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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");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return cg.nextTensorId++}nextVariableId(){return cg.nextVariableId++}clone(e){let t=B.runKernel(gi,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(ti,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,A0(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=J2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(J2(e)){let{kernelName:c,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=A0(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:S,dtype:N}=b;return this.makeTensorFromDataId(v,S,N)});if(n){let b=this.getTensorsForGradient(c,m,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,m=f=>{!n||(r=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:d}=e,h=J2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=pg(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&Es(e[0])&&(a=e.map(o=>fh(o)));let s=n.write(a,t,r),i=new nt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Nv(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new nt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Lp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*dg(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof Lp||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 r=e.size*dg(e.dtype);this.state.numBytes-=r}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,r=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of 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s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof nt,()=>"The result y returned by f() must be a tensor.");let s=YR(this.state.activeTape,t,a);if(!n&&s.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,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}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)}};jf.className="Adam";Hi(jf);var Hf=class extends as{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Hf.className="Adamax";Hi(Hf);var Sh=class extends as{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=mr(Se(-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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n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(At(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Nr(ce(l,le(At(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=ce(n,p);n.assign(c)}else{let 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.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 r=St(e),n=!0;for(let s of r)if(!(s instanceof pa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof pa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Eo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of St(e))s.push(i.shape);this.build(tn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=St(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=tn(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=QL(e),i=this.computeOutputShape(s),o,l=eB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new pa(l,u,this,St(e),t,this.name,d)):o=new pa(l,i,this,St(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 ja(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}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 ja(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${f}`);for(let b of A.outputTensors)m.push(b);f.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new da(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("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 r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}NA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${LA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=$g(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=St(e);let r=new Co;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return kp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=St(e);let r;return t==null?r=Oo(null,e.length):r=St(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=C0(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Hc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let d=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],A=`${f.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(tn(d)),p=C0(h),c=u.inboundNodes.indexOf(l);for(let m=0;m<p.length;m++){let f=`${u.name}_${c}_${m}`;r[f]=p[m]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Na(o in r),a.push(r[o])}return tn(a)}runInternalGraph(e,t){t==null&&(t=Oo(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let m of h)m.id in r&&c.push(r[m.id]);if(c.length===h.length){let m={},f,g,y,A;if(u.callArgs!=null&&(m=u.callArgs),c.length===1){let[x,b]=c[0];m.mask==null&&(m.mask=b),y=St(d.call(x,m)),A=St(d.computeMask(x,b)),f=[x],g=[b]}else f=c.map(x=>x[0]),g=c.map(x=>x[1]),m.mask==null&&(m.mask=g),y=St(d.call(f,m)),A=St(d.computeMask(f,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],S=A[x];r[b.id]=[v,S]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Na(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof Ta?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=Ta.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`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 q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=Ta.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=Ta.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let m=[];for(let f=0;f<h.inboundLayers.length;f++){let g=h.inboundLayers[f],y=h.nodeIndices[f],A=h.tensorIndices[f],x=Ta.nodeKey(g,y),b=t[x];b==null&&(b=0),m.push([g.name,b,A,c])}l.push(m)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Ta.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Ta.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],S=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(f,g);return}let N=a[b];if(N.inboundNodes.length<=v){i(f,g);return}let E=N.inboundNodes[v];y.push(E.outputTensors[S])}y.length>0&&f.apply(tn(y),A)}function l(f){let g=f.name,y=fa(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,f.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let f of d)l(f);for(;!TL(s);)for(let f of d){let g=a[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let f of c){let g=f[0],y=f[1],A=f[2];Na(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],A=f[2];Na(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("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(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function nU(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===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!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function x6(e,t){return nU(e,t,"classWeight")}async function b6(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Br(e);if(e.shape.length===2){if(e.shape[1]>1)return Rn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[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(r[o])}),Ct(i,"float32")}else return null}function aU(e,t){return L(e,t)}var sU=32;function v6(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=p4("input",e.inputNames,r),i=p4("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),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 l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function p4(e,t,r){if(r instanceof nt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function iU(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function oU(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=r.validationData!=null,s,i;if(a)if(h4(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=iU(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=p6(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=h6(d,h,r.epochs,null,null,lU(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let m=r.initialEpoch==null?0:r.initialEpoch,f=await t.iterator();for(;m<r.epochs;){let g={};await p.onEpochBegin(m);let y=0,A=0;for(n||(f=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await f.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${r.batchesPerEpoch*r.epochs} batches). 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load handlers for URL '${e}'`);e=r[0]}return bU(e,void 0,t)}async function bU(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=fa(jp(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. 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compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new da("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,r={}){if(!this.built)throw new da("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new da("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,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=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."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof _g))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=fa(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("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 q("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 r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},lm=_g;lm.className="Sequential";ue.registerClass(lm);function wU(e){return new Za(e)}function kU(e){return new lm(e)}function IU(e,t){return t==null&&(t={}),xU(e,t)}function I6(e){return t6(e)}function SU(e,t){PA.registerCallbackConstructor(e,t)}var on=class extends ue.Serializable{getConfig(){return{}}},S6=class extends on{apply(e,t=1){return VL(e,t)}};S6.className="elu";ue.registerClass(S6);var C6=class extends on{apply(e){return aA(e)}};C6.className="selu";ue.registerClass(C6);var T6=class extends on{apply(e){return Oa(e)}};T6.className="relu";ue.registerClass(T6);var N6=class extends on{apply(e){return K(()=>kh(6,Oa(e)))}};N6.className="relu6";ue.registerClass(N6);var E6=class extends on{apply(e){return e}};E6.className="linear";ue.registerClass(E6);var R6=class extends on{apply(e){return Cr(e)}};R6.className="sigmoid";ue.registerClass(R6);var $6=class extends on{apply(e){return GL(e)}};$6.className="hardSigmoid";ue.registerClass($6);var M6=class extends on{apply(e){return cd(e)}};M6.className="softplus";ue.registerClass(M6);var F6=class extends on{apply(e){return UL(e)}};F6.className="softsign";ue.registerClass(F6);var P6=class extends on{apply(e){return ku(e)}};P6.className="tanh";ue.registerClass(P6);var WA=class extends on{apply(e,t=-1){return gd(e,t)}};WA.className="softmax";ue.registerClass(WA);var _6=class extends on{apply(e,t=-1){return qy(e,t)}};_6.className="logSoftmax";ue.registerClass(_6);var z6=class extends on{apply(e,t=1){return K(()=>L(Cr(L(e,t)),e))}};z6.className="swish";ue.registerClass(z6);var O6=class extends on{apply(e){return K(()=>L(e,ku(cd(e))))}};O6.className="mish";ue.registerClass(O6);function js(e){return e.getClassName()}function sg(e,t={}){return Ch(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Hs(e){if(e==null){let t={};return t.className="linear",t.config={},sg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},sg(t)}else return e instanceof on?e:sg(e)}function VA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var D6=class extends ue.Serializable{},$h=class extends D6{constructor(e){super(),VA(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 K(()=>{let t=_t([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,nr(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Nh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};$h.className="L1L2";ue.registerClass($h);function CU(e){return VA(e),new $h({l1:e!=null?e.l1:null,l2:0})}function TU(e){return VA(e),new $h({l2:e!=null?e.l2:null,l1:0})}var g4={l1l2:"L1L2"};function xt(e){return AA(e)}function y4(e,t={}){return Ch(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function $t(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in g4?g4[e]:e,config:{}};return y4(t)}else return e instanceof D6?e:y4(e)}var UA=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let r=Oa(e);return this.maxValue!=null&&(r=cn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};UA.className="ReLU";ue.registerClass(UA);var GA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return Tf(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};GA.className="LeakyReLU";ue.registerClass(GA);var jA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Rt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=$t(e.alphaRegularizer),this.alphaConstraint=lr(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 q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new Xt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=je(e),Pf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:zt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:or(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};jA.className="PReLU";ue.registerClass(jA);var HA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return vh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};HA.className="ELU";ue.registerClass(HA);var qA=class extends st{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 r=je(e);return L(r,me(mn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qA.className="ThresholdedReLU";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new WA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Softmax";ue.registerClass(KA);function xu(e,t,r){if(typeof e=="number")return Oo(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!DL(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ma(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Ea(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Gs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function XA(e,t){return K(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function L6(e,t){return K(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function NU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=Aa()),jt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Ly(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=va(o,r)),o})}function A4(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=Aa()),jt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=XA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Us.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function EU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=Aa()),jt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=L6(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Vy(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=va(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var ZA=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ZA.verifyArgs(t),this.rank=e,gr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xu(t.kernelSize,e,"kernelSize"),this.strides=xu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ln(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=Hs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Rt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=lr(t.biasConstraint),this.biasRegularizer=$t(t.biasRegularizer),this.activityRegularizer=$t(t.activityRegularizer),this.dilationRate=xu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Na("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!xA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:js(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Mh=class extends ZA{constructor(e,t){super(e,t),this.kernel=null,Mh.verifyArgs(t),this.filters=t.filters,gr(this.filters,"filters"),this.kernelInitializer=Rt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=lr(t.kernelConstraint),this.kernelRegularizer=$t(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,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]:r}}],this.built=!0}call(e,t){return K(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=jw(this.activation.getClassName());if(a!=null&&this.rank===2)r=A4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=NU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=A4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=EU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=mt(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ma(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:zt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:or(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},B6=class extends Mh{constructor(e){super(2,e),B6.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!xA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},um=B6;um.className="Conv2D";ue.registerClass(um);var W6=class extends Mh{constructor(e){super(3,e),W6.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},dm=W6;dm.className="Conv3D";ue.registerClass(dm);var YA=class extends um{constructor(e){if(super(e),this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Ea(o,h,u,this.padding),m=Ea(l,p,d,this.padding),f=[a,c,m,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=Wy(r,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=va(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Ea(t[n],o,s,this.padding),t[a]=Ea(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};YA.className="Conv2DTranspose";ue.registerClass(YA);var JA=class extends dm{constructor(e){if(super(e),this.inputSpec=[new Xt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=Ea(l,m,h,this.padding),A=Ea(u,f,p,this.padding),x=Ea(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,4,1]));let v=X7(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=tt(v,[0,4,1,2,3])),this.bias!==null&&(v=va(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Ea(t[n],u,i,this.padding),t[a]=Ea(t[a],d,o,this.padding),t[s]=Ea(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};JA.className="Conv3DTranspose";ue.registerClass(JA);var V6=class extends Mh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=$t(t.depthwiseRegularizer),this.depthwiseConstraint=lr(t.depthwiseConstraint),this.pointwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=$t(t.pointwiseRegularizer),this.pointwiseConstraint=lr(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Xt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),r=gw(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=tt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.pointwiseInitializer=zt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseConstraint),e.pointwiseConstraint=or(this.pointwiseConstraint),e}};V6.className="SeparableConv";var QA=class extends V6{constructor(e){super(2,e)}};QA.className="SeparableConv2D";ue.registerClass(QA);var U6=class extends Mh{constructor(e){super(1,e),U6.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"&&!xA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ex=U6;ex.className="Conv1D";ue.registerClass(ex);var tx=class extends st{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 K(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=Kc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Kc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kc(r,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}};tx.className="Cropping2D";ue.registerClass(tx);var rx=class extends st{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,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,_L(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=tt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rx.className="UpSampling2D";ue.registerClass(rx);function RU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=Aa()),jt(a);let i=XA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=bh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var nx=class extends ZA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Rt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=lr(e.depthwiseConstraint),this.depthwiseRegularizer=$t(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=je(e);let r=RU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=ma(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseRegularizer),e}};nx.className="DepthwiseConv2D";ue.registerClass(nx);function G6(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function j6(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ya(2,l));if(t=tt(t,u),s!=null)throw new Ve("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."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Kt(a,-1)),a=tt(a,u)),n&&(t=_n(t,0),a!=null&&(a=_n(a,0)));let d=[],h,p=r,c=t.shape[0],m=nn(t),f;a!=null&&(f=nn(a));for(let y=0;y<c;++y){let A=m[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=f[y],S=ce(Pn(v),v),N=le(L(x[0],v),L(p[0],S)),E=p.map((R,z)=>le(L(x[1][z],v),L(R,S)));return{output:N,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=ur(d,1)),[h,g,p]})}var H6=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 Xt({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 ya(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ng(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Ng(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Xt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);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(s=>s.shape[s.shape.length-1]),a))throw new q(`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(s=>new Xt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new ja("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("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(n=>_t([r,n])):this.states_=[_t([r,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(n=>_t([r,n])):this.states_[0]=_t([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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 n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>mr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=G6(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof pa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=j6((p,c)=>{let m=this.cell.call([p].concat(c),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=_t(e.shape);return t=ke(t,[1,2]),t=Th(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Cg(t,[1,r]):t):this.cell.stateSize>1?[Cg(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 r=this.cell.getConfig();return this.getClassName()===H6.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=fa(n,r);return new e(Object.assign(t,{cell:a}))}},ss=H6;ss.className="RNN";ue.registerClass(ss);var Fh=class extends st{},pm=class extends Fh{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,gr(this.units,"units"),this.activation=Hs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=$t(e.kernelRegularizer),this.recurrentRegularizer=$t(e.recurrentRegularizer),this.biasRegularizer=$t(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Nu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nu([1,Gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=$a(L(e,s),this.kernel.read()):a=$a(e,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,$a(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};pm.className="SimpleRNNCell";ue.registerClass(pm);var ax=class extends ss{constructor(e){e.cell=new pm(e),super(e)}call(e,t){return K(()=>{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 r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};ax.className="SimpleRNN";ue.registerClass(ax);var hm=class extends Fh{constructor(e){if(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",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gr(this.units,"units"),this.activation=Hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=$t(e.kernelRegularizer),this.recurrentRegularizer=$t(e.recurrentRegularizer),this.biasRegularizer=$t(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Nu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nu([1,Gs([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=mt(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 K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=$a(e,this.kernel.read());this.useBias&&(u=va(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Zt(d,[2*this.units,this.units],d.rank-1),c=$a(n,h),[m,f,g]=Zt(u,3,u.rank-1),[y,A]=Zt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(m,y)),o=this.recurrentActivation.apply(le(f,A));let x=$a(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,Dt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),recurrentActivation:js(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};hm.className="GRUCell";ue.registerClass(hm);var sx=class extends ss{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 hm(e),super(e)}call(e,t){return K(()=>{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 r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sx.className="GRU";ue.registerClass(sx);var Ph=class extends Fh{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,gr(this.units,"units"),this.activation=Hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=$t(e.kernelRegularizer),this.recurrentRegularizer=$t(e.recurrentRegularizer),this.biasRegularizer=$t(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Nu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nu([1,Gs([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=mt(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,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 n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Zn{apply(i,o){let l=a.apply([s]),u=new Jf().apply([s]),d=a.apply([s*2]);return Zb(Zb(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=$a(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,$a(n,this.recurrentKernel.read())),this.useBias&&(h=va(h,this.bias.read()));let[p,c,m,f]=Zt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),recurrentActivation:js(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ph.className="LSTMCell";ue.registerClass(Ph);var ix=class extends ss{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 Ph(e),super(e)}call(e,t){return K(()=>{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 r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ix.className="LSTM";ue.registerClass(ix);var cm=class extends Fh{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 K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){Ng(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{Eo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(fa(a,r));return new e({cells:n})}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 r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Eg(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}NA(t)}};cm.className="StackedRNNCells";ue.registerClass(cm);function qs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):Jw(t(),r),o=()=>Eh(i,t,n);return!a||a<=1?mr(o().clone()):Array(a).fill(void 0).map(o).map(l=>mr(l.clone()))}var q6=class extends ss{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Xt({ndim:5})]}call(e,t){return K(()=>{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 q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}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 K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=_t(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new ja("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("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(a)):this.states_=[_t(a)];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(a)):this.states_[0]=_t(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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 s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>mr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ma(l,n[0],a,s[0],i[0]),h=ma(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};q6.className="ConvRNN2D";var fm=class extends Ph{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,gr(this.filters,"filters"),this.kernelSize=xu(r,2,"kernelSize"),this.kernelSize.forEach(o=>gr(o,"kernelSize")),this.strides=xu(n||1,2,"strides"),this.strides.forEach(o=>gr(o,"strides")),this.padding=a||"valid",Ln(this.padding),this.dataFormat=s||"channelsLast",jt(this.dataFormat),this.dilationRate=xu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>gr(o,"dilationRate"))}build(e){var t;e=mt(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Zn{apply(d,h){let p=l.apply([u]),c=hn([u]),m=l.apply([u*2]);return bA([p,c,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,Q)=>!ee||!ee[Q]?V:L(ee[Q],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,m=l(a,c,0),f=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,S]=Zt(this.kernel.read(),i,A),[N,E,R,z]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,N,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,S,z,this.padding);let[$,I,_,O]=Zt(this.recurrentKernel.read(),i,A);m=this.recurrentConv(m,$),f=this.recurrentConv(f,I),g=this.recurrentConv(g,_),y=this.recurrentConv(y,O);let j=this.recurrentActivation.apply(le(u,m)),X=this.recurrentActivation.apply(le(d,f)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),J=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[J,J,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Bs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?va(a,r,this.dataFormat):a}recurrentConv(e,t){return Bs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};fm.className="ConvLSTM2DCell";ue.registerClass(fm);var ox=class extends q6{constructor(e){let t=new fm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ox.className="ConvLSTM2D";ue.registerClass(ox);var mm=class extends st{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,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Eh(()=>Jw(r,this.rate,a,this.seed),()=>r,n)}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()}};mm.className="Dropout";ue.registerClass(mm);var lx=class extends mm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};lx.className="SpatialDropout1D";ue.registerClass(lx);var ux=class extends st{constructor(e){if(super(e),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,gr(this.units,"units"),this.activation=Hs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=lr(e.kernelConstraint),this.biasConstraint=lr(e.biasConstraint),this.kernelRegularizer=$t(e.kernelRegularizer),this.biasRegularizer=$t(e.biasRegularizer),this.activityRegularizer=$t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(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=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=jw(this.activation.getClassName()),a;return n!=null?a=$a(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=$a(r,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Dense";ue.registerClass(ux);var dx=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new q(`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],Fs(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=tt(r,n)}return WL(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};dx.className="Flatten";ue.registerClass(dx);var px=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=Hs(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.activation.apply(r)})}getConfig(){let e={activation:js(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};px.className="Activation";ue.registerClass(px);var hx=class extends st{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 K(()=>(e=je(e),LL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hx.className="RepeatVector";ue.registerClass(hx);var cx=class extends st{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 r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Fs(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){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 K(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};cx.className="Reshape";ue.registerClass(cx);var fx=class extends st{constructor(e){if(super(e),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 ${e.dims} instead.`);let t=ya(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return tt(je(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Permute";ue.registerClass(fx);var mx=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=je(e),n=-1;return k0(Cu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=k0(Cu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};mx.className="Masking";ue.registerClass(mx);var gx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(St(e.inputLength))}this.inputDim=e.inputDim,gr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,gr(this.outputDim,"outputDim"),this.embeddingsInitializer=Rt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=$t(e.embeddingsRegularizer),this.activityRegularizer=$t(e.activityRegularizer),this.embeddingsConstraint=lr(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 K(()=>this.maskZero?(e=je(e),Cu(e,at(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=St(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);r.dtype!