downloadPackedMatrixFromBuffer:()=>k1,getInternalFormatForFloat16MatrixTexture:()=>fv,getInternalFormatForFloat16PackedMatrixTexture:()=>bv,getInternalFormatForFloat32MatrixTexture:()=>hv,getInternalFormatForPackedMatrixTexture:()=>gv,getInternalFormatForUnsignedBytesMatrixTexture:()=>mv,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>y1});function u1(e){let t=hn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return PC(e,n)}function l1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return BC(e,t)}function c1(e){let t=new Uint16Array([0,1,2,2,1,3]);return VC(e,t)}function Yl(e,t,n,s,r,a){UC(t,n);let i=WC(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function hv(e){return e.internalFormatFloat}function d1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,hv(s),s.textureFormatFloat,e.FLOAT)}function fv(e){return e.internalFormatHalfFloat}function p1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,fv(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function mv(e){return e.downloadTextureFormat}function h1(e,t,n,s){let[r,a]=Xl(t,n);return Yl(e,r,a,mv(s),e.RGBA,e.UNSIGNED_BYTE)}function gv(e){return e.internalFormatPackedFloat}function f1(e,t,n,s){let[r,a]=eu(t,n);return Yl(e,r,a,gv(s),e.RGBA,e.FLOAT)}function bv(e){return e.internalFormatPackedHalfFloat}function m1(e,t,n,s){let[r,a]=eu(t,n);return Yl(e,r,a,bv(s),e.RGBA,s.textureTypeHalfFloat)}function g1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Mm(e,t,"clipSpacePos",n,3,20,0)&&Mm(e,t,"uv",n,2,20,12)}function b1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*s*4),o=e.FLOAT,u=a.internalFormatPackedFloat),i.set(r),X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,o,i)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,o,i)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function y1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function v1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let o=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function x1(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 w1(e,t,n,s){let[r,a]=Xl(t,n),i=4,o=new Uint8Array(m5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function k1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array(g5(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function I1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var qf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,p5(t,e)):this.gl=vs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Du(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Du(this.gl,a);else if(X().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),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Du(this.gl,s);else if(X().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",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=l1(this.gl),this.indexBuffer=c1(this.gl),this.framebuffer=GC(this.gl),this.textureConfig=lv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().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;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),d1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),h1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),y1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),b1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),m1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),f1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Lm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>w1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return k1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return x1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=v1(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(X().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 X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>I1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=u1(t));let n=MC(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),LC(t,n),this.debug&&sd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=g1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&sd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?qC(this.gl,e,t):jC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(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(),KC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=eu(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&&sd(this.gl,this.program),Fu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Du(this.gl,X().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(X().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(X().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,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().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=kK(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(),rd(this.gl,e,this.framebuffer),this.debug&&Fu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Fu(this.gl)):Lm(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;rd(s,e,this.framebuffer),this.debug&&Fu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(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 kK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:IK,bincountImpl:S1,bincountReduceImpl:SK,ceilImpl:CK,concatImpl:NK,equalImpl:TK,expImpl:$K,expm1Impl:_K,floorImpl:AK,gatherNdImpl:EK,gatherV2Impl:RK,greaterImpl:DK,greaterEqualImpl:FK,lessImpl:OK,lessEqualImpl:PK,linSpaceImpl:zK,logImpl:MK,maxImpl:LK,maximumImpl:BK,minimumImpl:VK,multiplyImpl:WK,negImpl:UK,notEqualImpl:GK,prodImpl:HK,rangeImpl:qK,rsqrtImpl:jK,sigmoidImpl:KK,simpleAbsImpl:C1,sliceImpl:XK,sparseFillEmptyRowsImpl:YK,sparseReshapeImpl:QK,sparseSegmentReductionImpl:N1,sqrtImpl:ZK,stridedSliceImpl:JK,stringNGramsImpl:eX,stringSplitImpl:tX,stringToHashBucketFastImpl:nX,subImpl:sX,tileImpl:rX,topKImpl:aX,transposeImpl:yv,uniqueImpl:iX}=Yy;function T1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:T1(e,t)}function oX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var uX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=kn(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=un("rc",this.rank),n=ot(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${r}
setOutput(vec4(${a}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${s};
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
cEdge ? 0. : getA(${t[1]}),
rEdge ? 0. : getA(${t[2]}),
rEdge || cEdge ? 0. : getA(${t[3]})`}},$1=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${lX(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?dv():cv(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function lX(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?_5(["r","c","d"],"inputShape"):gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var cX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=sw(t,n),r=rw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=nw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=sw(n,s),a=rw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=nw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dX(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;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nw(e,t,n,s,r){let a=pX(t,s),i;if(r){let[u,l]=eu(e[0],e[1]);i=u*l}else{let[u,l]=Xl(e[0],e[1]);i=u*l}let o=dX(n,a);return i*o}function pX(e,t){switch(e){case 3:return gv(t);case 4:return bv(t);case 1:return hv(t);case 0:return fv(t);case 2:return mv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function hX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function sw(e,t){if(e===1)return 3;if(e===0||e==null)return hX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function rw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},ts="if (isnan(x)) return x;",fX="return x;",aw="return abs(x);",mX="return (x >= 0.0) ? x : (exp(x) - 1.0);",gX=ts+`
return (x < 0.0) ? 0.0 : x;
`,bX=ts+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Pi="return x;",yX="return 1.0 / (1.0 + exp(-1.0 * x));",vX="return x;",xX=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,wX=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,kX=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,IX="return 1.0 / (1.0 + exp(-1.0 * x));",Yr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},SX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let t=e.length,n=un("rc",t),s=ot(t),r=oX(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${i}));
}
`}},CX=xs.whereImpl,NX=1e-7,TX=1e-4,Qc={};function $X(e){return e in Qc||(Qc[e]={}),Qc[e]}var _X=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),AX=600;function EX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*AX/1024/1024}var _1=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof qf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new qf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new qf(n),this.binaryCache=$X(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new cX(this.gpgpu),this.numMBBeforeWarning=EX(),this.texData=new Wd(this,Ss())}nextDataId(){return _1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().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:1,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(X().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:1,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:i,isPacked:o}=t;if(a!=null){let p;o?p=new Yr(i,Pi):p=new Hs(i,Pi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=N.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Yr(s,Pi):h=new Hs(s,Pi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Xc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Yr(r,Pi):d=new Hs(r,Pi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}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 De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Xc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?ad(t):t,o=a?new yK(i):new bK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().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:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_X){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return CX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=C1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aw,e.dtype);let t=new Hs(e.shape,aw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>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 Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new SX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ma(e.shape),...ga(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ma(t),...ga(t)],a=new $1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=ad(r),o;s?o=new gK(i):o=new mK(i);let u=!0,l=[t!=null?t:Xc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Xc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=fK(e,l,c),d=this.getAndSaveBinary(p,()=>pK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),hK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}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||(X().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=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?NX:TX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=QC(n,o),t.texShape=c),r!=null){let p=ad(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=eu(c[0],c[1])),o?d=new xK(p,m):d=new vK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=RX(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)}},A1=_1;A1.nextDataId=0;function RX(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;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var xpe="0.0.0";function DX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}cp.isBrowser()&&dp("webgl",()=>new A1,2);var wpe={forceHalfFloat:DX},E1=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,no=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},qp=`
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;
`,Ql=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=kn(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${ot(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let o=un("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${o[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${o[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${o[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${o[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function En(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 FX={kernelName:Ma,backendName:"webgl",kernelFunc:En};function Er(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=En({inputs:{x:s},backend:n}),u=En({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var OX={kernelName:qd,backendName:"webgl",kernelFunc:Er},R1="return (a < 0.) ? b * a : a;",D1=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(D1,r.shape,i.shape):new no(R1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var zX={kernelName:La,backendName:"webgl",kernelFunc:PX},F1="return (a < 0.) ? b * a : a;",O1=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function MX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(O1,s.shape,r.shape):new no(F1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var LX={kernelName:Qa,backendName:"webgl",kernelFunc:MX},iu="if (isnan(x)) return x;",BX=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,VX=`
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 Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Yr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:u.shape},T={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new no(e,u.shape,l.shape);return c.runWebGLProgram(E,[C,T],yn(x.dtype,k.dtype))}),y=Er({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||yn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?N.fromUint8ToStringArray(f):f,b=u.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ql(t,u.shape,l.shape,n):h=new no(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function jp(e,t=!1){if(e==="linear")return t?vX:fX;if(e==="relu")return t?wX:gX;if(e==="elu")return t?xX:mX;if(e==="relu6")return t?kX:bX;if(e==="prelu")return t?O1:F1;if(e==="leakyrelu")return t?D1:R1;if(e==="sigmoid")return t?IX:yX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=kn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:m=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${c}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${c}; i++) {
int batchA = ${y};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${g}
setOutput(result);
}
`}},iw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ow=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},uw="return a * b;";function vv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new ow(iw.REAL,s.shape,r.shape),c=new ow(iw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=WK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ql(uw,s.shape,r.shape):i=new no(uw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var WX={kernelName:Ka,backendName:"webgl",kernelFunc:vv};function UX(e,t,n){let s=[ma(e.shape),...ga(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ma(t),...ga(t)],i=new $1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!Ju(r.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?UX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var GX={kernelName:Ao,backendName:"webgl",kernelFunc:he},lw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${l}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}},HX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,c=n%4,p=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,d="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${l};
if (${c===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${c===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${c===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${u});
}
`}};function qX(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=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yi(e,t,n,s){let r=qX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new HX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var jX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=KX(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function KX(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var XX=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ot(this.rank),r=T1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${o}) {
