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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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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=dg(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=s.map(l=>t[l]);let 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UM=V({transform_:VM});function GM(e,t,n){M(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=$(e,"a","bandPart");M(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let s=r.shape,[a,o]=r.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=H(Nu(0,a,1,"int32"),[-1,1]),l=Nu(0,o,1,"int32"),u=he(i,l),c=ms($l(u,Ie(+t,"int32")),_l(u,Ie(-n,"int32"))),p=Ft([a,o],r.dtype);return H(cn(rr(H(r,[-1,a,o])).map(d=>Hn(c,d,p))),s)}var HM=V({bandPart_:GM});function jM(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let s=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===s,()=>`Gram-Schmidt: 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};jm.className="Adam";jo(jm);var qm=class extends aa{constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);K(()=>{let n=he(1,this.accBeta1),r=de(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((s,a)=>{let o=B.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:st(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:st(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[s];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ue(L(u,this.beta1),L(l,1-this.beta1)),d=L(c,this.beta2),h=rn(l),f=ra(d,h);u.assign(p),c.assign(f);let m=ue(L(de(r,n),de(p,ue(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ue(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ne(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};qm.className="Adamax";jo(qm);var Cp=class extends aa{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=B.registeredVariables[n];K(()=>{let o=ue(L(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=fn(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Xm.className="Momentum";jo(Xm);var Km=class extends aa{constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,r==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=B.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:K(()=>st(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:K(()=>st(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:K(()=>st(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,l=this.accumulatedMoments[r].variable;K(()=>{let u=ue(L(i,this.decay),L(xt(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[r].variable,p=ue(L(c,this.decay),L(o,1-this.decay)),d=de(L(o,this.learningRate),En(he(u,ue(xt(p),this.epsilon)))),h=ue(L(l,this.momentum),d);i.assign(u),c.assign(p),l.assign(h);let f=he(s,h);s.assign(f)}else{let c=ue(L(i,this.decay),L(xt(o),1-this.decay)),p=ue(L(l,this.momentum),de(L(o,this.learningRate),En(ue(c,this.epsilon))));i.assign(c),l.assign(p);let d=he(s,p);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],s=t[n];if(s==null)continue;let a=r.rank;if(s.ndim!=null&&a!==s.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${a}`);if(s.maxNDim!=null&&a>s.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(s.minNDim!=null&&a<s.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${a}.`);if(s.dtype!=null&&r.dtype!==s.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${r.dtype}.`);if(s.axes){let o=r.shape;for(let i in s.axes){let l=Number(i),u=s.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${o}.`)}}if(s.shape!=null)for(let o=0;o<s.shape.length;++o){let i=s.shape[o],l=r.shape[o];if(i!=null&&l!=null&&i!==l)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=It(e),r=!0;for(let a of n)if(!(a instanceof cs)){r=!1;break}let s=!0;for(let a of n)if(a instanceof cs){s=!1;break}if(r===s)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ni(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of It(e))a.push(o.shape);this.build(tr(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let a=this.call(e,t),o=It(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=tr(i),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=eB(e),o=this.computeOutputShape(a),i,l=tB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new cs(l,u,this,It(e),t,this.name,c)):i=new cs(l,o,this,It(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new js(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new js(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new j(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new j(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new j(`${a.length} of ${r} weights are not set: ${a}`)}NA(s)}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 n=_g(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return K(()=>{e=It(e);let n=new Ii;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Sd(this.outputs,n,t)})}computeMask(e,t){return K(()=>{e=It(e);let n;return t==null?n=Oi(null,e.length):n=It(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Nf(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(jh);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],x=`${m.name}_${g}_${y}`,A=n[x];c.push(A)}let p=u.computeOutputShape(tr(c)),d=Nf(p),h=u.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=d[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Ts(i in n),s.push(n[i])}return tr(s)}runInternalGraph(e,t){t==null&&(t=Oi(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(jh);for(let i of r){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,p=u.inputTensors,d=u.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,y,x;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[A,b]=h[0];f.mask==null&&(f.mask=b),y=It(c.call(A,f)),x=It(c.computeMask(A,b)),m=[A],g=[b]}else m=h.map(A=>A[0]),g=h.map(A=>A[1]),f.mask==null&&(f.mask=g),y=It(c.call(m,f)),x=It(c.computeMask(m,g));if(c.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let A=0;A<d.length;++A){let b=d[A],v=y[A],S=x[A];n[b.id]=[v,S]}}}}let s=[],a=[],o=[];for(let i of this.outputs){Ts(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),s.push(l),a.push(u)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Cs?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=Cs.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`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 j("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Cs.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Cs.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],y=p.nodeIndices[m],x=p.tensorIndices[m],A=Cs.nodeKey(g,y),b=t[A];b==null&&(b=0),f.push([g.name,b,x,h])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Cs.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];r.push([o.name,u,c])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Cs.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];s.push([o.name,u,c])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],x;for(let A of g){let b=A[0],v=A[1],S=A[2];if(x=A[3]==null?{}:A[3],!(b in s)){o(m,g);return}let I=s[b];if(I.inboundNodes.length<=v){o(m,g);return}let E=I.inboundNodes[v];y.push(E.outputTensors[S])}y.length>0&&m.apply(tr(y),x)}function l(m){let g=m.name,y=hs(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),s[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${A}`);o(y,A)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!NL(a);)for(let m of c){let g=s[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let x of y)i(g,x)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],x=m[2];Ts(g in s);let b=s[g].inboundNodes[y].outputTensors;p.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],x=m[2];Ts(g in s);let b=s[g].inboundNodes[y].outputTensors;d.push(b[x])}return new e({inputs:p,outputs:d,name:u})}get stateful(){if(this._stateful)throw new j("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 sU(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===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!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function A6(e,t){return sU(e,t,"classWeight")}async function x6(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=K(()=>{if(e.shape.length===1)return Gn(e);if(e.shape.length===2){if(e.shape[1]>1)return Rr(e,1);if(e.shape[1]===1)return H(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());ne(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Ct(o,"float32")}else return null}function aU(e,t){return L(e,t)}var oU=32;function b6(e,t){let n,r,s=t;n=s.xs,r=s.ys,w.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=dv("input",e.inputNames,n),o=dv("output",e.outputNames,r),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)w.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)w.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function dv(e,t,n){if(n instanceof rt)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let s of t){if(n[s]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}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 lU(e,t,n){let r=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(pv(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=iU(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;s?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=c6(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=d6(c,p,n.epochs,null,null,uU(t,n),null,s,u);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let y=0,x=0;for(r||(m=await t.iterator());!r||y<n.batchesPerEpoch;){let A=await m.next();if(r&&A.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.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, ${n.batchesPerEpoch*n.epochs} batches). 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if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=wi(e.metrics[a])}this.compile({loss:r,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=Cn.getSaveHandlers(e);if(l.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new j(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Cn.encodeWeights(this.getNamedWeights(t)),r=!1,s=null,o={modelTopology:this.toJSON(s,r),format:AU,generatedBy:`TensorFlow.js tfjs-layers v${LA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:u,specs:c}=await Cn.