=="int32"&&(r=Zf(r,"int32"));let n=Yw(this.embeddings.read(),G(r,[r.size]));return G(n,mt(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:zt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:or(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};gx.className="Embedding";ue.registerClass(gx);var Ol=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}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 r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ms(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Ms(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Gs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Th(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Fs(u.slice(1))));p=tt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ya(1,l).concat([0]);r.push(tt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(tt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ya(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Ms(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Kt(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=ga(r,t[n]);return r})}},yx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};yx.className="Add";ue.registerClass(yx);var Ax=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};Ax.className="Multiply";ue.registerClass(Ax);var xx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};xx.className="Average";ue.registerClass(xx);var bx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=rs(t,e[r]);return t})}};bx.className="Maximum";ue.registerClass(bx);var vx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=kh(t,e[r]);return t})}};vx.className="Minimum";ue.registerClass(vx);var wx=class extends Ol{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 q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>bA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Pn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Kt(t[s],-1)):n.push(t[s]);let a=It(n,this.axis);return _y(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wx.className="Concatenate";ue.registerClass(wx);function yp(e,t){for(;e<0;)e+=t;return e}function $U(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("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 r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(tt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=rt(o,u)}return o.shape.length===1&&(o=Kt(o,1)),o})}var kx=class extends Ol{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],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>yp(a,e[s].shape.length)):n=[yp(this.axes,t.shape.length),yp(this.axes,r.shape.length)],this.normalize&&(t=R0(t,n[0]),r=R0(r,n[1])),$U(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[yp(this.axes,e.length),yp(this.axes,t.length)],r}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(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};kx.className="Dot";ue.registerClass(kx);var Ix=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=je(e);return Eh(()=>le(Yf(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};Ix.className="GaussianNoise";ue.registerClass(Ix);var Sx=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Eh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Yf(r.shape,1,n))},()=>r,t.training||!1):r})}};Sx.className="GaussianDropout";ue.registerClass(Sx);var Cx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(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 K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Eh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ml(md(r),this.rate);o=Zf(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};Cx.className="AlphaDropout";ue.registerClass(Cx);function Hp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=B7(e,t,r,n,a,s);else if(e.rank===3)i=W7(e,t,r,n,a,s);else if(e.rank===4)i=V7(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function MU(e,t,r,n,a=.001){return K(()=>{let s=Mf(e,n),i=s.mean,o=s.variance;return[Hp(e,i,o,r,t,a),i,o]})}function FU(e,t,r,n,a=.001){return K(()=>{let s=Mf(e,n),i=s.mean,o=s.variance,l=[];for(let c of ya(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Hp(e,u,d,p,h,a),i,o]})}function PU(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ya(0,e.rank-1))?MU(e,t,r,n,a):FU(e,t,r,n,a)}var Tx=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Rt(e.betaInitializer||"zeros"),this.gammaInitializer=Rt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Rt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Rt(e.movingVarianceInitializer||"ones"),this.betaConstraint=lr(e.betaConstraint),this.gammaConstraint=lr(e.gammaConstraint),this.betaRegularizer=$t(e.betaRegularizer),this.gammaRegularizer=$t(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ya(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Oo(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ya(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Hp(n,g,y,A,x,this.epsilon)}else return Hp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,m]=PU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(ce(b,y),x);g.write(ce(b,v))})};return f(this.movingMean,c,this.momentum),f(this.movingVariance,m,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),movingMeanInitializer:zt(this.movingMeanInitializer),movingVarianceInitializer:zt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:or(this.betaConstraint),gammaConstraint:or(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Tx.className="BatchNormalization";ue.registerClass(Tx);var Nx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Rt(e.betaInitializer||"zeros"),this.gammaInitializer=Rt(e.gammaInitializer||"ones"),this.betaRegularizer=$t(e.betaRegularizer),this.gammaRegularizer=$t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ms(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=Mf(r,this.axis,!0),o=Oo(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Gn(s,h),i=Gn(i,h),u=Gn(u,p),d=Gn(d,p),Hp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="LayerNormalization";ue.registerClass(Nx);function _U(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=Aa()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Kn(e,n)})}var Ex=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Aa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=mt(e);let t,r;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?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(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?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>_U(je(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ex.className="ZeroPadding2D";ue.registerClass(Ex);function gm(e,t,r,n,a,s){return K(()=>{jt(a),qw(s),Ln(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=XA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=$f(e,t,r,o):i=kf(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function K6(e,t,r,n,a,s){return K(()=>{jt(a),qw(s),Ln(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=L6(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Yy(e,t,r,o):i=Oy(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var X6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gr(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ln(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=ma(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=Th(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return rt(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends X6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"max")}};Rx.className="MaxPooling1D";ue.registerClass(Rx);var $x=class extends X6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"avg")}};$x.className="AveragePooling1D";ue.registerClass($x);var Z6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`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];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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}},Mx=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"max")}};Mx.className="MaxPooling2D";ue.registerClass(Mx);var Fx=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"avg")}};Fx.className="AveragePooling2D";ue.registerClass(Fx);var Y6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`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];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),n=ma(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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}},Px=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),K6(e,t,r,n,a,"max")}};Px.className="MaxPooling3D";ue.registerClass(Px);var _x=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),K6(e,t,r,n,a,"avg")}};_x.className="AveragePooling3D";ue.registerClass(_x);var J6=class extends st{constructor(e){super(e),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},zx=class extends J6{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return Vt(r,1)})}};zx.className="GlobalAveragePooling1D";ue.registerClass(zx);var Ox=class extends J6{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return yr(r,1)})}};Ox.className="GlobalMaxPooling1D";ue.registerClass(Ox);var Q6=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Dx=class extends Q6{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Vt(r,[1,2]):Vt(r,[2,3])})}};Dx.className="GlobalAveragePooling2D";ue.registerClass(Dx);var Lx=class extends Q6{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?yr(r,[1,2]):yr(r,[2,3])})}};Lx.className="GlobalMaxPooling2D";ue.registerClass(Lx);var ek=class extends st{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,r={}){let n=t.layer,a=fa(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Bx=class extends ek{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new q(`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=mt(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=je(e),j6((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Bx.className="TimeDistributed";ue.registerClass(Bx);function zU(e){_l(PL,"BidirectionalMergeMode",e)}var OU="concat",Wx=class extends ek{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=fa(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=fa(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?OU:e.mergeMode,zU(this.mergeMode),e.weights)throw new Ve("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,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):tn(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=G6(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new Xt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof pa;for(let l of s)if(l instanceof pa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=_n(a,1));let i;return this.mergeMode==="concat"?i=bA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Eo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Eo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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implemented`)}},tH=(e,t,r)=>{switch(e.op){case"Equal":return[$n(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[Cu(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[mn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[Ml(k("a",e,t,r),k("b",e,t,r))];case"Less":return[Hy(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[Fl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[ga(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[Rf(k("a",e,t,r))];case"LogicalOr":return[Zy(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Wr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},nH=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Iu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[Iu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[sw(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[gd(k("x",e,t,r))];case"LogSoftmax":return[qy(k("x",e,t,r))];case"SparseToDense":return[hA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},aH=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[yr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Vt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Ws(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[_y(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[k0(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Rn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[$7(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Jy(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[S0(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[Gy(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[Dy(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[Z7(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sH=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[It(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Su(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[Su(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[_n(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[_n(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[xw(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=rt(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(rt(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[ur(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return nn(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Gn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Zt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[Cw(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Tw(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[hA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},iH=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=wp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=wp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[wp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[wp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},oH=(e,t,r)=>{switch(e.op){case"FFT":return[zf(k("x",e,t,r))];case"IFFT":return[Up(k("x",e,t,r))];case"RFFT":return[Of(k("x",e,t,r))];case"IRFFT":return[lA(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lH=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=a0.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=a0.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[a0.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},uH=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[Kt(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[rt(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[hw(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Kn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[Ff(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[If(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[Y7(k("x",e,t,r),n,a)]}case"BroadcastTo":return[$p(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[U7(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function S4(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>Lj(s,i,o));case"basic_math":return K(()=>Bj(s,i,o));case"control":return Hj(s,i,o);case"convolution":return K(()=>qj(s,i,o));case"creation":return K(()=>Kj(s,i,o));case"dynamic":return Xj(s,i,o);case"evaluation":return K(()=>Zj(s,i,o));case"image":return K(()=>eH(s,i,o));case"graph":return K(()=>Yj(s,i,o));case"logical":return K(()=>tH(s,i,o));case"matrices":return K(()=>rH(s,i,o));case"normalization":return K(()=>nH(s,i,o));case"reduction":return K(()=>aH(s,i,o));case"slice_join":return K(()=>sH(s,i,o));case"sparse":return K(()=>iH(s,i,o));case"spectral":return K(()=>oH(s,i,o));case"string":return K(()=>lH(s,i,o));case"transformation":return K(()=>uH(s,i,o));case"hash_table":return Qj(s,i,o,n);case"custom":let l=pk(s.op);if(l&&l.customExecutor)return l.customExecutor(new Dj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,r);return w.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var C4=class{constructor(e={},t={},r={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=n,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 r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}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 T4(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>dn(p)[0]),d=[];n!=null&&(d=n.map(p=>dn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((Fk(p)||fH(p)||mH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function dH(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>dn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var pH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],hH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],cH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Fk(e){return pH.indexOf(e.op)>=0}function fH(e){return hH.indexOf(e.op)>=0}function mH(e){return cH.indexOf(e.op)>=0}var qg=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(r=>{this._functionExecutorMap[r]=new qg(e.functions[r],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(r=>e[r].map(n=>n.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 r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=T4(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return dH(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=r.map(d=>this.graph.nodes[dn(d)[0]]),a=t.map(d=>dn(d)[0]),s=a.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return K(()=>{let d=new C4(this.weightMap,l,u,this.functionExecutorMap),h={...this.weightMap};Object.keys(e).forEach(m=>{let[f,g]=dn(m),y=[];y[g]=e[m],h[f]=y});let p=this.getFrozenTensorIds(h),c={};for(let m=0;m<o.length;m++){let f=o[m];if(!h[f.name]){let g=S4(f,h,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);h[f.name]=g,this.checkTensorForDisposal(f.name,f,h,d,p,a,c)}}return this.parent==null&&d.dispose(p),t.map(m=>Dr(m,h,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,r,n,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=yj(o.name,r,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!a.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[h,p]=Ra(t.name,n);this.intermediateTensors[h]?this.intermediateTensors[h][p]=u:(this.intermediateTensors[h]=[],this.intermediateTensors[h][p]=u)}delete i[u.id]}else d!=null&&i[u.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(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.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,r=!1,n={},a={}){r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Z().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new C4(this.weightMap,n,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(u=>Dr(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let n=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(n,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,n){let a=Object.keys(e),s=a.map(A=>this.graph.nodes[dn(A)[0]]),i=r.map(A=>dn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:h}=T4(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=dn(A),v=[];v[b]=e[A],c[x]=v});let m={},f=this.getFrozenTensorIds(c),g={};for(;p.length>0;){let A=this.processStack(s,p,t,c,g,f,i,m,l);await Promise.all(A)}d==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!Fk(A)&&!Dr(A.name,c,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${A}`)}return c}processStack(e,t,r,n,a,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();r.currentContext=d.contexts;let h="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,r)&&([h]=Ra(d.node.name,r)),n[d.node.name]==null){let p=S4(d.node,n,r,this._resourceManager);h||([h]=Ra(d.node.name,r));let c=r.currentContext;w.isPromise(p)?u.push(p.then(m=>(n[h]=m,r.currentContext=c,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l),m))):(n[h]=p,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l))}else this.processChildNodes(d.node,t,r,n,a,l)}return 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RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},Lk=class extends Dk{constructor(){super(Lk.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),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},Bk=Lk;Bk.INITIAL_CAPACITY=32;function Wk(e){return new $H(e)}function qx(e){return new MH(e)}function EH(e,t){return new Vk(e,t)}function RH(e,t=Uk.FAIL){return new WH(e,t)}var xr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new LH(this,e)}filter(e){return new OH(this,e)}map(e){return new DH(this,e)}mapAsync(e){return new N4(this,e)}serialMapAsync(e){return new N4(this,e).serial()}flatmap(e){return new BH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new zH(this,e,t)}columnMajorBatch(e,t=!0,r=zk){return this.rowMajorBatch(e,t).map(n=>IH(n,r))}concatenate(e,t){return new Vk(Wk([this,e]),t)}take(e){return e<0||e==null?this:new _H(this,e)}skip(e){return e<0||e==null?this:new PH(this,e)}prefetch(e){return new Gk(this,e)}shuffle(e,t){return new VH(this,e,t)}serial(){return new FH(this)}},$H=class extends xr{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let 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this.upstream.next()}},_H=class extends xr{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zH=class extends xr{constructor(e,t,r=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},OH=class extends xr{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},DH=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},LH=class extends xr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},N4=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Kx=class extends xr{constructor(){super(),this.outputQueue=new Bk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},BH=class extends Kx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await 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xr{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,r=0;function n(s){return s instanceof xr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Ok(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Gk=class extends xr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Dk(t)}summary(){return`${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()}},VH=class extends Gk{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=wH.alea(r||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}}},bd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),un(async()=>(await r.iterator()).columnMajorBatch(e,t,jH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,un(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,un(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return un(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,un(async()=>{let n=qx(async()=>({value:await t.iterator(),done:!1}));return EH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,un(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=vH.alea(t||w.now().toString());return un(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,un(async()=>(await t.iterator()).take(e),r)}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()}};bd.MAX_BUFFER_SIZE=1e4;function un(e,t=null){return new class extends bd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function UH(e){return un(async()=>Wk(e),e.length)}function GH(e){if(!$u(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return un(async()=>{let r=await Ok(e,n=>{if(n instanceof bd)return{value:n.iterator(),recurse:!1};if($u(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return RH(r,1)},t)}function jH(e){if(e===null)return null;let t=e[0];return SH(t)?{value:HH(e),recurse:!1}:{value:null,recurse:!0}}function HH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?ur(e):ft(e)}var jk=class extends bd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Yc='"',xp=Symbol("out"),E4=Symbol("field"),Jc=Symbol("quote"),og=Symbol("quoteafterquote"),R4=Symbol("quoteinquote"),Hk=class extends bd{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 jk(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((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!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),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=xp;for(let i=0;i<a;i++)switch(s){case xp:switch(e.charAt(i)){case Yc:n=i+1,s=Jc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=xp;break;default:s=E4,n=i;break}break;case E4:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=xp,n=i+1;break;default:}break;case Jc:switch(e.charAt(i)){case Yc:s=og;break;default:}break;case og:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=xp,n=i+1;break;case Yc:s=Jc;break;default:s=R4;break}break;case R4:switch(e.charAt(i)){case Yc:s=Jc;break;default:}break;default:}if(s===og?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},qk=class extends xr{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(!Z().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new qk(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(r){throw new Error(`Error thrown while initializing video stream: ${r.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,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({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,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ft(r,t)}},Kk=class extends xr{constructor(e,t){if(super(),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=Ct([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ca([s,a,o,i],[1,4])}else this.cropBox=ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Z().get("IS_BROWSER"))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 r=new Kk(e,t);return await r.start(),r}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=On.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Kt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Xk=class{},Zk=class extends xr{split(e){return new qH(this,e)}},qH=class extends Zk{constructor(e,t){super(),this.upstream=e,this.impl=new KH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},KH=class extends Kx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},XH=class extends xr{decodeUTF8(){return new ZH(this)}},ZH=class extends Zk{constructor(e){super(),this.upstream=e,this.impl=new YH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},YH=class extends Kx{constructor(e){if(super(),this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=vv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Z().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},Yk=class extends XH{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Z().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof 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Xk{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return Jk(this.url)?new Qk(this.url,this.fileOptions).iterator():JH(this.url,this.fileOptions)}};function eq(e,t={}){return new Hk(new e8(e),t)}function tq(e){let t=qx(e);return un(async()=>t)}function rq(e){return un(async()=>{let t=await e();return qx(()=>t.next())})}async function nq(e,t){return Kk.create(e,t)}async function aq(e){return qk.create(e)}var sq="0.0.0";function Ce(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var iq=Xn.whereImpl,t8=class extends _u{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Xp(this,ar())}nextDataId(){return t8.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,Z().get("IS_NODE")&&C.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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o=w.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),d=a;u!=null&&(d=sn({inputs:{x:a},backend:r,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,d.shape.length);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=w.sizeFromShape(p),m=w.makeZerosTypedArray(w.sizeFromShape(h),d.dtype),f=r.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let A=y*c,x=f[A];for(let b=0;b<c;++b){let v=f[A+b];x=x||v}m[y]=x}u!=null&&r.disposeIntermediateTensorInfo(d);let g=r.makeTensorInfo(h,d.dtype,m);if(i){let y=C.expandShapeToKeepDim(h,o),A=Mt({inputs:{x:g},backend:r,attrs:{shape:y}});return r.disposeIntermediateTensorInfo(g),A}return g}var CK={kernelName:Bu,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Ce(a,"argMax");let i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=sn({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=w.sizeFromShape(d),c=w.makeZerosTypedArray(p,"int32"),m=w.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let v=f[y+b];v>A&&(A=v,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var NK={kernelName:Js,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n;Ce(a,"argMin");let i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=sn({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[d,h]=C.computeOutAndReduceShapes(l.shape,i),p=w.sizeFromShape(d),c=w.makeZerosTypedArray(p,"int32"),m=w.sizeFromShape(h),f=r.data.get(l.dataId).values;for(let g=0;g<c.length;++g){let y=g*m,A=f[y],x=0;for(let b=0;b<m;++b){let v=f[y+b];v<A&&(A=v,x=b)}c[g]=x}return u.forEach(g=>r.disposeIntermediateTensorInfo(g)),r.makeTensorInfo(d,"int32",c)}var RK={kernelName:Wu,backendName:"cpu",kernelFunc:EK},$K=gt(Vu,e=>Math.asin(e)),MK={kernelName:Vu,backendName:"cpu",kernelFunc:$K},FK=gt(Uu,e=>Math.asinh(e)),PK={kernelName:Uu,backendName:"cpu",kernelFunc:FK},_K=gt(Gu,e=>Math.atan(e)),zK={kernelName:Gu,backendName:"cpu",kernelFunc:_K},OK=Jt((e,t)=>Math.atan2(e,t)),DK=br(Hu,OK),LK={kernelName:Hu,backendName:"cpu",kernelFunc:DK},BK=gt(ju,e=>Math.atanh(e)),WK={kernelName:ju,backendName:"cpu",kernelFunc:BK};function s5(e,t,r,n,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,u=a.dilationWidth,d=a.effectiveFilterHeight,h=a.effectiveFilterWidth,p=a.padInfo.top,c=a.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=We(a.outShape,r),g=f.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],A=a.outShape[2]*a.outShape[3],x=a.outShape[3];for(let b=0;b<a.batchSize;++b){let v=b*y,S=b*n[0];for(let N=0;N<a.inChannels;++N)for(let E=0;E<a.outHeight;++E){let 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j=O-N,X=f.get(g,I,O,y);X>z&&(z=X,a?$=s?((g*n.inHeight+I)*n.inWidth+O)*n.inChannels+y:(I*n.inWidth+O)*n.inChannels+y:$=_*p+j)}}i.set($,g,A,S,y)}}return i}function q8(e,t,r,n,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,u=a.dilationDepth,d=a.dilationHeight,h=a.dilationWidth,p=a.effectiveFilterDepth,c=a.effectiveFilterHeight,m=a.effectiveFilterWidth,f=a.padInfo.front,g=a.padInfo.top,y=a.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(a.outShape,r),b=x.values,v=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],S=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],E=a.outShape[4];for(let R=0;R<a.batchSize;++R){let z=R*v,$=R*n[0];for(let I=0;I<a.inChannels;++I)for(let _=0;_<a.outDepth;++_){let O=_*i-f,j=O;for(;j<0;)j+=u;let X=Math.min(a.inDepth,p+O),D=z+_*S;for(let J=0;J<a.outHeight;++J){let V=J*o-g,ee=V;for(;ee<0;)ee+=d;let Q=Math.min(a.inHeight,c+V),ie=D+J*N;for(let Y=0;Y<a.outWidth;++Y){let ae=Y*l-y,de=ae;for(;de<0;)de+=h;let Ae=Math.min(a.inWidth,m+ae),be=ie+Y*E,Ee=A,$e=0,De=0;for(let Ze=j;Ze<X;Ze+=u){let ot=$+Ze*n[1];for(let pt=ee;pt<Q;pt+=d){let ht=ot+pt*n[2];for(let Fe=de;Fe<Ae;Fe+=h){let wt=ht+Fe*n[3],yt=e[wt+I];if(s==="max"&&yt>Ee?Ee=yt:s==="avg"&&($e+=yt,De++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let Be=be+I;b[Be]=s==="avg"?$e/De:Ee}}}}return x}function VK(e,t){let r=We(t.outShape,"int32"),n=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,p=t.padInfo.front,c=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*n-p,x=A;for(;x<0;)x+=i;let b=Math.min(t.inDepth,u+A);for(let v=0;v<t.outHeight;++v){let S=v*a-c,N=S;for(;N<0;)N+=o;let E=Math.min(t.inHeight,d+S);for(let R=0;R<t.outWidth;++R){let z=R*s-m,$=z;for(;$<0;)$+=l;let I=Math.min(t.inWidth,h+z),_=Number.NEGATIVE_INFINITY,O=-1;for(let j=x;j<b;j+=i){let X=j-A;for(let D=N;D<E;D+=o){let J=D-S;for(let V=$;V<I;V+=l){let ee=V-z,Q=e.get(f,j,D,V,g);Q>=_&&(_=Q,O=X*d*h+J*d+ee)}}}r.set(O,f,y,v,R,g)}}}return r}function UK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Ce(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l),h;if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))h=_a({inputs:{x:a},backend:r});else{let p=r.data.get(a.dataId).values,c=w.computeStrides(a.shape),m=s5(p,a.shape,a.dtype,c,d,"avg");h=r.makeTensorInfo(d.outShape,a.dtype,m.values)}return h}var GK={kernelName:Qs,backendName:"cpu",kernelFunc:UK};function jK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ce(a,"avgPool3d");let d=C.computePool3DInfo(a.shape,s,i,1,o,l,u),h=r.data.get(a.dataId).values,p=q8(h,a.shape,a.dtype,w.computeStrides(a.shape),d,"avg");return r.makeTensorInfo(p.shape,"float32",p.values)}var HK={kernelName:Zp,backendName:"cpu",kernelFunc:jK};function qK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ce([a,s],"avgPool3DGrad");let d=C.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,m=d.filterDepth,f=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,v=d.effectiveFilterHeight,S=d.effectiveFilterWidth,N=b-1-d.padInfo.front,E=S-1-d.padInfo.left,R=v-1-d.padInfo.top,z=We(s.shape,"float32"),$=1/(m*f*g),I=r.bufferSync(a);for(let _=0;_<d.batchSize;++_)for(let O=0;O<d.inChannels;++O)for(let j=0;j<d.inDepth;++j)for(let X=0;X<d.inHeight;++X)for(let D=0;D<d.inWidth;++D){let J=j-N,V=X-R,ee=D-E,Q=0;for(let ie=0;ie<b;ie+=y){let Y=(J+ie)/h;if(!(Y<0||Y>=d.outDepth||Math.floor(Y)!==Y))for(let ae=0;ae<v;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let Ae=0;Ae<S;Ae+=x){let be=(ee+Ae)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(Q+=I.get(_,Y,de,be,O))}}}z.set(Q*$,_,j,X,D,O)}return r.makeTensorInfo(z.shape,z.dtype,z.values)}var KK={kernelName:Z0,backendName:"cpu",kernelFunc:qK};function XK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Ce([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,v=We(i.shape,"float32"),S=1/(c*m),N=r.data.get(a.dataId).values,E=We(a.shape,"float32",N);for(let R=0;R<d.batchSize;++R)for(let z=0;z<d.inChannels;++z)for(let $=0;$<d.inHeight;++$)for(let I=0;I<d.inWidth;++I){let _=$-b,O=I-x,j=0;for(let X=0;X<y;X+=f){let D=(_+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let J=0;J<A;J+=g){let V=(O+J)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,z))}}v.set(j*S,R,$,I,z)}return r.makeTensorInfo(v.shape,v.dtype,v.values)}var ZK={kernelName:X0,backendName:"cpu",kernelFunc:XK};function YK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ce([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let d=r.data.get(a.dataId).values,h=r.data.get(o.dataId).values,p=r.data.get(l.dataId).values,c=s?r.data.get(s.dataId).values:new Float32Array([1]),m=i?r.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),g=m.length,y=c.length,A=p.length,x=h.length,b=0,v=0,S=0,N=0;for(let E=0;E<d.length;++E)f[E]=m[b++]+(d[E]-h[v++])*c[S++]/Math.sqrt(p[N++]+u),b>=g&&(b=0),v>=x&&(v=0),S>=y&&(S=0),N>=A&&(N=0);return r.makeTensorInfo(a.shape,a.dtype,f)}var JK={kernelName:fi,backendName:"cpu",kernelFunc:YK};function QK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Ce([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),m=sn({inputs:{x:c},backend:r,attrs:{perm:u}}),f=Mt({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Lo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(f),g}var eX={kernelName:Ho,backendName:"cpu",kernelFunc:QK};function tX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Yx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var rX={kernelName:Y0,backendName:"cpu",kernelFunc:tX};function nX(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var aX={kernelName:J0,backendName:"cpu",kernelFunc:nX},sX=gt(es,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),iX={kernelName:es,backendName:"cpu",kernelFunc:sX},oX=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(w.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},lX={kernelName:Jp,backendName:"cpu",kernelFunc:oX};function Mu(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.imag,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var uX={kernelName:rh,backendName:"cpu",kernelFunc:Mu};function Fu(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>w.sizeFromShape(f.shape)>0);if(o.length===1)return _a({inputs:{x:o[0]},backend:r});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(b=>Do({inputs:{input:b},backend:r})),g=o.map(b=>Mu({inputs:{input:b},backend:r})),y=Fu({inputs:f,backend:r,attrs:{axis:s}}),A=Fu({inputs:g,backend:r,attrs:{axis:s}}),x=pn({inputs:{real:y,imag:A},backend:r});return 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ir(p.outShape,a.dtype),v=w.computeStrides(a.shape),S=w.computeStrides(s.shape),N=v[0],E=x?v[1]:v[2],R=x?v[2]:1,z=x?1:v[1],$=b.strides[0],I=x?b.strides[1]:b.strides[2],_=x?b.strides[2]:1,O=x?1:b.strides[1],j=r.data.get(a.dataId).values,X=r.data.get(s.dataId).values,D=b.values;for(let J=0;J<p.batchSize;++J){let V=J*N,ee=J*$;for(let Q=0;Q<p.outHeight;++Q){let ie=ee+Q*I,Y=Q*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=Y+ae*f;if(de<0||de>=p.inHeight)continue;let Ae=ae*S[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let $e=ie+Ee*_,De=Ee*p.strideWidth-y;for(let Be=0;Be<m;++Be){let Ze=De+Be*g;if(Ze<0||Ze>=p.inWidth)continue;let ot=Ae+Be*S[1],pt=be+Ze*R,ht=ot;for(let Fe=0;Fe<p.inChannels;++Fe){let wt=j[pt+Fe*z];for(let yt=0;yt<p.outChannels;++yt)D[$e+yt*O]+=wt*X[ht+yt];ht+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var pX={kernelName:ni,backendName:"cpu",kernelFunc:K8};function hX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Ce([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:m,filterHeight:f,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new ir(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=r.data.get(a.dataId).values,S=r.data.get(s.dataId).values,N=new ir(a.shape,a.dtype,v),E=new ir(s.shape,s.dtype,S);for(let R=0;R<f;++R){let z=Math.max(0,Math.ceil((b-R)/c)),$=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let I=0;I<g;++I){let _=Math.max(0,Math.ceil((x-I)/m)),O=Math.min(p.outWidth,(p.inWidth+x-I)/m);for(let j=0;j<p.inChannels;++j)for(let X=0;X<p.outChannels;++X){let D=0;for(let J=0;J<p.batchSize;++J)for(let V=z;V<$;++V){let ee=R+V*c-b;for(let Q=_;Q<O;++Q){let ie=I+Q*m-x;y?D+=N.get(J,ee,ie,j)*E.get(J,V,Q,X):D+=N.get(J,j,ee,ie)*E.get(J,X,V,Q)}}A.set(D,R,I,j,X)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var cX={kernelName:Q0,backendName:"cpu",kernelFunc:hX};function fX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Ce([a,s],"conv2dBackpropInput");let h=w.computeStrides(s.shape),p=w.computeStrides(a.shape),c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),f=new ir(m.inShape,"float32"),g=f.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=h,{batchSize:S,filterHeight:N,filterWidth:E,inChannels:R,inHeight:z,inWidth:$,outChannels:I,outHeight:_,outWidth:O,strideHeight:j,strideWidth:X}=m;c=m.dataFormat;let D=N-1-m.padInfo.top,J=E-1-m.padInfo.left,V=c==="channelsLast",ee=f.strides[0],Q=V?f.strides[1]:f.strides[2],ie=V?f.strides[2]:1,Y=V?1:f.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<S;++Ee)for(let $e=0;$e<R;++$e)for(let De=0;De<z;++De){let 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u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,m=h.strideWidth,f=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new ir(h.filterShape,"float32"),x=A.values,[b,v,S,N]=A.strides,E=r.data.get(s.dataId).values,[R,z,$,I]=d,_=r.data.get(a.dataId).values,[O,j,X,D]=u,J=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let Q=0;Q<f;++Q){let ie=Math.max(0,Math.ceil((J-Q)/p)),Y=Math.min(h.outDepth,(h.inDepth+J-Q)/p),ae=Q*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*v+ae;for(let $e=0;$e<y;++$e){let De=Math.max(0,Math.ceil((V-$e)/m)),Be=Math.min(h.outWidth,(h.inWidth+V-$e)/m),Ze=$e*S+Ee;for(let ot=0;ot<h.inChannels;++ot){let pt=ot*N+Ze;for(let ht=0;ht<h.outChannels;++ht){let Fe=0;for(let wt=0;wt<h.batchSize;++wt){let yt=wt*O,Fr=wt*R;for(let hr=ie;hr<Y;++hr){let Jr=(Q+hr*p-J)*j+yt,tr=hr*z+Fr;for(let cr=Ae;cr<be;++cr){let 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h=Tr(u.dtype,"int32"),p=w.makeOnesTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?1:c[x];else{let b=f(y,A-1);p[x]=i?c[b]*p[b]:c[x]*p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var EX={kernelName:Ko,backendName:"cpu",kernelFunc:NX};function RX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Ce(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=sn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=C.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Tr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,A)=>y+m-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=m)for(let A=0;A<m;A++){let x=f(y,A);if(A===0)p[x]=i?0:c[x];else{let b=f(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var $X={kernelName:oi,backendName:"cpu",kernelFunc:RX};function MX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Yx(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=a8(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