result[1] = ${u};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${o}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Kp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XX(e.shape,t):new jX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function YX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Kp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=lp(e.dtype),v=yi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Xp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return YX(r,a,i,n)}var QX={kernelName:ai,backendName:"webgl",kernelFunc:Xp};function cn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=yv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Kp(r,a,i);return l}var ZX={kernelName:ci,backendName:"webgl",kernelFunc:cn},z1=1e3;function Pd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=n?T.shape[1]:T.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?jp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>z1&&q===!1){let Z=T,te=E;n&&(Z=cn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=vv({inputs:{a:ne,b:re},backend:r});K=Xp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=yn(e.dtype,t.dtype),te=new P1(k,C,[P,h,f],n,s,F,W,$,z),ee=[T,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function JX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Pd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var e8={kernelName:na,backendName:"webgl",kernelFunc:JX},cw="return abs(x);";function t8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=C1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,cw):r=new Hs(s.shape,cw),n.runWebGLProgram(r,[s],s.dtype)}var n8={kernelName:ao,backendName:"webgl",kernelFunc:t8},s8=ts+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,r8=Ke({opSnippet:s8}),a8={kernelName:nl,backendName:"webgl",kernelFunc:r8},i8=ts+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:sl,backendName:"webgl",kernelFunc:o8},dw="return a + b;",l8=qt({opSnippet:dw,packedOpSnippet:dw,supportsComplex:!0,cpuKernelImpl:IK}),c8={kernelName:kr,backendName:"webgl",kernelFunc:l8},d8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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(`
`)}
float result = ${s};
setOutput(result);
}
`}},p8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function ud(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return En({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ud({inputs:s.slice(0,u),backend:n}),c=ud({inputs:s.slice(u),backend:n});return ud({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>yn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new p8(s[0].shape,a):new d8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var h8={kernelName:xa,backendName:"webgl",kernelFunc:ud};function f8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("all",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var m8={kernelName:rl,backendName:"webgl",kernelFunc:f8};function g8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("any",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var b8={kernelName:al,backendName:"webgl",kernelFunc:g8},y8=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 i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},v8=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${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 i=this.outputShape,o=i.length,u=ot(o),l=un("coords",o),c,p;if(a===1){p=o+1;let T=ot(p);c=`
${T} sourceLocR = ${T}(${l.join()}, 0);
++${l[o-1]};
${T} sourceLocG = ${T}(${l.join()}, 0);
++${l[o-2]};
${T} sourceLocA = ${T}(${l.join()}, 0);
--${l[o-1]};
${T} sourceLocB = ${T}(${l.join()}, 0);
--${l[o-2]};`}else p=o,c=`
${u} sourceLocR = coords;
++${l[o-1]};
${u} sourceLocG = coords;
++${l[o-2]};
${u} sourceLocA = coords;
--${l[o-1]};
${u} sourceLocB = coords;
--${l[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map(T=>"int "+T),m=un("sourceLocR",p-1).concat("inIdx.r"),g=un("sourceLocG",p-1).concat("inIdx.g"),b=un("sourceLocB",p-1).concat("inIdx.b"),y=un("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${y.join()})));`,k=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,C=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${C}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};
${c}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${k};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${k};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function M1(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 i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new y8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=M1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function L1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new v8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=L1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function B1(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=M1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return L1(e,t,s)}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=B1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var w8={kernelName:wa,backendName:"webgl",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=B1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var I8={kernelName:il,backendName:"webgl",kernelFunc:k8},S8=ts+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,C8=Ke({opSnippet:S8}),N8={kernelName:ol,backendName:"webgl",kernelFunc:C8},T8=ts+"return log(x + sqrt(x * x + 1.0));",$8=Ke({opSnippet:T8}),_8={kernelName:ul,backendName:"webgl",kernelFunc:$8},A8=ts+`
return atan(x);
`,E8=Ke({opSnippet:A8}),R8={kernelName:ll,backendName:"webgl",kernelFunc:E8},D8=BX+`
return atan(a, b);
`,F8=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+VX+`
return result;
`,O8=qt({opSnippet:D8,packedOpSnippet:F8}),P8={kernelName:dl,backendName:"webgl",kernelFunc:O8},z8=ts+`
if ((x < -1.0) || (x > 1.0)) return NAN;
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const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${c};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${l}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${T} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(a/4)*4,k=a%4,C=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${b};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${c};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${l};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
getValue(batch, xR, xC + 3 * ${l}, d)
);
${C}
}
int xC = xCCorner + ${x};
if (${k===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${C}
} else if (${k===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
initializationValue,
initializationValue
);
${C}
} else if (${k===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
initializationValue
);
${C}
}
}
setOutput(${v});
}
`}},xv=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,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${p}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${A} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let C=Math.floor(a/4)*4,T=a%4,E=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
const float initializationValue = ${v};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${C}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${E}
}
int xC = xCCorner + ${C};
if (${T===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${T===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${T===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${E}
}
}
setOutput(${k});
}
}
`}};function B8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new el(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var V8={kernelName:ka,backendName:"webgl",kernelFunc:B8};function W8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new xv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var U8={kernelName:Hd,backendName:"webgl",kernelFunc:W8},G8=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,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${l}, ${c});
const float avgMultiplier = float(${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},H8=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${c};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${l}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function q8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new H8(d);return n.runWebGLProgram(h,[r],i.dtype)}var j8={kernelName:ag,backendName:"webgl",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;tu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=new G8(c);return n.runWebGLProgram(p,[r],i.dtype)}var X8={kernelName:rg,backendName:"webgl",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Pd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Q8={kernelName:Ia,backendName:"webgl",kernelFunc:Y8},Z8=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},J8=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},eY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=X().getBool("WEBGL_PACK_NORMALIZATION")?new J8(s.shape,r.shape,a.shape,c,p,u):new Z8(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},tY={kernelName:Pa,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=sY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Wm[i]} = start[${i}] + coords.${Wm[i]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Wm=["x","y","z","w","u","v"];function sY(e){if(e===1)return"sourceLoc";if(e<=6)return Wm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ot(this.rank),n=un("coords",this.rank),s=un("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function aY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function ou(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=XK(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rY(u):new nY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),aY(r,o,u,n)}var iY={kernelName:Oo,backendName:"webgl",kernelFunc:ou},oY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=ou({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},uY={kernelName:io,backendName:"webgl",kernelFunc:oY};function lY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=S1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var cY={kernelName:ig,backendName:"webgl",kernelFunc:lY};function dY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var pY={kernelName:og,backendName:"webgl",kernelFunc:dY},hY="return float(a != b);",V1=qt({opSnippet:hY,cpuKernelImpl:GK,dtype:"bool"}),fY={kernelName:Io,backendName:"webgl",kernelFunc:V1};function Zl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return En({inputs:{x:r.complexTensorInfos.real},backend:n})}var mY={kernelName:tp,backendName:"webgl",kernelFunc:Zl},gY="return float(int(x));";function bY(e,t){let n=new Hs(e.shape,gY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Um(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return En({inputs:{x:r},backend:n});let i=$t(r.shape),o=Um({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Er({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Zl({inputs:{input:r},backend:n}),o=Um({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=En({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return bY(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=V1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var yY={kernelName:Sa,backendName:"webgl",kernelFunc:Um},pw="return ceil(x);",vY=Ke({opSnippet:pw,packedOpSnippet:pw,cpuKernelImpl:CK}),xY={kernelName:Ca,backendName:"webgl",kernelFunc:vY},wY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},kY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function IY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool("WEBGL_PACK_CLIP")?o=new kY(r.shape):o=new wY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var SY={kernelName:Ir,backendName:"webgl",kernelFunc:IY},CY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function hw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new CY(s.shape),i=[hw(s,r.complexTensorInfos.real),hw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var TY={kernelName:jd,backendName:"webgl",kernelFunc:NY},$Y=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},_Y=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ot(s),a=un("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {
return getChannel(
getT0(${c}), vec2(${l.join()}));
}`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
if (${u} < ${o[f]} && ${u} >= ${o[f-1]}) {
return getChannel(
getT${f}(${Zc(i,u,m)}),
vec2(${Zc(l,u,m)}));
}`}let d=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${d}(${Zc(i,u,h)}),
vec2(${Zc(l,u,h)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Zc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Yp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return En({inputs:{x:r.complexTensorInfos.imag},backend:n})}var AY={kernelName:Qd,backendName:"webgl",kernelFunc:Yp};function Wi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Zl({inputs:{input:m},backend:n})),p=e.map(m=>Yp({inputs:{input:m},backend:n})),d=Wi(c,t,n),h=Wi(p,t,n),f=Er({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=N.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=NK(p,d,s,h),m=N.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=Wi(e.slice(0,c),t,n),d=Wi(e.slice(c),t,n),h=Wi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new _Y(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=EY(e,t,n),o=new $Y(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function EY(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function W1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return En({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Wi(o,a,n)}var RY={kernelName:oo,backendName:"webgl",kernelFunc:W1},U1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${o}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${b}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${k}
${x}
setOutput(result);
}
`}},DY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${c}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},FY=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=kn(this.outputShape.length);let{dataFormat:n}=t,s=hn(),r=n==="channelsLast",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
blockIndex = rc.y + ${c};
pos = rc.x + ${l};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${l*2+c}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${l*2+c}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${u}
${s.output} = result;
}
`}};function G1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>z1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},C=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(Ju(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(T);let E=Pd({a:k,b:T,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=C,A.shape=n.outShape,g=En({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),T=Pd({a:k,b:C,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:T},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(C),b.push(T)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function H1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(C);let T=new FY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(T,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?jp(o,!0):null,W=new P1(P.shape,C.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,C];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function OY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=G1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=H1({x:r,filter:a,convInfo:d,backend:n});else{let m=new U1(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var PY={kernelName:Na,backendName:"webgl",kernelFunc:OY},zY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},MY=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",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${c}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},LY=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,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},BY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${u}, ${l});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new zY(d);return n.runWebGLProgram(h,[r,a],"float32")}var WY={kernelName:ug,backendName:"webgl",kernelFunc:VY};function UY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new MY(d);return n.runWebGLProgram(h,[r,a],"float32")}var GY={kernelName:Ta,backendName:"webgl",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new DY(l);return n.runWebGLProgram(c,[r,a],"float32")}var qY={kernelName:Kd,backendName:"webgl",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=N.computeConv3DInfo(r.shape,u,i,1,o),c=new LY(l);return n.runWebGLProgram(c,[r,a],"float32")}var KY={kernelName:lg,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=N.computeConv3DInfo(u,a.shape,o,1,i),c=new BY(l);return n.runWebGLProgram(c,[r,a],"float32")}var YY={kernelName:cg,backendName:"webgl",kernelFunc:XY},QY=iu+`
return cos(x);
`,ZY=Ke({opSnippet:QY}),JY={kernelName:$a,backendName:"webgl",kernelFunc:ZY},e9=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,t9=Ke({opSnippet:e9}),n9={kernelName:_a,backendName:"webgl",kernelFunc:t9},s9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},r9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new s9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},a9={kernelName:lo,backendName:"webgl",kernelFunc:r9},fw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${mw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${ot(s)} coords = getOutputCoords();
int end = ${gw(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${gw(s,"coords")} = idx;
val += getX(${mw(s,"coords")});
}
setOutput(val);
}