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...c),n.data=Cn.concatenateArrayBuffers([n.data,u])}return this.userDefinedMetadata!=null&&(cv(this.userDefinedMetadata,this.name,!0),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){cv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Ys.className="Model";ce.registerClass(Ys);var w6=class extends Ys{};w6.className="Functional";ce.registerClass(w6);async function xU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=jd(n),s=hs(r,t);if(e.weightsManifest!=null){let a=await Cn.loadWeights(e.weightsManifest,e.pathPrefix,s.weights.map(i=>i.originalName)),o={};for(let i of s.weights)o[i.originalName]=a[i.originalName];s.loadWeights(o),ne(a)}return s}async function bU(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Cn.getLoadHandlers(e,t);if(n.length===0)n.push(Cn.browserHTTPRequest(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return vU(e,void 0,t)}async function vU(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),s=r.modelTopology;s.model_config!=null&&(s=s.model_config);let a=n.strict==null?!0:n.strict,o=r.weightData!=null&&r.weightSpecs!=null&&a,i=hs(jd(s),t,o),l=r.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),r.userDefinedMetadata!=null&&i.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");s=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."),s=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Fg))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of s){let u=hs(i,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new j("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 j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},u0=Fg;u0.className="Sequential";ce.registerClass(u0);function kU(e){return new Ys(e)}function SU(e){return new u0(e)}function IU(e,t){return t==null&&(t={}),bU(e,t)}function k6(e){return e6(e)}function CU(e,t){PA.registerCallbackConstructor(e,t)}var ir=class extends ce.Serializable{getConfig(){return{}}},S6=class extends ir{apply(e,t=1){return UL(e,t)}};S6.className="elu";ce.registerClass(S6);var I6=class extends ir{apply(e){return sA(e)}};I6.className="selu";ce.registerClass(I6);var C6=class extends ir{apply(e){return Os(e)}};C6.className="relu";ce.registerClass(C6);var T6=class extends ir{apply(e){return K(()=>Sp(6,Os(e)))}};T6.className="relu6";ce.registerClass(T6);var N6=class extends ir{apply(e){return e}};N6.className="linear";ce.registerClass(N6);var E6=class extends ir{apply(e){return 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t={};return t.className="linear",t.config={},og(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},og(t)}else return e instanceof ir?e:og(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: ${e}`)}var M6=class extends ce.Serializable{},$p=class extends M6{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=Ft([1]);return this.hasL1&&(t=ue(t,ke(L(this.l1,rn(e))))),this.hasL2&&(t=ue(t,ke(L(this.l2,Ep(e))))),H(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};$p.className="L1L2";ce.registerClass($p);function TU(e){return VA(e),new $p({l1:e!=null?e.l1:null,l2:0})}function NU(e){return VA(e),new $p({l2:e!=null?e.l2:null,l1:0})}var gv={l1l2:"L1L2"};function bt(e){return AA(e)}function yv(e,t={}){return Tp(e,ce.SerializationMap.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in gv?gv[e]:e,config:{}};return yv(n)}else return e instanceof M6?e:yv(e)}var UA=class extends at{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=He(e);let n=Os(e);return this.maxValue!=null&&(n=yr(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};UA.className="ReLU";ce.registerClass(UA);var GA=class extends at{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=He(e);return Nm(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};GA.className="LeakyReLU";ce.registerClass(GA);var HA=class extends at{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=un(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 j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=He(e),Fm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ot(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};HA.className="PReLU";ce.registerClass(HA);var jA=class extends at{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=He(e);return wp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jA.className="ELU";ce.registerClass(jA);var qA=class extends at{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=He(e);return L(n,ge(Ar(n,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";ce.registerClass(qA);var XA=class extends at{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 n=He(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};XA.className="Softmax";ce.registerClass(XA);function bu(e,t,n){if(typeof e=="number")return Oi(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!LL(s))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function fs(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function Ns(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ua([n-t,0]);else if(r==="same")e=e*t;else throw new j(`Unsupport padding mode: ${r}.`);return e}function KA(e,t){return K(()=>(Ht(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function z6(e,t){return K(()=>(Ht(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function EU(e,t,n,r=1,s="valid",a,o=1){return K(()=>{if(a==null&&(a=ys()),Ht(a),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=et(e,[0,2,1])),s==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Ly(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=bs(i,n)),i})}function Av(e,t,n,r=[1,1],s="valid",a,o,i=null){return K(()=>{if(a==null&&(a=ys()),Ht(a),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=KA(e,a);if(s==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Va.conv2d({x:l,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=et(l,[0,3,1,2])),l})}function RU(e,t,n,r=[1,1,1],s="valid",a,o){return K(()=>{if(a==null&&(a=ys()),Ht(a),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=z6(e,a);if(s==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Vy(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=bs(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var ZA=class extends at{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ZA.verifyArgs(t),this.rank=e,mn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=bu(t.kernelSize,e,"kernelSize"),this.strides=bu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Lr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ht(this.dataFormat),this.activation=Ha(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Rt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=un(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=bu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`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 j(`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 j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ts("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!xA(e.kernelSize,"number",1,3))throw new j(`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:Ga(this.activation),useBias:this.useBias,biasInitializer:Ot(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Dp=class extends ZA{constructor(e,t){super(e,t),this.kernel=null,Dp.verifyArgs(t),this.filters=t.filters,mn(this.filters,"filters"),this.kernelInitializer=Rt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=un(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 j(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return K(()=>{e=He(e);let n,r=this.bias==null?null:this.bias.read(),s=G7(this.activation.getClassName());if(s!=null&&this.rank===2)n=Av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=EU(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Av(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=RU(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=mt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=fs(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Ot(this.kernelInitializer),kernelRegularizer:bt(this.kernelRegularizer),kernelConstraint:ln(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 j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},L6=class extends Dp{constructor(e){super(2,e),L6.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 j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},c0=L6;c0.className="Conv2D";ce.registerClass(c0);var B6=class extends Dp{constructor(e){super(3,e),B6.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 j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},d0=B6;d0.className="Conv3D";ce.registerClass(d0);var YA=class extends c0{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=He(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],l=r[o],u=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(i,p,u,this.padding),f=Ns(l,d,c,this.padding),m=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=Wy(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=bs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Ns(t[r],i,a,this.padding),t[s]=Ns(t[s],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};YA.className="Conv2DTranspose";ce.registerClass(YA);var JA=class extends d0{constructor(e){if(super(e),this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=He(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=r[i],u=r[a],c=r[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Ns(l,f,p,this.padding),x=Ns(u,m,d,this.padding),A=Ns(c,g,h,this.padding),b=[s,y,x,A,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let v=Xw(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=et(v,[0,4,1,2,3])),this.bias!