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}
#define isnan(value) isnan_custom(value)
`,l="",u=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",r="varying",n="varying",a="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function p5(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var NI=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
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`),s=e.map(p=>EQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
`),i=t.texShape,o=Hr(),l=MQ(o),u,d,h=_Q(o);return t.isPacked?(u=RQ(t.logicalShape,i,r.enableShapeUniforms),d=PQ(o)):(u=$Q(t.logicalShape,i,r.enableShapeUniforms),d=FQ(o)),r.packedInputs&&(h+=LQ),[h,l,d,a,u,s,r.userCode].join(`
`)}function Id(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return YQ(e,t);case 1:return QQ(e,t);case 2:return tee(e,t);case 3:return nee(e,t);case 4:return see(e,t);case 5:return iee(e);case 6:return oee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function RI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return ZQ(e);case 1:return JQ(e,t);case 2:return eee(e,t);case 3:return ree(e,t);default:return aee(e,t)}}function EQ(e,t,r=!1,n){let a="";r?a+=RI(e,n):a+=Id(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=lee(e,t):a+=uee(e,t)),a}function RQ(e,t,r){switch(e.length){case 0:return $I();case 1:return BQ(e,t,r);case 2:return KQ(e,t,r);case 3:return VQ(e,t,r);default:return GQ(e,t,r)}}function $Q(e,t,r){switch(e.length){case 0:return $I();case 1:return WQ(e,t,r);case 2:return XQ(e,t,r);case 3:return UQ(e,t,r);case 4:return jQ(e,t,r);case 5:return HQ(e,t);case 6:return qQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function MQ(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function FQ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function PQ(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function _Q(e){return`${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);
}
${zQ}
${OQ}
${DQ}
`}var zQ=`
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);
}
`,OQ=`
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);
}
`,DQ=`
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);
}
`,LQ=`
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 $I(){return`
int getOutputCoords() {
return 0;
}
`}function BQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?r?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:r?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function WQ(e,t,r){return t[0]===1?r?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?r?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:r?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function VQ(e,t,r){if(r)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec3(b, r, c);
}
`}function UQ(e,t,r){if(r)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let n=Dl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function GQ(e,t,r){if(r)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
int b${u} = index / ${i};
index -= b${u} * ${i};
`+o,l=`b${u}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${o}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${l});
}
`}function jQ(e,t,r){if(r)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let n=Dl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function HQ(e,t){let r=Dl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function qQ(e,t){let r=Dl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function KQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return r?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function XQ(e,t,r){return w.arraysEqual(e,t)?r?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Ll(e){return`offset${e}`}function ZQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Hr();return`
vec4 ${r}() {
return ${n.texture2D}(${t}, halfCR);
}
`}function YQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
float ${n}() {
return sampleTexture(${r}, halfCR);
}
`;let i=Ll(r);if(t)return`
float ${n}() {
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
return sampleTexture(${r}, uv);
}
`;let[o,l]=e.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
return sampleTexture(${r}, uv);
}
`}function JQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Hr();if(t)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${r}, uv);
}
`;let i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${r}, uv);
}
`}function QQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Sd(e)}
}
`;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${r}, halfCR);
}
`;let o=Ll(r);return i===1?t?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
return sampleTexture(${r}, uv);
}
`:s===1?t?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:t?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${o});
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
return sampleTexture(${r}, uv);
}
`}function eee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Hr();if(s!=null&&w.arraysEqual(r,s))return t?`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${l.texture2D}(${n}, uv);
}
`:`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${l.texture2D}(${n}, uv);
}
`;if(t)return`
vec4 ${a}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${n}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],d=Math.ceil(r[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${n}, uv);
}
`}function tee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`;let p=s[0],c=s[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let p=Cd(e,l),c=["row","col"];return`
${Id(p,t)}
float ${a}(int row, int col) {
return ${a}(${Td(c,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
${Sd(e)}
}
`;let u=s[0],d=s[1],h=Ll(n);return d===1?t?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?t?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n}Shape[1] + col + ${h};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r[1]} + col + ${h};
vec2 uv = uvFromFlat(${u}, ${d}, index);
return sampleTexture(${n}, uv);
}
`}function ree(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],m=Cd(e,p),f=["b","row","col"];return`
${RI(m,t)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${Td(f,c)});
}
`}let o=Hr();if(t)return`
vec4 ${a}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${n}, uv);
}
`;let l=i[0],u=i[1],d=Math.ceil(r[2]/2),h=d*Math.ceil(r[1]/2);return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${h}, ${d}, b, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function nee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let f=Cd(e,u),g=["row","col","depth"];return`
${Id(f,t)}
float ${a}(int row, int col, int depth) {
return ${a}(${Td(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${Sd(e)}
}
`;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
float ${a}(int row, int col, int depth) {
int stride1 = ${n}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(p===i&&c==null)return t?`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=Ll(n);return t?`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${n}Shape[1] * ${n}Shape[2];
int stride1 = ${n}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${m};
vec2 uv = uvFromFlat(${h}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function aee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Hr();if(t)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${r}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}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 ${a.texture2D}(${r}, uv);
}
`;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],d=l[1],h=Math.ceil(s[i-1]/2),p=h*Math.ceil(s[i-2]/2),c="int b, int row, int col",m=`b * ${p} + (row / 2) * ${h} + (col / 2)`;for(let f=2;f<i-1;f++)c=`int b${f}, `+c,p*=s[i-f-1],m=`b${f} * ${p} + `+m;return`
vec4 ${n}(${c}) {
int index = ${m};
int texR = index / ${d};
int texC = index - texR * ${d};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
return ${a.texture2D}(${r}, uv);
}
`}function see(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=Cd(e,l),x=["row","col","depth","depth2"];return`
${Id(A,t)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${Td(x,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, 1)));
${Sd(e)}
}
`;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],m=`int stride2 = ${n}Shape[3];`,f=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
float ${a}(int row, int col, int depth, int depth2) {
${m}
${f}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(c===s&&d==null)return t?`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r[1]*r[2]}, ${r[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let y=Ll(n);return t?`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${m}
${f}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
return sampleTexture(${n}, uv);
}
`:`
float ${a}(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 * ${s} + depth2;
vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
return sampleTexture(${n}, uv);
}
`}function iee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let f=Cd(e,l),g=["row","col","depth","depth2","depth3"];return`
${Id(f)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${Td(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${Sd(e)}
}
`;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(c===a&&d==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;let m=Ll(r);return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${a} + depth3 + ${m};
vec2 uv = uvFromFlat(${p}, ${c}, index);
return sampleTexture(${r}, uv);
}
`}function oee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=Cd(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Id(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${Td(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${d}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Sd(e)}
}
`;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],m=p[1];if(m===d&&h==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${c}.0);
return sampleTexture(${r}, uv);
}
`;if(m===i&&h==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${c}.0);
return sampleTexture(${r}, uv);
}
`;let f=Ll(r);return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${d} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${c}, ${m}, index);
return sampleTexture(${r}, uv);
}
`}function Sd(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
for (int i = 0; i < ${r}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function lee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=EI(e.shapeInfo.logicalShape,t.logicalShape),l=vt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
`);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)c=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?c=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:c=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?c="return vec4(outputValue.x);":o.indexOf(g)>-1?c="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(c="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${a}() {
${l} coords = getOutputCoords();
${d}
vec4 outputValue = get${n}(${p});
${c}
}
`}function uee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${r}, resultUV);
}
`;let u=vt(l),d=EI(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(f=>`coords.${c[f+h]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${c[g+h]}`).join(", "),`
float ${a}() {
${u} coords = getOutputCoords();
${p}
return get${n}(${m});
}
`}function vt(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 ${e} is not yet supported`)}function h5(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Cd(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Td(e,t){return t.map(r=>e[r]).join(", ")}function dee(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=NQ(a,i,t),l=oI(e.gl,o),u=e.createProgram(l);return Z().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...MI(e,t,u)}}function MI(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Z().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let m=t.variableNames[c];n[m]=e.getUniformLocation(r,m,p),n[`offset${m}`]=e.getUniformLocation(r,`offset${m}`,p),t.enableShapeUniforms&&(a[`${m}Shape`]=e.getUniformLocation(r,`${m}Shape`,p),s[`${m}TexShape`]=e.getUniformLocation(r,`${m}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,m)=>{i[m]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function F4(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((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function pee(e,t,r,n,a){t.program.enableShapeUniforms||(F4(t.inShapeInfos,r),F4([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],m=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:f}=h5(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(c,new Int32Array(f));break;case 2:e.gl.uniform2iv(c,new Int32Array(f));break;case 3:e.gl.uniform3iv(c,new Int32Array(f));break;case 4:e.gl.uniform4iv(c,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(h,f)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function hee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=h5(e.packedInputs,i.shape,l),p="",c="",m="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);m=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let f=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=C.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&f===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Z().getNumber("WEBGL_VERSION")}`,s}function ln(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var cee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):Dl(["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;
}
`}},fee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):Dl(["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;
}
`}},mee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${NI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${NI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},yee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Hr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?p5():d5(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 = ${r.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];
}
${r.output} = vec4(${n}, 0., 0., 0.);
}
`}},Aee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Hr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${s};
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 = ${r.texture2D}(A, uv);
if (offset == 0) {
result[${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?p5():d5(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${n}
${r.output} = ${a};
}
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${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return iI(e,r)}function _I(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 dI(e,t)}function zI(e){let t=new Uint16Array([0,1,2,2,1,3]);return pI(e,t)}function Dh(e,t,r,n,a,s){cI(t,r);let i=hI(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Z().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(o,0,n,t,r,0,a,s,null)):we(e,()=>e.texStorage2D(o,1,n,t,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[r,t]}}function c5(e){return e.internalFormatFloat}function OI(e,t,r,n){let[a,s]=Oh(t,r);return Dh(e,a,s,c5(n),n.textureFormatFloat,e.FLOAT)}function f5(e){return e.internalFormatHalfFloat}function DI(e,t,r,n){let[a,s]=Oh(t,r);return 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ste=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=ln(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Lr("rc",this.rank),r=vt(this.rank),n=this.getOutOfBoundsCondition(t),a=this.getSetup(t),s=this.getOutput(t);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${n}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${s}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let r=0;r<=1;r++)for(let n=0;n<=1;n++){let a=`${r===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)a=`${e[e.length-1-s]},`+a;t.push(a)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let r=this.rank-2;r<this.rank;r++)t+=`${e[r]} >= ${this.enableShapeUniforms?`outShape[${r}]`:this.outputShape[r]}`,r<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),r=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${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 >= ${r};
bool rEdge = rp1 >= ${n};
`}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]})`}},eS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
${a}
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${n}] =
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${n>0?"}":""}
`}this.userCode=`
${ite(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?p5():d5(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]};
${r}
setOutput(result);
}
`}};function ite(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?TQ(["r","c","d"],"inputShape"):Dl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var ote=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=_4(t,r),a=z4(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=P4(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=_4(r,n),s=z4(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=P4(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.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 lte(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function P4(e,t,r,n,a){let s=ute(t,n),i;if(a){let[l,u]=wd(e[0],e[1]);i=l*u}else{let[l,u]=Oh(e[0],e[1]);i=l*u}let o=lte(r,s);return i*o}function ute(e,t){switch(e){case 3:return g5(t);case 4:return y5(t);case 1:return c5(t);case 0:return f5(t);case 2:return m5(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function dte(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function _4(e,t){if(e===1)return 3;if(e===0||e==null)return dte(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function z4(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Xa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Yn="if (isnan(x)) return x;",pte="return x;",O4="return abs(x);",hte="return (x >= 0.0) ? x : (exp(x) - 1.0);",cte=Yn+`
return (x < 0.0) ? 0.0 : x;
`,fte=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,uu="return x;",mte="return 1.0 / (1.0 + exp(-1.0 * x));",gte="return x;",yte=`
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;
`,Ate=`
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;
`,xte=`
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;
`,bte="return 1.0 / (1.0 + exp(-1.0 * x));",To=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},vte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let t=e.length,r=Lr("rc",t),n=vt(t),a=ate(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},wte=Xn.whereImpl,kte=1e-7,Ite=1e-4,ug={};function Ste(e){return e in ug||(ug[e]={}),ug[e]}var Cte=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Tte=600;function Nte(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*Tte/1024/1024}var tS=class extends _u{constructor(e){if(super(),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,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof bu)t=e;else{let r=xa(Z().getNumber("WEBGL_VERSION"),e);t=new bu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=xa(Z().getNumber("WEBGL_VERSION"));t=new bu(r),this.binaryCache=Ste(Z().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new ote(this.gpgpu),this.numMBBeforeWarning=Nte(),this.texData=new Xp(this,ar())}nextDataId(){return tS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}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,r,n,a){if(Z().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. 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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Qc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Z().getBool("WEBGL_PACK")&&n===!0,i=s?l0(t):t,o=s?new gee(i):new mee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Z().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:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Cte){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return wte(e.shape,t)}packedUnaryOp(e,t,r){let n=new To(e.shape,t),a=this.compileAndRun(n,[e],r);return ar().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=YI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,O4,e.dtype);let t=new Xa(e.shape,O4),r=this.compileAndRun(t,[e]);return ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return ar().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new vte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ste(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Bo(e.shape),...Wo(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Bo(t),...Wo(t)],s=new eS(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=l0(a),o;n?o=new fee(i):o=new cee(i);let l=!0,u=[t!=null?t:Qc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Qc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=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 y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=Z().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!qp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=hee(e,u,d),p=this.getAndSaveBinary(h,()=>dee(this.gpgpu,e,u,d)),c=this.activeTimers!=null,m;c&&(m=this.startTimer()),Z().get("ENGINE_COMPILE_ONLY")||pee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=w.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Z().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Z().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?kte:Ite}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=vI(r,o),t.texShape=d),a!=null){let h=l0(r),p,c=d[1],m=d[0],f=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!f)&&([c,m]=wd(d[0],d[1])),o?p=new Aee(h,f):p=new yee(h,f);let g=f?[m,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);f?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,m,a);let x=[[m,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),S=this.texData.get(v.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,Z().get("ENGINE_COMPILE_ONLY")?this.disposeData(v.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(v.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=Ete(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await gA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(u5(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=MI(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Lh=tS;Lh.nextDataId=0;function Ete(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var Rte="0.0.0";function rS(){Z().set("WEBGL_FORCE_F16_TEXTURES",!0)}gh.isBrowser()&&$l("webgl",()=>new Lh,2);var $te={forceHalfFloat:rS},nS=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Pu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},wm=`
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;
`,Bh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=ln(a);let s="";if(n)if(a===0||w.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${vt(a)} coords = getOutputCoords();
`,a===1)this.enableShapeUniforms?s+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Lr("coords",a);this.enableShapeUniforms?s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= outShape[${a} - 2];
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= outShape[${a} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= ${this.outputShape[a-2]};
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= ${this.outputShape[a-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function fn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Mte={kernelName:gi,backendName:"webgl",kernelFunc:fn};function Ki(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=fn({inputs:{x:n},backend:r}),l=fn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Fte={kernelName:Yp,backendName:"webgl",kernelFunc:Ki},aS="return (a < 0.) ? b * a : a;",sS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Pte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(sS,a.shape,i.shape):new Pu(aS,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var _te={kernelName:yi,backendName:"webgl",kernelFunc:Pte},iS="return (a < 0.) ? b * a : a;",oS=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function zte(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(oS,n.shape,a.shape):new Pu(iS,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var Ote={kernelName:Ei,backendName:"webgl",kernelFunc:zte},Nd="if (isnan(x)) return x;",Dte=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Lte=`
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 it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new To(i.shape,t):d=new Xa(i.shape,e),o.runWebGLProgram(d,[i],l)}}function vr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let m=d.texData.get(l.dataId),f=d.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[b,v]=x,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Pu(e,l.shape,u.shape);return d.runWebGLProgram(E,[S,N],Tr(b.dtype,v.dtype))}),A=Ki({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Tr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let m=d.texData.get(l.dataId).values,f=d.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Bh(t,l.shape,u.shape,r):c=new Pu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function km(e,t=!1){if(e==="linear")return t?gte:pte;if(e==="relu")return t?Ate:cte;if(e==="elu")return t?yte:hte;if(e==="relu6")return t?xte:fte;if(e==="prelu")return t?oS:iS;if(e==="leakyrelu")return t?sS:aS;if(e==="sigmoid")return t?bte:mte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var lS=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=ln(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${A};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${c[0]} * ${m[0]});
result += (${c[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},D4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},L4=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},B4="return a * b;";function x5(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=C.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new L4(D4.REAL,n.shape,a.shape),d=new L4(D4.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=Ki({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Lee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Bh(B4,n.shape,a.shape):i=new Pu(B4,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Bte={kernelName:Ci,backendName:"webgl",kernelFunc:x5};function Wte(e,t,r){let n=[Bo(e.shape),...Wo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Bo(t),...Wo(t)],i=new eS(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!qp(a.shape,l)&&!(d.texture!==null&&qp(d.shape,l))?Wte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Vte={kernelName:fl,backendName:"webgl",kernelFunc:ve},W4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
if (inIdx < 0 || inIdx >= ${a}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${u}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},Ute=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(r/4)*4,d=r%4,h=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let c="";a%r>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${d===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${d===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function Gte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=C.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Bl(e,t,r,n){let a=Gte(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new W4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new W4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Ute({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var jte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=vt(this.rank),a=Hte(t);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function Hte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var qte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=vt(this.rank),a=QI("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${r[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Im(e,t,r){let n=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qte(e.shape,t):new jte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function Kte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Im(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=C.expandShapeToKeepDim(h,i));let m=w.sizeFromShape(p),f=w.sizeFromShape(e.shape)/m,g=ve({inputs:{x:d},attrs:{shape:[f,m]},backend:n}),y=mh(e.dtype),A=Bl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function Sm(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Kte(a,s,i,r)}var Xte={kernelName:Di,backendName:"webgl",kernelFunc:Sm};function Vr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=A5(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=Im(a,s,i);return u}var Zte={kernelName:Ui,backendName:"webgl",kernelFunc:Vr},uS=1e3;function W0({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(f),A=w.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,m,p]:[A,p,m],S=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),N=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,N],R=Math.max(y,A),z=r?S.shape[1]:S.shape[2],$=s!=null,I=i!=null,_=l==="leakyrelu",O=l!=null?km(l,!0):null,j=$||I||_||O!=null,X;if((c===1||m===1)&&z>uS&&j===!1){let J=S,V=N;r&&(J=Vr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(J)),n&&(V=Vr({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=m!==1,Q=m===1,ie=J;ee&&(ie=ve({inputs:{x:J},backend:a,attrs:{shape:[R,z,1]}}),E.push(ie));let Y=m===1?2:1,ae=V;Q&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,z]}}),E.push(ae));let de=x5({inputs:{a:ie,b:ae},backend:a});X=Sm({inputs:{x:de},backend:a,attrs:{axis:Y,keepDims:!0}}),E.push(de)}else{let J=Tr(e.dtype,t.dtype),V=new lS(b,v,[R,c,m],r,n,$,O,I,_),ee=[S,N];if(s!=null&&ee.push(s),I&&ee.push(i),_){let Q=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(Q),E.push(Q)}X=a.runWebGLProgram(V,ee,J)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let J of E)a.disposeIntermediateTensorInfo(J);return D}function Yte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return W0({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Jte={kernelName:_s,backendName:"webgl",kernelFunc:Yte},V4="return abs(x);";function Qte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=YI(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new To(n.shape,V4):a=new Xa(n.shape,V4),r.runWebGLProgram(a,[n],n.dtype)}var ere={kernelName:jo,backendName:"webgl",kernelFunc:Qte},tre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,rre=it({opSnippet:tre}),nre={kernelName:Ou,backendName:"webgl",kernelFunc:rre},are=Yn+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,sre=it({opSnippet:are}),ire={kernelName:Du,backendName:"webgl",kernelFunc:sre},U4="return a + b;",ore=vr({opSnippet:U4,packedOpSnippet:U4,supportsComplex:!0,cpuKernelImpl:bee}),lre={kernelName:Qa,backendName:"webgl",kernelFunc:ore},ure=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
float result = ${n};
setOutput(result);
}
`}},dre=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
vec4 result = ${n};
setOutput(result);
}
`}};function p0(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return fn({inputs:{x:n[0]},backend:r});if(n.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=p0({inputs:n.slice(0,o),backend:r}),u=p0({inputs:n.slice(o),backend:r});return p0({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Tr(o,l)),s=n.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new dre(n[0].shape,s):new ure(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var pre={kernelName:Ys,backendName:"webgl",kernelFunc:p0};function hre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=w.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"all",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var cre={kernelName:Lu,backendName:"webgl",kernelFunc:hre};function fre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=w.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"any",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var mre={kernelName:Bu,backendName:"webgl",kernelFunc:fre},gre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"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 * ${n};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${n}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},yre=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=vt(o),u=Lr("coords",o),d,h;if(s===1){h=o+1;let N=vt(h);d=`
${N} sourceLocR = ${N}(${u.join()}, 0);
++${u[o-1]};
${N} sourceLocG = ${N}(${u.join()}, 0);
++${u[o-2]};
${N} sourceLocA = ${N}(${u.join()}, 0);
--${u[o-1]};
${N} sourceLocB = ${N}(${u.join()}, 0);
--${u[o-2]};`}else h=o,d=`
${l} sourceLocR = coords;
++${u[o-1]};
${l} sourceLocG = coords;
++${u[o-2]};
${l} sourceLocA = coords;
--${u[o-1]};
${l} sourceLocB = coords;
--${u[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],m=p.map(N=>"int "+N),f=Lr("sourceLocR",h-1).concat("inIdx.r"),g=Lr("sourceLocG",h-1).concat("inIdx.g"),y=Lr("sourceLocB",h-1).concat("inIdx.b"),A=Lr("sourceLocA",h-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=n?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()})));`,v=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,S=n?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${S}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
${d}
ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},
sourceLocB${c}, sourceLocA${c}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${v};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${v};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function dS(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new gre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=dS(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function pS(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new yre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=pS(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function hS(e,t,r,n){let a=[r];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=C.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=dS(e,p,n);s.push(c);let m=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return pS(e,t,n)}function Are(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=hS(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var xre={kernelName:Js,backendName:"webgl",kernelFunc:Are};function bre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=hS(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var vre={kernelName:Wu,backendName:"webgl",kernelFunc:bre},wre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,kre=it({opSnippet:wre}),Ire={kernelName:Vu,backendName:"webgl",kernelFunc:kre},Sre=Yn+"return log(x + sqrt(x * x + 1.0));",Cre=it({opSnippet:Sre}),Tre={kernelName:Uu,backendName:"webgl",kernelFunc:Cre},Nre=Yn+`