`}};function mw(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 ${e} is not yet supported`)}function gw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function i9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=N.getAxesPermutation([a],u),c=r;l!=null&&(c=cn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=En({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new fw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new fw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var o9={kernelName:uo,backendName:"webgl",kernelFunc:i9};function u9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=S1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=SK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var l9={kernelName:dg,backendName:"webgl",kernelFunc:u9},c9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function d9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new c9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var p9={kernelName:co,backendName:"webgl",kernelFunc:d9},q1=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=kn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?u=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:u=`
float activation(float x) {
${n}
}
`,l="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${u}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${o};
int q = d2 - d1 * ${o};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${c}
${l}
setOutput(result);
}
`}},j1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;d+=`
for (int r = 0; r < ${l}; r++) {
`;for(let g=0;g<c;g++)d+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;d+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`
xC = xCCorner + ${b*u};
`,o===1){if(b<c&&(i%2===1?(d+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
`,u===1&&b>0?d+=`
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
`:d+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${b} = vec4(previous.zw, xTexelC${b}.xy);
} else {
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
}
`):d+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xC${b} = xTexelC${b};
`,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`
xCOffset = xC + imod(pads[1], 2) + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
`,u>1&&(d+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
xTexelC${b}Ready = 1;
}
`),d+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
`):y===1?d+=`
xC${b+1} = xTexelC${b};
`:d+=`
xCOffset = xC + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b+1} = xTexelC${b+1};
`}}else b<c&&(i%2===1?(d+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`,b+1<c&&(d+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
`)):(d+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(
xTexelC${b}.xy, xTexelC${b+1}.xy);
`,b+1<c&&(d+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<c&&(d+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<c&&(d+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}d+=`
}
`,d+=`
}
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${d}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function h9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new j1(p):d=new q1(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var f9={kernelName:Aa,backendName:"webgl",kernelFunc:h9},m9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},g9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new m9(p);return n.runWebGLProgram(d,[r,a],"float32")}var y9={kernelName:pg,backendName:"webgl",kernelFunc:b9};function v9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new g9(p);return n.runWebGLProgram(d,[r,a],"float32")}var x9={kernelName:hg,backendName:"webgl",kernelFunc:v9},w9=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function k9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new w9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var I9={kernelName:fg,backendName:"webgl",kernelFunc:k9},S9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${c}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${l};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function C9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new S9(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var N9={kernelName:Xd,backendName:"webgl",kernelFunc:C9};function T9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=vv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Xp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $9={kernelName:Yd,backendName:"webgl",kernelFunc:T9},_9="return (x >= 0.0) ? x : (exp(x) - 1.0);",A9=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,E9=Ke({opSnippet:_9,packedOpSnippet:A9}),R9={kernelName:Ra,backendName:"webgl",kernelFunc:E9},D9="return (b >= 1.0) ? a : a * (b + 1.0);",F9=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,O9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(F9,s.shape,r.shape):new no(D9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},P9={kernelName:mg,backendName:"webgl",kernelFunc:O9},z9=`
return vec4(equal(a, b));
`,M9="return float(a == b);",L9=qt({opSnippet:M9,packedOpSnippet:z9,dtype:"bool",cpuKernelImpl:TK}),B9={kernelName:po,backendName:"webgl",kernelFunc:L9},V9=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,W9=Ke({opSnippet:V9}),U9={kernelName:pl,backendName:"webgl",kernelFunc:W9},G9=iu+`
return exp(x);
`,H9=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,K1=Ke({opSnippet:G9,packedOpSnippet:H9,cpuKernelImpl:$K,dtype:"float32"}),q9={kernelName:Da,backendName:"webgl",kernelFunc:K1};function Gm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var j9={kernelName:ho,backendName:"webgl",kernelFunc:Gm},bw="return exp(x) - 1.0;",K9=Ke({opSnippet:bw,packedOpSnippet:bw,cpuKernelImpl:_K}),X9={kernelName:fo,backendName:"webgl",kernelFunc:K9},yw=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",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function X1(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new yw("real",u,t),c=new yw("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Y9(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!1,n)}var Q9={kernelName:gg,backendName:"webgl",kernelFunc:Y9},Z9=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 Jl(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Z9(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var J9={kernelName:hl,backendName:"webgl",kernelFunc:Jl},eQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},tQ={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new eQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},vw="return floor(x);",nQ=Ke({opSnippet:vw,packedOpSnippet:vw,cpuKernelImpl:AK}),sQ={kernelName:Fa,backendName:"webgl",kernelFunc:nQ},rQ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,aQ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,iQ=qt({opSnippet:rQ,packedOpSnippet:aQ,dtype:"int32"}),oQ={kernelName:Oa,backendName:"webgl",kernelFunc:iQ},uQ=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},lQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},cQ={kernelName:hd,backendName:"webgl",kernelFunc:dQ},zi;function dQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(zi==null&&(zi=document.createElement("canvas").getContext("2d")),zi.canvas.width=u,zi.canvas.height=l,zi.drawImage(r,0,0,u,l),r=zi.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=X().getBool("WEBGL_PACK")?new lQ(p):new uQ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,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"))b=G1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=H1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,C=h==="leakyrelu",T=h?jp(h,!1):null,E=new U1(g,x,T,k,C),A=[r,a];if(i&&A.push(i),o&&A.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));A.push(P),y.push(P)}b=n.runWebGLProgram(E,A,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var hQ={kernelName:sa,backendName:"webgl",kernelFunc:pQ};function fQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?jp(d,b):null,v=[r,a],x=i!=null,k=o!=null,C=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let T;b?T=new j1(g,x,y,k,C):T=new q1(g,x,y,k,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=n.runWebGLProgram(T,v,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),A}var mQ={kernelName:ra,backendName:"webgl",kernelFunc:fQ},gQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ot(t.length),r=ot(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function bQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=EK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new gQ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var yQ={kernelName:bo,backendName:"webgl",kernelFunc:bQ},vQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),s=xQ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${s}));
}
`}};function xQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=RK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new vQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var wQ={kernelName:go,backendName:"webgl",kernelFunc:Y1},kQ="return float(a > b);",IQ=`
return vec4(greaterThan(a, b));
`,SQ=qt({opSnippet:kQ,packedOpSnippet:IQ,cpuKernelImpl:DK,dtype:"bool"}),CQ={kernelName:yo,backendName:"webgl",kernelFunc:SQ},NQ="return float(a >= b);",TQ=`
return vec4(greaterThanEqual(a, b));
`,$Q=qt({opSnippet:NQ,packedOpSnippet:TQ,dtype:"bool",cpuKernelImpl:FK}),_Q={kernelName:za,backendName:"webgl",kernelFunc:$Q};function AQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!0,n)}var EQ={kernelName:bg,backendName:"webgl",kernelFunc:AQ},RQ="return float(!isnan(x) && !isinf(x));",DQ=Ke({opSnippet:RQ,dtype:"bool"}),FQ={kernelName:fl,backendName:"webgl",kernelFunc:DQ},OQ="return float(isinf(x));",PQ=Ke({opSnippet:OQ,dtype:"bool"}),zQ={kernelName:ml,backendName:"webgl",kernelFunc:PQ},MQ="return float(isnan(x));",LQ=Ke({opSnippet:MQ,dtype:"bool"}),BQ={kernelName:gl,backendName:"webgl",kernelFunc:LQ},VQ="return float(a < b);",WQ=`
return vec4(lessThan(a, b));
`,UQ=qt({opSnippet:VQ,packedOpSnippet:WQ,cpuKernelImpl:OK,dtype:"bool"}),GQ={kernelName:vo,backendName:"webgl",kernelFunc:UQ},HQ="return float(a <= b);",qQ=`
return vec4(lessThanEqual(a, b));
`,jQ=qt({opSnippet:HQ,packedOpSnippet:qQ,cpuKernelImpl:PK,dtype:"bool"}),KQ={kernelName:xo,backendName:"webgl",kernelFunc:jQ};function XQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=zK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var YQ={kernelName:yg,backendName:"webgl",kernelFunc:XQ},QQ=iu+`
return x < 0.0 ? 0./0. : log(x);
`,ZQ=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,JQ=Ke({opSnippet:QQ,packedOpSnippet:ZQ,cpuKernelImpl:MK}),eZ={kernelName:Ba,backendName:"webgl",kernelFunc:JQ},tZ=iu+`
return log(1.0 + x);
`,nZ=Ke({opSnippet:tZ}),sZ={kernelName:bl,backendName:"webgl",kernelFunc:nZ},rZ="return float(a >= 1.0 && b >= 1.0);",aZ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,iZ=qt({opSnippet:rZ,packedOpSnippet:aZ,dtype:"bool"}),oZ={kernelName:wo,backendName:"webgl",kernelFunc:iZ},uZ="return float(!(x >= 1.0));",lZ=Ke({opSnippet:uZ}),cZ={kernelName:yl,backendName:"webgl",kernelFunc:lZ},dZ="return float(a >= 1.0 || b >= 1.0);",pZ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,hZ=qt({opSnippet:dZ,packedOpSnippet:pZ,dtype:"bool"}),fZ={kernelName:Zd,backendName:"webgl",kernelFunc:hZ},mZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},gZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},bZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool("WEBGL_PACK_NORMALIZATION")?new gZ(r.shape,a,i,o,u):new mZ(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},yZ={kernelName:Jd,backendName:"webgl",kernelFunc:bZ},vZ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},xZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new vZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},wZ={kernelName:vg,backendName:"webgl",kernelFunc:xZ};function kZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,e.dtype,"max",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function Q1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[c[T]];let k=yv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let C=n.texData.get(h.dataId);C.values=k}else h=Kp(r,c,n);l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("max",l,o);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=N.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=LK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=kZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var IZ={kernelName:Va,backendName:"webgl",kernelFunc:Q1},SZ=E1+`
return max(a, b);
`,CZ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+qp+`
return result;
`,NZ=qt({opSnippet:SZ,packedOpSnippet:CZ,cpuKernelImpl:BK}),TZ={kernelName:Wa,backendName:"webgl",kernelFunc:NZ};function $Z(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new el(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var _Z={kernelName:Ua,backendName:"webgl",kernelFunc:$Z};function AZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new xv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var EZ={kernelName:ep,backendName:"webgl",kernelFunc:AZ},RZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},DZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
const ivec3 pads = ivec3(${c}, ${p}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${l} +
wR * ${l} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function FZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new xv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new DZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var OZ={kernelName:wg,backendName:"webgl",kernelFunc:FZ};function PZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;tu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=N.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new el(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new RZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var zZ={kernelName:xg,backendName:"webgl",kernelFunc:PZ};function MZ(e,t,n,s){let r=new el(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new el(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var LZ={kernelName:kg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=N.computePool2DInfo(s.shape,r,a,l,i),[p,d]=MZ(s,o,c,u);return[p,d]}};function BZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var VZ={kernelName:Ga,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let C=yv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let T=i.texData.get(f.dataId);T.values=C}else f=Kp(s,c,i);h.push(f),l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=N.expandShapeToKeepDim(m,u));let y=BZ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function WZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"min",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var UZ={kernelName:Ha,backendName:"webgl",kernelFunc:WZ},GZ=E1+`
return min(a, b);
`,HZ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+qp+`
return result;
`,qZ=qt({opSnippet:GZ,packedOpSnippet:HZ,cpuKernelImpl:VK}),jZ={kernelName:qa,backendName:"webgl",kernelFunc:qZ},KZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=ot(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},XZ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${c});
${o[s-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${c});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${c});
${o[s-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${c});
}
rc = outputLoc;
${o[s-2]} += 1;
if(${o[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${u.join()}), ${c});
${o[s-1]} += 1;
if(${l}) {
${h}
result[3] = getChannel(getX(${u.join()}), ${c});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},YZ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XZ(s.shape,r,a):new KZ(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},QZ={kernelName:ja,backendName:"webgl",kernelFunc:YZ},ZZ=`if (b == 0.0) return NAN;
return mod(a, b);`,JZ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+qp+`
return result;
`,e7=qt({opSnippet:ZZ,packedOpSnippet:JZ}),t7={kernelName:vl,backendName:"webgl",kernelFunc:e7},n7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},s7=`
if (a == b) {
return 1.0;
};
return a / b;`,r7=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,Z1=qt({opSnippet:s7,packedOpSnippet:r7,checkOutOfBounds:!0}),a7={kernelName:Ea,backendName:"webgl",kernelFunc:Z1},xw="return a - b;",J1=qt({opSnippet:xw,packedOpSnippet:xw,supportsComplex:!0,cpuKernelImpl:sX}),i7={kernelName:ui,backendName:"webgl",kernelFunc:J1};function e2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=Q1({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=he({inputs:{x:o},backend:n,attrs:{shape:u}}),c=J1({inputs:{a:r,b:l},backend:n}),p=K1({inputs:{x:c},backend:n}),d=Xp({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Z1({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var o7={kernelName:ii,backendName:"webgl",kernelFunc:e2};function u7(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:e2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new n7(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var l7={kernelName:Ig,backendName:"webgl",kernelFunc:u7},c7=ts+`