==null&&(v=bs(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=mt(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=Ns(t[r],u,o,this.padding),t[s]=Ns(t[s],c,i,this.padding),t[a]=Ns(t[a],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};JA.className="Conv3DTranspose";ce.registerClass(JA);var W6=class extends Dp{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 j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("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 j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=un(t.depthwiseConstraint),this.pointwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=un(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${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 j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{e=He(e);let n;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=m7(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=bs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=et(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ot(this.depthwiseInitializer),e.pointwiseInitializer=Ot(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}};W6.className="SeparableConv";var QA=class extends W6{constructor(e){super(2,e)}};QA.className="SeparableConv2D";ce.registerClass(QA);var V6=class extends Dp{constructor(e){super(1,e),V6.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 j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ex=V6;ex.className="Conv1D";ce.registerClass(ex);var tx=class extends at{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=He(e),this.dataFormat==="channelsLast"){let n=Xh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Cropping2D";ce.registerClass(tx);var nx=class extends at{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,OL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return K(()=>{let n=He(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=et(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[s,a]):Se.resizeBilinear(n,[s,a]);return et(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[s,a]):Se.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};nx.className="UpSampling2D";ce.registerClass(nx);function _U(e,t,n=[1,1],r="valid",s,a){return K(()=>{s==null&&(s=ys()),Ht(s);let o=KA(e,s);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=vp(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var rx=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=un(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=He(e);let n=_U(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=bs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=fs(t,this.kernelSize[0],this.padding,this.strides[0]),a=fs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ot(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}};rx.className="DepthwiseConv2D";ce.registerClass(rx);function U6(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function G6(e,t,n,r=!1,s,a,o=!1,i=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new j(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(gs(2,l));if(t=et(t,u),a!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ge(ge(s,"bool"),"float32"),s.rank===l-1&&(s=Kt(s,-1)),s=et(s,u)),r&&(t=Fr(t,0),s!=null&&(s=Fr(s,0)));let c=[],p,d=n,h=t.shape[0],f=rr(t),m;s!=null&&(m=rr(s));for(let y=0;y<h;++y){let x=f[y],A=K(()=>e(x,d));if(s==null)p=A[0],d=A[1];else{let b=K(()=>{let v=m[y],S=he(Pr(v),v),I=ue(L(A[0],v),L(d[0],S)),E=d.map((R,P)=>ue(L(A[1][P],v),L(R,S)));return{output:I,newStates:E}});p=b.output,d=b.newStates}i&&c.push(p)}let g;return i&&(g=cn(c,1)),[p,g,d]})}var H6=class extends at{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new f0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gs(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 n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Ng(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Zt({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));this.cell.build(s);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(o=>o.shape[o.shape.length-1]),a))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new js("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("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(r=>Ft([n,r])):this.states_=[Ft([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ft([n,r])):this.states_[0]=Ft([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):ne(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(s.shape,o))throw new j(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>fn(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=U6(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof cs){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return K(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=He(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new j(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=G6((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],p=l[2];this.stateful&&this.resetStates(p,r);let d=this.returnSequences?c:u;return this.returnState?[d].concat(p):d})}getInitialState(e){return K(()=>{let t=Ft(e.shape);return t=ke(t,[1,2]),t=Np(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Cg(t,[1,n]):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 n=this.cell.getConfig();return this.getClassName()===H6.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let r=t.cell,s=hs(r,n);return new e(Object.assign(t,{cell:s}))}},oa=H6;oa.className="RNN";ce.registerClass(oa);var Pp=class extends at{},p0=class extends Pp{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,mn(this.units,"units"),this.activation=Ha(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=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Eu([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Eu([1,Ua([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 j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ja({ones:()=>Pr(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ja({ones:()=>Pr(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=Rs(L(e,a),this.kernel.read()):s=Rs(e,this.kernel.read()),this.bias!=null&&(s=bs(s,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(s,Rs(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ga(this.activation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),recurrentInitializer:Ot(this.recurrentInitializer),biasInitializer:Ot(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};p0.className="SimpleRNNCell";ce.registerClass(p0);var sx=class extends oa{constructor(e){e.cell=new p0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};sx.className="SimpleRNN";ce.registerClass(sx);var h0=class extends Pp{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 j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,mn(this.units,"units"),this.activation=Ha(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ha(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=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Eu([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Eu([1,Ua([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 j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ja({ones:()=>Pr(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ja({ones:()=>Pr(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let u=Rs(e,this.kernel.read());this.useBias&&(u=bs(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,a[0]));let c=this.recurrentKernel.read(),[p,d]=Yt(c,[2*this.units,this.units],c.rank-1),h=Rs(r,p),[f,m,g]=Yt(u,3,u.rank-1),[y,x]=Yt(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,y)),i=this.recurrentActivation.apply(ue(m,x));let A=Rs(L(i,r),d);l=this.activation.apply(ue(g,A));let b=ue(L(o,r),L(ue(1,zt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ga(this.activation),recurrentActivation:Ga(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),recurrentInitializer:Ot(this.recurrentInitializer),biasInitializer:Ot(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};h0.className="GRUCell";ce.registerClass(h0);var ax=class extends oa{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 h0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ax.className="GRU";ce.registerClass(ax);var Fp=class extends Pp{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,mn(this.units,"units"),this.activation=Ha(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ha(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=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Eu([1,Ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Eu([1,Ua([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 n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Zr{apply(o,i){let l=s.apply([a]),u=new Qm().apply([a]),c=s.apply([a*2]);return Zb(Zb(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ja({ones:()=>Pr(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ja({ones:()=>Pr(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let p=Rs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,o[0])),p=ue(p,Rs(r,this.recurrentKernel.read())),this.useBias&&(p=bs(p,this.bias.read()));let[d,h,f,m]=Yt(p,4,p.rank-1);i=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),u=ue(L(l,s),L(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=L(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ga(this.activation),recurrentActivation:Ga(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),recurrentInitializer:Ot(this.recurrentInitializer),biasInitializer:Ot(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Fp.className="LSTMCell";ce.registerClass(Fp);var ox=class extends oa{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 Fp(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ox.className="LSTM";ce.registerClass(ox);var f0=class extends Pp{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 n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Ng(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ni(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(hs(s,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Eg(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}NA(t)}};f0.className="StackedRNNCells";ce.registerClass(f0);function ja(e){let{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Y7(t(),n),i=()=>Rp(o,t,r);return!s||s<=1?fn(i().clone()):Array(s).fill(void 0).map(i).map(u=>fn(u.clone()))}var