return atan(x);
`,Ere=it({opSnippet:Nre}),Rre={kernelName:Gu,backendName:"webgl",kernelFunc:Ere},$re=Dte+`
return atan(a, b);
`,Mre=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Lte+`
return result;
`,Fre=vr({opSnippet:$re,packedOpSnippet:Mre}),Pre={kernelName:Hu,backendName:"webgl",kernelFunc:Fre},_re=Yn+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zre=it({opSnippet:_re}),Ore={kernelName:ju,backendName:"webgl",kernelFunc:zre},Kp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),r){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${d};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${u}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?a?f:g:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,v=s%4,S=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${A}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${c});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${d};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC += 4) {
int xC = xCCorner + wC * ${u};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
getValue(batch, xR, xC + 3 * ${u}, d)
);
${S}
}
int xC = xCCorner + ${b};
if (${v===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${S}
} else if (${v===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${S}
} else if (${v===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${S}
}
}
setOutput(${x});
}
`}},b5=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${p};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${c};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${h}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let S=Math.floor(s/4)*4,N=s%4,E=`
if (${A}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${c};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${S}; wC += 4) {
int xC = xCCorner + wC * ${h};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
);
${E}
}
int xC = xCCorner + ${S};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
initializationValue
);
${E}
}
}
setOutput(${v});
}
}
`}};function Dre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;kd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return fn({inputs:{x:a},backend:r});let h=new Kp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Lre={kernelName:Qs,backendName:"webgl",kernelFunc:Dre};function Bre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new b5(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Wre={kernelName:Zp,backendName:"webgl",kernelFunc:Bre},Vre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
const ivec2 pads = ivec2(${u}, ${d});
const float avgMultiplier = float(${h});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${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) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Ure=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,m=h-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
const ivec3 pads = ivec3(${c}, ${m}, ${f});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${d};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${a}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${h};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${p};
wC += ${u}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function Gre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new Ure(p);return r.runWebGLProgram(c,[a],i.dtype)}var jre={kernelName:Z0,backendName:"webgl",kernelFunc:Gre};function Hre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;kd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=C.computePool2DInfo(i.shape,o,l,1,u),h=new Vre(d);return r.runWebGLProgram(h,[a],i.dtype)}var qre={kernelName:X0,backendName:"webgl",kernelFunc:Hre};function Kre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return W0({a,b:s,transposeA:i,transposeB:o,backend:r})}var Xre={kernelName:ei,backendName:"webgl",kernelFunc:Kre},Zre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},Yre=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},Jre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Yre(n.shape,a.shape,s.shape,d,h,l):new Zre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Qre={kernelName:fi,backendName:"webgl",kernelFunc:Jre},ene=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=tne(this.rank),n,a=e.map((s,i)=>`sourceLoc.${ey[i]} = start[${i}] + coords.${ey[i]};`);n=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
void main() {
${n}
setOutput(getSource(${r}));
}
`}},ey=["x","y","z","w","u","v"];function tne(e){if(e===1)return"sourceLoc";if(e<=6)return ey.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rne=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),r=Lr("coords",this.rank),n=Lr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
result.x = ${s};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.y = ${s};
--${n[this.rank-1]};
}
`,o=this.rank===1?"":`
--${r[this.rank-1]};
if (++${r[this.rank-2]} < ${e[this.rank-2]}) {
++${n[this.rank-2]};
result.z = ${s};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${n[d]} = ${r[d]} + start[${d}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function nne(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=Ot.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Ed(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Ot.parseSliceParams(a,s,i);if(Ot.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=Hee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=Ot.isSliceContinous(a.shape,o,l);if(u||!d){let h=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rne(l):new ene(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),nne(a,o,l,r)}var ane={kernelName:xl,backendName:"webgl",kernelFunc:Ed},sne=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Vr({inputs:{x:m},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:f},backend:r,attrs:{shape:d}}),y=Ed({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},ine={kernelName:Ho,backendName:"webgl",kernelFunc:sne};function one(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=ZI(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var lne={kernelName:Y0,backendName:"webgl",kernelFunc:one};function une(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var dne={kernelName:J0,backendName:"webgl",kernelFunc:une},pne="return float(a != b);",cS=vr({opSnippet:pne,cpuKernelImpl:Wee,dtype:"bool"}),hne={kernelName:ll,backendName:"webgl",kernelFunc:cS};function Wh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return fn({inputs:{x:a.complexTensorInfos.real},backend:r})}var cne={kernelName:ih,backendName:"webgl",kernelFunc:Wh},fne="return float(int(x));";function mne(e,t){let r=new Xa(e.shape,fne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function ty(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return fn({inputs:{x:a},backend:r});let i=_t(a.shape),o=ty({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Ki({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Wh({inputs:{input:a},backend:r}),o=ty({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=fn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return mne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=cS({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var gne={kernelName:ti,backendName:"webgl",kernelFunc:ty},G4="return ceil(x);",yne=it({opSnippet:G4,packedOpSnippet:G4,cpuKernelImpl:wee}),Ane={kernelName:ri,backendName:"webgl",kernelFunc:yne},xne=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));
}
`}},bne=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 vne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Z().getBool("WEBGL_PACK_CLIP")?o=new bne(a.shape):o=new xne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var wne={kernelName:es,backendName:"webgl",kernelFunc:vne},kne=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 j4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Ine(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new kne(n.shape),i=[j4(n,a.complexTensorInfos.real),j4(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Sne={kernelName:Jp,backendName:"webgl",kernelFunc:Ine},Cne=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${r.join(`
`)}
}
`}},Tne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=vt(n),s=Lr("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${u.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];h+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${t0(i,l,f)}),
vec2(${t0(u,l,f)}));
}`}let p=o.length,c=o[o.length-1];h+=`
return getChannel(
getT${p}(${t0(i,l,c)}),
vec2(${t0(u,l,c)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[n-1]} = ${s[n-1]} + 1;
if (${s[n-1]} < ${r[n-1]}) {
result.g = getValue(${s});
}
${s[n-2]} = ${s[n-2]} + 1;
if (${s[n-2]} < ${r[n-2]}) {
result.a = getValue(${s});
}
${s[n-1]} = ${s[n-1]} - 1;
if (${s[n-2]} < ${r[n-2]} &&
${s[n-1]} < ${r[n-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function t0(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function Cm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return fn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Nne={kernelName:rh,backendName:"webgl",kernelFunc:Cm};function mu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(f=>Wh({inputs:{input:f},backend:r})),h=e.map(f=>Cm({inputs:{input:f},backend:r})),p=mu(d,t,r),c=mu(h,t,r),m=Ki({inputs:{real:p,imag:c},backend:r});return d.forEach(f=>r.disposeIntermediateTensorInfo(f)),h.forEach(f=>r.disposeIntermediateTensorInfo(f)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=C.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,m=kee(h,p,n,c),f=C.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(f,n,m);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=mu(e.slice(0,d),t,r),p=mu(e.slice(d),t,r),c=mu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Tne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=Ene(e,t,r),o=new Cne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function Ene(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function fS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return fn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),mu(o,s,r)}var Rne={kernelName:qo,backendName:"webgl",kernelFunc:fS},mS=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,A=f?3:1,x="",b="";r&&(n?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:x=`
float activation(float x) {
${r}
}
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${A}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${c}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${c}) *
getW(wR, wC, ${c}, d2);
} else {
dotProd +=
getX(batch, ${c}, xR, xC) *
getW(wR, wC, ${c}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${c}),
getX(batch, xR, xC, ${c} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${c}, xR, xC),
getX(batch, ${c} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2),
getW(wR, wC, ${c} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${c}),
getX(batch, xR, xC, ${c} + 1),
getX(batch, xR, xC, ${c} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${c}, xR, xC),
getX(batch, ${c} + 1, xR, xC),
getX(batch, ${c} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${v}
${b}
setOutput(result);
}
`}},$ne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${r}, ${n});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${d}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${c}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${c}) *
getW(wF, wR, wC, ${c}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${c}),
getX(batch, xF, xR, xC, ${c} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${c}, d2),
getW(wF, wR, wC, ${c} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${c}),
getX(batch, xF, xR, xC, ${c} + 1),
getX(batch, xF, xR, xC, ${c} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${c}, d2),
getW(wF, wR, wC, ${c} + 1, d2),
getW(wF, wR, wC, ${c} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},Mne=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=ln(this.outputShape.length);let{dataFormat:r}=t,n=Hr(),a=r==="channelsLast",s=a?0:1,i=a?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
blockIndex = rc.y + ${d};
pos = rc.x + ${u};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${s}] && 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[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${a}) {
innerDims = vec2(d1, ch);
result[${u*2+d}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+d}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${n.output} = result;
}
`}};function gS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(!((h===1||p===1)&&d>uS)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(qp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let S=W0({a:x,b:v,backend:n,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=n.texData.get(S.dataId);w.assert(N.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,N.shape=r.outShape,g=fn({inputs:{x:S},backend:n}),g.shape=r.outShape,y.push(S)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=W0({a:x,b,transposeA:m,transposeB:f,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function yS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,m=c==="channelsLast",f=l*u*d,g=p*h,y=[f,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),S=ve({inputs:{x:t},backend:n,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});b.push(v),b.push(S);let N=new Mne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(N,[v],"float32",E),z=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(z);let $=a!=null,I=s!=null,_=o==="leakyrelu",O=o?km(o,!0):null,j=new lS(z.shape,S.shape,[1,g,r.outChannels],A,x,$,O,I,_),X=[z,S];if(a&&X.push(a),I&&X.push(s),_){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),J=m?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:J}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function Fne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=gS({x:a,filter:s,convInfo:p,backend:r});else if(Z().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=yS({x:a,filter:s,convInfo:p,backend:r});else{let f=new mS(p);c=r.runWebGLProgram(f,[a,s],"float32")}let m=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),m}var Pne={kernelName:ni,backendName:"webgl",kernelFunc:Fne},_ne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},zne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${d}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.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 < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},One=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${a};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${r} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Dne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${u});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${a}.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 < ${r}; 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 = ${r} - 1 - wR;
for (int wC = 0; wC < ${n}; 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 = ${n} - 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 Lne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new _ne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Bne={kernelName:Q0,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new zne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Vne={kernelName:ai,backendName:"webgl",kernelFunc:Wne};function Une(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new $ne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Gne={kernelName:Qp,backendName:"webgl",kernelFunc:Une};function jne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(a.shape,l,i,1,o),d=new One(u);return r.runWebGLProgram(d,[a,s],"float32")}var Hne={kernelName:ef,backendName:"webgl",kernelFunc:jne};function qne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),d=new Dne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Kne={kernelName:tf,backendName:"webgl",kernelFunc:qne},Xne=Nd+`
return cos(x);
`,Zne=it({opSnippet:Xne}),Yne={kernelName:si,backendName:"webgl",kernelFunc:Zne},Jne=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,Qne=it({opSnippet:Jne}),eae={kernelName:ii,backendName:"webgl",kernelFunc:Qne},tae=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${A});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${c} ) {
setOutput(float(${a}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},rae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new tae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},nae={kernelName:Xo,backendName:"webgl",kernelFunc:rae},H4=class{constructor(e,t,r,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.op=e,this.outputShape=t;let a=t.length,s=this.op==="*"?"1.0":"0.0",i=r?s:`getX(${q4(a,"coords",this.op)})`,o=t[t.length-1],l="",u="";r?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${vt(a)} coords = getOutputCoords();
int end = ${K4(a,"coords",this.op)};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${l}) {
int idx = ${u};
${K4(a,"coords",this.op)} = idx;
val ${this.op}= getX(${q4(a,"coords",this.op)});
}
setOutput(val);
}
`}};function q4(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function K4(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function AS(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Vr({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=fn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new H4(e,l.shape,!1,s),m=[[p]],f=h;h=r.runWebGLProgram(c,[h],h.dtype,m),r.disposeIntermediateTensorInfo(f)}if(a){let p=new H4(e,l.shape,a,s),c=h;h=r.runWebGLProgram(p,[h],h.dtype),r.disposeIntermediateTensorInfo(c)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Vr({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(l),c}return h}function aae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return AS("*",a,r,s,i,o)}var sae={kernelName:Ko,backendName:"webgl",kernelFunc:aae};function iae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return AS("+",a,r,s,i,o)}var oae={kernelName:oi,backendName:"webgl",kernelFunc:iae};function lae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=ZI(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=vee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var uae={kernelName:rf,backendName:"webgl",kernelFunc:lae},dae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,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 pae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=new dae(m,s,i);return r.runWebGLProgram(f,[a],a.dtype)}var hae={kernelName:Zo,backendName:"webgl",kernelFunc:pae},xS=class{constructor(e,t=!1,r=null,n=!1,a=!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=ln(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:l=`
float activation(float x) {
${r}
}
`,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${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 < ${s}; 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;
${d}
${u}
setOutput(result);
}
`}},bS=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;p+=`
for (int r = 0; r < ${u}; r++) {
`;for(let g=0;g<d;g++)p+=`
xTexelC${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);`;p+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(h+1)/2;g++){let y=g*2;if(p+=`
xC = xCCorner + ${y*l};
`,o===1){if(y<d&&(i%2===1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,l===1&&y>0?p+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:p+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<d)){let A=i%2===0?w.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${A};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,l>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):A===1?p+=`
xC${y+1} = xTexelC${y};
`:p+=`
xCOffset = xC + ${A};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<d&&(i%2===1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<d&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<d&&(p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<d&&(p+=`
wTexel = getW(r, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<d&&(p+=`
wTexel = getW(r, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let c="",m="";r&&(n?c=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?c=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${r}
}`:c=`vec4 activation(vec4 x) {
${r}
}`,m="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${c}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${f}
${m}
setOutput(result);
}
`}};function cae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new bS(h):p=new xS(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var fae={kernelName:li,backendName:"webgl",kernelFunc:cae},mae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},gae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.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 < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function yae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=C.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new mae(h);return r.runWebGLProgram(p,[a,s],"float32")}var Aae={kernelName:nf,backendName:"webgl",kernelFunc:yae};function xae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=C.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new gae(h);return r.runWebGLProgram(p,[a,s],"float32")}var bae={kernelName:af,backendName:"webgl",kernelFunc:xae},vae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function wae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new vae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var kae={kernelName:sf,backendName:"webgl",kernelFunc:wae},Iae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${d}, ${h});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${u};
if (wIn >= 0 && wIn < ${r}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Sae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Iae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Cae={kernelName:eh,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Vr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=x5({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=Sm({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeIntermediateTensorInfo(f);return p}var Nae={kernelName:th,backendName:"webgl",kernelFunc:Tae},Eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Rae=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,$ae=it({opSnippet:Eae,packedOpSnippet:Rae}),Mae={kernelName:di,backendName:"webgl",kernelFunc:$ae},Fae="return (b >= 1.0) ? a : a * (b + 1.0);",Pae=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,_ae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(Pae,n.shape,a.shape):new Pu(Fae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},zae={kernelName:of,backendName:"webgl",kernelFunc:_ae},Oae=`
return vec4(equal(a, b));
`,Dae="return float(a == b);",Lae=vr({opSnippet:Dae,packedOpSnippet:Oae,dtype:"bool",cpuKernelImpl:Iee}),Bae={kernelName:Yo,backendName:"webgl",kernelFunc:Lae},Wae=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${C.ERF_P};
float a1 = ${C.ERF_A1};
float a2 = ${C.ERF_A2};
float a3 = ${C.ERF_A3};
float a4 = ${C.ERF_A4};
float a5 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Vae=it({opSnippet:Wae}),Uae={kernelName:qu,backendName:"webgl",kernelFunc:Vae},Gae=Nd+`
return exp(x);
`,jae=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,vS=it({opSnippet:Gae,packedOpSnippet:jae,cpuKernelImpl:See,dtype:"float32"}),Hae={kernelName:pi,backendName:"webgl",kernelFunc:vS};function ry(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var qae={kernelName:Jo,backendName:"webgl",kernelFunc:ry},X4="return exp(x) - 1.0;",Kae=it({opSnippet:X4,packedOpSnippet:X4,cpuKernelImpl:Cee}),Xae={kernelName:Qo,backendName:"webgl",kernelFunc:Kae},Z4=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.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 = ${a};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${n});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${n}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function wS(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Z4("real",l,t),d=new Z4("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),m=Ki({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let f=ve({inputs:{x:m},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(m),f}function Zae(e){let{inputs:t,backend:r}=e,{input:n}=t;return wS(n,!1,r)}var Yae={kernelName:lf,backendName:"webgl",kernelFunc:Zae},Jae=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 Vh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Jae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Qae={kernelName:Ku,backendName:"webgl",kernelFunc:Vh},ese=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},tse={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new ese(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Y4="return floor(x);",rse=it({opSnippet:Y4,packedOpSnippet:Y4,cpuKernelImpl:Tee}),nse={kernelName:hi,backendName:"webgl",kernelFunc:rse},ase=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,sse=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,ise=vr({opSnippet:ase,packedOpSnippet:sse,dtype:"int32"}),ose={kernelName:ci,backendName:"webgl",kernelFunc:ise},lse=class{constructor(e){this.variableNames=["A"];let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${r}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},use=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${r}.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;
}
`}},dse={kernelName:zp,backendName:"webgl",kernelFunc:pse},du;function pse(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(du==null&&(du=document.createElement("canvas").getContext("2d")),du.canvas.width=l,du.canvas.height=u,du.drawImage(a,0,0,l,u),a=du.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Z().getBool("WEBGL_PACK")?new use(h):new lse(h),m=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),m}function hse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=gS({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=yS({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,v=o!=null,S=c==="leakyrelu",N=c?km(c,!1):null,E=new mS(g,b,N,v,S),R=[a,s];if(i&&R.push(i),o&&R.push(o),S){let z=r.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));R.push(z),A.push(z)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var cse={kernelName:zs,backendName:"webgl",kernelFunc:hse};function fse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=[],f=d;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=C.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?km(p,y):null,x=[a,s],b=i!=null,v=o!=null,S=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),S){let z=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(z),m.push(z)}let N;y?N=new bS(g,b,A,v,S):N=new xS(g,b,A,v,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(N,x,"float32",E);return m.forEach(z=>r.disposeIntermediateTensorInfo(z)),R}var mse={kernelName:Os,backendName:"webgl",kernelFunc:fse},gse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=vt(t.length),a=vt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${n} strides = ${n}(${this.strides});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function yse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=Nee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let m=new gse(i,h,[u,d]),f=r.runWebGLProgram(m,[c,p],c.dtype),g=ve({inputs:{x:f},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),g}var Ase={kernelName:rl,backendName:"webgl",kernelFunc:yse},xse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=vt(this.rank),n=bse(e,2);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${n}));
}
`}};function bse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function kS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(Z().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=Eee(x,A,m);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let f=new xse(p.shape,m),g=r.runWebGLProgram(f,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var vse={kernelName:tl,backendName:"webgl",kernelFunc:kS},wse="return float(a > b);",kse=`
return vec4(greaterThan(a, b));
`,Ise=vr({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:Ree,dtype:"bool"}),Sse={kernelName:nl,backendName:"webgl",kernelFunc:Ise},Cse="return float(a >= b);",Tse=`
return vec4(greaterThanEqual(a, b));
`,Nse=vr({opSnippet:Cse,packedOpSnippet:Tse,dtype:"bool",cpuKernelImpl:$ee}),Ese={kernelName:mi,backendName:"webgl",kernelFunc:Nse};function Rse(e){let{inputs:t,backend:r}=e,{input:n}=t;return wS(n,!0,r)}var $se={kernelName:uf,backendName:"webgl",kernelFunc:Rse},Mse="return float(!isnan(x) && !isinf(x));",Fse=it({opSnippet:Mse,dtype:"bool"}),Pse={kernelName:Xu,backendName:"webgl",kernelFunc:Fse},_se="return float(isinf(x));",zse=it({opSnippet:_se,dtype:"bool"}),Ose={kernelName:Zu,backendName:"webgl",kernelFunc:zse},Dse="return float(isnan(x));",Lse=it({opSnippet:Dse,dtype:"bool"}),Bse={kernelName:Yu,backendName:"webgl",kernelFunc:Lse},Wse="return float(a < b);",Vse=`
return vec4(lessThan(a, b));
`,Use=vr({opSnippet:Wse,packedOpSnippet:Vse,cpuKernelImpl:Mee,dtype:"bool"}),Gse={kernelName:al,backendName:"webgl",kernelFunc:Use},jse="return float(a <= b);",Hse=`
return vec4(lessThanEqual(a, b));
`,qse=vr({opSnippet:jse,packedOpSnippet:Hse,cpuKernelImpl:Fee,dtype:"bool"}),Kse={kernelName:sl,backendName:"webgl",kernelFunc:qse};function Xse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=Pee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var Zse={kernelName:df,backendName:"webgl",kernelFunc:Xse},Yse=Nd+`
return x < 0.0 ? 0./0. : log(x);
`,Jse=`
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;
`,Qse=it({opSnippet:Yse,packedOpSnippet:Jse,cpuKernelImpl:_ee}),eie={kernelName:Ai,backendName:"webgl",kernelFunc:Qse},tie=Nd+`
return log(1.0 + x);
`,rie=it({opSnippet:tie}),nie={kernelName:Ju,backendName:"webgl",kernelFunc:rie},aie="return float(a >= 1.0 && b >= 1.0);",sie=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,iie=vr({opSnippet:aie,packedOpSnippet:sie,dtype:"bool"}),oie={kernelName:il,backendName:"webgl",kernelFunc:iie},lie="return float(!(x >= 1.0));",uie=it({opSnippet:lie}),die={kernelName:Qu,backendName:"webgl",kernelFunc:uie},pie="return float(a >= 1.0 || b >= 1.0);",hie=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,cie=vr({opSnippet:pie,packedOpSnippet:hie,dtype:"bool"}),fie={kernelName:nh,backendName:"webgl",kernelFunc:cie},mie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},gie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},yie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Z().getBool("WEBGL_PACK_NORMALIZATION")?new gie(a.shape,s,i,o,l):new mie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Aie={kernelName:ah,backendName:"webgl",kernelFunc:yie},xie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${n}) * norm + float(${r});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${n})
* float(${a})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},bie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new xie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},vie={kernelName:pf,backendName:"webgl",kernelFunc:bie};function wie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Bl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function IS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let S=0;S<x.length;S++)x[S]=a.shape[d[S]];let b=A5(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=Im(a,d,r);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(c.shape,u),g=m;i&&(g=C.expandShapeToKeepDim(m,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=zee(A,w.sizeFromShape(f),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=wie(c,f,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var kie={kernelName:xi,backendName:"webgl",kernelFunc:IS},Iie=nS+`
return max(a, b);
`,Sie=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wm+`
return result;
`,Cie=vr({opSnippet:Iie,packedOpSnippet:Sie,cpuKernelImpl:Oee}),Tie={kernelName:bi,backendName:"webgl",kernelFunc:Cie};function Nie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;kd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return fn({inputs:{x:a},backend:r});let h=new Kp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var Eie={kernelName:vi,backendName:"webgl",kernelFunc:Nie};function Rie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new b5(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var $ie={kernelName:sh,backendName:"webgl",kernelFunc:Rie},Mie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${a};
wR += ${n}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${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);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Fie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${a}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${r}.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) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${c} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${u} +
wR * ${u} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Pie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,u,d),c=new b5(p,"max",!0),m=r.runWebGLProgram(c,[i],i.dtype),f=new Fie(p),g=r.runWebGLProgram(f,[a,m],i.dtype);return r.disposeIntermediateTensorInfo(m),g}var _ie={kernelName:cf,backendName:"webgl",kernelFunc:Pie};function zie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;kd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=C.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,m=new Kp(p,"max",c),f=r.runWebGLProgram(m,[o],o.dtype),g=new Mie(p),y=r.runWebGLProgram(g,[a,f],o.dtype);return r.disposeIntermediateTensorInfo(f),y}var Oie={kernelName:hf,backendName:"webgl",kernelFunc:zie};function Die(e,t,r,n){let a=new Kp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Kp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Lie={kernelName:ff,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=C.computePool2DInfo(n.shape,a,s,u,i),[h,p]=Die(n,o,d,l);return[h,p]}};function Bie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Bl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Wie={kernelName:wi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=C.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],m=n;if(h){if(p){let x=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=n.shape[d[N]];let v=A5(x,n.shape,n.dtype,d,b);m=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(m.dataId);S.values=v}else m=Im(n,d,i);c.push(m),u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=C.computeOutAndReduceShapes(m.shape,u),y=f;a&&(y=C.expandShapeToKeepDim(f,l));let A=Bie(m,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Vie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=C.getAxesPermutation(u,o),h=a;d!=null&&(h=Vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=C.getInnerMostAxes(u.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",u,o);let[p,c]=C.computeOutAndReduceShapes(h.shape,u),m=w.sizeFromShape(c),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,m]}}),g=Bl(f,f.dtype,"min",r),y;if(i){let A=C.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Uie={kernelName:ki,backendName:"webgl",kernelFunc:Vie},Gie=nS+`