return -x;
`,d7=`
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;function p7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=UK(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,d7):r=new Hs(s.shape,c7),n.runWebGLProgram(r,[s],s.dtype)}var h7={kernelName:ko,backendName:"webgl",kernelFunc:p7},f7=xs.nonMaxSuppressionV3Impl;function m7(e){N.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:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=f7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var g7={kernelName:So,backendName:"webgl",kernelFunc:m7},b7=xs.nonMaxSuppressionV4Impl;function y7(e){N.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:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=b7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var v7={kernelName:xl,backendName:"webgl",kernelFunc:y7},x7=xs.nonMaxSuppressionV5Impl;function w7(e){N.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:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=x7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var k7={kernelName:Co,backendName:"webgl",kernelFunc:w7},I7=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)));
}
`}},S7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new I7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},C7={kernelName:To,backendName:"webgl",kernelFunc:S7};function zd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Zl({inputs:{input:s},backend:n}),a=zd({inputs:{x:r},backend:n}),i=Yp({inputs:{input:s},backend:n}),o=zd({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var N7={kernelName:Go,backendName:"webgl",kernelFunc:zd};function t2(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=Zl({inputs:{input:s},backend:n}),a=t2({inputs:{x:r},backend:n}),i=Yp({inputs:{input:s},backend:n}),o=zd({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var T7={kernelName:No,backendName:"webgl",kernelFunc:t2};function $7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Gm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Gm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=W1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var _7={kernelName:$o,backendName:"webgl",kernelFunc:$7},A7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=ot(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},E7=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=ot(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
if(${l}) {
`,s===1?"":`}
rc = outputLoc;
${o[s-2]} += 1;
if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${o[s-1]} += 1;
if(${l}) {`],d=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${p[f]}
if (${d}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${u.join()}), ${c});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},n2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return Jl({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new E7(r.shape,a,i):new A7(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},R7={kernelName:Xa,backendName:"webgl",kernelFunc:n2},D7=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,F7=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+qp+`
return result;
`,O7=qt({opSnippet:D7,packedOpSnippet:F7}),P7={kernelName:Ya,backendName:"webgl",kernelFunc:O7};function z7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=N.getAxesPermutation(c,o),d=r;p!=null&&(d=cn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,o),u.push(d)),N.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=HK(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=lp(r.dtype),v=yi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=N.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var M7={kernelName:_o,backendName:"webgl",kernelFunc:z7},s2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=qK(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},L7={kernelName:wl,backendName:"webgl",kernelFunc:s2},B7="return 1.0 / x;",V7=Ke({opSnippet:B7}),W7={kernelName:kl,backendName:"webgl",kernelFunc:V7},U7=ts+`
return (x < 0.0) ? 0.0 : x;
`,G7=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,H7=Ke({opSnippet:U7,packedOpSnippet:G7}),q7={kernelName:Za,backendName:"webgl",kernelFunc:H7},j7=ts+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,K7=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,X7=Ke({opSnippet:j7,packedOpSnippet:K7}),Y7={kernelName:ei,backendName:"webgl",kernelFunc:X7},Q7=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/c[0]},
${l[1]/c[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},Z7=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/c[0]},
${l[1]/c[1]},
${l[1]/c[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function J7(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Z7(r.shape,u,l,a,i):new Q7(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var eJ={kernelName:Ja,backendName:"webgl",kernelFunc:J7},tJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new tJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var sJ={kernelName:Cg,backendName:"webgl",kernelFunc:nJ},rJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/c[0]},
${l[1]/c[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},aJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/c[0]},
${l[1]/c[1]},
${l[1]/c[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aJ(r.shape,u,l,a,i):new rJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var oJ={kernelName:Il,backendName:"webgl",kernelFunc:iJ},uJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function lJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new uJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var cJ={kernelName:Sg,backendName:"webgl",kernelFunc:lJ},dJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(","),a=ot(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},pJ=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-${n} tensor is not yet supported`);this.outputShape=e;let s=un("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(s.slice())};
if(${r}){
result.g = ${u(s.slice())};
}
if(${a}) {
result.b = ${l(s.slice())};
if(${r}) {
result.a = ${c(s.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return En({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pJ(r.shape,o):new dJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var fJ={kernelName:Eo,backendName:"webgl",kernelFunc:hJ},mJ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},gJ={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new mJ(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},bJ=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,yJ=Ke({opSnippet:bJ}),vJ={kernelName:Ro,backendName:"webgl",kernelFunc:yJ},xJ="return inversesqrt(x);",wJ=Ke({opSnippet:xJ,cpuKernelImpl:jK}),kJ={kernelName:ti,backendName:"webgl",kernelFunc:wJ},r2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${c});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function IJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new r2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var SJ={kernelName:Do,backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function NJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new CJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var TJ={kernelName:Fo,backendName:"webgl",kernelFunc:NJ},$J=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,_J=Ke({opSnippet:$J}),AJ={kernelName:Sl,backendName:"webgl",kernelFunc:_J},EJ=iu+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,RJ=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ,cpuKernelImpl:KK}),FJ={kernelName:si,backendName:"webgl",kernelFunc:DJ},OJ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,PJ=Ke({opSnippet:OJ}),zJ={kernelName:Cl,backendName:"webgl",kernelFunc:PJ},MJ=iu+`
return sin(x);
`,LJ=Ke({opSnippet:MJ}),BJ={kernelName:ni,backendName:"webgl",kernelFunc:LJ},VJ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Po,backendName:"webgl",kernelFunc:WJ},GJ=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,HJ=Ke({opSnippet:GJ}),qJ={kernelName:Nl,backendName:"webgl",kernelFunc:HJ},jJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=n2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},KJ={kernelName:zo,backendName:"webgl",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=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:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=YK(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var YJ={kernelName:np,backendName:"webgl",kernelFunc:XJ};function QJ(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 i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=QK(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var ZJ={kernelName:Tl,backendName:"webgl",kernelFunc:QJ};function JJ(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 i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var eee={kernelName:sp,backendName:"webgl",kernelFunc:JJ};function tee(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
${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var nee={kernelName:rp,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=new r2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var ree={kernelName:ap,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=ou({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var iee={kernelName:Mo,backendName:"webgl",kernelFunc:aee},ww="return sqrt(x);",oee=Ke({opSnippet:ww,packedOpSnippet:ww,cpuKernelImpl:ZK}),uee={kernelName:ri,backendName:"webgl",kernelFunc:oee},lee="return x * x;",cee=Ke({opSnippet:lee}),dee={kernelName:$l,backendName:"webgl",kernelFunc:cee},kw="return (a - b) * (a - b);",pee=qt({opSnippet:kw,packedOpSnippet:kw}),hee={kernelName:oi,backendName:"webgl",kernelFunc:pee};function fee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ts+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var mee={kernelName:di,backendName:"webgl",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ot(n.length),a=ot(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),E=ou({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=JK(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new gee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let C=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),C}var yee={kernelName:Lo,backendName:"webgl",kernelFunc:bee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=eX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var xee={kernelName:ip,backendName:"webgl",kernelFunc:vee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=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(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=tX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var kee={kernelName:Ng,backendName:"webgl",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=nX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var See={kernelName:Tg,backendName:"webgl",kernelFunc:Iee},Cee="return tan(x);",Nee=Ke({opSnippet:Cee}),Tee={kernelName:Bo,backendName:"webgl",kernelFunc:Nee},$ee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,_ee=Ke({opSnippet:$ee}),Aee={kernelName:li,backendName:"webgl",kernelFunc:_ee},Eee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=Ree(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Ree(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Eee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var Dee={kernelName:Sr,backendName:"webgl",kernelFunc:a2},Fee=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];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Oee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function Wr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Iw(e){let t=1;for(;t<e;)t*=2;return t}function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=aX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,Jl({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Wr(n,h);let b=Iw(a),y=Iw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new Fee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Wr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Oee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Wr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let C=v;v=ou({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Wr(n,C);let T=Y1({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Wr(n,g);let E=l.slice(0,-1);E.push(a),C=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Wr(n,C);let A=T;return T=he({inputs:{x:T},attrs:{shape:E},backend:n}),Wr(n,A),[T,v]}var zee={kernelName:Vo,backendName:"webgl",kernelFunc:Pee},Mee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Lee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Mee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Bee={kernelName:Wo,backendName:"webgl",kernelFunc:Lee};function Vee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=iX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var Wee={kernelName:$g,backendName:"webgl",kernelFunc:Vee};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gee={kernelName:Uo,backendName:"webgl",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
sumValue += dot(values, segFilter);
`,d="";r%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${p}
}
int inIdx = inOffset + ${l};
if (${c===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${p}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${p}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${p}
}
setOutput(${u});
}
`}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=N.getAxesPermutation([l],o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=N.getInnerMostAxes(1,o)[0]);let d=N.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=lp(r.dtype),g=(x,k,C,T,E)=>{let A=x.shape[0],P=x.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new Hee(F,k),z=n.compileAndRun($,[x,C],T);if(u.push(z),z.shape[1]===E)return z;let W=s2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=a2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,T,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jee={kernelName:op,backendName:"webgl",kernelFunc:qee},Kee=[e8,n8,a8,u8,c8,h8,m8,b8,w8,I8,N8,_8,R8,P8,L8,V8,U8,j8,X8,Q8,tY,uY,cY,pY,yY,xY,SY,OX,TY,RY,PY,WY,GY,qY,KY,YY,JY,n9,a9,o9,l9,p9,f9,y9,x9,I9,N9,$9,R9,P9,B9,U9,q9,j9,X9,Q9,J9,tQ,sQ,oQ,cQ,hQ,mQ,yQ,wQ,CQ,_Q,FX,EQ,AY,FQ,zQ,BQ,zX,GQ,KQ,YQ,eZ,sZ,oZ,cZ,fZ,yZ,wZ,IZ,TZ,_Z,EZ,OZ,zZ,LZ,VZ,UZ,jZ,QZ,t7,l7,WX,h7,g7,v7,k7,fY,C7,T7,_7,R7,P7,LX,M7,L7,mY,a7,W7,q7,Y7,GX,eJ,sJ,oJ,cJ,fJ,gJ,vJ,kJ,SJ,TJ,AJ,FJ,zJ,BJ,UJ,iY,o7,qJ,KJ,YJ,ZJ,eee,nee,ree,iee,uee,dee,hee,mee,yee,xee,kee,See,i7,QX,Tee,Aee,Dee,zee,Bee,ZX,Wee,Gee,jee,N7];for(let e of Kee)_l(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Xee(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function ld(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function wv(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Rr(){return`
${wv()}
fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
localId = LocalId;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
`}function Ue(){return`
${Rr()}
let index = getGlobalIndex();
`}function Yee(e,t,n,s=!1){let r=[];if(r.push(`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),s===!0)return r.push(`
struct Matrix0 {
numbers: array<${ld(t.dtype,n.isVec4)}>;
};
struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
@group(0) @binding(0) var<storage, write> result : Matrix0;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[Sw,r.join(`
`),Cw(t.shape),n.getUserCode()].join(`
`);let a="struct Uniforms { NAN : f32; ";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}; `}),a+=`outShape : ${Wt(t.shape.length)} ; `;let i=t.shape.length-1;a+=`
outShapeStrides: ${Wt(i)}; `,n.size&&(a+="size : i32; "),n.uniforms&&(a+=n.uniforms),a+="};",r.push(a),n.atomic?r.push(`
struct Matrix0 {
numbers: array<atomic<i32>>;
};
@group(0) @binding(0) var<storage, read_write> result : Matrix0;
`):r.push(`
struct Matrix0 {
numbers: array<${ld(t.dtype,n.isVec4)}>;
};
@group(0) @binding(0) var<storage, write> result : Matrix0;
`),n.variableNames.forEach((p,d)=>{r.push(`
struct Matrix${1+d} {
numbers: array<${ld(e[d].dtype,n.isVec4)}>;
};
@group(0) @binding(${1+d}) var<storage, read> ${p} : Matrix${1+d};
`)}),a!==""&&r.push(`
@group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms : Uniforms;
`);let[o,u]=nte(t.shape,n.dispatchLayout),l=[Sw,r.join(`
`),Cw(t.shape),o,Qee(t.shape.length)];if(n.atomic||l.push(Zee(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Jee(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);l.push(p)}return l.push(n.getUserCode()),l.join(`
`)}var Sw=`
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) && all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) && all(coord < shape);
}
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) && all(coord < shape);
}
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(shape.y, 1));
}
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
}
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
// NaN defination in IEEE 754-1985 is :
// - sign = either 0 or 1.