j6=class extends oa{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 Zt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}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,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=Ft(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new js("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new j("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(()=>Ft(s)):this.states_=[Ft(s)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ft(s)):this.states_[0]=Ft(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=s;if(!w.arraysEqual(i.shape,l))throw new j(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>fn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=fs(l,r[0],s,a[0],o[0]),p=fs(u,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};j6.className="ConvRNN2D";var m0=class extends Fp{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super({...e,units:t}),this.filters=t,mn(this.filters,"filters"),this.kernelSize=bu(n,2,"kernelSize"),this.kernelSize.forEach(i=>mn(i,"kernelSize")),this.strides=bu(r||1,2,"strides"),this.strides.forEach(i=>mn(i,"strides")),this.padding=s||"valid",Lr(this.padding),this.dataFormat=a||"channelsLast",Ht(this.dataFormat),this.dilationRate=bu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>mn(i,"dilationRate"))}build(e){var t;e=mt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends Zr{apply(c,p){let d=l.apply([u]),h=gr([u]),f=l.apply([u*2]);return bA([d,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ja({ones:()=>Pr(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(W,ee,Q)=>!ee||!ee[Q]?W:L(ee[Q],W),u=l(r,i,0),c=l(r,i,1),p=l(r,i,2),d=l(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ja({ones:()=>Pr(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(s,h,0),m=l(s,h,1),g=l(s,h,2),y=l(s,h,3),x=3,[A,b,v,S]=Yt(this.kernel.read(),o,x),[I,E,R,P]=this.useBias?Yt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,A,I,this.padding),c=this.inputConv(c,b,E,this.padding),p=this.inputConv(p,v,R,this.padding),d=this.inputConv(d,S,P,this.padding);let[_,D,T,F]=Yt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,_),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),y=this.recurrentConv(y,F);let U=this.recurrentActivation.apply(ue(u,f)),X=this.recurrentActivation.apply(ue(c,m)),z=ue(L(X,a),L(U,this.activation.apply(ue(p,g)))),Z=L(this.recurrentActivation.apply(ue(d,y)),this.activation.apply(z));return[Z,Z,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,r){let s=La(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?bs(s,n,this.dataFormat):s}recurrentConv(e,t){return La(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};m0.className="ConvLSTM2DCell";ce.registerClass(m0);var ix=class extends j6{constructor(e){let t=new m0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ix.className="ConvLSTM2D";ce.registerClass(ix);var g0=class extends at{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Rp(()=>Y7(n,this.rate,s,this.seed),()=>n,r)}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()}};g0.className="Dropout";ce.registerClass(g0);var lx=class extends g0{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};lx.className="SpatialDropout1D";ce.registerClass(lx);var ux=class extends at{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,mn(this.units,"units"),this.activation=Ha(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=un(e.kernelConstraint),this.biasConstraint=un(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 n=He(e),r=G7(this.activation.getClassName()),s;return r!=null?s=Rs(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=Rs(n,this.kernel.read()),this.bias!=null&&(s=bs(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ga(this.activation),useBias:this.useBias,kernelInitializer:Ot(this.kernelInitializer),biasInitializer:Ot(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Dense";ce.registerClass(ux);var cx=class extends at{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 j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],$a(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=et(n,r)}return VL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};cx.className="Flatten";ce.registerClass(cx);var dx=class extends at{constructor(e){super(e),this.supportsMasking=!0,this.activation=Ha(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ga(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};dx.className="Activation";ce.registerClass(dx);var px=class extends at{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=He(e),BL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};px.className="RepeatVector";ce.registerClass(px);var hx=class extends at{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let l=r[i];if(this.isUnknown(l))if(a===null)a=i;else throw new j("Can only specifiy one unknown dimension.");else s*=l}let o=$a(e);if(a!==null){if(s===0||o%s!==0)throw new j(n);r[a]=o/s}else if(o!==s)throw new j(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return H(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};hx.className="Reshape";ce.registerClass(hx);var fx=class extends at{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=gs(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 Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return et(He(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Permute";ce.registerClass(fx);var mx=class extends at{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=He(e),r=-1;return If(Tu(n,this.maskValue),r)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e),r=-1,s=!0,a=If(Tu(n,this.maskValue),r,s);return L(n,ge(a,n.dtype))})}};mx.className="Masking";ce.registerClass(mx);var gx=class extends at{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(It(e.inputLength))}this.inputDim=e.inputDim,mn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,mn(this.outputDim,"outputDim"),this.embeddingsInitializer=Rt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=un(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=He(e),Tu(e,st(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=He(e);n.dtype!=="int32"&&(n=Ym(n,"int32"));let r=Z7(this.embeddings.read(),H(n,[n.size]));return H(r,mt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ot(this.embeddingsInitializer),embeddingsRegularizer:bt(this.embeddingsRegularizer),activityRegularizer:bt(this.activityRegularizer),embeddingsConstraint:ln(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};gx.className="Embedding";ce.registerClass(gx);var Ol=class extends at{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 n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=_a(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let r=e.map(s=>s.length);e.indexOf(null)===-1&&_a(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=Ua(r);for(let a of e){let o=a.rank;for(let i=0;i<s-o;++i)a=Np(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],p=u.slice(1).concat([c]),d=H(i,[c].concat($a(u.slice(1))));d=et(d,[1,0]),d=H(d,p),n.push(d),s=!0}else if(l>1){let u=gs(1,l).concat([0]);n.push(et(i,u)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=H(et(H(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(gs(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=_a(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Kt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=ms(n,t[r]);return n})}},yx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return t})}};yx.className="Add";ce.registerClass(yx);var Ax=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};Ax.className="Multiply";ce.registerClass(Ax);var xx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return L(1/e.length,t)})}};xx.className="Average";ce.registerClass(xx);var bx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ra(t,e[n]);return t})}};bx.className="Maximum";ce.registerClass(bx);var vx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Sp(t,e[n]);return t})}};vx.className="Minimum";ce.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 j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let s=e[r].slice();s.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new j("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 j("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let s of t.slice(1)){if(n[r]==null||s[r]==null){n[r]=null;break}n[r]+=s[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let r=[];for(let a=0;a<e.length;++a)t[a]==null?r.push(ge(Pr(e[a]),"bool")):t[a].rank<e[a].rank?r.push(Kt(t[a],-1)):r.push(t[a]);let s=St(r,this.axis);return Fy(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wx.className="Concatenate";ce.registerClass(wx);function Ad(e,t){for(;e<0;)e+=t;return e}function $U(e,t,n){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 n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return K(()=>{let o;if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);t=H(t,t.shape.concat(l))}else if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);e=H(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(L(e,t),a[0]):i=ke(L(et(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=Ye(e,t,l,u)}if(o>0){let l;r>s?l=r+s-3:l=r-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=tt(i,u)}return i.shape.length===1&&(i=Kt(i,1)),i})}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],n=e[1];if(t.length>3||n.