return min(a, b);
`,jie=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wm+`
return result;
`,Hie=vr({opSnippet:Gie,packedOpSnippet:jie,cpuKernelImpl:Dee}),qie={kernelName:Ii,backendName:"webgl",kernelFunc:Hie},Kie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=vt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
for (int i = 0; i < ${n}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${a} coords = outC - start;
setOutput(getX(${o}));
}
`}},Xie=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,m)=>c[0]+e[m]+c[1]);let n=e.length,a=vt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,m)=>c[0]+e[m]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
${a} source = rc;
if (source < start) {
source = start * 2 - source - ${h};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;p=`
${a} rc = outputLoc;
${c}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[1] = getChannel(getX(${l.join()}), ${d});
}
`}else{let c=`
${a} source = rc;
${a} lt = ${a}(lessThan(source, start));
${a} gte = ${a}(greaterThanEqual(source, end));
${a} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${h}) +
gte * ((end - 1) * 2 - source + ${h});
source -= start;
`;p=`
${a} rc = outputLoc;
${c}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[1] = getChannel(getX(${l.join()}), ${d});
}
rc = outputLoc;
${o[n-2]} += 1;
if(${o[n-2]} < ${this.outputShape[n-2]}) {
${c}
result[2] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[3] = getChannel(getX(${l.join()}), ${d});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},Zie=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xie(n.shape,a,s):new Kie(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Yie={kernelName:Si,backendName:"webgl",kernelFunc:Zie},Jie=`if (b == 0.0) return NAN;
return mod(a, b);`,Qie=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+wm+`
return result;
`,eoe=vr({opSnippet:Jie,packedOpSnippet:Qie}),toe={kernelName:ed,backendName:"webgl",kernelFunc:eoe},roe=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],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}));
}
`}},noe=`
if (a == b) {
return 1.0;
};
return a / b;`,aoe=`
// 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;
`,SS=vr({opSnippet:noe,packedOpSnippet:aoe,checkOutOfBounds:!0}),soe={kernelName:ui,backendName:"webgl",kernelFunc:SS},J4="return a - b;",CS=vr({opSnippet:J4,packedOpSnippet:J4,supportsComplex:!0,cpuKernelImpl:ete}),ioe={kernelName:Wi,backendName:"webgl",kernelFunc:CS};function TS(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=IS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=CS({inputs:{a,b:u},backend:r}),h=vS({inputs:{x:d},backend:r}),p=Sm({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),m=SS({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),m}var ooe={kernelName:Li,backendName:"webgl",kernelFunc:TS};function loe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:TS({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new roe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var uoe={kernelName:mf,backendName:"webgl",kernelFunc:loe},doe=Yn+`
return -x;
`,poe=`
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 hoe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=Bee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new To(n.shape,poe):a=new Xa(n.shape,doe),r.runWebGLProgram(a,[n],n.dtype)}var coe={kernelName:ol,backendName:"webgl",kernelFunc:hoe},foe=Xn.nonMaxSuppressionV3Impl;function moe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=foe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var goe={kernelName:ul,backendName:"webgl",kernelFunc:moe},yoe=Xn.nonMaxSuppressionV4Impl;function Aoe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=yoe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var xoe={kernelName:td,backendName:"webgl",kernelFunc:Aoe},boe=Xn.nonMaxSuppressionV5Impl;function voe(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=boe(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var woe={kernelName:dl,backendName:"webgl",kernelFunc:voe},koe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${r}),
float(index == coords.y)));
}
`}},Ioe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new koe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Soe={kernelName:hl,backendName:"webgl",kernelFunc:Ioe};function V0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Wh({inputs:{input:n},backend:r}),s=V0({inputs:{x:a},backend:r}),i=Cm({inputs:{input:n},backend:r}),o=V0({inputs:{x:i},backend:r}),l=Ki({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Vh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Coe={kernelName:Nl,backendName:"webgl",kernelFunc:V0};function NS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Wh({inputs:{input:n},backend:r}),s=NS({inputs:{x:a},backend:r}),i=Cm({inputs:{input:n},backend:r}),o=V0({inputs:{x:i},backend:r}),l=Ki({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Vh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Toe={kernelName:pl,backendName:"webgl",kernelFunc:NS};function Noe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return ry({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=ry({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=fS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var Eoe={kernelName:cl,backendName:"webgl",kernelFunc:Noe},Roe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=vt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},$oe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let n=e.length,a=vt(n),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
if(${u}) {
`,n===1?"":`}
rc = outputLoc;
${o[n-2]} += 1;
if(${o[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${o[n-1]} += 1;
if(${u}) {`],p=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let m=0,f=n===1?2:4;m<f;m++)c+=`
${h[m]}
if (${p}) {
result[${m}] = float(value);
} else {
${a} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${d});
}
`;c+=n===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},ES=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Vh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $oe(a.shape,s,i):new Roe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},Moe={kernelName:Ti,backendName:"webgl",kernelFunc:ES},Foe=`
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);
`,Poe=`
// 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));
`+wm+`
return result;
`,_oe=vr({opSnippet:Foe,packedOpSnippet:Poe}),zoe={kernelName:Ni,backendName:"webgl",kernelFunc:_oe};function Ooe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=C.getAxesPermutation(d,o),p=a;h!=null&&(p=Vr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=C.getInnerMostAxes(d.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let m=r.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:y}=Vee(p.shape,p.dtype,m,d);c=r.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(f),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=mh(a.dtype),x=Bl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(c);let m=C.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:m}})}return l.forEach(m=>r.disposeIntermediateTensorInfo(m)),c}var Doe={kernelName:Ri,backendName:"webgl",kernelFunc:Ooe},RS=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Uee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Loe={kernelName:rd,backendName:"webgl",kernelFunc:RS},Boe="return 1.0 / x;",Woe=it({opSnippet:Boe}),Voe={kernelName:nd,backendName:"webgl",kernelFunc:Woe},Uoe=Yn+`
return (x < 0.0) ? 0.0 : x;
`,Goe=`
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;
`,joe=it({opSnippet:Uoe,packedOpSnippet:Goe}),Hoe={kernelName:$i,backendName:"webgl",kernelFunc:joe},qoe=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Koe=`
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;
`,Xoe=it({opSnippet:qoe,packedOpSnippet:Koe}),Zoe={kernelName:Fi,backendName:"webgl",kernelFunc:Xoe},Yoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},Joe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${r-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 Qoe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Joe(a.shape,l,u,s,i):new Yoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var ele={kernelName:Mi,backendName:"webgl",kernelFunc:Qoe},tle=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${c});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${n-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), ${a-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 rle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new tle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var nle={kernelName:yf,backendName:"webgl",kernelFunc:rle},ale=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},sle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${r-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function ile(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sle(a.shape,l,u,s,i):new ale(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var ole={kernelName:ad,backendName:"webgl",kernelFunc:ile},lle=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,m=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${c});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${n}) - 1),
${r} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${r} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function ule(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new lle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var dle={kernelName:gf,backendName:"webgl",kernelFunc:ule},ple=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>n(o)).join(","),s=vt(r);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},hle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Lr("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=vt(r);r===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${a}){
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 = ${o(n.slice())};
if(${a}){
result.g = ${l(n.slice())};
}
if(${s}) {
result.b = ${u(n.slice())};
if(${a}) {
result.a = ${d(n.slice())};
}
}
setOutput(result);
}
`;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let m=e.map((y,A)=>p(A,c)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(c,m){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${m[c]} - 1`:`${m[c]}`}}};function cle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return fn({inputs:{x:a},backend:r});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hle(a.shape,o):new ple(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var fle={kernelName:ml,backendName:"webgl",kernelFunc:cle},mle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
vec3 fill = vec3(${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]));
${a}
if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},gle={kernelName:El,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new mle(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},yle=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Ale=it({opSnippet:yle}),xle={kernelName:gl,backendName:"webgl",kernelFunc:Ale},ble="return inversesqrt(x);",vle=it({opSnippet:ble,cpuKernelImpl:Gee}),wle={kernelName:Pi,backendName:"webgl",kernelFunc:vle},$S=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=vt(a.length),l=vt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${a});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${d});
flattenedIndex += index * ${c};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function kle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new $S(l,o,c.shape.length,m.shape.length,d,p),y=r.runWebGLProgram(g,[m,c,f],m.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(f),A}var Ile={kernelName:yl,backendName:"webgl",kernelFunc:kle},Sle=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=vt(r);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function Cle(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Sle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Tr(a.dtype,s.dtype))}var Tle={kernelName:Al,backendName:"webgl",kernelFunc:Cle},Nle=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Ele=it({opSnippet:Nle}),Rle={kernelName:sd,backendName:"webgl",kernelFunc:Ele},$le=Nd+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Mle=`
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;
`,Fle=it({opSnippet:$le,packedOpSnippet:Mle,cpuKernelImpl:jee}),Ple={kernelName:zi,backendName:"webgl",kernelFunc:Fle},_le=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,zle=it({opSnippet:_le}),Ole={kernelName:id,backendName:"webgl",kernelFunc:zle},Dle=Nd+`
return sin(x);
`,Lle=it({opSnippet:Dle}),Ble={kernelName:_i,backendName:"webgl",kernelFunc:Lle},Wle=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Vle=it({opSnippet:Wle}),Ule={kernelName:bl,backendName:"webgl",kernelFunc:Vle},Gle=`
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;
`,jle=it({opSnippet:Gle}),Hle={kernelName:od,backendName:"webgl",kernelFunc:jle},qle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=ES({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Vr({inputs:{x:m},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Kle={kernelName:vl,backendName:"webgl",kernelFunc:qle};function Xle(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,m,f]=qee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),r.makeTensorInfo([f.length],n.dtype,new Int32Array(f))]}var Zle={kernelName:oh,backendName:"webgl",kernelFunc:Xle};function Yle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=Kee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Jle={kernelName:ld,backendName:"webgl",kernelFunc:Yle};function Qle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=JI(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var eue={kernelName:lh,backendName:"webgl",kernelFunc:Qle};function tue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=JI(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var rue={kernelName:uh,backendName:"webgl",kernelFunc:tue};function nue(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=C.calculateShapes(s,a,o),p=!1,c=new $S(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGLProgram(c,[s,a,i],s.dtype),f=ve({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(m),f}var aue={kernelName:dh,backendName:"webgl",kernelFunc:nue};function sue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=Ed({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var iue={kernelName:wl,backendName:"webgl",kernelFunc:sue},Q4="return sqrt(x);",oue=it({opSnippet:Q4,packedOpSnippet:Q4,cpuKernelImpl:Xee}),lue={kernelName:Oi,backendName:"webgl",kernelFunc:oue},uue="return x * x;",due=it({opSnippet:uue}),pue={kernelName:ud,backendName:"webgl",kernelFunc:due},ev="return (a - b) * (a - b);",hue=vr({opSnippet:ev,packedOpSnippet:ev}),cue={kernelName:Bi,backendName:"webgl",kernelFunc:hue};function fue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Yn+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Xa(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var mue={kernelName:Gi,backendName:"webgl",kernelFunc:fue},gue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=vt(r.length),s=vt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${a} begin = ${a}(${e});
${a} strides = ${a}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function yue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(f)v=ve({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let N=Ot.computeOutShape(A,x,b),E=Ed({inputs:{x:a},backend:r,attrs:{begin:A,size:N}});v=ve({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let N=r.readSync(a.dataId),E=We(a.shape,a.dtype,N),R=Zee(c,E,b,A);v=r.makeTensorInfo(m,a.dtype,R.values)}else{let N=new gue(A,b,c);v=r.runWebGLProgram(N,[a],a.dtype)}let S=ve({inputs:{x:v},backend:r,attrs:{shape:m}});return r.disposeIntermediateTensorInfo(v),S}var Aue={kernelName:kl,backendName:"webgl",kernelFunc:yue};function xue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Yee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var bue={kernelName:ph,backendName:"webgl",kernelFunc:xue};function vue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Jee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var wue={kernelName:Af,backendName:"webgl",kernelFunc:vue};function kue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Qee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Iue={kernelName:xf,backendName:"webgl",kernelFunc:kue},Sue="return tan(x);",Cue=it({opSnippet:Sue}),Tue={kernelName:Il,backendName:"webgl",kernelFunc:Cue},Nue=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Eue=it({opSnippet:Nue}),Rue={kernelName:Vi,backendName:"webgl",kernelFunc:Eue},$ue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=vt(this.rank),a=Mue(e);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function Mue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function MS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=tte(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new $ue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Fue={kernelName:ts,backendName:"webgl",kernelFunc:MS},Pue=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));
}
}
`}},_ue=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 yo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function tv(e){let t=1;for(;t<e;)t*=2;return t}function zue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[z,$]=rte(R,u,a.dtype,s,i);return[r.makeTensorInfo(z.shape,z.dtype,z.values),r.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Vh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,m=w.sizeFromShape(u)/d,f=ve({inputs:{x:c},attrs:{shape:[m,d]},backend:r});p&&yo(r,c);let g=tv(s),y=tv(d),A=null,x=()=>A===null?[f,f]:[f,A],b=(R,z,$)=>{let I=x(),_=new Pue($),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[z]],j=A;A=r.runWebGLProgram(_,I,"int32",O),yo(r,j)};for(let R=1;R<g;R*=2){let z=R*2;for(let $=R;$>=1;$/=2)b(z,$,[m,y])}for(let R=y;R>g;R/=2){let z=x(),$=new _ue([m,R/2]),I=[[d],[A===null?1:0],[g]],_=A;A=r.runWebGLProgram($,z,"int32",I),yo(r,_);let O=g/2,j=O*2;for(let X=O;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=Ed({inputs:{x:A},backend:r,attrs:{begin:0,size:[m,s]}}),yo(r,v);let S=kS({inputs:{x:f,indices:A},backend:r,attrs:{axis:1,batchDims:1}});yo(r,f);let N=u.slice(0,-1);N.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:N},backend:r}),yo(r,v);let E=S;return S=ve({inputs:{x:S},attrs:{shape:N},backend:r}),yo(r,E),[S,A]}var Oue={kernelName:Sl,backendName:"webgl",kernelFunc:zue},Due=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){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 < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${a});
}
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(${a});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${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 Lue(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new Due(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Bue={kernelName:Cl,backendName:"webgl",kernelFunc:Lue};function Wue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;kd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=nte(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Vue={kernelName:bf,backendName:"webgl",kernelFunc:Wue};function Uue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=Ed({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeIntermediateTensorInfo(f)),m}var Gue={kernelName:Tl,backendName:"webgl",kernelFunc:Uue},jue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
sumValue += dot(values, segFilter);
`,p="";a%r>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let c="";a%r>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${c}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${r}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${d===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${d===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function Hue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=C.getAxesPermutation([u],o),h=a;d!=null&&(h=Vr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(m);let f=mh(a.dtype),g=(b,v,S,N,E)=>{let R=b.shape[0],z=b.shape[1],$=C.segment_util.segOpComputeOptimalWindowSize(z,E),I={windowSize:$,inSize:z,batchSize:R,numSegments:E},_=new jue(I,v),O=r.compileAndRun(_,[b,S],N);if(l.push(O),O.shape[1]===E)return O;let j=RS({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=MS({inputs:{x:j},backend:r,attrs:{reps:[z/$]}});return l.push(j),l.push(X),g(O,v,X,N,E)},y=g(m,"unsortedSegmentSum",s,f,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=C.getUndoAxesPermutation(d);x=Vr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var que={kernelName:hh,backendName:"webgl",kernelFunc:Hue},Kue=[Jte,ere,nre,ire,lre,pre,cre,mre,xre,vre,Ire,Tre,Rre,Pre,Ore,Lre,Wre,jre,qre,Xre,Qre,ine,lne,dne,gne,Ane,wne,Fte,Sne,Rne,Pne,Bne,Vne,Gne,Hne,Kne,Yne,eae,nae,sae,oae,uae,hae,fae,Aae,bae,kae,Cae,Nae,Mae,zae,Bae,Uae,Hae,qae,Xae,Yae,Qae,tse,nse,ose,dse,cse,mse,Ase,vse,Sse,Ese,Mte,$se,Nne,Pse,Ose,Bse,_te,Gse,Kse,Zse,eie,nie,oie,die,fie,Aie,vie,kie,Tie,Eie,$ie,_ie,Oie,Lie,Wie,Uie,qie,Yie,toe,uoe,Bte,coe,goe,xoe,woe,hne,Soe,Toe,Eoe,Moe,zoe,Ote,Doe,Loe,cne,soe,Voe,Hoe,Zoe,Vte,ele,nle,ole,dle,fle,gle,xle,wle,Ile,Tle,Rle,Ple,Ole,Ble,Ule,ane,ooe,Hle,Kle,Zle,Jle,eue,rue,aue,iue,lue,pue,cue,mue,Aue,bue,wue,Iue,ioe,Xte,Tue,Rue,Fue,Oue,Bue,Zte,Vue,Gue,que,Coe];for(let e of Kue)qn(e);var Da=Z();Da.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Da.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Da.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Da.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Da.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Da.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Da.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Da.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Da.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Da.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Xue="return a + b;",Zue="return areal * breal - aimag * bimag;",Yue="return areal * bimag + aimag * breal;",Jue="return a / b;",Que="return a * b;",ede="return (a - b) * (a - b);",tde="return a - b;",rde="return f32(a == b);",nde="return vec4<f32>(a == b);",ade="return f32(a > b);",sde="return vec4<f32>(a > b);",ide="return f32(a >= b);",ode="return vec4<f32>(a >= b);",lde="return f32(a < b);",ude="return vec4<f32>(a < b);",dde="return f32(a <= b);",pde="return vec4<f32>(a <= b);",hde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",cde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,fde=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,FS=`
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;
}
`,mde=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,gde=`
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);
`,yde="return f32(a != b);",Ade="return vec4<f32>(a != b);",xde=`
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);
`,bde=`
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;
${FS}
return resultTemp;
`,vde="if (a < 0.0) { return b * a; } return a;",wde=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function rv(e,t){let r=t?FS:fde;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+r+`
return resultTemp;
`:r+`
return ${e}(a, b);
`}function Uh(e,t){switch(e){case 0:return Que;case 1:return Xue;case 2:return tde;case 3:return Jue;case 4:return t?nde:rde;case 5:return t?sde:ade;case 6:return t?ode:ide;case 7:return t?ude:lde;case 8:return t?pde:dde;case 9:return t?cde:hde;case 10:return t?Ade:yde;case 11:return ede;case 12:return t?gde:mde;case 14:return t?wde:vde;case 15:return rv("max",t);case 16:return rv("min",t);case 13:return t?bde:xde;case 17:return Zue;case 18:return Yue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var kde="return abs(a);",Ide="return ceil(a);",Sde="return cos(a);",Cde=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Tde="return exp(a) - 1.0;",Nde="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Ede=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Rde="return exp(a);",$de="return floor(a);",Mde="return a;",Fde=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Pde="return f32(!(a >= 1.0));",_de="return -a;",zde="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ode=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`,Dde="if(a < 0.0) { return 0.0; } return a;",Lde="return clamp(a, 0.0, 6.0);",Bde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Wde=`
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;
`,Vde="return 1.0/sqrt(a);",Ude="return 1.0 / (1.0 + exp(-1.0 * a));",Gde="return sin(a);",jde=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,Hde="return sqrt(a);",qde="return a * a;",Kde=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Xde="return f32(i32((a)));";function bo(e,t){switch(e){case 0:return kde;case 2:return Sde;case 3:return Cde;case 1:return Ide;case 4:return t?Ede:Nde;case 5:return Rde;case 6:return Tde;case 7:return $de;case 8:return Mde;case 9:return Fde;case 10:return Pde;case 11:return _de;case 14:return t?Ode:zde;case 12:return t?Wde:Dde;case 13:return t?Bde:Lde;case 15:return Vde;case 18:return Ude;case 16:return Gde;case 17:return jde;case 19:return Hde;case 20:return qde;case 21:return Kde;case 22:return Xde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function os(e,t=!1){if(e===null)return null;if(e==="linear")return bo(8);if(e==="relu")return bo(12,t);if(e==="elu")return bo(4,t);if(e==="relu6")return bo(13,t);if(e==="prelu")return Uh(14,t);if(e==="sigmoid")return bo(18,t);if(e==="leakyrelu")return bo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Zde(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function Ar(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 h0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function v5(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Xi(){return`
${v5()}
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 et(){return`
${Xi()}
let index = getGlobalIndex();
`}function Yde(e,t,r,n=!1){let a=[];if(a.push(`
let workGroupSizeX = ${r.workGroupSize[0]}u;
let workGroupSizeY = ${r.workGroupSize[1]}u;
let workGroupSizeZ = ${r.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),n===!0)return a.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
dispatchSize : vec3<u32>,
};
@group(0) @binding(0) var<storage, write> result: array<${h0(t.dtype,r.isVec4)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[nv,a.join(`
`),av(t.shape),r.getUserCode()].join(`
`);let s="struct Uniforms { NAN : f32, ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${Ar(e[h].shape.length)}, `}),s+=`outShape : ${Ar(t.shape.length)}, `;let i=t.shape.length-1;s+=`
outShapeStrides: ${Ar(i)}, `,r.size&&(s+="size : i32, "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`):a.push(`
@group(0) @binding(0) var<storage, write> result: array<${h0(t.dtype,r.isVec4)}>;
`),r.variableNames.forEach((d,h)=>{a.push(`
@group(0) @binding(${1+h}) var<storage, read> ${d}: array<${h0(e[h].dtype,r.isVec4)}>;
`)}),s!==""&&a.push(`
@group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
`);let[o,l]=npe(t.shape,r.dispatchLayout),u=[nv,a.join(`
`),av(t.shape),o,Jde(t.shape.length)];if(r.atomic||u.push(Qde(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>epe(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
`);u.push(d)}return u.push(r.getUserCode()),u.join(`
`)}var nv=`
// 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 Jde(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 Qde(e,t,r){let n=e.length,a=h0(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result[flatIndex] = ${a}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result[flatIndex] = ${a}(value);
}`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result[flatIndex] = ${a}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result[flatIndex] = ${a}(value);
}`,n>=2){let i=["d0","d1","d2","d3"].slice(0,n),o=Ar(n);r?s+=`
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:s+=`
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return s}function epe(e,t,r,n){let a=tpe(e,r);return e.shape.length<=t.length&&(a+=rpe(e,t,r,n)),a}function tpe(e,t){let r=e.name,n=e.shape.length,a=Ar(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
fn ${s}() -> vec4<f32> {
return vec4<f32>(${r}[0]);
}
`:`
fn ${s}() ->f32 {
return f32(${r}[0]);
}
`;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
fn ${s}(${o}) -> vec4<f32> {
return vec4<f32>(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
${l}) / 4]);
}
`:`
fn ${s}(${o}) -> f32 {
return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
${l})]);
}
`}function rpe(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=Ar(l);if(w.arraysEqual(e.shape,t)&&n)return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${a}[globalIndex]);
}
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
return vec4<f32>(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
return f32(${a}[globalIndex]);
}
fn ${i}Coords(coords : ${u}) -> f32 {
return f32(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let d=C.getBroadcastDims(e.shape,t),h=l-o,p="";if(o===0)return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return get${s}();
}
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
return get${s}();
}
`:`
fn ${i}Index(globalIndex : i32) -> f32{
return get${s}();
}
fn ${i}Coords(coords : ${u}) -> f32{
return get${s}();
}
`;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords[${g+h}] = 0;`).join(`
`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=Ar(o),y=e.shape.map((A,x)=>`coords[${x+h}]`).join(", ");c=`${g}(${y})`}else c="coords";let m=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,f=`${o}D`;return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${p}
return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
}
fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${a}[getIndexFromCoords${f}(${c}, ${m}) / 4];
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${p}
return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
}
fn ${i}Coords(coordsIn : ${u}) -> f32 {
var coords = coordsIn;
${p}
return f32(${a}[getIndexFromCoords${f}(${c}, ${m})]);
}
`}function npe(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${Ar(s)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let m=Zde(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let f=0;f<m.length;f++)i+=`let d${c[f]} = index${p} / ${m[f]};`,f===m.length-1?i+=`let d${c[f+1]} = index${p} - d${c[f]} * ${m[f]};`:i+=`index${p} = index${p} - d${c[f]} * ${m[f]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=Ar(l),h=`fn getOutputCoords() -> ${d} {
${i}
`;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function av(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=Ar(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.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 s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
fn getCoordsFromIndex(index : i32) -> ${n} {
${s}
return ${n}(${a.join(",")});
}
`}var PS={};Le(PS,{ArrayBufferToTypedArray:()=>zS,GPUBytesPerElement:()=>ny,computeDispatch:()=>ze,computeWorkGroupSizeForConv2d:()=>w5,computeWorkGroupSizeForMatMul:()=>_S,computeWorkPerThreadForConv2d:()=>k5,flatDispatchLayout:()=>Ke,isWebGPUSupported:()=>I5,tilesFitEvenlyIntoShape:()=>Ya});var $o=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Ya(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function ze(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil($o(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil($o(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil($o(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function w5(e,t){let r=$o(e.x.map(a=>t[a])),n=$o(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function _S(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function k5(e,t){let r=$o(e.x.map(a=>t[a])),n=$o(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Ke(e){return{x:e.map((t,r)=>r)}}function ny(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function zS(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 I5(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function OS(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${n};
${Xi()}
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 ape=class{constructor(e,t,r,n,a,s=null,i=null,o=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=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&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=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=n,this.batchBEqualOne=a,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Ya(n,this.aShape.slice(1)),Ya(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=os(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${s}
}`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
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> {
${this.batchBEqualOne?`
let batchBSize = 0;
let batch = 0;
`:`
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);
${a}
${n}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${OS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function S5(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
${Xi()}
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) / ${a} + 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 = ${a} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${a} / ${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 * ${a} + 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 * ${a} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${a}; 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 spe(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Xi()}
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 DS=class{constructor(e,t,r,n,a,s=!1,i=!1,o=null,l=null,u=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 d=s?e[1]:e[2];this.workGroupSize=_S(t[1],d,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let h=o!=null,p=u!=null;h&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=s,this.transposeB=i,this.addBias=h,this.activation=l,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=a;let c=this.outputShape[2],m=this.transposeB?[this.outputShape[0],c,d]:[this.outputShape[0],d,c];[this.fitA,this.fitB]=this.getShapeFit(m),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${i}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Ya(a,this.aShape.slice(1)),Ya(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}
`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
let batch = i32(globalId.z);
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
`}
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
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);
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
${this.outputShape[1]>1?S5([this.workPerThread,this.workPerThread,1],this.workGroupSize):spe(this.workGroupSize)}
`}};function ipe(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Xi()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var ope=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=a,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=r,this.shaderKey=`matMulReduce_${this.activation}_${n}_${a}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}
`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = batchIn;
`}
${e}
}
fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
let batch = batchIn;
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);
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
${ipe()}
`}};function lpe(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${r}>, ${n}>;
// 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.