// - biased exponent = all 1 bits.
// - fraction = anything except all 0 bits (since all 0 bits represents infinity).
// https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
fn isnan(val: f32) -> bool {
let floatToUint: u32 = bitcast<u32>(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
}
`;function Qee(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputIndexFromCoords(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
}
`;break;case 3:t+=`
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
}
`;break;case 4:t+=`
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
}
`;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Zee(e,t,n){let s=e.length,r=ld(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result.numbers[flatIndex] = ${r}(value);
}`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result.numbers[flatIndex] = ${r}(value);
}`,s>=2){let i=["d0","d1","d2","d3"].slice(0,s),o=Wt(s);n?a+=`
fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:a+=`
fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return a}function Jee(e,t,n,s){let r=ete(e,n);return e.shape.length<=t.length&&(r+=tte(e,t,n,s)),r}function ete(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}.numbers[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}.numbers[0]);
}
`;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l="1D"),t?`
fn ${a}(${o}) -> vec4<f32> {
return vec4<f32>(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(",")}),
${u}) / 4]);
}
`:`
fn ${a}(${o}) -> f32 {
return f32(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(",")}),
${u})]);
}
`}function tte(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${i}Coords(coords : ${l}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${u>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${i}Coords(coords : ${l}) -> f32 {
return f32(${r}.numbers[${u>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let c=N.getBroadcastDims(e.shape,t),p=u-o,d="";if(o===0)return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${i}Coords(coords : ${l}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${i}Index(globalIndex : i32) -> f32{
return get${a}();
}
fn ${i}Coords(coords : ${l}) -> f32{
return get${a}();
}
`;u<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords[${g+p}] = 0;`).join(`
`);let h="";if(u<2&&o>0)h="coords";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${d}
return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {
var coords = coordsIn;
${d}
return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${d}
return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);
}
fn ${i}Coords(coordsIn : ${l}) -> f32 {
var coords = coordsIn;
${d}
return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);
}
`}function nte(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Xee(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
${i}
`;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Cw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return` fn getCoordsFromIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join("");return`
fn getCoordsFromIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}var i2={};Ae(i2,{ArrayBufferToTypedArray:()=>u2,GPUBytesPerElement:()=>Hm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>kv,computeWorkGroupSizeForMatMul:()=>o2,computeWorkPerThreadForConv2d:()=>Iv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Sv,tilesFitEvenlyIntoShape:()=>Ks});var ea=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ea(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(ea(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(ea(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function kv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function o2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Iv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function Hm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function u2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Sv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var ste="return a + b;",rte="return areal * breal - aimag * bimag;",ate="return areal * bimag + aimag * breal;",ite="return a / b;",ote="return a * b;",ute="return (a - b) * (a - b);",lte="return a - b;",cte="return f32(a == b);",dte="return vec4<f32>(a == b);",pte="return f32(a > b);",hte="return vec4<f32>(a > b);",fte="return f32(a >= b);",mte="return vec4<f32>(a >= b);",gte="return f32(a < b);",bte="return vec4<f32>(a < b);",yte="return f32(a <= b);",vte="return vec4<f32>(a <= b);",xte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",wte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,kte=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,l2=`
if (isNaN.r) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a) {
resultTemp.a = uniforms.NAN;
}
`,Ite=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,Ste=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,Cte="return f32(a != b);",Nte="return vec4<f32>(a != b);",Tte=`
if(a < 0.0 && floor(b) < b) {
return uniforms.NAN;
}
if (b == 0.0) {
return 1.0;
}
if (round(abs(b) % 2.0) != 1.0) {
return pow(abs(a), b);
}
return sign(a) * pow(abs(a), b);
`,$te=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
${l2}
return resultTemp;
`,_te="if (a < 0.0) { return b * a; } return a;",Ate=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function Nw(e,t){let n=t?l2:kte;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function ec(e,t){switch(e){case 0:return ote;case 1:return ste;case 2:return lte;case 3:return ite;case 4:return t?dte:cte;case 5:return t?hte:pte;case 6:return t?mte:fte;case 7:return t?bte:gte;case 8:return t?vte:yte;case 9:return t?wte:xte;case 10:return t?Nte:Cte;case 11:return ute;case 12:return t?Ste:Ite;case 14:return t?Ate:_te;case 15:return Nw("max",t);case 16:return Nw("min",t);case 13:return t?$te:Tte;case 17:return rte;case 18:return ate;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ete="return abs(a);",Rte="return ceil(a);",Dte="return cos(a);",Fte=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Ote="return exp(a) - 1.0;",Pte="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",zte=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Mte="return exp(a);",Lte="return floor(a);",Bte="return a;",Vte=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Wte="return f32(!(a >= 1.0));",Ute="return -a;",Gte="return (a < 0.0) ? b * a : a;",Hte="if (a < 0.0) { return uniforms.alpha * a; } return a;",qte="if(a < 0.0) { return 0.0; } return a;",jte="return clamp(a, 0.0, 6.0);",Kte="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Xte=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isnanVec4(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,Yte="return 1.0/sqrt(a);",Qte="return 1.0 / (1.0 + exp(-1.0 * a));",Zte="return sin(a);",Jte=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,ene="return sqrt(a);",tne="return a * a;",nne=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,sne="return f32(i32((a)));";function Ui(e,t){switch(e){case 0:return Ete;case 2:return Dte;case 3:return Fte;case 1:return Rte;case 4:return t?zte:Pte;case 5:return Mte;case 6:return Ote;case 7:return Lte;case 8:return Bte;case 9:return Vte;case 10:return Wte;case 11:return Ute;case 12:return Gte;case 15:return Hte;case 13:return t?Xte:qte;case 14:return t?Kte:jte;case 16:return Yte;case 19:return Qte;case 17:return Zte;case 18:return Jte;case 20:return ene;case 21:return tne;case 22:return nne;case 23:return sne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Ui(8);if(e==="relu")return Ui(13,t);if(e==="elu")return Ui(4,t);if(e==="relu6")return Ui(14,t);if(e==="prelu")return ec(14,t);if(e==="sigmoid")return Ui(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function c2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${s};
${Rr()}
let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
let tileCol = i32(localId.x);
let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, RowPerThread>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / i32(workGroupSizeY);
let tileRowB = i32(localId.y) * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
}
globalColA = globalColA + TileInner / ColPerThread;
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
for (var i = 0; i < RowPerThread; i = i + 1) {
ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
acc[i] = BCached[3] * ACached.w + acc[i];
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}var rne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${i}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function Cv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Rr()}
let tileRow = i32(localId.y) * ${e[1]};
let tileCol = i32(localId.x) * ${e[0]};
let globalRow = i32(globalId.y) * ${e[1]};
let globalCol = i32(globalId.x) * ${e[0]};
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${r} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${r} / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileColA + innerCol;
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * ${r} + inputCol, globalId);
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${r} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; k = k + 1) {
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
ACached = mm_Asub[tileRow + innerRow][k];
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
if ((globalCol + innerCol) < uniforms.dimBOuter &&
(globalRow + innerRow) < uniforms.dimAOuter) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
}
`}function ane(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Rr()}
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var d2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=o2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
${this.outputShape[1]>1?Cv([this.workPerThread,this.workPerThread,1],this.workGroupSize):ane(this.workGroupSize)}
`}};function ine(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Rr()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var one=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
${e}
}
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
${ine()}
`}};function une(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${Rr()}
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var lne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${i}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${i}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
}
${une(this.workGroupSize)}
`}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var cne={kernelName:Ao,backendName:"webgpu",kernelFunc:Me};function Nv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new one([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new lne(k,C,[P,h,f],a,u,i):R?F=new rne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new d2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[T,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function dne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Nv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var pne={kernelName:na,backendName:"webgpu",kernelFunc:dne},Tw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${ec(this.op,!1)}
}
${Ue()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},hne=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBByOutputCoords(coords);`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${ec(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Ue()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},fne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${ec(this.op,this.isVec4)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},p2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${ec(this.op,!1)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function $w(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new fne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new hne(e,t,n,a):new p2(e,t,n)}function Zn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var mne={kernelName:Ma,backendName:"webgpu",kernelFunc:Zn};function uu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=Zn({inputs:{x:s},backend:n}),u=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var gne={kernelName:qd,backendName:"webgpu",kernelFunc:uu},tc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${Ui(this.op,!1)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new tc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function fn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=$w(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],yn(b.dtype,y.dtype))});else{let g=new Tw(17,i.shape,o.shape),b=new Tw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=uu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||yn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?N.fromUint8ToStringArray(p):p,f=i.dtype==="string"?N.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=$w(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:bne,ceilImpl:yne,concatImpl:vne,equalImpl:xne,expImpl:wne,expm1Impl:kne,floorImpl:Ine,gatherNdImpl:Sne,gatherV2Impl:Cne,greaterEqualImpl:Nne,greaterImpl:Tne,lessEqualImpl:$ne,lessImpl:_ne,logImpl:Ane,maxImpl:Ene,maximumImpl:Rne,minimumImpl:Dne,multiplyImpl:Fne,negImpl:One,notEqualImpl:Pne,prodImpl:zne,rangeImpl:Mne,rsqrtImpl:Lne,simpleAbsImpl:Bne,sliceImpl:Vne,stridedSliceImpl:Wne,stringNGramsImpl:Une,subImpl:Gne,tileImpl:Hne,topKImpl:qne,transposeImpl:jne,uniqueImpl:kpe}=Yy,Kne=jt({opType:0,cpuKernelImpl:Bne}),Xne={kernelName:ao,backendName:"webgpu",kernelFunc:Kne},Yne=fn({opSnippet:1,cpuKernelImpl:bne,supportsComplex:!0}),Qne={kernelName:kr,backendName:"webgpu",kernelFunc:Yne},Zne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Ue()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${e.join(`
`)}
setOutputAtIndex(flatIndex, ${t});
}
}
}
`}};function Jne(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Zn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>yn(o,u)),a=s.map(o=>o.shape),i=new Zne(a);return n.runWebGPUProgram(i,s,r)}var ese={kernelName:xa,backendName:"webgpu",kernelFunc:Jne},h2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${r}]`;return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
let outputCoords = getCoordsFromIndex(outputIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${n(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${t("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Ue()}
let outputIndex = index / i32(workGroupSizeX);