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new j(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((s,a)=>Ad(s,e[a].shape.length)):r=[Ad(this.axes,t.shape.length),Ad(this.axes,n.shape.length)],this.normalize&&(t=$f(t,r[0]),n=$f(n,r[1])),$U(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ad(this.axes,e.length),Ad(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}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";ce.registerClass(kx);var Sx=class extends at{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 n=He(e);return Rp(()=>ue(Jm(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Sx.className="GaussianNoise";ce.registerClass(Sx);var Ix=class extends at{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 n=He(e);return this.rate>0&&this.rate<1?Rp(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return L(n,Jm(n.shape,1,s))},()=>n,t.training||!1):n})}};Ix.className="GaussianDropout";ce.registerClass(Ix);var Cx=class extends at{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||He(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 n=this._getNoiseShape(e);return Rp(()=>{let s=He(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=_l(gc(n),this.rate);l=Ym(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,p=ue(L(s,l),L(ue(l,-1),i));return ue(L(p,u),c)},()=>He(e),t.training||!1)}return e})}};Cx.className="AlphaDropout";ce.registerClass(Cx);function qd(e,t,n,r,s,a=.001){let o;if(e.rank===2)o=Lw(e,t,n,r,s,a);else if(e.rank===3)o=Bw(e,t,n,r,s,a);else if(e.rank===4)o=Ww(e,t,n,r,s,a);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function DU(e,t,n,r,s=.001){return K(()=>{let a=Dm(e,r),o=a.mean,i=a.variance;return[qd(e,o,i,n,t,s),o,i]})}function PU(e,t,n,r,s=.001){return K(()=>{let a=Dm(e,r),o=a.mean,i=a.variance,l=[];for(let f of gs(0,e.rank))r.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=H(o,l),c=H(i,l),p=t==null?null:H(t,l),d=n==null?null:H(n,l);return[qd(e,u,c,d,p,s),o,i]})}function FU(e,t,n,r,s=.001){return w.arraysEqual(r.slice().sort(),gs(0,e.rank-1))?DU(e,t,n,r,s):PU(e,t,n,r,s)}var Tx=class extends at{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=un(e.betaConstraint),this.gammaConstraint=un(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,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let n=t.training==null?!1:t.training,r=He(e),s=r.shape,a=s.length,o=gs(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Oi(1,a);l[i]=s[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,gs(0,a).slice(0,a-1)),p=()=>{if(c){let y=H(this.movingMean.read(),l),x=H(this.movingVariance.read(),l),A=this.center?H(this.beta.read(),l):null,b=this.scale?H(this.gamma.read(),l):null;return qd(r,y,x,A,b,this.epsilon)}else return qd(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FU(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,x,A)=>{K(()=>{let b=1-A,v=y.read(),S=L(he(v,x),b);y.write(he(v,S))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ot(this.betaInitializer),gammaInitializer:Ot(this.gammaInitializer),movingMeanInitializer:Ot(this.movingMeanInitializer),movingVarianceInitializer:Ot(this.movingVarianceInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer),betaConstraint:ln(this.betaConstraint),gammaConstraint:ln(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Tx.className="BatchNormalization";ce.registerClass(Tx);var Nx=class extends at{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 s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==_a(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=He(e),r=n.shape,s=r.length;return K(()=>{let{mean:o,variance:i}=Dm(n,this.axis,!0),l=Oi(1,s);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==s?H(f,l):f,c=u(this.gamma.read()),p=u(this.beta.read()),d=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),h.push(1)):(d.push(1),h.push(r[f]));return o=Gr(o,d),i=Gr(i,d),c=Gr(c,h),p=Gr(p,h),qd(n,o,i,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ot(this.betaInitializer),gammaInitializer:Ot(this.gammaInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="LayerNormalization";ce.registerClass(Nx);function OU(e,t,n){return K(()=>{if(e.rank!==4)throw new j(`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 j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ys()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Xr(e,r)})}var Ex=class extends at{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ys():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=mt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return K(()=>OU(He(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";ce.registerClass(Ex);function y0(e,t,n,r,s,a){return K(()=>{Ht(s),j7(a),Lr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=ys()),a==null&&(a="max"),e=KA(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=$m(e,t,n,i):o=Sm(e,t,n,i),s==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function q6(e,t,n,r,s,a){return K(()=>{Ht(s),j7(a),Lr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=ys()),a==null&&(a="max"),e=z6(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Yy(e,t,n,i):o=My(e,t,n,i),s==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var X6=class extends at{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 j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(mn(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 j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Lr(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=fs(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=Np(He(e),2);let n=this.poolingFunction(He(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return tt(n,[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,n,r,s){return Ht(s),Lr(r),y0(e,t,n,r,s,"max")}};Rx.className="MaxPooling1D";ce.registerClass(Rx);var _x=class extends X6{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ht(s),Lr(r),y0(e,t,n,r,s,"avg")}};_x.className="AveragePooling1D";ce.registerClass(_x);var K6=class extends at{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 j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Lr(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=fs(t,this.poolSize[0],this.padding,this.strides[0]),n=fs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(He(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},$x=class extends K6{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ht(s),Lr(r),y0(e,t,n,r,s,"max")}};$x.className="MaxPooling2D";ce.registerClass($x);var Dx=class extends K6{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ht(s),Lr(r),y0(e,t,n,r,s,"avg")}};Dx.className="AveragePooling2D";ce.registerClass(Dx);var Z6=class extends at{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 j(`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];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Lr(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=fs(t,this.poolSize[0],this.padding,this.strides[0]),n=fs(n,this.poolSize[1],this.padding,this.strides[1]),r=fs(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(He(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 Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ht(s),Lr(r),q6(e,t,n,r,s,"max")}};Px.className="MaxPooling3D";ce.registerClass(Px);var Fx=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ht(s),Lr(r),q6(e,t,n,r,s,"avg")}};Fx.className="AveragePooling3D";ce.registerClass(Fx);var Y6=class extends at{constructor(e){super(e),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Ox=class extends Y6{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=He(e);return Vt(n,1)})}};Ox.className="GlobalAveragePooling1D";ce.registerClass(Ox);var Mx=class extends Y6{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=He(e);return gn(n,1)})}};Mx.className="GlobalMaxPooling1D";ce.registerClass(Mx);var J6=class extends at{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zx=class extends J6{call(e,t){return K(()=>{let n=He(e);return this.dataFormat==="channelsLast"?Vt(n,[1,2]):Vt(n,[2,3])})}};zx.className="GlobalAveragePooling2D";ce.registerClass(zx);var Lx=class extends J6{call(e,t){return K(()=>{let n=He(e);return this.dataFormat==="channelsLast"?gn(n,[1,2]):gn(n,[2,3])})}};Lx.className="GlobalMaxPooling2D";ce.registerClass(Lx);var Q6=class extends at{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,s=hs(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Bx=class extends Q6{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new j(`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)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return K(()=>(e=He(e),G6((a,o)=>[He(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Bx.className="TimeDistributed";ce.registerClass(Bx);function MU(e){Pl(FL,"BidirectionalMergeMode",e)}var zU="concat",Wx=class extends Q6{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=hs(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=hs(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zU:e.mergeMode,MU(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,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):tr(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=U6(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new Zt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(r!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof cs;for(let l of a)if(l instanceof cs!==i)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return K(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=Fr(s,1));let o;return this.mergeMode==="concat"?o=bA([r,s]):this.mergeMode==="sum"?o=ue(r,s):this.mergeMode==="ave"?o=L(.5,ue(r,s)):this.mergeMode==="mul"?o=L(r,s):this.mergeMode==null&&(o=[r,s]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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k("x",e,t,n).map(u=>Ct(u.shape));case"Size":return[Ie(k("x",e,t,n).size,"int32")];case"Rank":return[Ie(k("x",e,t,n).rank,"int32")];case"NoOp":return[Ie(1)];case"Print":let a=k("x",e,t,n),o=k("data",e,t,n),i=k("message",e,t,n),l=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<o.length;u++)console.log(Array.prototype.slice.call(o[u].