${Xi()}
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) / ${n} + 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 + ${n};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
}
} 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 + ${n};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${n}; 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 + ${n};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${n}; 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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var upe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.batchAEqualOne=e[0]===0,this.batchBEqualOne=t[0]===0,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
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 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
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;
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
}
${lpe(this.workGroupSize)}
`}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var dpe={kernelName:fl,backendName:"webgpu",kernelFunc:qe};function C5({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],m=n?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(f),A=w.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,m]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,m,p]:[A,p,m],S=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),N=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,N],R=Math.max(y,A),z=y===1,$=A===1,I=h%4===0&&m%4===0&&!r&&!n,_;c*m<=32?_=new ope([R,c,m],z,$,r,n,s,l,i):!r&&!n&&(c<=16&&(m<=512||p>=2*m)||m<=16&&(c<=512||h>=2*c))?_=new upe(b,v,[R,c,m],s,l,i):I?_=new ape(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),z,$,s,l,i):_=new DS(b,[R,c,m],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),z,$,r,n,s,l,i);let O=[S,N];s&&O.push(s),i&&O.push(i);let j=[{type:"int32",data:[c]},{type:"int32",data:[m]},{type:"int32",data:[h]}];l==="leakyrelu"&&(j.push({type:"float32",data:[o]}),_.uniforms+=" alpha : f32,");let X=a.runWebGPUProgram(_,O,e.dtype,j),D=qe({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let J of E)a.disposeData(J.dataId);return D}function ppe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return C5({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var hpe={kernelName:_s,backendName:"webgpu",kernelFunc:ppe},sv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${Uh(this.op,!1)}
}
${et()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},cpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${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 {
${Uh(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${et()}
// 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"}[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);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},fpe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${Uh(this.op,this.isVec4)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},LS=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${Uh(this.op,!1)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function iv(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new fpe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new cpe(e,t,r,a):new LS(e,t,r)}function zn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var mpe={kernelName:gi,backendName:"webgpu",kernelFunc:zn};function Rd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=zn({inputs:{x:n},backend:r}),l=zn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var gpe={kernelName:Yp,backendName:"webgpu",kernelFunc:Rd},Gh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${bo(this.op,!1)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function wr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Gh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function qr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,m;if(e!==0)[c,m]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=iv(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Tr(y.dtype,A.dtype))});else{let g=new sv(17,i.shape,o.shape),y=new sv(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),m=l.runWebGPUProgram(y,A,"float32")}let f=Rd({inputs:{real:c,imag:m},backend:l});return l.disposeData(c.dataId),l.disposeData(m.dataId),f}let u=n||Tr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?C.fromUint8ToStringArray(h):h,m=i.dtype==="string"?C.fromUint8ToStringArray(p):p,[f,g]=t(i.shape,o.shape,c,m,u);return l.makeTensorInfo(g,u,f)}let d=iv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:ype,ceilImpl:Ape,concatImpl:xpe,equalImpl:bpe,expImpl:vpe,expm1Impl:wpe,floorImpl:kpe,gatherNdImpl:Ipe,gatherV2Impl:Spe,greaterEqualImpl:Cpe,greaterImpl:Tpe,lessEqualImpl:Npe,lessImpl:Epe,logImpl:Rpe,maxImpl:$pe,maximumImpl:Mpe,minimumImpl:Fpe,multiplyImpl:Ppe,negImpl:_pe,notEqualImpl:zpe,prodImpl:Ope,rangeImpl:Dpe,rsqrtImpl:Lpe,simpleAbsImpl:Bpe,sliceImpl:Wpe,stridedSliceImpl:Vpe,stringNGramsImpl:Upe,subImpl:Gpe,tileImpl:jpe,topKImpl:Hpe,transposeImpl:qpe,uniqueImpl:Axe}=Am,Kpe=wr({opType:0,cpuKernelImpl:Bpe}),Xpe={kernelName:jo,backendName:"webgpu",kernelFunc:Kpe},Zpe=qr({opSnippet:1,cpuKernelImpl:ype,supportsComplex:!0}),Ype={kernelName:Qa,backendName:"webgpu",kernelFunc:Zpe},Jpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
${et()}
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, ${t});
}
}
}
`}};function Qpe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return zn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Tr(o,l)),s=n.map(o=>o.shape),i=new Jpe(s);return r.runWebGPUProgram(i,n,a)}var ehe={kernelName:Ys,backendName:"webgpu",kernelFunc:Qpe},BS=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=C.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;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 = ${r(`${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;
}
${et()}
let outputIndex = index / i32(workGroupSizeX);
let coordInfo = getInputCoordInfo(outputIndex);
let Length = ${r("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[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]);
}
}
`}},the=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=ze(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]}>;
${v5()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = A[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},rhe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Ar(this.outputShape.length),t=nhe(this.newDim);return`
${et()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function nhe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Ja(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=qpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new the(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new rhe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var ahe={kernelName:Ui,backendName:"webgpu",kernelFunc:Ja};function she(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ja({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new BS(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var ihe={kernelName:Js,backendName:"webgpu",kernelFunc:she};function ohe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ja({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new BS(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var lhe={kernelName:Wu,backendName:"webgpu",kernelFunc:ohe},WS=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=Ke(this.outputShape),this.dispatch=ze(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"),`
${et()}
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});
}
}
`}},VS=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=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${et()}
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 uhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new VS(d):(h=new WS(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var dhe={kernelName:Qs,backendName:"webgpu",kernelFunc:uhe};function phe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return C5({a,b:s,transposeA:i,transposeB:o,backend:r})}var hhe={kernelName:ei,backendName:"webgpu",kernelFunc:phe},che=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=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Ar(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Ar(this.rank),t=fhe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${ay[a]} = uniforms.start[${a}] + coords.${ay[a]};`),`
${et()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${r.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},ay=["x","y","z","w","u","v"];function fhe(e){if(e===1)return"sourceLoc";if(e<=6)return ay.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function $d(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Ot.parseSliceParams(a,s,i);if(Ot.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Wpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new che(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var mhe={kernelName:xl,backendName:"webgpu",kernelFunc:$d},ghe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=[],m=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Ja({inputs:{x:m},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:d}}),y=$d({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(m),c.push(f),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},yhe={kernelName:Ho,backendName:"webgpu",kernelFunc:ghe},US=qr({opSnippet:10,dtype:"bool",cpuKernelImpl:zpe}),Ahe={kernelName:ll,backendName:"webgpu",kernelFunc:US};function jh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.real},backend:r})}var xhe={kernelName:ih,backendName:"webgpu",kernelFunc:jh};function bhe(e,t){let r=new Gh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function sy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return zn({inputs:{x:a},backend:r});let i=_t(a.shape),o=sy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Rd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=jh({inputs:{input:a},backend:r}),o=sy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=zn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return bhe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=US({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var vhe={kernelName:ti,backendName:"webgpu",kernelFunc:sy},whe=wr({opType:1,cpuKernelImpl:Ape}),khe={kernelName:ri,backendName:"webgpu",kernelFunc:whe},Ihe=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=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${et()}
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);
}
}
`}},She=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=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${et()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Che(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(a.shape)%4===0?o=new Ihe(a.shape):o=new She(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var The={kernelName:es,backendName:"webgpu",kernelFunc:Che},Nhe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(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${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${et()}
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 Tm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Ehe={kernelName:rh,backendName:"webgpu",kernelFunc:Tm};function iy(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>jh({inputs:{input:A},backend:r})),m=e.map(A=>Tm({inputs:{input:A},backend:r})),f=iy(c,t,r),g=iy(m,t,r),y=Rd({inputs:{real:f,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),m.forEach(A=>r.disposeData(A.dataId)),r.disposeData(f.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),m=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=xpe(m,f,n,g),A=C.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=Rhe(e,t,r),o=s.map(c=>c.shape),l=new Nhe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function Rhe(e,t,r){let n=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function GS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),iy(o,s,r)}var $he={kernelName:qo,backendName:"webgpu",kernelFunc:GS},Mhe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ya(e,[r,a]),Ya(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x[divBy4Index${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${e} = x[divBy4Index${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=OS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`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[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);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} else if (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,r=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${t}
}
return vec4<f32>(0.0);
`,n=this.fitB?"return W[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,a="",s="";if(this.activation){let o=os(this.activation,this.isVec4);if(this.hasPreluActivationWeights)a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`;else{if(this.hasLeakyreluAlpha)throw a=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaByOutputCoords(outCoord);
${o}
}`,new Error("Leakyrelu is not supported.");a=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${o}
}`}s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${a}
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> {
${n}
}
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);
${i}
${s}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${e}
`}},Fhe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[1],y:[2,3],z:[0]},this.workGroupSize=w5(this.dispatchLayout,this.outputShape),this.elementsPerThread=k5(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.convInfo.outHeight*this.convInfo.outWidth,i=this.convInfo.outChannels,o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ya(n,[s,o]),Ya(a,[o,i])]}getUserCode(){let e=this.isChannelsLast?`
let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
`:`
let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
`,t=this.isChannelsLast?`
let outCoord = vec4<i32>(
batch,
row / outWidth,
row % outWidth,
col);
`:`
let outCoord = vec4<i32>(
batch,
col,
row / outWidth,
row % outWidth);
`,r=S5(this.elementsPerThread,this.workGroupSize),n=`
let inChannels = uniforms.wShape[2];
let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
let outRow = row / outWidth;
let outCol = row % outWidth;
let WRow = col / (uniforms.filterDims[1] * inChannels);
let WCol = col / inChannels % uniforms.filterDims[1];
let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
${e}
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,a=this.fitA?`${n}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${n}
}
return 0.0;
`,s=this.fitB?"return W[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W[row * uniforms.dimBOuter + col];
}
return 0.0;
`,i="",o="";if(this.activation){let u=os(this.activation,!1);this.hasPreluActivationWeights?i=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${u}
}`:i=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${u}
}
`,o="value = activation(value, outCoord);"}let l=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${a}
}
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 outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
${t}
${l}
${o}
result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${r}
`}},Phe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${n}
}
`,t="value = activation(value, outCoord);"}let r=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)) {
${r}
${t}
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Xi()}
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}},_he=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${et()}
for(var i = 0; i<${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[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputAtIndex(flatIndex, value);
}
}
}
`}};function zhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r.dataFormat==="channelsLast",u=!l,d=!1,h=l&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",p,c;if(h){let g=r.inHeight*r.inWidth*r.inChannels;p=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,g]}}),c=qe({inputs:{x:t},backend:n,attrs:{shape:[1,g,r.outChannels]}})}else p=qe({inputs:{x:e},backend:n,attrs:{shape:l?[r.batchSize,r.inHeight*r.inWidth,r.inChannels]:[r.batchSize,r.inChannels,r.inHeight*r.inWidth]}}),c=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});let m=C5({a:l?p:c,b:l?c:p,transposeA:u,transposeB:d,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),f=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(p.dataId),n.disposeData(c.dataId),n.disposeData(m.dataId),f}function Ohe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=f*m,S=[v,b],N=!1,E=!1,R=[],z=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),$=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(z),R.push($);let I=new _he(S,x),_=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[m]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],O=n.runWebGPUProgram(I,[z],z.dtype,_),j=qe({inputs:{x:O},backend:n,attrs:{shape:[1,S[0],S[1]]}});R.push(O),R.push(j);let X=[1,S[0],S[1]],D=new DS(X,[1,v,r.outChannels],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),!0,!0,N,E,a,o,s),J=X[1],V=X[2],ee=r.outChannels,Q=[{type:"int32",data:[J]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],ie=[j,$];a&&ie.push(a),s&&ie.push(s),o==="leakyrelu"&&(_.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Y=n.runWebGPUProgram(D,ie,j.dtype,Q),ae=x?[1,f,m,r.outChannels]:[1,r.outChannels,f,m],de=qe({inputs:{x:Y},backend:n,attrs:{shape:ae}});R.push(Y);for(let Ae of R)n.disposeData(Ae.dataId);return de}function jS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d=r.dataFormat==="channelsLast",h;if(d&&r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return zhe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Z().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return w.assert(d,()=>"TODO: NCHW is unimplemented"),Ohe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let p=Z().getBool("WEBGPU_USE_NAIVE_CONV2D"),c=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&d,m=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...m]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(p)w.assert(d,()=>"TODO: NCHW is unimplemented"),h=new Phe(r,l,o,u);else{c?h=new Mhe(r,l,o,u):h=new Fhe(r,l,o,u);let y=r.outHeight*r.outWidth,A=r.outChannels,x=r.filterHeight*r.filterWidth*r.inChannels;f.push({type:"int32",data:[y]},{type:"int32",data:[A]},{type:"int32",data:[x]})}let g=[e,t];return l&&g.push(a),u&&g.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),h.uniforms+=" alpha : f32,"),n.runWebGPUProgram(h,g,e.dtype,f)}function Dhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return jS({x:a,filter:s,convInfo:p,backend:n})}var Lhe={kernelName:ni,backendName:"webgpu",kernelFunc:Dhe},Bhe=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=w5(this.dispatchLayout,this.outputShape),this.elementsPerThread=k5(this.dispatchLayout,this.outputShape),this.dispatch=ze(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[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[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[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${S5(this.elementsPerThread,this.workGroupSize)}
`}},Whe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
${et()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${r}];
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 (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function Vhe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=C.convertConv2DDataFormat(u),p=C.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],m;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))m=new Whe(p);else{m=new Bhe(p);let f=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(m,[a,s],"float32",c)}var Uhe={kernelName:ai,backendName:"webgpu",kernelFunc:Vhe},Ghe=wr({opType:2}),jhe={kernelName:si,backendName:"webgpu",kernelFunc:Ghe},Hhe=wr({opType:3}),qhe={kernelName:ii,backendName:"webgpu",kernelFunc:Hhe},Khe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=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) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${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`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${r});
let width_ratio = f32(${s});
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 = ${n};
let width_scale = ${i};
let in_y = ${a};
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);
}
}
}
`}},Xhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Khe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Zhe={kernelName:Xo,backendName:"webgpu",kernelFunc:Xhe},ov=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${lv(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],a="",s="";return this.exclusive?(a=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(a=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
${et()}
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${uv(e,"coords",this.op)};
var val = ${r};
let pow2 = i32(pow(2.0, uniforms.index));
if (${a}) {
let idx = ${s};
${uv(e,"coords",this.op)} = idx;
val ${this.op}= getX(${lv(e,"coords",this.op)});
}
setOutputAtIndex(index, val);
}
}
`}};function lv(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function uv(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function HS(e,t,r,n,a,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ja({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=zn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new ov(e,l.shape,!1,s),m=h,f=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,f),r.disposeData(m.dataId)}if(a){let p=new ov(e,l.shape,a,s),c=h,m=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,m),r.disposeData(c.dataId)}if(o!=null){let p=C.getUndoAxesPermutation(o),c=Ja({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function Yhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return HS("*",a,r,s,i,o)}var Jhe={kernelName:Ko,backendName:"webgpu",kernelFunc:Yhe};function Qhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return HS("+",a,r,s,i,o)}var ece={kernelName:oi,backendName:"webgpu",kernelFunc:Qhe},tce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${et()}
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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function rce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=[{type:"int32",data:[s]}],g=new tce(m,i);return r.runWebGPUProgram(g,[a],a.dtype,f)}var nce={kernelName:Zo,backendName:"webgpu",kernelFunc:rce},qS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${n}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${v5()}
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)) {
${r}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},KS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${n}
}
`,t="dotProd = activation(dotProd, coords);"}let r=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);
}
}
${Xi()}
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;
}
}
}
${r}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function ace(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new qS(h):(c=new KS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var sce={kernelName:li,backendName:"webgpu",kernelFunc:ace},XS=qr({opSnippet:0,cpuKernelImpl:Ppe,supportsComplex:!0}),ice={kernelName:Ci,backendName:"webgpu",kernelFunc:XS},oce=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${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[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let r=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;
}
${et()}
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[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) {
${r}
}
}
`}};function Hh(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=C.getAxesPermutation(l,s),d=e;u!=null&&(d=Ja({inputs:{x:e},attrs:{perm:u},backend:a}),l=C.getInnerMostAxes(l.length,s),i.push(d)),C.assertAxesAreInnerMostDims(n,l,s);let[h,p]=C.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=C.expandShapeToKeepDim(h,o));let m;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let f=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=$pe(f,w.sizeFromShape(p),c,e.dtype);m=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Ope(d.shape,d.dtype,f,l);m=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},A=n==="mean"?"float32":mh(e.dtype),x=[{type:"int32",data:[f]}],b=new oce(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),m=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(f=>a.disposeData(f.dataId)),m}function T5(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"sum",r)}var lce={kernelName:Di,backendName:"webgpu",kernelFunc:T5};function uce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=C.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,m=[];for(let f=0;f<h;++f){for(let g of d[f]){let{permutationIndices:y,expandDims:A}=C.getEinsumPermutation(c,l[g]),x;C.isIdentityPermutation(y)?x=s[g]:(x=Ja({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),m.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),m.push(x)),p===null?p=x:(p=XS({inputs:{a:x,b:p},backend:r}),m.push(p))}f<h-1&&(u[f]>=0&&(p=T5({inputs:{x:p},backend:r,attrs:{axis:u[f]-(i.length-c),keepDims:!1}}),m.push(p)),c--)}for(let f of m)f!==p&&r.disposeData(f.dataId);return p}var dce={kernelName:th,backendName:"webgpu",kernelFunc:uce},pce=wr({opType:4}),hce={kernelName:di,backendName:"webgpu",kernelFunc:pce},cce=qr({opSnippet:4,dtype:"bool",cpuKernelImpl:bpe}),fce={kernelName:Yo,backendName:"webgpu",kernelFunc:cce},ZS=wr({opType:5,cpuKernelImpl:vpe,dtype:"float32"}),mce={kernelName:pi,backendName:"webgpu",kernelFunc:ZS};function oy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var gce={kernelName:Jo,backendName:"webgpu",kernelFunc:oy},yce=wr({opType:6,cpuKernelImpl:wpe}),Ace={kernelName:Qo,backendName:"webgpu",kernelFunc:yce},xce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function Md(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new xce(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var bce={kernelName:Ku,backendName:"webgpu",kernelFunc:Md},vce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`}},wce={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new vce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},kce=wr({opType:7,cpuKernelImpl:kpe}),Ice={kernelName:hi,backendName:"webgpu",kernelFunc:kce},Sce=qr({opSnippet:12,dtype:"int32"}),Cce={kernelName:ci,backendName:"webgpu",kernelFunc:Sce},Tce=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){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>"};
${et()}
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[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}},Nce={kernelName:zp,backendName:"webgpu",kernelFunc:Ece},pu;function Ece(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Z().getBool("WEBGPU_USE_IMPORT")&&i)return dv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=d,pu.canvas.height=h,pu.drawImage(a,0,0,d,h),a=pu.canvas),u||l||i||o)return dv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,m=c;if(s!=null&&s!==4){m=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(m[A++]=c[x])}let f=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(f.dataId);return g.values=new Int32Array(m),r.maybeReleaseBuffer(f.dataId),r.uploadToGPU(f.dataId),f}function dv(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=new Tce(a,s),d=[{type:"uint32",data:[o]},{type:"uint32",data:[i]},{type:"uint32",data:[...l]},{type:"uint32",data:[...u.dispatch]}];return r.runFromPixelsProgram(u,a,d,s,t)}var Rce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,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)"),`
${et()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},$ce={kernelName:fi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Rce(n.shape,i.shape,o.shape,h,p),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,m)}};function Mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(d),g=C.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,f);return jS({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:c})}var Fce={kernelName:zs,backendName:"webgpu",kernelFunc:Mce};function Pce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,m=d;m==null&&(m=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{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]}],b;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.dilationHeight===1&&f.dilationWidth===1&&f.filterHeight===3&&f.inChannels%4===0?b=new qS(f,y,p,A):(b=new KS(f,y,p,A),x.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var _ce={kernelName:Os,backendName:"webgpu",kernelFunc:Pce},zce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Ar(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${et()}
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 Oce(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=C.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=Ipe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let m=new zce(i,[u,d]),f=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(m,[c,p],c.dtype,f),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var Dce={kernelName:rl,backendName:"webgpu",kernelFunc:Oce},Lce=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=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Bce(this.aShape);return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`}};function Bce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function YS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let m=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),S=Spe(v,x,m);return h.forEach(N=>r.disposeData(N.dataId)),r.makeTensorInfo(u.outputShape,S.dtype,S.values)}let f=new Lce(p.shape,m),g=r.runWebGPUProgram(f,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var Wce={kernelName:tl,backendName:"webgpu",kernelFunc:YS},Vce=qr({opSnippet:5,cpuKernelImpl:Tpe,dtype:"bool"}),Uce={kernelName:nl,backendName:"webgpu",kernelFunc:Vce},Gce=qr({opSnippet:6,dtype:"bool",cpuKernelImpl:Cpe}),jce={kernelName:mi,backendName:"webgpu",kernelFunc:Gce};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Gh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var qce={kernelName:yi,backendName:"webgpu",kernelFunc:Hce},Kce=qr({opSnippet:7,dtype:"bool",cpuKernelImpl:Epe}),Xce={kernelName:al,backendName:"webgpu",kernelFunc:Kce},Zce=qr({opSnippet:8,dtype:"bool",cpuKernelImpl:Npe}),Yce={kernelName:sl,backendName:"webgpu",kernelFunc:Zce},Jce=wr({opType:9,cpuKernelImpl:Rpe}),Qce={kernelName:Ai,backendName:"webgpu",kernelFunc:Jce},e0e=qr({opSnippet:9,dtype:"bool"}),t0e={kernelName:il,backendName:"webgpu",kernelFunc:e0e},r0e=wr({opType:10}),n0e={kernelName:Qu,backendName:"webgpu",kernelFunc:r0e};function JS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Hh(a,s,i,"max",r)}var a0e={kernelName:xi,backendName:"webgpu",kernelFunc:JS},s0e=qr({opSnippet:15,cpuKernelImpl:Mpe}),i0e={kernelName:bi,backendName:"webgpu",kernelFunc:s0e};function o0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=C.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});h=new VS(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new WS(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var l0e={kernelName:vi,backendName:"webgpu",kernelFunc:o0e};function u0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Hh(a,i,s,"mean",r)}var d0e={kernelName:wi,backendName:"webgpu",kernelFunc:u0e};function p0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"min",r)}var h0e={kernelName:ki,backendName:"webgpu",kernelFunc:p0e},c0e=qr({opSnippet:16,cpuKernelImpl:Fpe}),f0e={kernelName:Ii,backendName:"webgpu",kernelFunc:c0e},m0e=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Ar(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${et()}
if (index < uniforms.size) {
let start = ${i}(${t});
let end = ${i}(${r});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${s} < ${n}) {
${s} = ${n} * 2 - ${s} - ${this.offset};
} else if(${s} >= ${a}) {
${s} = (${a} - 1) * 2 - ${s} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${o}));
}
}
`}},g0e={kernelName:Si,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new m0e(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function y0e(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=_pe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Gh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var A0e={kernelName:ol,backendName:"webgpu",kernelFunc:y0e};function x0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Xn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var b0e={kernelName:ul,backendName:"webgpu",kernelFunc:x0e};function v0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Xn.nonMaxSuppressionV5Impl(d,h,p,c,m,f);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var w0e={kernelName:dl,backendName:"webgpu",kernelFunc:v0e};function U0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=jh({inputs:{input:n},backend:r}),s=U0({inputs:{x:a},backend:r}),i=Tm({inputs:{input:n},backend:r}),o=U0({inputs:{x:i},backend:r}),l=Rd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Md({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var k0e={kernelName:Nl,backendName:"webgpu",kernelFunc:U0};function QS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=jh({inputs:{input:n},backend:r}),s=QS({inputs:{x:a},backend:r}),i=Tm({inputs:{input:n},backend:r}),o=U0({inputs:{x:i},backend:r}),l=Rd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Md({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var I0e={kernelName:pl,backendName:"webgpu",kernelFunc:QS};function S0e(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return oy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=oy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=GS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var C0e={kernelName:cl,backendName:"webgpu",kernelFunc:S0e},T0e=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Ar(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${et()}
if (index < uniforms.size) {
let start = ${a};
let end = ${s};
let outC = getCoordsFromIndex(index);
if (${i} || ${o}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${l}));
}
}
}
`}},e9=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return zn({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Md({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new T0e(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},N0e={kernelName:Ti,backendName:"webgpu",kernelFunc:e9},E0e=qr({opSnippet:13}),R0e={kernelName:Ni,backendName:"webgpu",kernelFunc:E0e};function $0e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new LS(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var M0e={kernelName:Ei,backendName:"webgpu",kernelFunc:$0e};function F0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"prod",r)}var P0e={kernelName:Ri,backendName:"webgpu",kernelFunc:F0e},_0e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Dpe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},z0e={kernelName:rd,backendName:"webgpu",kernelFunc:_0e},t9=qr({opSnippet:3}),O0e={kernelName:ui,backendName:"webgpu",kernelFunc:t9},D0e=wr({opType:12}),L0e={kernelName:$i,backendName:"webgpu",kernelFunc:D0e},B0e=wr({opType:13}),W0e={kernelName:Fi,backendName:"webgpu",kernelFunc:B0e},V0e=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${et()}
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 U0e(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new V0e(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var G0e={kernelName:Mi,backendName:"webgpu",kernelFunc:U0e},j0e=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}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",`
${et()}
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 H0e(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new j0e(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var q0e={kernelName:ad,backendName:"webgpu",kernelFunc:H0e},K0e=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(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`