let coordInfo = getInputCoordInfo(outputIndex);
let Length = ${n("uniforms.axis")};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
if (!isnan(candidate) && candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = k;
}
}
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = bestIndex;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
if (candidate ${this.op} bestValue) {
bestValue = candidate;
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
}
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
}
}
`}},tse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${wv()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},nse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=sse(this.newDim);return`
${Ue()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function sse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=jne(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new tse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new nse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var rse={kernelName:ci,backendName:"webgpu",kernelFunc:vi};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var ise={kernelName:wa,backendName:"webgpu",kernelFunc:ase};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var use={kernelName:il,backendName:"webgpu",kernelFunc:ose},f2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutputAtIndex(index, ${t});
}
}
`}},m2=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`}};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new m2(c):(p=new f2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var cse={kernelName:ka,backendName:"webgpu",kernelFunc:lse};function dse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Nv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var pse={kernelName:Ia,backendName:"webgpu",kernelFunc:dse},hse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=fse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${qm[a]} = uniforms.start[${a}] + coords.${qm[a]};`),`
${Ue()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${n.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},qm=["x","y","z","w","u","v"];function fse(e){if(e===1)return"sourceLoc";if(e<=6)return qm.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Vne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new hse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var mse={kernelName:Oo,backendName:"webgpu",kernelFunc:lu},gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},bse={kernelName:io,backendName:"webgpu",kernelFunc:gse},g2=fn({opSnippet:10,dtype:"bool",cpuKernelImpl:Pne}),yse={kernelName:Io,backendName:"webgpu",kernelFunc:g2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var vse={kernelName:tp,backendName:"webgpu",kernelFunc:nc};function xse(e,t){let n=new tc(e.shape,23),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Zn({inputs:{x:r},backend:n});let i=$t(r.shape),o=jm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=uu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=nc({inputs:{input:r},backend:n}),o=jm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return xse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=g2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var wse={kernelName:Sa,backendName:"webgpu",kernelFunc:jm},kse=jt({opType:1,cpuKernelImpl:yne}),Ise={kernelName:Ca,backendName:"webgpu",kernelFunc:kse},Sse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${Ue()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isnan(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputAtIndex(index, clampedValue);
}
}
`}},Cse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${Ue()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Nse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new Sse(r.shape):o=new Cse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Tse={kernelName:Ir,backendName:"webgpu",kernelFunc:Nse},$se=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${Ue()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${e.join(`
`)}
}
}
}
`}};function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var _se={kernelName:Qd,backendName:"webgpu",kernelFunc:Qp};function Km(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>nc({inputs:{input:y},backend:n})),f=e.map(y=>Qp({inputs:{input:y},backend:n})),m=Km(h,t,n),g=Km(f,t,n),b=uu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=vne(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Ase(e,t,n),o=a.map(h=>h.shape),u=new $se(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Ase(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function b2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Km(o,a,n)}var Ese={kernelName:oo,backendName:"webgpu",kernelFunc:b2},Rse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Ue()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
uniforms.pad[0];
let d1 = offsetX + uniforms.dilation[0] * ((pos %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = pos % uniforms.inChannels;
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputAtIndex(flatIndex, value);
}
}
}
`}},Dse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x.numbers[divBy4Index${e}];
if (divBy4Remainder${e} == 0) {
temp = curData${e};
} else {
// TODO: This could end up being a redundant load with another one in
// the same shader invocation. Perhaps there's an opportunity for
// optimization
let nextData${e} = x.numbers[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} else if (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} else if (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let e=c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];
} else {
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
${this.getSampleAWithRemainder(1)}
resData = temp;
if (WCol == (uniforms.filterDims[1] - 1)) {
coord = vec4<i32>(
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} else if (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${s}
}
return vec4<f32>(0.0);
`,a=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",o="";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${c}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaByOutputCoords(outCoord);
${c}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${c}
}`}o="value = activation(value, outCoord);"}let u=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${r}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${a}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${u}
${o}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${e}
`}},Fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Cv(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${u}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${u}
}
`,a="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${i}
${a}
result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},Ose=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${r}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Rr()}
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}};function Pse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Nv({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function zse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,C=[k,x],T=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Rse(C,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,C[0],C[1]]}});A.push(z),A.push(W);let q=[1,C[0],C[1]],K=new d2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a);let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function y2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Pse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return zse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Ose(n,u,o,l);else{h?c=new Dse(n,u,o,l):c=new Fse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),s.runWebGPUProgram(c,g,e.dtype,m)}function Mse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return y2({x:r,filter:a,convInfo:d,backend:s})}var Lse={kernelName:Na,backendName:"webgpu",kernelFunc:Mse},Bse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W.numbers[getIndexFromCoords4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${Cv(this.elementsPerThread,this.workGroupSize)}
`}},Vse=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Ue()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function Wse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Vse(d);else{f=new Bse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Use={kernelName:Ta,backendName:"webgpu",kernelFunc:Wse},Gse=jt({opType:2}),Hse={kernelName:$a,backendName:"webgpu",kernelFunc:Gse},qse=jt({opType:3}),jse={kernelName:_a,backendName:"webgpu",kernelFunc:qse},Kse=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${i};
let in_y = ${r};
if( in_y < 0.0 || in_y > ${e} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let in_x = ${o};
if( in_x < 0.0 || in_x > ${t} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
setOutputAtIndex(index, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
setOutputAtIndex(index, newValue);
}
}
}
`}},Xse=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Kse(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},Yse={kernelName:lo,backendName:"webgpu",kernelFunc:Xse},Qse=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new Qse(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Jse={kernelName:co,backendName:"webgpu",kernelFunc:Zse},v2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${wv()}
fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},x2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${r}
}
`,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
${e}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Rr()}
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / uniforms.channelMul;
let q = d2 - d1 * uniforms.channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + uniforms.filterHeight *
uniforms.dilation[0];
let inputColEnd = inputColStart + uniforms.filterWidth *
uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] &&
inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${n}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.filterHeight===3&&p.inChannels%4===0?h=new v2(p):(h=new x2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var tre={kernelName:Aa,backendName:"webgpu",kernelFunc:ere},w2=fn({opSnippet:0,cpuKernelImpl:Fne,supportsComplex:!0}),nre={kernelName:Ka,backendName:"webgpu",kernelFunc:w2},sre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
if (isnan(candidate)) {
bestValue = uniforms.NAN;
} else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
{ bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`}
fn getOffset(outputIndex : i32) -> i32 {
let outputCoords = getCoordsFromIndex(outputIndex);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Ue()}
let outputIndex = index / i32(workGroupSizeX);
let offset = getOffset(outputIndex);
var bestValue = ${t};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x.numbers[offset + k]);
${e}
}
xBestValues[localId.x] = bestValue;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
${e}
xBestValues[localId.x] = bestValue;
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
${n}
}
}
`}};function sc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=vi({inputs:{x:e},attrs:{perm:l},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(c)),N.assertAxesAreInnerMostDims(s,u,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Ene(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=zne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":lp(e.dtype),x=[{type:"int32",data:[m]}],k=new sre(y,s),C=r.runWebGPUProgram(k,[c],v,x);i.push(C),f=Me({inputs:{x:C},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Tv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"sum",n)}var rre={kernelName:ai,backendName:"webgpu",kernelFunc:Tv};function are(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=vi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=w2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Tv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var ire={kernelName:Yd,backendName:"webgpu",kernelFunc:are},ore=jt({opType:4}),ure={kernelName:Ra,backendName:"webgpu",kernelFunc:ore},lre=fn({opSnippet:4,dtype:"bool",cpuKernelImpl:xne}),cre={kernelName:po,backendName:"webgpu",kernelFunc:lre},k2=jt({opType:5,cpuKernelImpl:wne,dtype:"float32"}),dre={kernelName:Da,backendName:"webgpu",kernelFunc:k2};function Xm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var pre={kernelName:ho,backendName:"webgpu",kernelFunc:Xm},hre=jt({opType:6,cpuKernelImpl:kne}),fre={kernelName:fo,backendName:"webgpu",kernelFunc:hre},mre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function cu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new mre(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var gre={kernelName:hl,backendName:"webgpu",kernelFunc:cu},bre=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`}},yre={kernelName:mo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bre(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},vre=jt({opType:7,cpuKernelImpl:Ine}),xre={kernelName:Fa,backendName:"webgpu",kernelFunc:vre},wre=fn({opSnippet:12,dtype:"int32"}),kre={kernelName:Oa,backendName:"webgpu",kernelFunc:wre},Ire=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},I2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Yee(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function S2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function _w(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=S2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>I2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Sre={kernelName:hd,backendName:"webgpu",kernelFunc:Cre},Mi;function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return _w({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Mi==null&&(Mi=document.createElement("canvas").getContext("2d")),Mi.canvas.width=c,Mi.canvas.height=p,Mi.drawImage(r,0,0,c,p),r=Mi.canvas),l||u||i||o)return _w({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Nre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