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QH=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,fn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ie(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return 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implemented`)}},nj=(e,t,n)=>{switch(e.op){case"Equal":return[_r(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[Tu(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[Ar(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[_l(k("a",e,t,n),k("b",e,t,n))];case"Less":return[jy(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[$l(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[ms(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[_m(k("a",e,t,n))];case"LogicalOr":return[Zy(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[Hn(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},sj=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Iu(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[Iu(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[s7(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[yc(k("x",e,t,n))];case"LogSoftmax":return[qy(k("x",e,t,n))];case"SparseToDense":return[pA(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},aj=(e,t,n)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[gn(k("x",e,t,n),o,i)]}case"Mean":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[Vt(k("x",e,t,n),o,i)]}case"Min":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[Ba(k("x",e,t,n),o,i)]}case"Sum":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[ke(k("x",e,t,n),o,i)]}case"All":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[Fy(k("x",e,t,n),o,i)]}case"Any":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[If(k("x",e,t,n),o,i)]}case"ArgMax":{let o=k("axis",e,t,n);return[Rr(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[Rw(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),i=k("keepDims",e,t,n);return[Jy(k("x",e,t,n),o,i)]}case"Cumprod":{let o=k("axis",e,t,n),i=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[Tf(k("x",e,t,n),o,i,l)]}case"Cumsum":{let o=k("axis",e,t,n),i=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[Gy(k("x",e,t,n),o,i,l)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),a=k("size",e,t,n);return[zy(r,s,a)];case"DenseBincount":{let o=k("x",e,t,n),i=k("weights",e,t,n),l=k("size",e,t,n),u=k("binaryOutput",e,t,n);return[Kw(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},oj=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),a=k("tensors",e,t,n);return a=a.slice(0,r),[St(a,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[Cu(r,ge(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),a=k("x",e,t,n),o=k("indices",e,t,n);return[Cu(a,ge(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let a=k("x",e,t,n);return[Fr(a,s)]}case"ReverseV2":{let r=k("axis",e,t,n),s=k("x",e,t,n);return[Fr(s,r)]}case"Slice":{let r=k("begin",e,t,n),s=k("size",e,t,n);return[Pe(k("x",e,t,n),r,s)]}case"StridedSlice":{let r=k("begin",e,t,n),s=k("end",e,t,n),a=k("strides",e,t,n),o=k("beginMask",e,t,n),i=k("endMask",e,t,n),l=k("ellipsisMask",e,t,n),u=k("newAxisMask",e,t,n),c=k("shrinkAxisMask",e,t,n),p=k("x",e,t,n);return[A7(p,r,s,a,o,i,l,u,c)]}case"Pack":return K(()=>{let r=k("axis",e,t,n),s=k("tensors",e,t,n),a=s[0].shape,o=tt(s[0]).shape,i=s.map(l=>{let u=w.arraysEqual(l.shape,a);if(!u&&!w.arraysEqual(tt(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:H(l,a)});return[cn(i,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return rr(s,r)}case"Tile":{let r=k("reps",e,t,n);return[Gr(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),a=k("x",e,t,n);return Yt(a,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),a=k("shape",e,t,n);return[I7(r,s,a)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[C7(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),a=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[pA(r,a,s,a.dtype===o.dtype?o:ge(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ij=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=kd.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=kd.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[kd.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[kd.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lj=(e,t,n)=>{switch(e.op){case"FFT":return[Mm(k("x",e,t,n))];case"IFFT":return[Gd(k("x",e,t,n))];case"RFFT":return[zm(k("x",e,t,n))];case"IRFFT":return[lA(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},uj=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=af.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:a}=af.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,a]}case"StringToHashBucketFast":return[af.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},cj=(e,t,n)=>{switch(e.op){case"Cast":return[ge(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[Kt(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[tt(k("x",e,t,n),r)]}case"Reshape":return[H(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[d7(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Xr(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[Pm(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[Im(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[Zw(k("x",e,t,n),r,s)]}case"BroadcastTo":return[$d(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[Vw(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Iv(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return K(()=>BH(a,o,i));case"basic_math":return K(()=>WH(a,o,i));case"control":return qH(a,o,i);case"convolution":return K(()=>XH(a,o,i));case"creation":return K(()=>KH(a,o,i));case"dynamic":return ZH(a,o,i);case"evaluation":return K(()=>YH(a,o,i));case"image":return K(()=>tj(a,o,i));case"graph":return K(()=>JH(a,o,i));case"logical":return K(()=>nj(a,o,i));case"matrices":return K(()=>rj(a,o,i));case"normalization":return K(()=>sj(a,o,i));case"reduction":return K(()=>aj(a,o,i));case"slice_join":return K(()=>oj(a,o,i));case"sparse":return K(()=>ij(a,o,i));case"spectral":return K(()=>lj(a,o,i));case"string":return K(()=>uj(a,o,i));case"transformation":return K(()=>cj(a,o,i));case"hash_table":return ej(a,o,i,r);case"custom":let l=ck(a.op);if(l&&l.customExecutor)return l.customExecutor(new LH(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Cv=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Tv(e,t,n,r){let s=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(d=>fr(d)[0]),c=[];r!=null&&(c=r.map(d=>fr(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if(($k(d)||mj(d)||gj(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function dj(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(c=>fr(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{r.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{r.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&r.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&a.push(p)})}return u}var pj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],hj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],fj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function $k(e){return pj.indexOf(e.op)>=0}function mj(e){return hj.indexOf(e.op)>=0}function gj(e){return fj.indexOf(e.op)>=0}var qg=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new qg(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=Tv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return dj(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[fr(c)[0]]),s=t.map(c=>fr(c)[0]),a=s.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return K(()=>{let c=new Cv(this.weightMap,l,u,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=fr(f),y=[];y[g]=e[f],p[m]=y});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=Iv(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,s,h)}}return this.parent==null&&c.dispose(d),t.map(f=>Vn(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=AH(i.name,n,r);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!s.has(u.id)){let c=o[u.id];if(c===1){if(!this.keepTensorForDebug)u.dispose();else{let[p,d]=Es(t.name,r);this.intermediateTensors[p]?this.intermediateTensors[p][d]=u:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=u)}delete o[u.id]}else c!=null&&o[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(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let a=new Cv(this.weightMap,r,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(u=>Vn(u,this.tensorsMap,a)),i=o.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let r=e.reduce((s,a,o)=>(s[this.inputs[o].name]=a,s),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let s=Object.keys(e),a=s.map(x=>this.graph.nodes[fr(x)[0]]),o=n.map(x=>fr(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:p}=Tv(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(x=>{let[A,b]=fr(x),v=[];v[b]=e[x],h[A]=v});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(a,d,t,h,g,m,o,f,l);await Promise.all(x)}c==null&&!r&&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=i.filter(x=>!$k(x)&&!Vn(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw c!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${s}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,n,r,s,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([p]=Es(c.node.name,n)),r[c.node.name]==null){let d=Iv(c.node,r,n,this._resourceManager);p||([p]=Es(c.node.name,n));let h=n.currentContext;w.isPromise(d)?u.push(d.then(f=>(r[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,l),f))):(r[p]=d,this.checkTensorForDisposal(p,c.node,r,n,a,o,i),this.processChildNodes(c.node,t,n,r,s,l))}else this.processChildNodes(c.node,t,n,r,s,l)}return 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t=Cn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Cn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Cn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function bj(e,t={}){if(e==null)throw 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this.set(t,this.pop()),n}},zk=class extends Mk{constructor(){super(zk.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},Lk=zk;Lk.INITIAL_CAPACITY=32;function Bk(e){return new $j(e)}function qx(e){return new Dj(e)}function Rj(e,t){return new Wk(e,t)}function _j(e,t=Vk.FAIL){return new Vj(e,t)}var An=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await 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this.upstream.next()}},Oj=class extends An{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Mj=class extends An{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},zj=class extends An{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;ne(e.value)}}},Lj=class extends An{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ds.getTensorsInContainer(e.value),n=this.transform(e.value),r=ds.getTensorsInContainer(n);for(let s of t)ds.