${et()}
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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},X0e={kernelName:El,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new K0e(n.shape,s),[u,d]=C.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Z0e=wr({opType:15,cpuKernelImpl:Lpe}),Y0e={kernelName:Pi,backendName:"webgpu",kernelFunc:Z0e},J0e=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Ke(e),this.dispatch=ze(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=Ar(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(n="coords[0], coords[1]",a="vec2<i32>(flattenedIndex, coords[1])",s=`
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(${n})`,o=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
var assumed = atomicLoad(&(result[flatIndex]));
var success = 0;
for (; success == 0;) {
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
assumed = resValue[0];
success = resValue[1];
}
`;return`
${s}
${et()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${r};
}
let updateValue = ${i};
let flatIndex = getOutputIndexFromCoords(${a});
${o}
}
}`}};function Q0e(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=C.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),m=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),f=m.dtype,g=Md({backend:r,attrs:{shape:p,value:0,dtype:f}}),y=w.sizeFromShape(m.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new J0e(m.shape,o,c.shape.length,m.shape.length,d,p,f),b=r.runWebGPUProgram(x,[m,c],f,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(b.dataId),v}var efe={kernelName:yl,backendName:"webgpu",kernelFunc:Q0e},tfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function rfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new tfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Tr(a.dtype,s.dtype))}var nfe={kernelName:Al,backendName:"webgpu",kernelFunc:rfe},afe=wr({opType:18}),sfe={kernelName:zi,backendName:"webgpu",kernelFunc:afe},ife=wr({opType:16}),ofe={kernelName:_i,backendName:"webgpu",kernelFunc:ife},lfe=wr({opType:17}),ufe={kernelName:bl,backendName:"webgpu",kernelFunc:lfe},r9=qr({opSnippet:2,cpuKernelImpl:Gpe,supportsComplex:!0}),dfe={kernelName:Wi,backendName:"webgpu",kernelFunc:r9};function pfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=JS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=r9({inputs:{a,b:u},backend:r}),h=ZS({inputs:{x:d},backend:r}),p=T5({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),m=t9({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),m}var hfe={kernelName:Li,backendName:"webgpu",kernelFunc:pfe},cfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=e9({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(d.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),c=C.getReshapedPermuted(d.shape,s,o,!1),m=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),f=Ja({inputs:{x:m},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:f},backend:r,attrs:{shape:c}});return u.push(d),u.push(m),u.push(f),u.forEach(y=>r.disposeData(y.dataId)),g},ffe={kernelName:vl,backendName:"webgpu",kernelFunc:cfe},mfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=Ar(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${et()}
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 < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function gfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=C.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],m=new mfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGPUProgram(m,[s,a,i],s.dtype,c),g=qe({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeData(f.dataId),g}var yfe={kernelName:dh,backendName:"webgpu",kernelFunc:gfe};function Afe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let m=$d({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,m})}var xfe={kernelName:wl,backendName:"webgpu",kernelFunc:Afe},bfe=wr({opType:19}),vfe={kernelName:Oi,backendName:"webgpu",kernelFunc:bfe},wfe={kernelName:ud,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Gh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},kfe=qr({opSnippet:11}),Ife={kernelName:Bi,backendName:"webgpu",kernelFunc:kfe},Sfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Ar(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function Cfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(f)v=qe({inputs:{x:a},backend:r,attrs:{shape:m}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=Ot.computeOutShape(A,x,b),N=$d({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=qe({inputs:{x:N},backend:r,attrs:{shape:m}}),r.disposeData(N.dataId)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),N=We(a.shape,a.dtype,S),E=Vpe(c,N,b,A);v=r.makeTensorInfo(m,a.dtype,E.values)}else{let S=new Sfe(c),N=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(S,[a],a.dtype,N);v=qe({inputs:{x:E},backend:r,attrs:{shape:m}}),r.disposeData(E.dataId)}return v}var Tfe={kernelName:kl,backendName:"webgpu",kernelFunc:Cfe};function Nfe(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[m,f]=Upe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([m.length],"string",m),r.makeTensorInfo(h.shape,"int32",f)]}var Efe={kernelName:ph,backendName:"webgpu",kernelFunc:Nfe},Rfe=wr({opType:21}),$fe={kernelName:Vi,backendName:"webgpu",kernelFunc:Rfe},Mfe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Ffe(this.rank,"uniforms.");return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Ffe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function Pfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=jpe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Mfe(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var _fe={kernelName:ts,backendName:"webgpu",kernelFunc:Pfe},zfe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
${et()}
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));
}
}
}
`}},Ofe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
${et()}
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 hu(e,t){t!==null&&e.disposeData(t.dataId)}function pv(e){let t=1;for(;t<e;)t*=2;return t}function Dfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,S]=Hpe(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Md({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=pv(s),p=pv(l),c=null,m=()=>c===null?[d,d]:[d,c],f=(b,v,S)=>{let N=m(),E=new zfe(S),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],z=c;c=r.runWebGPUProgram(E,N,"int32",R),hu(r,z)};for(let b=1;b<h;b*=2){let v=b*2;for(let S=b;S>=1;S/=2)f(v,S,[u,p])}for(let b=p;b>h;b/=2){let v=m(),S=new Ofe([u,b/2]),N=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(S,v,"int32",N),hu(r,E);let R=h/2,z=R*2;for(let $=R;$>=1;$/=2)f(z,$,c.shape)}let g=c;c=$d({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),hu(r,g);let y=YS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});hu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),hu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),hu(r,x),[y,c]}var Lfe={kernelName:Sl,backendName:"webgpu",kernelFunc:Dfe},Bfe=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=Ke(this.outputShape),this.dispatch=ze(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;
}
${et()}
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 Wfe(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[m,f]=u!=null?u:[h,p],g=[d,m,f,c],y=new Bfe(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var Vfe={kernelName:Cl,backendName:"webgpu",kernelFunc:Wfe};function Ufe(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(u[d++]=i.shape[f]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){p[s]=f;let g=$d({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});m[f]=y,h.push(g)}return h.forEach(f=>r.disposeData(f.dataId)),m}var Gfe={kernelName:Tl,backendName:"webgpu",kernelFunc:Ufe},jfe=[hpe,Xpe,Ype,ehe,ihe,lhe,dhe,hhe,yhe,vhe,khe,The,gpe,$he,Lhe,Uhe,jhe,qhe,Zhe,Jhe,ece,nce,sce,dce,hce,fce,mce,gce,Ace,bce,wce,Nce,Ice,Cce,$ce,Fce,_ce,Dce,Wce,Uce,jce,mpe,Ehe,qce,Xce,Yce,Qce,t0e,n0e,a0e,i0e,l0e,d0e,h0e,f0e,g0e,ice,A0e,b0e,w0e,Ahe,I0e,C0e,N0e,R0e,M0e,P0e,z0e,xhe,O0e,L0e,W0e,dpe,G0e,q0e,X0e,Y0e,efe,nfe,sfe,ofe,ufe,mhe,Tfe,Efe,hfe,ffe,yfe,xfe,vfe,wfe,Ife,dfe,lce,$fe,_fe,Lfe,Vfe,ahe,Gfe,k0e];for(let e of jfe)qn(e);var Hfe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=hv(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=hv(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function hv(e,t){return`${e}_${t}`}var qfe=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,r,n){let a=fv(r),s=e*t*a,i=cv(e,t,r,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:r,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,r,n,a){if(this.freeTextures.size===0)return;let s=cv(t,r,n,a);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=fv(n),u=t*r*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function cv(e,t,r,n){return`${e}_${t}_${r}_${n}`}function fv(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Kfe=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},mv=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Yde(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function gv(e,t,r=[],n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}var Xfe=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),yv=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},n9=class extends _u{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!I5())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 Hfe(this.device),this.textureManager=new qfe(this.device),this.tensorMap=new Xp(this,ar()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().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 n9.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.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.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}getTextureManager(){return this.textureManager}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,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*ny(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*ny(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}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}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),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 r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=C.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=zS(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.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,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,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 r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let o;switch(i.data.length){case 1:o=4;break;case 2:o=8;break;case 3:o=16;break;case 4:o=16;break;default:w.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}t=Math.ceil(t/o)*o,r.push(t),t+=i.data.length*4});let n=new ArrayBuffer(t);e.forEach((i,o)=>{let l=r[o];i.type==="int32"?new Int32Array(n,l,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(n,l,i.data.length).set(i.data):new Float32Array(n,l,i.data.length).set(i.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,n,0,t);let s={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(s),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=yv(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,N)=>{if(S.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(S.dataId),{dtype:this.tensorMap.get(S.dataId).dtype,shape:S.shape,name:e.variableNames[N]}}),h=d.map(S=>S.dtype).concat(a.dtype),p=d.map(S=>C.getBroadcastDims(S.shape,a.shape)),c=d.map(S=>w.arraysEqual(S.shape,a.shape)).join("_"),m=p.map(S=>S.join("_")).join(";"),f=gv(e,i,h,m,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(f,()=>mv(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Kfe(this.device,g,t.map(S=>this.tensorToBinding(S)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let v=this.getComputePass();return x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(S=>{this.commandQueueOwnedIds.add(S.dataId)}),this.commandQueueOwnedIds.add(a.dataId),Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}copyExternalImageToTexture(e,t){let r=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,n="rgba8unorm",a=this.textureManager.acquireTexture(t[1],t[0],n,r),s=a.createView();this.queue.copyExternalImageToTexture({source:e},{texture:a},[t[1],t[0]]);let i={width:t[1],height:t[0],format:n,usage:r,texture:a};return this.textureDisposalQueue.push(i),s}runFromPixelsProgram(e,t,r,n,a){e.dispatch=yv(this.device,e);let s=this.makeTensorInfo(t,"int32");if(w.sizeFromShape(s.shape)===0){let f=this.tensorMap.get(s.dataId);return f.values=w.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId);let i=gv(e,[s.shape]),o=this.getFromPixelTextureLayout(n),l=this.getAndSavePipeline(i,()=>mv(this.device,e,o.pipelineLayout,[],s,!0)),u;if(n){let f={source:a};u=this.device.importExternalTexture(f)}else u=this.copyExternalImageToTexture(a,s.shape);let d=this.tensorToBinding(s),h=this.makeUniforms(r),p=this.device.createBindGroup({layout:o.bindGroupLayout,entries:[{binding:0,resource:{buffer:d.buffer}},{binding:1,resource:u},{binding:2,resource:{buffer:h.buffer}}]});this.ensureCommandEncoderReady();let c=this.getComputePass(),m=this.activeTimers!=null;return m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,0),c.setPipeline(l),c.setBindGroup(0,p),c.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),m&&this.supportTimeQuery&&c.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s.dataId),this.dispatchNumberInEncoder++,Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),m&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=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,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=Xfe){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},N5=n9;N5.nextDataId=0;var a9={};Le(a9,{WebGPUBackend:()=>N5,webgpu_util:()=>PS});I5()&&$l("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?n.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. 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supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:r.dataIdMap.get(o.dataId).id,g=Nm[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,S=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return s9(p,S,a.shape.length,c,N,s.shape.length,l,u,g,m,f,h||0,v),b}var Jfe={kernelName:_s,backendName:"wasm",setupFunc:Zfe,kernelFunc:Yfe};function kr(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Qfe=kr(jo);function Kr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,d.shape),f=o.makeOutput(m,c);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(f.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),f}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var eme=!0,tme=Kr(Qa,eme),i9;function rme(e){i9=e.wasm.cwrap(Ys,null,["array","number","number","number"])}function nme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return i9(s,a.length,Ut[n.dtype],i),n}var ame={kernelName:Ys,backendName:"wasm",setupFunc:rme,kernelFunc:nme};function Em(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var sme={kernelName:gi,backendName:"wasm",kernelFunc:Em},o9;function ime(e){o9=e.wasm.cwrap(Ui,null,["number","array","number","number","number","array","number"])}function Xs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=lme(t.x.shape,n.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=ome(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=Em({inputs:t,backend:r});return m.shape=o,m}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return o9(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function ome(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function lme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var ume={kernelName:Ui,backendName:"wasm",kernelFunc:Xs,setupFunc:ime};function Zi(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=C.getInnerMostAxes(i.length,a),l=Xs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var l9;function dme(e){l9=e.wasm.cwrap(Lu,null,["number, number, number"])}function pme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Zi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("all",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;l9(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var hme={kernelName:Lu,backendName:"wasm",setupFunc:dme,kernelFunc:pme},u9;function cme(e){u9=e.wasm.cwrap(Bu,null,["number, number, number"])}function fme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Zi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;C.assertAxesAreInnerMostDims("any",d,c);let[m,f]=C.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;u9(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=C.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var mme={kernelName:Bu,backendName:"wasm",setupFunc:cme,kernelFunc:fme},d9;function gme(e){d9=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function yme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Zi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),m=t.dataIdMap.get(c.dataId).id,f=w.sizeFromShape(c.shape),g=l.shape[d[0]];return d9(o,Ut[l.dtype],f,g,m),h&&t.disposeData(u.dataId),c}var Ame={kernelName:Js,backendName:"wasm",kernelFunc:yme,setupFunc:gme},p9;function xme(e){p9=e.wasm.cwrap(Qs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=C.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. 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t.dtype==="string"?h.stringBytes=l.slice(m,m+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(i))),u}if(t.dtype==="string"){let m=D0(l,s,i,t.shape,t.dtype);return h.stringBytes=m,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Cme(l,d[0],p,s,i);else if(c===3)Tme(l,d[0],d[1],p,s,i);else if(c===4)Nme(l,d[0],d[1],d[2],p,s,i);else{let m=D0(l,s,i,t.shape,t.dtype);p.set(m)}return u}function Cme(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function Tme(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let m=p*t+c*r+u;n.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function Nme(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],m=s[3];for(let f=l;f<h;f++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=f*t+g*r+y*n+m;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Eme={kernelName:xl,backendName:"wasm",kernelFunc:Vo};function Rme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),d=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(d,i,s.length),c=rn({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Xs({inputs:{x:c},backend:r,attrs:{perm:u}}),f=rn({inputs:{x:m},backend:r,attrs:{shape:d}}),g=Vo({inputs:{x:f},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(m.dataId),r.disposeData(c.dataId),g}var $me={kernelName:Ho,backendName:"wasm",kernelFunc:Rme};function qh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var Mme={kernelName:ti,backendName:"wasm",kernelFunc:qh},Fme=kr(ri),c9;function Pme(e){c9=e.wasm.cwrap(es,null,["number","number","number","number"])}function _me(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o=r.dataIdMap.get(a.dataId).id,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(l.dataId).id;return c9(o,s,i,u),l}var zme={kernelName:es,backendName:"wasm",setupFunc:Pme,kernelFunc:_me};function f9(e){let{inputs:t,backend:r}=e,n=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>w.sizeFromShape(c.shape)>0);if(s.length===1)return Em({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(w.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(C.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=w.sizeFromShape(x.shape.slice(n));return rn({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),m=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=C.computeOutShape(c.map(x=>x.shape),1);let f=c[0].shape[0]===1,g=Jx(m,a,t[0].dtype,f),y=C.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=C.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let m=w.sizeFromShape(c.shape.slice(n));return u+=m,m}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let m=c*u;for(let f=0;f<h.length;f++){let g=d[f],y=c*g,A=h[f].subarray(y,y+g);p.set(A,m),m+=g}}return i}var Ome={kernelName:qo,backendName:"wasm",kernelFunc:f9},m9;function Dme(e){m9=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lme(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,A=m.padInfo.right,x=m.padInfo.bottom,b=m.padInfo.left,v=m.dilationHeight,S=m.dilationWidth,N=m.strideHeight,E=m.strideWidth,R=m.inChannels,z=m.outChannels,$=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let I=n.makeOutput(m.outShape,"float32"),_=n.dataIdMap.get(I.dataId).id;return m9(i,a.shape[0],a.shape[1],a.shape[2],o,f,g,y,A,x,b,$,v,S,N,E,R,z,_),I}var Bme={kernelName:ni,backendName:"wasm",setupFunc:Dme,kernelFunc:Lme},g9;function Wme(e){g9=e.wasm.cwrap(ai,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vme(e){let{backend:t,inputs:r,attrs:n}=e,{dy:a,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=n,h=1,p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:v,outWidth:S,strideHeight:N,strideWidth:E}=c,R=f-1-c.padInfo.top,z=g-1-c.padInfo.left,$=c.dataFormat==="channelsLast",I=w.computeStrides(c.inShape),_=w.computeStrides(a.shape),[O,j,X]=w.computeStrides(s.shape),D=I[0],J=$?I[1]:I[2],V=$?I[2]:1,ee=$?1:I[1],Q=_[0],ie=$?_[1]:_[2],Y=$?_[2]:1,ae=$?1:_[1],de=t.makeOutput(c.inShape,"float32"),Ae=t.dataIdMap.get(de.dataId).id,be=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return g9(be,Ee,m,f,g,A,x,y,v,S,b,N,E,R,z,O,j,X,D,J,V,ee,Q,ie,Y,ae,Ae),de}var Ume={kernelName:ai,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme},Gme=kr(si),jme=kr(ii),y9=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(y9||{}),A9;function Hme(e){A9=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function qme(e){let{backend:t,inputs:r,attrs:n}=e,{method:a,extrapolationValue:s,cropSize:i}=n,{image:o,boxes:l,boxInd:u}=r,d=l.shape[0],[h,p]=i,c=[d,h,p,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=qh({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(o.shape).buffer);return A9(g,y,A,d,v,h,p,y9[a],s,b),f!=null&&t.disposeData(f.dataId),x}var Kme={kernelName:Xo,backendName:"wasm",setupFunc:Hme,kernelFunc:qme},x9;function Xme(e){x9=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number"])}function Zme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;w.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;x9(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var Yme={kernelName:Ko,backendName:"wasm",setupFunc:Xme,kernelFunc:Zme},b9;function Jme(e){b9=e.wasm.cwrap(oi,null,["number","number","number","number","number","number"])}function Qme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;w.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],m=r.dataIdMap.get(d.dataId).id,f=r.dataIdMap.get(p.dataId).id;b9(m,i?1:0,o?1:0,c,f,Ut[a.dtype]);let g=p;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var e1e={kernelName:oi,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme},v9;function t1e(e){v9=e.wasm.cwrap(Zo,null,["number","number","number","array","number","array","array","number","number"])}function r1e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),m=i==="NHWC"?[o,h,p,c]:[o,c,h,p],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return v9(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,m.length,b),f}var n1e={kernelName:Zo,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},w9;function a1e(e){w9=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function s1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=C.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),m=c.filterHeight,f=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,S=c.strideHeight,N=c.strideWidth,E=c.inChannels,R=c.outChannels,z=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. 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vge={kernelName:vl,backendName:"wasm",kernelFunc:bge},J9;function wge(e){J9=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function kge(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,m=t.makeOutput(d,n.dtype),f=t.dataIdMap.get(m.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),v=t.dataIdMap.get(b.dataId).id,S=t.makeOutput([4],"int32"),N=t.dataIdMap.get(S.dataId).id,E=J9(h,p,Ut[a.dtype],o,u,l,c,f,y,x,v,N),R=t.readSync(S.dataId),z;switch(R[0]){case 1:{z=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 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precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;var hC=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`,cC=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`,fC=`
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`,mC=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`,gC=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
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re(i),o},Om=(e,t)=>{let r=zm(e),n=_d(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},Dm=e=>{let t=zm(e),r=_d(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},WC=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Z5=[[1,0,0],[0,1,0],[0,0,1]],Vye=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Uye=(e,t)=>Vye(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var DC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Vl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},Gye=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},LC=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Vl(e[a],Gye(t,s)))}return r},VC=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=DC(t[0],t[1]),i=LC(s,a),o=DC(-t[0],-t[1]);return LC(i,o)},jye=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Vl(t[0],r),-Vl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},Hye=(e,t)=>[Vl(e,t[0]),Vl(e,t[1])];function UC(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function GC(e,t,r,n,a){let s=_d(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?VC(r,[0,0]):Z5,u=o?i.map(c=>[...Hye(c,l),c[2]]):i,d=o?jye(n):Z5,h=zm(t),p=[Vl(h,d[0]),Vl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function jC(e,t,r,n){let a=t.landmarks.length>=H5.count?H5.symmetryLine:Zh.symmetryLine,s=0,i=Z5,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=Uye(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=zm(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=VC(-s,u),o=X5(t,h,[n,n]),re(h)}else o=X5(t,r,[n,n]);else o=X5(t,r,[n,n]);return[s,i,o]}var qye=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},HC=(e,t)=>{let r=qye(e),n=_d(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var qC=6,Kye=1.4,La,KC=null,Ji=0,Jh=null,Lm=()=>Ji;async function XC(e){var t;return he.initial&&(La=null),La?e.debug&&se("cached model:",La.modelUrl):La=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Ji=La.inputs[0].shape?La.inputs[0].shape[2]:0,Jh=Se(Ji,"int32"),KC=ca(UC(Ji)),La}function Xye(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,KC),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Jh),t.centersNormalized=pe(t.centers,Jh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Jh),t.endNormalized=L(t.ends,Jh);let r=pd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function ZC(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Ji,Ji]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=La==null?void 0:La.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=It([h[0],h[2]],2),r.concat512=It([h[1],h[3]],2),r.concat=It([r.concat512,r.concat384],1),r.batch=rt(r.concat,0)}else r.batch=rt(n);re(n),r.boxes=Xye(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Cr(r.logits),r.scores=rt(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],qC-1],[1,-1]),c.squeeze=rt(c.slice),c.landmarks=G(c.squeeze,[qC,-1]);let m=await c.bbox.data(),f={startPoint:[m[0],m[1]],endPoint:[m[2],m[3]],landmarks:await c.landmarks.array(),confidence:p},g=BC(f,[(e.shape[2]||0)/Ji,(e.shape[1]||0)/Ji]),y=Om(g,t.face.scale||Kye),A=Dm(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var Bm={};ks(Bm,{connected:()=>t3,kpt:()=>e3});var e3=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],t3={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var JC=224,Zye,Yye=5,Wm=[8,16,32,32,32];async function QC(){let e=[],t=0;for(;t<Yye;){let r=0,n=t;for(;n<Wm.length&&Wm[n]===Wm[t];)r+=2,n++;let a=Wm[t],s=Math.ceil(JC/a),i=Math.ceil(JC/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}Zye={x:Ct(e.map(r=>r.x)),y:Ct(e.map(r=>r.y))}}function ds(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function eT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function Vm(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var nT={initial:!0},yn={detector:null,landmarks:null},zd={detector:[224,224],landmarks:[256,256]},r3=Number.MAX_SAFE_INTEGER,Qye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Gm=null,Qh,Qi=[[0,0],[0,0],[0,0],[0,0]],tT=0,rT=e=>1-1/(1+Math.exp(e));async function aT(e){if(nT.initial&&(yn.detector=null),!yn.detector&&e.body.detector&&e.body.detector.modelPath){yn.detector=await Ge(e.body.detector.modelPath);let t=Object.values(yn.detector.modelSignature.inputs);zd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,zd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&yn.detector&&se("cached model:",yn.detector.modelUrl);return await QC(),yn.detector}async function sT(e){if(nT.initial&&(yn.landmarks=null),yn.landmarks)e.debug&&se("cached model:",yn.landmarks.modelUrl);else{yn.landmarks=await Ge(e.body.modelPath);let t=Object.values(yn.landmarks.modelSignature.inputs);zd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,zd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return yn.landmarks}async function eAe(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Qh&&(r.cropped=Ie.cropAndResize(e,[Qh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Qi=[[0,0],a,s,[0,0]],r.pad=Kn(r.cropped||e,Qi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function tAe(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Qi[2][0]+Qi[2][1])/t[0]-Qi[2][0]),Math.trunc(r.position[1]*(t[1]+Qi[1][0]+Qi[1][1])/t[1]-Qi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Qh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Qh[1],r.positionRaw[1]+Qh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function rAe(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function nAe(e,t,r){var m;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(m=yn.landmarks)==null?void 0:m.execute(e,Qye.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>re(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let g=rT(s[l*f+3]),y=rT(s[l*f+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*f+0]/zd.landmarks[0],s[l*f+1]/zd.landmarks[1],s[l*f+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:e3[f],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;rAe(o);let u=tAe(o,r),d=u.map(f=>f.position),h=ds(d,[r[0],r[1]]),p={};for(let[f,g]of Object.entries(t3)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[f]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function n3(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-tT,a=r3<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&Gm!==null)r3++;else{let s={};s.landmarks=await eAe(e,256),Gm=await nAe(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),tT=oe(),r3=0}return Gm?[Gm]:[]}var Od=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ps,Ul=0,a3=[],oT=0,s3=Number.MAX_SAFE_INTEGER;async function lT(e){if(he.initial&&(ps=null),ps)e.debug&&se("cached model:",ps.modelUrl);else{ps=await Ge(e.object.modelPath);let t=Object.values(ps.modelSignature.inputs);Ul=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ps}async function aAe(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=rt(e);let i=Zt(n.squeeze,6,1);n.stack=ur([i[1],i[0],i[3],i[2]],1),n.boxes=rt(n.stack),n.scores=rt(i[4]),n.classes=rt(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Od[h].label,[c,m]=[s[0][u][0]/Ul,s[0][u][1]/Ul],f=[c,m,s[0][u][2]/Ul-c,s[0][u][3]/Ul-m],g=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:f})}return Object.keys(n).forEach(u=>re(n[u])),a}async function i3(e,t){let r=(t.object.skipTime||0)>oe()-oT,n=s3<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&a3.length>0?(s3++,a3):(s3=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ul,Ul]),o=t.object.enabled?ps==null?void 0:ps.execute(i,["tower_0/detections"]):null;oT=oe(),re(i);let l=await aAe(o,s,t);a3=l,a(l)}))}var jm={};ks(jm,{connected:()=>l3,kpt:()=>o3});var o3=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],l3={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Rr,dT=0,Zr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},u3=Number.MAX_SAFE_INTEGER;async function pT(e){return he.initial&&(Rr=null),Rr?e.debug&&se("cached model:",Rr.modelUrl):Rr=await Ge(e.body.modelPath),Rr}async function sAe(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=yr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Rn(a,0),l=fd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function d3(e,t){let r=(t.body.skipTime||0)>oe()-dT,n=u3<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Zr.keypoints).length>0?(u3++,[Zr]):(u3=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Rr!=null&&Rr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Rr.inputs[0].shape[2],Rr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Rr==null?void 0:Rr.execute(s)),dT=oe(),re(s),i){Zr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let m=0;m<c.length;m++){let[f,g,y]=await sAe(c[m],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Zr.keypoints.push({score:Math.round(100*y)/100,part:o3[m],positionRaw:[f/Rr.inputs[0].shape[2],g/Rr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/Rr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Rr.inputs[0].shape[1])]})}c.forEach(m=>re(m))}Zr.score=Zr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Zr.keypoints.map(p=>p.position[0]),l=Zr.keypoints.map(p=>p.position[1]);Zr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Zr.keypoints.map(p=>p.positionRaw[0]),d=Zr.keypoints.map(p=>p.positionRaw[1]);Zr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(l3)){let m=[];for(let f=0;f<c.length-1;f++){let g=Zr.keypoints.find(A=>A.part===c[f]),y=Zr.keypoints.find(A=>A.part===c[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}Zr.annotations[p]=m}a([Zr])}))}var iAe=["angry","disgust","fear","happy","sad","surprise","neutral"],Bn,Hm=[],cT=0,fT=0,p3=Number.MAX_SAFE_INTEGER;async function mT(e){var t;return he.initial&&(Bn=null),Bn?e.debug&&se("cached model:",Bn.modelUrl):Bn=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),Bn}async function h3(e,t,r,n){var i,o;if(!Bn)return[];let a=p3<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-fT;return t.skipAllowed&&s&&a&&cT===n&&Hm[r]&&Hm[r].length>0?(p3++,Hm[r]):(p3=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=Bn!=null&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=Bn==null?void 0:Bn.execute(p.grayscaleMul),fT=oe();let m=await p.emotion.data();for(let f=0;f<m.length;f++)m[f]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*m[f])/100),emotion:iAe[f]});u.sort((f,g)=>g.score-f.score),Object.keys(p).forEach(f=>re(p[f]))}Hm[r]=u,cT=n,l(u)}))}var An,c3=[],yT=0,AT=0,xT=Number.MAX_SAFE_INTEGER;async function bT(e){return he.initial&&(An=null),An?e.debug&&se("cached 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0:t.modelPath),eo=hs.inputs[0].shape?hs.inputs[0].shape[2]:0,eo===-1&&(eo=64),hs}function qm(e,t,r,n){for(let a=0;a<q5.length;a++){let{key:s,indices:i}=q5[a],o=ea[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var lAe=e=>{let t=e[Dd.leftBounds[0]][2],r=e[Dd.rightBounds[0]][2];return t-r},wT=(e,t,r,n,a,s=!1)=>{let i=Dm(Om(WC([e[r],e[n]]),oAe)),o=_d(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[eo,eo]);if(s&&he.