${Ue()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},Tre={kernelName:Pa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Nre(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function $re(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return y2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var _re={kernelName:sa,backendName:"webgpu",kernelFunc:$re};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d}=s,h=c;h==null&&(h=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let f=N.computeConv2DInfo(r.shape,a.shape,u,h,l,p,!0),m=[r,a],g=i!=null,b=o!=null;g&&m.push(i),b&&m.push(o);let y=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],v;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4===0?v=new v2(f,g,d,b):(v=new x2(f,g,d,b),y.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),n.runWebGPUProgram(v,m,"float32",y)}var Ere={kernelName:ra,backendName:"webgpu",kernelFunc:Are},Rre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Wt(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
}
}
`}};function Dre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Sne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Rre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Fre={kernelName:bo,backendName:"webgpu",kernelFunc:Dre},Ore=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Pre(this.aShape,"i32");return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Pre(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),C=n.tensorMap.get(d.dataId).values,T=De(d.shape,d.dtype,C),E=Cne(T,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Ore(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var zre={kernelName:go,backendName:"webgpu",kernelFunc:C2},Mre=fn({opSnippet:5,cpuKernelImpl:Tne,dtype:"bool"}),Lre={kernelName:yo,backendName:"webgpu",kernelFunc:Mre},Bre=fn({opSnippet:6,dtype:"bool",cpuKernelImpl:Nne}),Vre={kernelName:za,backendName:"webgpu",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new tc(r.shape,15);return o.uniforms="alpha : f32;",n.runWebGPUProgram(o,[r],"float32",i)}var Ure={kernelName:La,backendName:"webgpu",kernelFunc:Wre},Gre=fn({opSnippet:7,dtype:"bool",cpuKernelImpl:_ne}),Hre={kernelName:vo,backendName:"webgpu",kernelFunc:Gre},qre=fn({opSnippet:8,dtype:"bool",cpuKernelImpl:$ne}),jre={kernelName:xo,backendName:"webgpu",kernelFunc:qre},Kre=jt({opType:9,cpuKernelImpl:Ane}),Xre={kernelName:Ba,backendName:"webgpu",kernelFunc:Kre},Yre=fn({opSnippet:9,dtype:"bool"}),Qre={kernelName:wo,backendName:"webgpu",kernelFunc:Yre},Zre=jt({opType:10}),Jre={kernelName:yl,backendName:"webgpu",kernelFunc:Zre};function N2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return sc(r,a,i,"max",n)}var eae={kernelName:Va,backendName:"webgpu",kernelFunc:N2},tae=fn({opSnippet:15,cpuKernelImpl:Rne}),nae={kernelName:Wa,backendName:"webgpu",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});p=new m2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new f2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var rae={kernelName:Ua,backendName:"webgpu",kernelFunc:sae};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return sc(r,i,a,"mean",n)}var iae={kernelName:Ga,backendName:"webgpu",kernelFunc:aae};function oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"min",n)}var uae={kernelName:Ha,backendName:"webgpu",kernelFunc:oae},lae=fn({opSnippet:16,cpuKernelImpl:Dne}),cae={kernelName:qa,backendName:"webgpu",kernelFunc:lae},dae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Ue()}
if (index < uniforms.size) {
let start = ${i}(${t});
let end = ${i}(${n});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} else if(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${o}));
}
}
`}},pae={kernelName:ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new dae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=One(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new tc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var fae={kernelName:ko,backendName:"webgpu",kernelFunc:hae};function mae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var gae={kernelName:So,backendName:"webgpu",kernelFunc:mae};function bae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var yae={kernelName:Co,backendName:"webgpu",kernelFunc:bae};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var vae={kernelName:Go,backendName:"webgpu",kernelFunc:Md};function T2(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=nc({inputs:{input:s},backend:n}),a=T2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xae={kernelName:No,backendName:"webgpu",kernelFunc:T2};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Xm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Xm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var kae={kernelName:$o,backendName:"webgpu",kernelFunc:wae},Iae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Ue()}
if (index < uniforms.size) {
let start = ${r};
let end = ${a};
let outC = getCoordsFromIndex(index);
if (${i} || ${o}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${u}));
}
}
}
`}},$2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Zn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return cu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Iae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Sae={kernelName:Xa,backendName:"webgpu",kernelFunc:$2},Cae=fn({opSnippet:13}),Nae={kernelName:Ya,backendName:"webgpu",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new p2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var $ae={kernelName:Qa,backendName:"webgpu",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"prod",n)}var Aae={kernelName:_o,backendName:"webgpu",kernelFunc:_ae},Eae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Mne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:wl,backendName:"webgpu",kernelFunc:Eae},_2=fn({opSnippet:3}),Dae={kernelName:Ea,backendName:"webgpu",kernelFunc:_2},Fae=jt({opType:13}),Oae={kernelName:Za,backendName:"webgpu",kernelFunc:Fae},Pae=jt({opType:14}),zae={kernelName:ei,backendName:"webgpu",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`}};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new Mae(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Bae={kernelName:Ja,backendName:"webgpu",kernelFunc:Lae},Vae=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${e};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Vae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Uae={kernelName:Il,backendName:"webgpu",kernelFunc:Wae},Gae=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},Hae={kernelName:Ho,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Gae(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},qae=jt({opType:16,cpuKernelImpl:Lne}),jae={kernelName:ti,backendName:"webgpu",kernelFunc:qae},Kae=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let i=`getUpdates(${s})`,o=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
var assumed = atomicLoad(&(result.numbers[flatIndex]));
var success = 0;
for (; success == 0;) {
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
assumed = resValue[0];
success = resValue[1];
}
`;return`
${a}
${Ue()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${i};
let flatIndex = getOutputIndexFromCoords(${r});
${o}
}
}`}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=cu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Kae(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Yae={kernelName:Do,backendName:"webgpu",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function Zae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Qae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var Jae={kernelName:Fo,backendName:"webgpu",kernelFunc:Zae},eie=jt({opType:19}),tie={kernelName:si,backendName:"webgpu",kernelFunc:eie},nie=jt({opType:17}),sie={kernelName:ni,backendName:"webgpu",kernelFunc:nie},rie=jt({opType:18}),aie={kernelName:Po,backendName:"webgpu",kernelFunc:rie},A2=fn({opSnippet:2,cpuKernelImpl:Gne,supportsComplex:!0}),iie={kernelName:ui,backendName:"webgpu",kernelFunc:A2};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=N2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=A2({inputs:{a:r,b:l},backend:n}),p=k2({inputs:{x:c},backend:n}),d=Tv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=_2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var uie={kernelName:ii,backendName:"webgpu",kernelFunc:oie},lie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=$2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},cie={kernelName:zo,backendName:"webgpu",kernelFunc:lie},die=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Ue()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new die(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var hie={kernelName:ap,backendName:"webgpu",kernelFunc:pie};function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var mie={kernelName:Mo,backendName:"webgpu",kernelFunc:fie},gie=jt({opType:20}),bie={kernelName:ri,backendName:"webgpu",kernelFunc:gie},yie={kernelName:$l,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new tc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},vie=fn({opSnippet:11}),xie={kernelName:oi,backendName:"webgpu",kernelFunc:vie},wie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=wt.computeOutShape(y,v,x),T=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=Me({inputs:{x:T},backend:n,attrs:{shape:f}}),n.disposeData(T.dataId)}else if(n.shouldExecuteOnCPU([r])){let T=n.readSync(r.dataId),E=De(r.shape,r.dtype,T),A=Wne(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let T=new wie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(T,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Iie={kernelName:Lo,backendName:"webgpu",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Une(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Cie={kernelName:ip,backendName:"webgpu",kernelFunc:Sie},Nie=jt({opType:22}),Tie={kernelName:li,backendName:"webgpu",kernelFunc:Nie},$ie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=_ie(this.rank,"uniforms.");return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function _ie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Aie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=Hne(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new $ie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Eie={kernelName:Sr,backendName:"webgpu",kernelFunc:Aie},Rie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced
// above, Figure5(a) shows that element[1] is in the second half of
// the group when group size is 2, but it is in the first half of
// the group when group size is 4.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}},Die=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
// (k=4), we only need to output the indices at positions |, the
// indices at positions _ can be thrown away, see Figure5(b) After
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
// above.
// For example, the paper shows we only need to output the orange
// bars. The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back to
// the previous sequence to find the corresponding value, we need
// to double the index. When we double the index, we basically
// interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
// position of each 2k positions by - elemIdx % k. E.g. for output
// at index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}};function Li(e,t){t!==null&&e.disposeData(t.dataId)}function Aw(e){let t=1;for(;t<e;)t*=2;return t}function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[C,T]=qne(k,o,r.dtype,a,i);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(T.shape,T.dtype,T.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,cu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Aw(a),h=Aw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,C,T)=>{let E=m(),A=new Rie(T),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[C]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Li(n,F)};for(let k=1;k<d;k*=2){let C=k*2;for(let T=k;T>=1;T/=2)g(C,T,[c,h])}for(let k=h;k>d;k/=2){let C=m(),T=new Die([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(T,C,"int32",A),Li(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=lu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Li(n,b);let y=C2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Li(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Li(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Li(n,x),[y,f]}var Oie={kernelName:Vo,backendName:"webgpu",kernelFunc:Fie},Pie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutputAtIndex(index, outputValue);
}
}
`}};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Pie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Mie={kernelName:Wo,backendName:"webgpu",kernelFunc:zie};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Bie={kernelName:Uo,backendName:"webgpu",kernelFunc:Lie},Vie=[pne,Xne,Qne,ese,ise,use,cse,pse,bse,wse,Ise,Tse,gne,Ese,Lse,Use,Hse,jse,Yse,Jse,tre,ire,ure,cre,dre,pre,fre,gre,yre,Sre,xre,kre,Tre,_re,Ere,Fre,zre,Lre,Vre,mne,_se,Ure,Hre,jre,Xre,Qre,Jre,eae,nae,rae,iae,uae,cae,pae,nre,fae,gae,yae,yse,xae,kae,Sae,Nae,$ae,Aae,Rae,vse,Dae,Oae,zae,cne,Bae,Uae,Hae,jae,Yae,Jae,tie,sie,aie,mse,Iie,Cie,uie,cie,hie,mie,bie,yie,xie,iie,rre,Tie,Eie,Oie,Mie,rse,Bie,vae];for(let e of Vie)_l(e);var Wie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Ew(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Ew(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Ew(e,t){return`${e}_${t}`}var E2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
@binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Ue()}
let flatIndexBase = index * uniforms.numChannels;
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndexBase);
let values = ${e};