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Bj=class extends An{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Nv=class extends An{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ds.getTensorsInContainer(e.value),n=await this.transform(e.value),r=ds.getTensorsInContainer(n);for(let s of t)ds.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Xx=class extends An{constructor(){super(),this.outputQueue=new Lk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wj=class extends Xx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await 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An{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(a){return a instanceof An?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let s=await Ok(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Uk=class extends An{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Mk(t)}summary(){return`${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()}},Uj=class extends Uk{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=kj.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},vc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===1/0||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),hr(async()=>(await n.iterator()).columnMajorBatch(e,t,jj),r)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,hr(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,hr(async()=>(await t.iterator()).filter(r=>K(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return hr(async()=>(await t.iterator()).map(n=>K(()=>e(n))),this.size)}mapAsync(e){let t=this;return hr(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return hr(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,hr(async()=>{let r=qx(async()=>({value:await t.iterator(),done:!1}));return Rj(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,hr(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,s=wj.alea(t||w.now().toString());return hr(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await r.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,hr(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};vc.MAX_BUFFER_SIZE=1e4;function hr(e,t=null){return new class extends vc{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function Gj(e){return hr(async()=>Bk(e),e.length)}function Hj(e){if(!$u(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return hr(async()=>{let n=await Ok(e,r=>{if(r instanceof vc)return{value:r.iterator(),recurse:!1};if($u(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return _j(n,1)},t)}function jj(e){if(e===null)return null;let t=e[0];return Cj(t)?{value:qj(e),recurse:!1}:{value:null,recurse:!0}}function qj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?cn(e):pt(e)}var Gk=class extends vc{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(r=>(r.endsWith("\r")&&(r=r.slice(0,-1)),r))}},Yh='"',bd=Symbol("out"),Ev=Symbol("field"),Jh=Symbol("quote"),lg=Symbol("quoteafterquote"),Rv=Symbol("quoteinquote"),Hk=class extends vc{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 Gk(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((r,s)=>(r[s]=r[s]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[s],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?r[a]=l:n[a]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,s=e.length,a=bd;for(let o=0;o<s;o++)switch(a){case bd:switch(e.charAt(o)){case Yh:r=o+1,a=Jh;break;case this.delimiter:if(r=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=bd;break;default:a=Ev,r=o;break}break;case Ev:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o)),a=bd,r=o+1;break;default:}break;case Jh:switch(e.charAt(o)){case Yh:a=lg;break;default:}break;case lg:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(r,o-1)),a=bd,r=o+1;break;case Yh:a=Jh;break;default:a=Rv;break}break;case Rv:switch(e.charAt(o)){case Yh:a=Jh;break;default:}break;default:}if(a===lg?n.push(e.substring(r,s-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},jk=class extends An{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(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new jk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),pt(n,t)}},qk=class extends An{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 n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=ps([a,s,i,o],[1,4])}else this.cropBox=ps([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().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 n=new qk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Mr.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Kt(ge(e,"float32"),0),n;n=Se.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Xk=class{},Kk=class extends An{split(e){return new Xj(this,e)}},Xj=class extends Kk{constructor(e,t){super(),this.upstream=e,this.impl=new Kj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Kj=class extends Xx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Zj=class extends An{decodeUTF8(){return new Yj(this)}},Yj=class extends Kk{constructor(e){super(),this.upstream=e,this.impl=new Jj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Jj=class extends Xx{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=b4();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Zk=class extends Zj{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},s.onabort=o=>n(new Error("Aborted")),s.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,r);s.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function Qj(e,t={},n){let r,s;typeof e=="string"?r=e:(r=e.url,s=eq(e));let a=await(n||w.fetch)(r,s);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new Zk(o,t)}else throw new Error(a.statusText)}var eq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Yk(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var Jk=class extends Xk{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(Yk(this.input)&&Y().get("IS_NODE")){let e=dy();this.input=e.readFileSync(this.input.slice(7))}return new Zk(this.input,this.options)}},Qk=class extends Xk{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return Yk(this.url)?new Jk(this.url,this.fileOptions).iterator():Qj(this.url,this.fileOptions)}};function tq(e,t={}){return new Hk(new Qk(e),t)}function nq(e){let t=qx(e);return hr(async()=>t)}function rq(e){return hr(async()=>{let t=await e();return qx(()=>t.next())})}async function sq(e,t){return qk.create(e,t)}async function aq(e){return jk.create(e)}var oq="0.0.0";function Ce(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var iq=Kr.whereImpl,eS=class extends Ou{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Zd(this,sn())}nextDataId(){return eS.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().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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mX={kernelName:Qa,backendName:"cpu",kernelFunc:GS};function gX(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=r,d,h,f,m=[];d=GS({inputs:{a:s,b:a},attrs:{transposeA:l,transposeB:u},backend:n}),o&&(h=Op({inputs:{a:d,b:o},backend:n}),m.push(d),d=h),c&&(f=s3(n,d,c,i,p),m.push(d),d=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return d}var yX={kernelName:Pa,backendName:"cpu",kernelFunc:gX},AX=gt(zu,e=>Math.acos(e)),xX={kernelName:zu,backendName:"cpu",kernelFunc:AX},bX=gt(Lu,e=>Math.acosh(e)),vX={kernelName:Lu,backendName:"cpu",kernelFunc:bX};function wX(e){let{inputs:t,backend:n}=e,r=t;Ce(t,"addN");let s=r.map(i=>n.data.get(i.dataId).values),a=We(r[0].shape,r[0].dtype),o=a.values;for(let i=0;i<r.length;i++){let l=s[i];for(let u=0;u<o.length;u++)o[u]+=l[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var kX={kernelName:Za,backendName:"cpu",kernelFunc:wX};function 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c=C.computePool3DInfo(a.shape,o,i,1,l,u),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,x=c.dilationHeight,A=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,S=c.effectiveFilterWidth,I=b-1-c.padInfo.front,E=S-1-c.padInfo.left,R=v-1-c.padInfo.top,P=We(a.shape,"float32"),_=1/(f*m*g),D=n.bufferSync(s);for(let T=0;T<c.batchSize;++T)for(let F=0;F<c.inChannels;++F)for(let U=0;U<c.inDepth;++U)for(let X=0;X<c.inHeight;++X)for(let z=0;z<c.inWidth;++z){let Z=U-I,W=X-R,ee=z-E,Q=0;for(let ae=0;ae<b;ae+=y){let J=(Z+ae)/p;if(!(J<0||J>=c.outDepth||Math.floor(J)!==J))for(let se=0;se<v;se+=x){let ie=(W+se)/d;if(!(ie<0||ie>=c.outHeight||Math.floor(ie)!==ie))for(let me=0;me<S;me+=A){let be=(ee+me)/h;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;Q+=D.get(T,J,ie,be,F)}}}P.set(Q*_,T,U,X,z,F)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var KX={kernelName:Jf,backendName:"cpu",kernelFunc:XX};function 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n.makeTensorInfo(s.shape,s.dtype,m)}var QX={kernelName:fo,backendName:"cpu",kernelFunc:JX};function eK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;Ce([s],"batchToSpaceND");let i=a.reduce((y,x)=>y*x),l=C.getReshaped(s.shape,a,i),u=C.getPermuted(l.length,a.length),c=C.getReshapedPermuted(s.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=$t({inputs:{x:s},backend:n,attrs:{shape:l}}),f=ar({inputs:{x:h},backend:n,attrs:{perm:u}}),m=$t({inputs:{x:f},backend:n,attrs:{shape:c}}),g=zi({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tK={kernelName:Hi,backendName:"cpu",kernelFunc:eK};function nK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,l=n.data.get(a.dataId).values,u=Yx(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var rK={kernelName:Qf,backendName:"cpu",kernelFunc:nK};function sK(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var aK={kernelName:em,backendName:"cpu",kernelFunc:sK},oK=gt(ta,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),iK={kernelName:ta,backendName:"cpu",kernelFunc:oK},lK=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(w.