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},kT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Ld.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/eo:i/eo)*r[0]+t.startPoint[0],o/eo*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Ld.index)}},IT=(e,t,r)=>{let n=e[ea[`${r}EyeUpper0`][Ld.upperCenter]][2],a=e[ea[`${r}EyeLower0`][Ld.lowerCenter]][2],s=(n+a)/2;return 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xn={eyeLLower:[33,7,163,144,145,153,154,155,133],eyeRLower:[263,249,390,373,374,380,381,382,362],lips:[185,96,90,181,84,17,314,405,320,307,409,40,39,73,37,0,267,269,270,409,40,88,178,178,87,14,268,402,318,324,409,80,41,38,87,12,268,303,318,324,185,95,80,81,85,16,315,404,319,325,409,40,39,73,72,0,302,303,270,408,185,88,88,81,82,15,316,403,319,324,409,80,41,38,87,12,268,303,318,324],eyeL:[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],eyeR:[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417]};async function NT(e,t){let r={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},n=xn.eyeRLower.reduce((s,i)=>s+=e[i][2],0)/xn.eyeRLower.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],n]);let a=xn.eyeLLower.reduce((s,i)=>s+=e[i][2],0)/xn.eyeLLower.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[xn.eyeL[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[xn.eyeL[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[xn.eyeR[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[xn.eyeR[s]][2]];for(let s=0;s<r.lips.length/2;s++)e[xn.lips[s]]=[r.lips[2*s+0],r.lips[2*s+1],e[xn.lips[s]][2]];return e}var Ba={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Wa=null,Bd=0;async function ET(e,t){var o,l,u,d,h,p,c,m,f,g;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Ba.timestamp,n=Ba.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Ba.boxes.length===0?(Ba.boxes=await ZC(e,t),Ba.timestamp=oe(),Ba.skipped=0):Ba.skipped++;let a=[],s=[],i=0;for(let y=0;y<Ba.boxes.length;y++){let A=Ba.boxes[y],x=0,b,v={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([x,b,v.tensor]=jC((u=t.face.detector)==null?void 0:u.rotation,A,e,(d=t.face.mesh)!=null&&d.enabled?Bd:Lm()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let S=await Rm(v.tensor);re(v.tensor),v.tensor=S}if(v.boxScore=Math.round(100*A.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!Wa)t.debug&&se("face mesh detection requested, but model is not loaded");else{let S=Wa.execute(v.tensor),N=S.find(I=>I.shape[I.shape.length-1]===1),E=S.find(I=>I.shape[I.shape.length-1]===1404),R=await N.data();v.faceScore=Math.round(100*R[0])/100;let z=G(E,[-1,3]),$=await z.array();if(v.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))A.confidence=v.faceScore;else{(m=t.face.attention)!=null&&m.enabled?$=await NT($,S):(f=t.face.iris)!=null&&f.enabled&&($=await CT($,v.tensor,t,Bd)),v.mesh=GC($,A,x,b,Bd),v.meshRaw=v.mesh.map(_=>[_[0]/(e.shape[2]||0),_[1]/(e.shape[1]||0),(_[2]||0)/Bd]);for(let _ of Object.keys(ea))v.annotations[_]=ea[_].map(O=>v.mesh[O]);v.score=v.faceScore;let I={...HC(v.mesh,A),confidence:A.confidence,landmarks:A.landmarks};v.box=Y5(I,e),v.boxRaw=J5(I,e),s.push(I)}re([...S,z])}else{v.box=Y5(A,e),v.boxRaw=J5(A,e),v.score=v.boxScore,v.mesh=A.landmarks.map(S=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*S[0]/Lm(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*S[1]/Lm()]),v.meshRaw=v.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/Bd]);for(let S of Object.keys(Zh))v.annotations[S]=[v.mesh[Zh[S]]]}v.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?a.push(v):re(v.tensor)}return Ba.boxes=s,a}async function RT(e){var t,r,n;return he.initial&&(Wa=null),Wa?e.debug&&se("cached model:",Wa.modelUrl):(t=e.face.attention)!=null&&t.enabled?Wa=await Ge((r=e.face.attention)==null?void 0:r.modelPath):Wa=await Ge((n=e.face.mesh)==null?void 0:n.modelPath),Bd=Wa.inputs[0].shape?Wa.inputs[0].shape[2]:0,Wa}var $T=Wl,MT=Yh;var bn,Km=[],FT=0,PT=0,A3=Number.MAX_SAFE_INTEGER;async function _T(e){var t;return he.initial&&(bn=null),bn?e.debug&&se("cached model:",bn.modelUrl):bn=await Ge((t=e.face.description)==null?void 0:t.modelPath),bn}function x3(e){let t=e.image||e.tensor||e;if(!(bn!=null&&bn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[bn.inputs[0].shape[2],bn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function b3(e,t,r,n){var i,o,l,u;if(!bn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=A3<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-FT;return t.skipAllowed&&a&&s&&PT===n&&((l=Km[r])==null?void 0:l.age)&&((u=Km[r])==null?void 0:u.age)>0?(A3++,Km[r]):(A3=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let m=x3(e),f=bn==null?void 0:bn.execute(m);FT=oe(),re(m);let y=await(await f.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Rn(f.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let S=await f.find(R=>R.shape[1]===100).data();h.age=Math.round(S[b-1]>S[b+1]?10*b-100*S[b-1]:10*b+100*S[b+1])/10;let N=f.find(R=>R.shape[1]===1024),E=N?await N.data():[];h.descriptor=Array.from(E),f.forEach(R=>re(R))}Km[r]=h,PT=n,d(h)}))}function Xm(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=rt(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Cr(n.slice),n.scores=rt(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},m=LT(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(m),Object.keys(l).forEach(f=>re(l[f]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var fAe=5,GT=1.65,jT=[0,5,9,13,17,1,2],mAe=0,gAe=2,HT=0,Qm=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>k3([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return Zm(Ym(a),fAe)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=Zm(Ym(r),GT);n.palmLandmarks=[];for(let a=0;a<jT.length;a++)n.palmLandmarks.push(t[jT[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=Xm(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=w3(n,[0,0]),u=o.map(c=>[...k3(c,l),c[2]]),d=WT(a),h=[...ec(r),1],p=[to(h,d[0]),to(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-HT,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?BT(u.palmLandmarks[mAe],u.palmLandmarks[gAe]):0,h=ec(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),m=w3(-d,h),f=n?this.getBoxForPalmLandmarks(u.palmLandmarks,m):u,g=DT(f,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);HT=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),S=await v.array();re(x),re(v);let N=this.transformRawCoords(S,f,d,m),E=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:N,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=Zm(Ym(u),GT),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Yr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Yr.nameMapping[e],getPoints:e=>Yr.pointsMapping[e]},no={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>no.nameMapping[e]},Bt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Bt.nameMapping[e]},ro=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:wa,index:cs,middle:fs,ring:Gl,pinky:jl}=Yr,{none:ka,half:AAe,full:Ia}=no,{verticalUp:Wd,verticalDown:A6e,horizontalLeft:I3,horizontalRight:xAe,diagonalUpRight:bAe,diagonalUpLeft:Vd,diagonalDownRight:x6e,diagonalDownLeft:b6e}=Bt,ao=new ro("thumbs up");ao.curl(wa,ka,1);ao.direction(wa,Wd,1);ao.direction(wa,Vd,.25);ao.direction(wa,bAe,.25);for(let e of[Yr.index,Yr.middle,Yr.ring,Yr.pinky])ao.curl(e,Ia,1),ao.direction(e,I3,1),ao.direction(e,xAe,1);var er=new ro("victory");er.curl(wa,AAe,.5);er.curl(wa,ka,.5);er.direction(wa,Wd,1);er.direction(wa,Vd,1);er.curl(cs,ka,1);er.direction(cs,Wd,.75);er.direction(cs,Vd,1);er.curl(fs,ka,1);er.direction(fs,Wd,1);er.direction(fs,Vd,.75);er.curl(Gl,Ia,1);er.direction(Gl,Wd,.2);er.direction(Gl,Vd,1);er.direction(Gl,I3,.2);er.curl(jl,Ia,1);er.direction(jl,Wd,.2);er.direction(jl,Vd,1);er.direction(jl,I3,.2);er.weight(cs,2);er.weight(fs,2);var so=new ro("point");so.curl(wa,Ia,1);so.curl(cs,ka,.5);so.curl(fs,Ia,.5);so.curl(Gl,Ia,.5);so.curl(jl,Ia,.5);so.weight(cs,2);so.weight(fs,2);var io=new ro("middle finger");io.curl(wa,ka,1);io.curl(cs,Ia,.5);io.curl(fs,Ia,.5);io.curl(Gl,Ia,.5);io.curl(jl,Ia,.5);io.weight(cs,2);io.weight(fs,2);var Ud=new ro("open palm");Ud.curl(wa,ka,.75);Ud.curl(cs,ka,.75);Ud.curl(fs,ka,.75);Ud.curl(Gl,ka,.75);Ud.curl(jl,ka,.75);var qT=[ao,er,so,io,Ud];var vAe=.7,Hl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function KT(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function ZT(e,t){if(!e||!t)return[0,0];let r=KT(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=KT(e[1],e[2],t[1],t[2]);return[r,n]}function XT(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function wAe(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),m=Math.sqrt(s*s+l*l+h*h),f=(m*m+p*p-c*c)/(2*m*p);f>1?f=1:f<-1&&(f=-1);let g=Math.acos(f);g=57.2958*g%180;let y;return g>Hl.NO_CURL_START_LIMIT?y=no.none:g>Hl.HALF_CURL_START_LIMIT?y=no.half:y=no.full,y}function YT(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:n===Math.abs(t)?t>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:r>0?a=Bt.horizontalLeft:a=Bt.horizontalRight,a}function JT(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Bt.verticalDown:a=Bt.verticalUp:n===Math.abs(t)?t<0?a=Bt.verticalDown:a=Bt.verticalUp:r<0?a=Bt.verticalDown:a=Bt.verticalUp,a}function kAe(e,t,r,n,a,s,i,o){let l,u=JT(e,t,r,n),d=YT(a,s,i,o);return u===Bt.verticalUp?d===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:d===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function IAe(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,m=0,f=h/(d+1e-5);f>1.5?p+=Hl.DISTANCE_VOTE_POWER:f>.66?c+=Hl.DISTANCE_VOTE_POWER:m+=Hl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],S=r[0],N=r[1];x===g?(S=r[0],N=r[1]):x===A&&(b=t[0],v=t[1]);let z=ZT([b,v],[S,N]),$=XT(z,Hl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],c+=$[1],m+=$[2];for(let _ of n){let O=XT(_,Hl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],c+=O[1],m+=O[2]}let I;return p===Math.max(p,c,m)?I=JT(l,o,u,h):m===Math.max(c,m)?I=YT(s,a,i,d):I=kAe(l,o,u,h,s,a,i,d),I}function QT(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Yr.all){let i=Yr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=ZT(d,h),c=p[0],m=p[1];o.push(c),l.push(m)}t.push(o),r.push(l)}for(let s of Yr.all){let i=s===Yr.thumb?1:0,o=Yr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=wAe(l,u,d),p=IAe(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function e1(e){if(!e||e.length===0)return null;let t=QT(e),r={};for(let n of Yr.all)r[Yr.getName(n)]={curl:no.getName(t.curls[n]),direction:Bt.getName(t.directions[n])};return r}function eN(e){let t=[];if(!e||e.length===0)return t;let r=QT(e);for(let n of qT){let a=n.matchAgainst(r.curls,r.directions);a>=vAe&&t.push({name:n.name,confidence:a})}return t}var tN={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Gd,jd,rN;async function C3(e,t){let r=await rN.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(tN))s[d]=tN[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=e1(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function T3(e){var r,n;he.initial&&(Gd=null,jd=null),!Gd||!jd?[Gd,jd]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&se("cached model:",Gd.modelUrl),e.debug&&se("cached model:",jd.modelUrl));let t=new Jm(Gd);return rN=new Qm(t,jd),[Gd,jd]}var pr=[null,null],SAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oo=[[0,0],[0,0]],CAe=["hand","fist","pinch","point","face","tip","pinchtip"],aN=4,sN=1.6,TAe=512,NAe=1.4,t1=Number.MAX_SAFE_INTEGER,N3=0,ms=[0,0],Ht={boxes:[],hands:[]},iN={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function oN(e){var t;if(he.initial&&(pr[0]=null),pr[0])e.debug&&se("cached model:",pr[0].modelUrl);else{r1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),pr[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(pr[0].modelSignature.inputs);oo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,oo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[0]}async function lN(e){var t;if(he.initial&&(pr[1]=null),pr[1])e.debug&&se("cached model:",pr[1].modelUrl);else{pr[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(pr[1].modelSignature.inputs);oo[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,oo[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[1]}async function EAe(e,t){let r=[];if(!e||!pr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,TAe),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await pr[0].executeAsync(n.cast,SAe),n.boxes=rt(n.rawBoxes,[0,2]),n.scores=rt(n.rawScores,[0]);let o=nn(n.scores,1);re(o[aN]),o.splice(aN,1),n.filtered=ur(o,1),re(o),n.max=yr(n.filtered,1),n.argmax=Rn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),m=await c.data();re(c);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=Vm(f,NAe),y=[Math.trunc(f[0]*ms[0]),Math.trunc(f[1]*ms[1]),Math.trunc(f[2]*ms[0]),Math.trunc(f[3]*ms[1])],A=d[p],x=CAe[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function E3(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&pr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[oo[1][0],oo[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=pr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/oo[1][1],h[1]/oo[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[ms[0]*(h[0]+t.boxRaw[0]),ms[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=e1(n.keypoints);for(let h of Object.keys(iN))n.annotations[h]=iN[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function R3(e,t){var a,s;if(!pr[0]||!pr[1]||!((a=pr[0])!=null&&a.inputs[0].shape)||!((s=pr[1])!=null&&s.inputs[0].shape))return[];ms=[e.shape[2]||0,e.shape[1]||0],t1++;let r=(t.hand.skipTime||0)>oe()-N3,n=t1<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Ht.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-N3,l=t1<3*(t.hand.skipFrames||0);t.skipAllowed&&Ht.hands.length===t.hand.maxDetected?Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))):t.skipAllowed&&o&&l&&Ht.hands.length>0?Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))):(Ht.boxes=await EAe(e,t),N3=oe(),Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))),t1=0);let u=[...Ht.boxes];if(Ht.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Ht.hands.length;d++){let h=eT(Ht.hands[d].keypoints,ms);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Ht.hands[d].fingerScore&&Ht.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=Vm(h.box,sN),c=Vm(h.boxRaw,sN);Ht.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Ht.hands.length;d++){let h=ds(Ht.hands[d].keypoints,ms);Ht.hands[d].box=h.box,Ht.hands[d].boxRaw=h.boxRaw}i(Ht.hands)})}var $r,n1=[],$3=Number.MAX_SAFE_INTEGER,dN=0,pN=0;async function hN(e){var t;return he.initial&&($r=null),$r?e.debug&&se("cached model:",$r.modelUrl):$r=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),$r}async function M3(e,t,r,n){var i,o;if(!$r)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-pN,s=$3<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&dN===n&&n1[r]?($3++,n1[r]):($3=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[$r!=null&&$r.inputs[0].shape?$r.inputs[0].shape[2]:0,$r!=null&&$r.inputs[0].shape?$r.inputs[0].shape[1]:0],!1),d=$r==null?void 0:$r.execute(u),h=(await d.data())[0];n1[r]=Math.round(100*h)/100,dN=n,pN=oe(),re([u,d]),l(n1[r])}))}var tc={};ks(tc,{connected:()=>s1,horizontal:()=>F3,kpt:()=>a1,relative:()=>_3,vertical:()=>P3});var a1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],F3=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],P3=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],_3=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],s1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var fN=.005,vn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function z3(e){for(let t of F3){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of P3){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of _3){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function mN(e){for(let t=0;t<e.length;t++)if(e[t]&&vn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-vn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-vn.keypoints[t].positionRaw[1])];r[0]<fN&&r[1]<fN?e[t]=vn.keypoints[t]:vn.keypoints[t]=e[t]}else vn.keypoints[t]=e[t];return e}function gN(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;vn.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Kn(e,vn.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function yN(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+vn.padding[2][0]+vn.padding[2][1])/t[0]-vn.padding[2][0],n.position[1]*(t[1]+vn.padding[1][0]+vn.padding[1][1])/t[1]-vn.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=ds(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var wn,i1=0,O3=Number.MAX_SAFE_INTEGER,ql={boxes:[],bodies:[],last:0};async function AN(e){return he.initial&&(wn=null),wn?e.debug&&se("cached model:",wn.modelUrl):(r1(["size"],e),wn=await Ge(e.body.modelPath)),i1=wn.inputs[0].shape?wn.inputs[0].shape[2]:0,i1<64&&(i1=256),wn}async function $Ae(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:a1[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ds(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(s1)){let p=[];for(let c=0;c<h.length-1;c++){let m=a.find(g=>g.part===h[c]),f=a.find(g=>g.part===h[c+1]);m&&f&&m.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&p.push([m.position,f.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return z3(u),i.push(u),i}async function MAe(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:a1[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ds(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(s1)){let c=[];for(let m=0;m<p.length-1;m++){let f=o.find(y=>y.part===p[m]),g=o.find(y=>y.part===p[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([f.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};z3(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function D3(e,t){if(!wn||!(wn!=null&&wn.inputs[0].shape))return[];t.skipAllowed||(ql.boxes.length=0),O3++;let r=(t.body.skipTime||0)>oe()-ql.last,n=O3<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?ql.bodies:new Promise(async a=>{let s={};O3=0,s.input=gN(e,i1),s.res=wn==null?void 0:wn.execute(s.input),ql.last=oe();let i=await s.res.array();ql.bodies=s.res.shape[2]===17?await $Ae(i,t,e):await MAe(i,t,e);for(let o of ql.bodies)yN(o,[e.shape[2]||1,e.shape[1]||1]),mN(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(ql.bodies)})}var Hd,o1=[],bN=0,L3=Number.MAX_SAFE_INTEGER,u1=0,l1=2.5;async function vN(e){if(!Hd||he.initial){Hd=await Ge(e.object.modelPath);let t=Object.values(Hd.modelSignature.inputs);u1=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&se("cached model:",Hd.modelUrl);return Hd}async function FAe(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=rt(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)===Od.length)),h=rt(e.find(f=>f.shape[1]===u**2&&(f.shape[2]||0)<Od.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),m=await d.array();for(let f=0;f<d.shape[0];f++)for(let g=0;g<d.shape[1];g++){let y=m[f][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(f%u))/u,x=(.5+Math.trunc(f/u))/u,b=c[f].map(I=>I*(u/l/u1)),[v,S]=[A-l1/l*b[0],x-l1/l*b[1]],[N,E]=[A+l1/l*b[2]-v,x+l1/l*b[3]-S],R=[v,S,N,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let z=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],$={id:n++,score:Math.round(100*y)/100,class:g+1,label:Od[g].label,box:z.map(I=>Math.trunc(I)),boxRaw:R};a.push($)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function B3(e,t){let r=(t.object.skipTime||0)>oe()-bN,n=L3<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&o1.length>0?(L3++,o1):(L3=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?o1:new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[u1,u1],!1),o=pe(i,Qe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let u;t.object.enabled&&(u=Hd.execute(l)),bN=oe(),re(l);let d=await FAe(u,s,t);o1=d,a(d)}))}var nc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],PAe=nc.length,rc=nc.reduce((e,t,r)=>(e[t]=r,e),{}),_Ae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],G6e=_Ae.map(([e,t])=>[rc[e],rc[t]]),kN=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function IN(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function SN(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var d1=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=r}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let r=2*t;if(r<this.numberOfElements&&this.less(r,r+1)&&r++,!this.less(t,r))break;this.exchange(t,r),t=r}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,r){return this.getValueAt(t)<this.getValueAt(r)}exchange(t,r){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[r],this.priorityQueue[r]=n}};function W3(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+PAe)}}function V3(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=W3(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function U3(e,t,r){return e<t?t:e>r?r:e}function CN(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function G3(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Sa,OAe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],p1=1,qd=16,DAe=50**2;function TN(e,t,r,n,a,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:U3(Math.round(y.y/qd),0,A-1),x:U3(Math.round(y.x/qd),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),m=G3(t.position,p);for(let y=0;y<i;y++){let A=l(m,u,d),x=W3(A.y,A.x,r,a);m=G3({x:A.x*qd,y:A.y*qd},{x:x.x,y:x.y})}let f=l(m,u,d),g=n.get(f.y,f.x,r);return{position:m,part:nc[r],score:g}}function LAe(e,t,r,n,a){let 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r=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=dt.useDepth?"rgba(255, 200, 255, 0.3)":dt.color,t.beginPath();let r=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}}function XAe(e,t){var r;if(dt.drawGaze&&((r=e.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Kl(e.rotation.angle.yaw)/90,a=e.box[1]+e.box[3]/2+e.box[2]*Kl(e.rotation.angle.pitch)/90,s=new Path2D(`
M ${e.box[0]+e.box[2]/2} ${e.box[1]}
C
${n} ${e.box[1]},
${n} ${e.box[1]+e.box[3]},
${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
`),i=new Path2D(`
M ${e.box[0]} ${e.box[1]+e.box[3]/2}
C
${e.box[0]} ${a},
${e.box[0]+e.box[2]} ${a},
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`);t.stroke(i),t.stroke(s)}}function ZAe(e,t){var r,n,a,s;if(dt.drawGaze&&((n=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:n.strength)&&((s=(a=e.rotation)==null?void 0:a.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let i=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[i[0],i[1]],4);let o=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[o[0],o[1]],4)}}function YAe(e,t){if(dt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let r=0;r<Wl.length/3;r++){let n=[Wl[r*3+0],Wl[r*3+1],Wl[r*3+2]].map(a=>e.mesh[a]);Z3(t,n,dt)}KAe(e,t)}}function JAe(e,t){if(dt.drawPoints&&e.mesh.length>=468)for(let r=0;r<e.mesh.length;r++)ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2],dt),dt.drawAttention&&(xn.lips.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]+127,dt),xn.eyeL.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt),xn.eyeR.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt))}function QAe(e,t){dt.drawBoxes&&Ua(t,e.box[0],e.box[1],e.box[2],e.box[3],dt)}async function Kd(e,t,r){if(dt=Gt(Mr,r),!t||!e)return;let n=Wn(e);if(!!n){n.font=dt.font,n.strokeStyle=dt.color,n.fillStyle=dt.color;for(let a of t)QAe(a,n),qAe(a,n),a.mesh&&a.mesh.length>0&&(JAe(a,n),YAe(a,n),XAe(a,n),ZAe(a,n))}}async function Xd(e,t,r){var s;let n=Gt(Mr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Ua(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=gs(t[i].keypoints[o].position[2],n),ys(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=gs(o.position[2],n),a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)_N(a,l,n)}}}async function Zd(e,t,r){let n=Gt(Mr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Ua(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=gs(i[2],n),ys(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||-256;a.fillStyle=gs(u,n),a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=gs(l*u,n),a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function Yd(e,t,r){let n=Gt(Mr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Ua(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function Jd(e,t,r){let n=Gt(Mr,r);if(!(!t||!e)&&n.drawGestures){let a=Wn(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var J3=0;async function Q3(e,t,r){let n=Gt(Mr,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Ua(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person 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ea.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Qd&&Qd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Qd:i.x-Qd,y:i.y>.5?i.y+Qd:i.y-Qd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)exe(i/t,o/t,a)||(n.set(nb*n.get(0,o,i,0),0,o,i,0),n.set(nb*n.get(0,o,i,1),0,o,i,1),n.set(nb*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var rxe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},ON=(e,t)=>{let r=f=>{let g=Math.sqrt(f[0]*f[0]+f[1]*f[1]+f[2]*f[2]);return f[0]/=g,f[1]/=g,f[2]/=g,f},n=(f,g)=>{let y=f[0]-g[0],A=f[1]-g[1],x=f[2]-g[2];return[y,A,x]},a=(f,g)=>{let y=f[1]*g[2]-f[2]*g[1],A=f[2]*g[0]-f[0]*g[2],x=f[0]*g[1]-f[1]*g[0];return[y,A,x]},s=f=>{let[g,y,A,x,b,v,S,N,E]=f,R,z,$;return x<1?x>-1?($=Math.asin(x),z=Math.atan2(-S,g),R=Math.atan2(-v,b)):($=-Math.PI/2,z=-Math.atan2(N,E),R=0):($=Math.PI/2,z=Math.atan2(N,E),R=0),isNaN(R)&&(R=0),isNaN(z)&&(z=0),isNaN($)&&($=0),{pitch:2*-R,yaw:2*-z,roll:2*-$}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(f=>[f[0]*t[0]/o,f[1]*t[1]/o,f[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),m=i.length===478?rxe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:m}};var ab=async(e,t)=>{var c,m,f,g,y,A,x,b,v,S,N,E,R,z,$,I,_,O,j,X,D,J;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await ET(t,e.config);if(e.performance.face=he.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){se("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await zN(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?ON(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(m=e.config.face.emotion)!=null&&m.enabled?h3(p[V].tensor||ft([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(f=e.config.face.emotion)!=null&&f.enabled?await h3(p[V].tensor||ft([]),e.config,V,p.length):[],e.performance.emotion=he.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?j5(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await j5(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.antispoof=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?M3(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await M3(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.liveness=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?D5(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(v=e.config.face.gear)!=null&&v.enabled?await D5(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(S=e.config.face.ssrnet)!=null&&S.enabled?B5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(N=e.config.face.ssrnet)!=null&&N.enabled?U5(p[V].tensor||ft([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await B5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await U5(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(z=e.config.face.mobilefacenet)!=null&&z.enabled?f3(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=($=e.config.face.mobilefacenet)!=null&&$.enabled?await f3(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?b3(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(_=e.config.face.description)!=null&&_.enabled?await b3(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Q=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ie=(J=e.config.face.detector)!=null&&J.return?rt(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Y={...p[V],id:V};d!=null&&d.age&&(Y.age=d.age),d!=null&&d.gender&&(Y.gender=d.gender),d!=null&&d.genderScore&&(Y.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Y.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Y.race=d==null?void 0:d.race),i&&(Y.emotion=i),l&&(Y.real=l),u&&(Y.live=u),Q&&Q!==0&&(Y.iris=Math.trunc(500/Q/11.7)/100),ee&&(Y.rotation=ee),ie&&(Y.tensor=ie),h.push(Y),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var DN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},LN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},BN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],m=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(m<.01||c<.01||m>.022||c>.022)&&(u=!1),(m<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(m>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},WN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=eN(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},sb=0;function VN(e,t){var i,o,l,u,d,h,p,c,m,f,g,y,A,x,b,v,S,N,E,R,z,$,I,_,O,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ne.canvas=e.canvas),e.error&&(Ne.error=e.error),!Ne.body||e.body.length!==Ne.body.length)Ne.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let J=e.body[D].box.map((Y,ae)=>((a-1)*Ne.body[D].box[ae]+Y)/a),V=e.body[D].boxRaw.map((Y,ae)=>((a-1)*Ne.body[D].boxRaw[ae]+Y)/a),ee=e.body[D].keypoints.map((Y,ae)=>{var de,Ae,be,Ee,$e,De,Be,Ze,ot;return{score:Y.score,part:Y.part,position:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[0]||0)+(Y.position[0]||0))/a:Y.position[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[1]||0)+(Y.position[1]||0))/a:Y.position[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[2]||0)+(Y.position[2]||0))/a:Y.position[2]],positionRaw:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[0]||0)+(Y.positionRaw[0]||0))/a:Y.positionRaw[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[1]||0)+(Y.positionRaw[1]||0))/a:Y.positionRaw[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[2]||0)+(Y.positionRaw[2]||0))/a:Y.positionRaw[2]],distance:[Ne.body[D].keypoints[ae]?((a-1)*(((de=Ne.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Y.distance)==null?void 0:Ae[0])||0))/a:(be=Y.distance)==null?void 0:be[0],Ne.body[D].keypoints[ae]?((a-1)*(((Ee=Ne.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+((($e=Y.distance)==null?void 0:$e[1])||0))/a:(De=Y.distance)==null?void 0:De[1],Ne.body[D].keypoints[ae]?((a-1)*(((Be=Ne.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Y.distance)==null?void 0:Ze[2])||0))/a:(ot=Y.distance)==null?void 0:ot[2]]}}),Q={},ie={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?ie=jm:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?ie=Bm:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(ie=tc);for(let[Y,ae]of Object.entries(ie.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find($e=>$e.part===ae[Ae]),Ee=ee.find($e=>$e.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}Q[Y]=de}Ne.body[D]={...e.body[D],box:J,boxRaw:V,keypoints:ee,annotations:Q}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let J=e.hand[D].box.map((ie,Y)=>((a-1)*Ne.hand[D].box[Y]+ie)/a),V=e.hand[D].boxRaw.map((ie,Y)=>((a-1)*Ne.hand[D].boxRaw[Y]+ie)/a);Ne.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ne.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((ie,Y)=>ie.map((ae,de)=>((a-1)*(Ne.hand[D].keypoints[Y][de]||1)+(ae||0))/a)):[],Q={};if(Object.keys(Ne.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ne.hand[D].annotations=e.hand[D].annotations,Q=Ne.hand[D].annotations;else if(e.hand[D].annotations)for(let ie of Object.keys(e.hand[D].annotations))Q[ie]=e.hand[D].annotations[ie]&&e.hand[D].annotations[ie][0]?e.hand[D].annotations[ie].map((Y,ae)=>Y.map((de,Ae)=>((a-1)*Ne.hand[D].annotations[ie][ae][Ae]+de)/a)):null;Ne.hand[D]={...e.hand[D],box:J,boxRaw:V,keypoints:ee,annotations:Q}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let J=e.face[D].box.map((ee,Q)=>((a-1)*Ne.face[D].box[Q]+ee)/a),V=e.face[D].boxRaw.map((ee,Q)=>((a-1)*Ne.face[D].boxRaw[Q]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((m=(c=Ne.face[D].rotation)==null?void 0:c.angle)==null?void 0:m.roll)||0)+(((g=(f=e.face[D].rotation)==null?void 0:f.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ne.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((S=(v=Ne.face[D].rotation)==null?void 0:v.angle)==null?void 0:S.pitch)||0)+(((E=(N=e.face[D].rotation)==null?void 0:N.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((z=(R=Ne.face[D].rotation)==null?void 0:R.gaze)==null?void 0:z.bearing)||0)+(((I=($=e.face[D].rotation)==null?void 0:$.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((O=(_=Ne.face[D].rotation)==null?void 0:_.gaze)==null?void 0:O.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ne.face[D]={...e.face[D],rotation:ee,box:J,boxRaw:V}}Ne.face[D]={...e.face[D],box:J,boxRaw:V}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let J=e.object[D].box.map((ee,Q)=>((a-1)*Ne.object[D].box[Q]+ee)/a),V=e.object[D].boxRaw.map((ee,Q)=>((a-1)*Ne.object[D].boxRaw[Q]+ee)/a);Ne.object[D]={...e.object[D],box:J,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ne.persons||D.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(D));else for(let J=0;J<D.length;J++)Ne.persons[J].box=D[J].box.map((V,ee)=>((a-1)*Ne.persons[J].box[ee]+V)/a)}e.gesture&&(Ne.gesture=e.gesture);let s=oe();return sb=he.perfadd?sb+Math.round(s-r):Math.round(s-r),e.performance&&(Ne.performance={...e.performance,interpolate:sb}),Ne}var lb={};ks(lb,{distance:()=>sc,match:()=>ob,similarity:()=>ib});function sc(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var UN=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function ib(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=sc(e,t,r);return UN(n,r.order||2,r.min||0,r.max||1)}function ob(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=sc(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let 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d3(o.tensor,p):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(u=this.config.body.enabled?await D3(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Gt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?C3(o.tensor,c):[]:($=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&$.includes("handtrack")&&(d=this.config.hand.enabled?R3(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(_=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&_.includes("handdetect")?d=this.config.hand.enabled?await C3(o.tensor,c):[]:(j=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await R3(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?B3(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?i3(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(J=this.config.object.modelPath)!=null&&J.includes("nanodet")?h=this.config.object.enabled?await B3(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await i3(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(a=oe(),m=[...LN(l),...DN(u),...WN(d),...BN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let f=((Q=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:u,hand:d,gesture:m,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return GN(l,u,d,m,f)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};ep=new WeakMap,ic=new WeakMap,oc=new WeakMap,y1=new WeakMap;return GE(fxe);})();
/**
* @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 2022 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 2022 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 2022 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 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the 'License');
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* Human main module
* @default Human Library
* @summary <https://github.com/vladmandic/human>
* @author <https://github.com/vladmandic>
* @copyright <https://github.com/vladmandic>
* @license MIT
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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