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Uie=class extends E2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gie=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Rw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},R2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Sv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Wie(this.device),this.tensorMap=new Wd(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return R2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Hm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*Hm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new E2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Uie),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=u2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&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{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let T=this.tensorMap.get(r.dataId);return T.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Rw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(T=>T.shape),o="int32";i.map(T=>{a.push({type:o,data:T})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let T=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?T/4:T]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=c.map(T=>T.dtype).concat(r.dtype),d=c.map(T=>N.getBroadcastDims(T.shape,r.shape)),h=c.map(T=>w.arraysEqual(T.shape,r.shape)).join("_"),f=d.map(T=>T.join("_")).join(";"),m=S2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>I2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ire(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let C={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(C),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Rw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gie){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},$v=R2;$v.nextDataId=0;var Hie={};Ae(Hie,{WebGPUBackend:()=>$v,webgpu_util:()=>i2});Sv()&&dp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new $v(a,r)},3);var Ct=(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",e))(Ct||{}),Zp=(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",e))(Zp||{}),D2;function qie(e){D2=e.wasm.cwrap(na,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Zp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(a.shape).buffer);return D2(d,C,r.shape.length,h,T,a.shape.length,u,l,g,f,m,p||0,k),x}var Kie={kernelName:na,backendName:"wasm",setupFunc:qie,kernelFunc:jie};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Ct[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Xie=Kt(ao);function mn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,Ct[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Yie=!0,Qie=mn(kr,Yie),F2;function Zie(e){F2=e.wasm.cwrap(xa,null,["array","number","number","number"])}function Jie(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(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return F2(a,r.length,Ct[s.dtype],i),s}var eoe={kernelName:xa,backendName:"wasm",setupFunc:Zie,kernelFunc:Jie};function Jp(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 toe={kernelName:Ma,backendName:"wasm",kernelFunc:Jp},O2;function noe(e){O2=e.wasm.cwrap(ci,null,["number","array","number","number","number","array","number"])}function so(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=roe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=soe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Jp({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return O2(c,h,u.shape.length,Ct[u.dtype],p,d,a.length),l}function soe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function roe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var aoe={kernelName:ci,backendName:"wasm",kernelFunc:so,setupFunc:noe};function Dr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=N.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=N.getInnerMostAxes(i.length,r),u=so({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var P2;function ioe(e){P2=e.wasm.cwrap(rl,null,["number, number, number"])}function ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("all",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;P2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:rl,backendName:"wasm",setupFunc:ioe,kernelFunc:ooe},z2;function loe(e){z2=e.wasm.cwrap(al,null,["number, number, number"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("any",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;z2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var doe={kernelName:al,backendName:"wasm",setupFunc:loe,kernelFunc:coe},M2;function poe(e){M2=e.wasm.cwrap(wa,null,["number","number","number","number","number"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Dr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return M2(o,Ct[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var foe={kernelName:wa,backendName:"wasm",kernelFunc:hoe,setupFunc:poe},L2;function moe(e){L2=e.wasm.cwrap(ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function goe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return L2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var boe={kernelName:ka,backendName:"wasm",setupFunc:moe,kernelFunc:goe};function bn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. 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transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],C=bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(C.dataId).id,A=n.dataIdMap.get(T.dataId).id,P=i?C.shape[2]:C.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],C.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(C.shape).buffer),q=new Uint8Array(new Int32Array(T.shape).buffer);return B2(E,W,C.shape.length,A,q,T.shape.length,i,o,z),n.disposeData(C.dataId),n.disposeData(T.dataId),$.shape=v,$}var woe={kernelName:Ia,backendName:"wasm",setupFunc:voe,kernelFunc:xoe};function ba(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Dd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)koe(u,c[0],d,a,i);else if(h===3)Ioe(u,c[0],c[1],d,a,i);else if(h===4)Soe(u,c[0],c[1],c[2],d,a,i);else{let f=Dd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function koe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ioe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Soe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Coe={kernelName:Oo,backendName:"wasm",kernelFunc:ba};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=bn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=so({inputs:{x:h},backend:n,attrs:{perm:l}}),m=bn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:io,backendName:"wasm",kernelFunc:Noe};function rc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $oe={kernelName:Sa,backendName:"wasm",kernelFunc:rc},_oe=Kt(Ca),V2;function Aoe(e){V2=e.wasm.cwrap(Ir,null,["number","number","number","number"])}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return V2(o,a,i,l),u}var Roe={kernelName:Ir,backendName:"wasm",setupFunc:Aoe,kernelFunc:Eoe};function W2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return Jp({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(N.assertParamsConsistent(o,s),a[0].dtype==="string"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return bn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=Jy(f,r,t[0].dtype,m),b=N.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Doe={kernelName:oo,backendName:"wasm",kernelFunc:W2},U2;function Foe(e){U2=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ooe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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ele={kernelName:Ha,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},tle=!1,nle=mn(qa,tle),uN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(uN||{}),lN;function sle(e){lN=e.wasm.cwrap(ja,null,["number","array","number","number","array","array","number","number"])}function rle(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]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return lN(i,l,t.shape.length,Ct[t.dtype],d,h,uN[r],u),o}var ale={kernelName:ja,backendName:"wasm",kernelFunc:rle,setupFunc:sle},ile=!0,ole=mn(Ka,ile),ule=Kt(ko);function _v(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var cN;function lle(e){cN=e.wasm.cwrap(So,"number",["number","number","number","number","number"])}function cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=cN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=_v(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var dle={kernelName:So,backendName:"wasm",setupFunc:lle,kernelFunc:cle},dN;function ple(e){dN=e.wasm.cwrap(xl,"number",["number","number","number","number","number","bool"])}function hle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=dN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_v(t,d);t.wasm._free(m);let 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Nle={kernelName:$o,backendName:"wasm",kernelFunc:Cle},fN;function Tle(e){fN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function $le(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return Y2({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return fN(i,c,t.shape.length,Ct[t.dtype],h,f,r,l),o}var mN={kernelName:Xa,backendName:"wasm",kernelFunc:$le,setupFunc:Tle},_le=!1,Ale=mn(Ya,_le),gN;function Ele(e){gN=e.wasm.cwrap(Qa,null,["number","number","number"])}function 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fce={kernelName:zo,backendName:"wasm",kernelFunc:hce},CN;function mce(e){CN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function gce(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[o+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,C=t.makeOutput([4],"int32"),T=t.dataIdMap.get(C.dataId).id,E=CN(p,d,Ct[r.dtype],o,l,u,h,m,b,v,k,T),A=t.readSync(C.dataId),P;switch(A[0]){case 1:{P=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);break}case 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as ResizeBilinear,Cg as ResizeBilinearGrad,Il as ResizeNearestNeighbor,Sg as ResizeNearestNeighborGrad,Eo as Reverse,Ho as RotateWithOffset,Ro as Round,ti as Rsqrt,kp as SGDOptimizer,Do as ScatterNd,Fo as Select,Sl as Selu,Vb as Sequential,si as Sigmoid,Cl as Sign,ni as Sin,Po as Sinh,Oo as Slice,ii as Softmax,Nl as Softplus,zo as SpaceToBatchND,np as SparseFillEmptyRows,Tl as SparseReshape,sp as SparseSegmentMean,rp as SparseSegmentSum,ap as SparseToDense,Mo as SplitV,ri as Sqrt,$l as Square,oi as SquaredDifference,di as Step,Lo as StridedSlice,ip as StringNGrams,Ng as StringSplit,Tg as StringToHashBucketFast,ui as Sub,ai as Sum,$s as SymbolicTensor,Bo as Tan,li as Tanh,et as Tensor,Vt as TensorBuffer,Sr as Tile,Vo as TopK,Wo as Transform,ci as Transpose,$g as Unique,Uo as Unpack,op as UnsortedSegmentSum,md as Variable,Go as ZerosLike,na as _FusedMatMul,Mt as abs,OA as acos,zA as acosh,ie as add,LA as addN,Bk as all,cm as any,Gu as argMax,GA as argMin,qA as asin,KA as asinh,YA as atan,ZA as atan2,eE as atanh,Wg as avgPool,Gk as avgPool3d,$A as backend,N as backend_util,Ade as basicLSTMCell,qu as batchNorm,vE as batchNorm2d,wE as batchNorm3d,IE as batchNorm4d,Ug as batchToSpaceND,Hk as bincount,rpe as booleanMaskAsync,NE as broadcastArgs,td as broadcastTo,qo as broadcast_util,xk as browser,De as buffer,fpe as callbacks,ce as cast,_E as ceil,Bn as clipByValue,lr as clone,aa as complex,Ft as concat,RE as concat1d,FE as concat2d,PE as concat3d,ME as concat4d,$M as constraints,qk as conv1d,ua as conv2d,jk as conv2dTranspose,Kk as conv3d,qE as conv3dTranspose,mde as copyRegisteredKernels,Hg as cos,Yk as cosh,wI as cosineWindow,Qk as cumsum,js as customGrad,k4 as data,QE as denseBincount,Mk as deprecationWarn,JE as depthToSpace,pp as depthwiseConv2d,gpe as deregisterOp,cp as device_util,Ede as diag,sR as dilation2d,vde as disableDeprecationWarnings,Re as dispose,xde as disposeVariables,xe as div,uR as divNoNan,Rde as dot,H3 as dropout,dR as einsum,hp as elu,yde as enableDebugMode,bde as enableProdMode,q3 as enclosingPowerOfTwo,Ss as engine,X as env,qn as equal,fR as erf,jn as exp,On as expandDims,yR as expm1,Zk as eye,ob as fft,Fl as fill,Tde as findBackend,$de as findBackendFactory,fp as floor,Lk as floorDiv,DX as forceHalfFloat,da as fused,ju as gather,W3 as gatherND,kk as gather_util,Cde as getBackend,Jv as getGradient,Yf as getKernel,Qf as getKernelsForBackend,Npe as getThreadsCount,wK as gpgpu_util,Ode as grad,Pde as grads,Wn as greater,jo as greaterEqual,kd as ifft,qg as imag,ds as image,ipe as inTopKAsync,PM as initializers,CB as input,_n as io,fI as irfft,Dde as isFinite,Fde as isInf,_R as isNaN,Ht as keep,xs as kernel_impls,yL as layers,jg as leakyRelu,Jk as less,Ko as lessEqual,AO as linalg,DR as linspace,bpe as loadGraphModel,ppe as loadLayersModel,OR as localResponseNormalization,Kn as log,Kg as log1p,Lde as logSigmoid,eI as logSoftmax,XR as logSumExp,Ds as logicalAnd,Qg as logicalNot,rI as logicalOr,Bde as logicalXor,lpe as losses,We as matMul,Q_ as math,As as max,Zg as maxPool,aI as maxPool3d,sD as maxPoolWithArgmax,Tr as maximum,It as mean,lm as memory,Vde as meshgrid,KV as metrics,pm as min,gp as minimum,lD as mirrorPad,dD as mod,cpe as model,cW as models,Jg as moments,ape as movingAverage,V as mul,Wde as multiRNNCell,gD as multinomial,kt as neg,RO as nextFrame,vI as norm,Ku as notEqual,yd as oneHot,zn as ones,Xn as onesLike,L as op,Ude as outerProduct,pi as pad,Gde as pad1d,Hde as pad2d,qde as pad3d,jde as pad4d,Kde as pool,ca as pow,tb as prelu,F_ as print,iI as prod,wde as profile,Xde as rand,Yde as randomGamma,zD as randomNormal,Pl as randomUniform,Xu as range,Sde as ready,xd as real,VD as reciprocal,dp as registerBackend,hpe as registerCallbackConstructor,S$ as registerGradient,_l as registerKernel,mpe as registerOp,dW as regularizers,Xs as relu,oI as relu6,Nde as removeBackend,G as reshape,Yn as reverse,Qde as reverse1d,Zde as reverse2d,Jde as reverse3d,epe as reverse4d,ub as rfft,uI as round,lI as rsqrt,Ie as scalar,M3 as scatterND,Sk as scatter_util,cI as selu,JD as separableConv2d,dpe as sequential,ae as serialization,Ide as setBackend,_de as setPlatform,Cpe as setThreadsCount,Ipe as setWasmPath,Spe as setWasmPaths,p5 as setWebGLContext,t3 as setdiff1dAsync,Yy as shared,qs as sigmoid,s3 as sign,upe as signal,dI as sin,pI as sinh,He as slice,rb as slice1d,hI as slice2d,ab as slice3d,wd as slice4d,wt as slice_util,ib as softmax,Ol as softplus,eb as spaceToBatchND,Vc as sparse,xI as sparseToDense,ope as spectral,Ln as split,ln as sqrt,ct as square,mI as squaredDifference,mr as squeeze,Qn as stack,bp as step,k3 as stridedSlice,Pf as string,ge as sub,ye as sum,lp as sumOutType,S3 as tan,Hu as tanh,hs as tensor,Qt as tensor1d,ji as tensor2d,sA as tensor3d,tpe as tensor4d,npe as tensor5d,spe as tensor6d,_s as tensor_util,xA as test_util,j as tidy,cs as tile,kde as time,N3 as topk,Fi as train,qe as transpose,lb as truncatedNormal,cx as unique,fde as unregisterGradient,hde as unregisterKernel,A3 as unsortedSegmentSum,Fs as unstack,yn as upcastType,w as util,zde as valueAndGrad,Mde as valueAndGrads,R3 as variable,MR as variableGrads,$pe as version,ype as version_converter,gde as version_core,vpe as version_cpu,t0 as version_layers,Tpe as version_wasm,xpe as version_webgl,wpe as webgl,d5 as webgl_util,Hie as webgpu,vn as where,bI as whereAsync,$t as zeros,je as zerosLike};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use backend file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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