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];r[u]=Math.hypot(c,p)}return n.makeOutput(r,t.shape,"float32")},uK={kernelName:Qd,backendName:"cpu",kernelFunc:lK};function 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m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(x),A}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return $t({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=C.computeOutShape(u.map(m=>m.shape),1);let p=u[0].shape[0]===1,d=Jx(c,o,t[0].dtype,p),h=C.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var dK={kernelName:ji,backendName:"cpu",kernelFunc:Pu};function qS(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=r;Ce([s,a],"conv2d");let p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(s.shape,a.shape,o,u,i,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new on(d.outShape,s.dtype),v=w.computeStrides(s.shape),S=w.computeStrides(a.shape),I=v[0],E=A?v[1]:v[2],R=A?v[2]:1,P=A?1:v[1],_=b.strides[0],D=A?b.strides[1]:b.strides[2],T=A?b.strides[2]:1,F=A?1:b.strides[1],U=n.data.get(s.dataId).values,X=n.data.get(a.dataId).values,z=b.values;for(let Z=0;Z<d.batchSize;++Z){let W=Z*I,ee=Z*_;for(let Q=0;Q<d.outHeight;++Q){let ae=ee+Q*D,J=Q*d.strideHeight-x;for(let se=0;se<h;++se){let ie=J+se*m;if(ie<0||ie>=d.inHeight)continue;let me=se*S[0],be=W+ie*E;for(let Ee=0;Ee<d.outWidth;++Ee){let Re=ae+Ee*T,ze=Ee*d.strideWidth-y;for(let Be=0;Be<f;++Be){let nt=ze+Be*g;if(nt<0||nt>=d.inWidth)continue;let it=me+Be*S[1],lt=be+nt*R,ft=it;for(let $e=0;$e<d.inChannels;++$e){let wt=U[lt+$e*P];for(let At=0;At<d.outChannels;++At)z[Re+At*F]+=wt*X[ft+At];ft+=d.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var pK={kernelName:no,backendName:"cpu",kernelFunc:qS};function hK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=r;Ce([s,a],"conv2dBackpropFilter");let p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(s.shape,c,o,1,i,u,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,y=d.dataFormat==="channelsLast",x=new on(d.filterShape,"float32"),A=d.padInfo.left,b=d.padInfo.top,v=n.data.get(s.dataId).values,S=n.data.get(a.dataId).values,I=new on(s.shape,s.dtype,v),E=new on(a.shape,a.dtype,S);for(let R=0;R<m;++R){let P=Math.max(0,Math.ceil((b-R)/h)),_=Math.min(d.outHeight,(d.inHeight+b-R)/h);for(let D=0;D<g;++D){let T=Math.max(0,Math.ceil((A-D)/f)),F=Math.min(d.outWidth,(d.inWidth+A-D)/f);for(let U=0;U<d.inChannels;++U)for(let X=0;X<d.outChannels;++X){let z=0;for(let Z=0;Z<d.batchSize;++Z)for(let W=P;W<_;++W){let ee=R+W*h-b;for(let Q=T;Q<F;++Q){let ae=D+Q*f-A;y?z+=I.get(Z,ee,ae,U)*E.get(Z,W,Q,X):z+=I.get(Z,U,ee,ae)*E.get(Z,X,W,Q)}}x.set(z,R,D,U,X)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var fK={kernelName:tm,backendName:"cpu",kernelFunc:hK};function mK(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=r;Ce([s,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=C.convertConv2DDataFormat(u),f=C.computeConv2DInfo(o,a.shape,i,1,l,c,!1,h),m=new on(f.inShape,"float32"),g=m.values,y=n.data.get(s.dataId).values,x=n.data.get(a.dataId).values,[A,b,v]=p,{batchSize:S,filterHeight:I,filterWidth:E,inChannels:R,inHeight:P,inWidth:_,outChannels:D,outHeight:T,outWidth:F,strideHeight:U,strideWidth:X}=f;h=f.dataFormat;let z=I-1-f.padInfo.top,Z=E-1-f.padInfo.left,W=h==="channelsLast",ee=m.strides[0],Q=W?m.strides[1]:m.strides[2],ae=W?m.strides[2]:1,J=W?1:m.strides[1],se=d[0],ie=W?d[1]:d[2],me=W?d[2]:1,be=W?1:d[1];for(let Ee=0;Ee<S;++Ee)for(let Re=0;Re<R;++Re)for(let ze=0;ze<P;++ze){let Be=ze-z,nt=Math.max(0,Math.ceil(Be/U)),it=Math.min(T,(I+Be)/U);for(let lt=0;lt<_;++lt){let ft=lt-Z,$e=Math.max(0,Math.ceil(ft/X)),wt=Math.min(F,(E+ft)/X),At=0;for(let qt=nt;qt<it;++qt){let ur=qt*U-Be;for(let nn=$e;nn<wt;++nn){let Fn=nn*X-ft,cr=se*Ee+ie*qt+me*nn,dr=A*(I-1-ur)+b*(E-1-Fn)+v*Re;for(let Sn=0;Sn<D;++Sn){let Ir=y[cr+be*Sn],On=x[dr+Sn];At+=Ir*On}}}let Pn=ee*Ee+Q*ze+ae*lt+J*Re;g[Pn]=At}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var gK={kernelName:ro,backendName:"cpu",kernelFunc:mK};function yK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r;Ce([s,a],"conv3d");let u=C.computeConv3DInfo(s.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,x=g.left,A=g.top,b=new on(u.outShape,s.dtype),v=n.data.get(s.dataId).values,S=n.data.get(a.dataId).values,I=b.values,E=w.computeStrides(s.shape),R=w.computeStrides(a.shape);for(let P=0;P<u.batchSize;++P){let _=P*E[0],D=P*b.strides[0];for(let T=0;T<u.outDepth;++T){let F=D+T*b.strides[1],U=T*u.strideDepth-y;for(let X=0;X<c;++X){let z=U+X*h;if(z<0||z>=u.inDepth)continue;let Z=X*R[0],W=_+z*E[1];for(let ee=0;ee<u.outHeight;++ee){let Q=F+ee*b.strides[2],ae=ee*u.strideHeight-A;for(let J=0;J<p;++J){let se=ae+J*f;if(se<0||se>=u.inHeight)continue;let ie=Z+J*R[1],me=W+se*E[2];for(let be=0;be<u.outWidth;++be){let Ee=Q+be*u.outChannels,Re=be*u.strideWidth-x;for(let ze=0;ze<d;++ze){let Be=Re+ze*m;if(Be<0||Be>=u.inWidth)continue;let nt=ie+ze*R[2],it=me+Be*u.inChannels,lt=nt;for(let ft=0;ft<u.inChannels;++ft){let $e=v[it+ft];for(let wt=0;wt<u.outChannels;++wt)I[Ee+wt]+=$e*S[lt+wt];lt+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var AK={kernelName:ep,backendName:"cpu",kernelFunc:yK};function xK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:l}=r;Ce([s,a],"conv3dBackpropFilterV2");let u=w.computeStrides(s.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(s.shape,l,o,1,i),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,y=p.filterWidth,x=new on(p.filterShape,"float32"),A=x.values,[b,v,S,I]=x.strides,E=n.data.get(a.dataId).values,[R,P,_,D]=c,T=n.data.get(s.dataId).values,[F,U,X,z]=u,Z=p.padInfo.front,W=p.padInfo.left,ee=p.padInfo.top;for(let Q=0;Q<m;++Q){let ae=Math.max(0,Math.ceil((Z-Q)/d)),J=Math.min(p.outDepth,(p.inDepth+Z-Q)/d),se=Q*b;for(let ie=0;ie<g;++ie){let me=Math.max(0,Math.ceil((ee-ie)/h)),be=Math.min(p.outHeight,(p.inHeight+ee-ie)/h),Ee=ie*v+se;for(let Re=0;Re<y;++Re){let ze=Math.max(0,Math.ceil((W-Re)/f)),Be=Math.min(p.outWidth,(p.inWidth+W-Re)/f),nt=Re*S+Ee;for(let it=0;it<p.inChannels;++it){let lt=it*I+nt;for(let ft=0;ft<p.outChannels;++ft){let $e=0;for(let wt=0;wt<p.batchSize;++wt){let At=wt*F,Pn=wt*R;for(let qt=ae;qt<J;++qt){let nn=(Q+qt*d-Z)*U+At,Fn=qt*P+Pn;for(let cr=me;cr<be;++cr){let Sn=(ie+cr*h-ee)*X+nn,Ir=cr*_+Fn;for(let On=ze;On<Be;++On){let Gs=(Re+On*f-W)*z+Sn,Kl=On*D+Ir;$e+=T[Gs+it]*E[Kl+ft]}}}}A[lt+ft]=$e}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var bK={kernelName:nm,backendName:"cpu",kernelFunc:xK};function vK(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:l}=r;Ce([s],"conv3dBackpropInputV2");let u=w.computeStrides(s.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(l,a.shape,i,1,o),d=new on(p.inShape,"float32"),h=d.values,[f,m,g,y]=d.strides,x=n.data.get(s.dataId).values,[A,b,v,S]=u,I=n.data.get(a.dataId).values,[E,R,P,_]=c,{batchSize:D,filterDepth:T,filterHeight:F,filterWidth:U,inChannels:X,inDepth:z,inHeight:Z,inWidth:W,outChannels:ee,outDepth:Q,outHeight:ae,outWidth:J,strideDepth:se,strideHeight:ie,strideWidth:me}=p,be=T-1-p.padInfo.front,Ee=F-1-p.padInfo.top,Re=U-1-p.padInfo.left;for(let ze=0;ze<D;++ze)for(let Be=0;Be<X;++Be)for(let nt=0;nt<z;++nt){let it=nt-be,lt=Math.max(0,Math.ceil(it/se)),ft=Math.min(Q,(T+it)/se);for(let $e=0;$e<Z;++$e){let wt=$e-Ee,At=Math.max(0,Math.ceil(wt/ie)),Pn=Math.min(ae,(F+wt)/ie);for(let qt=0;qt<W;++qt){let ur=qt-Re,nn=Math.max(0,Math.ceil(ur/me)),Fn=Math.min(J,(U+ur)/me),cr=0;for(let dr=lt;dr<ft;++dr){let Sn=dr*se-it;for(let Ir=At;Ir<Pn;++Ir){let On=Ir*ie-wt;for(let Us=nn;Us<Fn;++Us){let Gs=Us*me-ur,Kl=A*ze+b*dr+v*Ir+S*Us,Aa=E*(T-1-Sn)+R*(F-1-On)+P*(U-1-Gs)+_*Be;for(let Hs=0;Hs<ee;++Hs){let nd=x[Kl+Hs],Zl=I[Aa+Hs];cr+=nd*Zl}}}}h[f*ze+m*nt+g*$e+y*qt+Be]=cr}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var wK={kernelName:rm,backendName:"cpu",kernelFunc:vK},kK=gt(so,e=>Math.cos(e)),SK={kernelName:so,backendName:"cpu",kernelFunc:kK},IK=gt(ao,e=>Math.cosh(e)),CK={kernelName:ao,backendName:"cpu",kernelFunc:IK};function TK(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=r,[c,p,d,h]=s.shape,f=a.shape[0],[m,g]=i,y=We([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,A=n.data.get(o.dataId).values,b=n.data.get(s.dataId).values,v=w.computeStrides(s.shape),S=w.computeStrides(y.shape);for(let I=0;I<f;I++){let E=I*4,R=x[E],P=x[E+1],_=x[E+2],D=x[E+3],T=A[I];if(T>=c)continue;let F=m>1?(_-R)*(p-1)/(m-1):0,U=g>1?(D-P)*(d-1)/(g-1):0;for(let X=0;X<m;X++){let z=m>1?R*(p-1)+X*F:.5*(R+_)*(p-1);if(z<0||z>p-1){for(let Z=0;Z<g;Z++)for(let W=0;W<h;W++){let ee=W+Z*S[2]+X*S[1]+I*S[0];y.values[ee]=u}continue}if(l==="bilinear"){let Z=Math.floor(z),W=Math.ceil(z),ee=z-Z;for(let Q=0;Q<g;Q++){let ae=g>1?P*(d-1)+Q*U:.5*(P+D)*(d-1);if(ae<0||ae>d-1){for(let me=0;me<h;me++){let be=me+Q*S[2]+X*S[1]+I*S[0];y.values[be]=u}continue}let J=Math.floor(ae),se=Math.ceil(ae),ie=ae-J;for(let me=0;me<h;me++){let be=me+J*v[2]+Z*v[1]+T*v[0],Ee=b[be];be=me+se*v[2]+Z*v[1]+T*v[0];let Re=b[be];be=me+J*v[2]+W*v[1]+T*v[0];let ze=b[be];be=me+se*v[2]+W*v[1]+T*v[0];let Be=b[be],nt=Ee+(Re-Ee)*ie,it=ze+(Be-ze)*ie;be=me+Q*S[2]+X*S[1]+I*S[0],y.values[be]=nt+(it-nt)*ee}}}else for(let Z=0;Z<g;++Z){let W=g>1?P*(d-1)+Z*U:.5*(P+D)*(d-1);if(W<0||W>d-1){for(let ae=0;ae<h;ae++){let J=ae+Z*S[2]+X*S[1]+I*S[0];y.values[J]=u}continue}let ee=Math.round(W),Q=Math.round(z);for(let ae=0;ae<h;ae++){let J=ae+ee*v[2]+Q*v[1]+T*v[0],se=ae+Z*S[2]+X*S[1]+I*S[0];y.values[se]=b[J]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var NK={kernelName:Xi,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;Ce(s,"cumprod");let l=C.getAxesPermutation([a],s.shape.length),u=s;l!=null&&(u=ar({inputs:{x:s},backend:n,attrs:{perm:l}}));let c=C.getInnerMostAxes(1,s.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=Nn(u.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(u.shape),p),h=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(y,x)=>y+f-x-1:(y,x)=>y+x;for(let y=0;y<h.length;y+=f)for(let x=0;x<f;x++){let A=m(y,x);if(x===0)d[A]=o?1:h[A];else{let b=m(y,x-1);d[A]=o?h[b]*d[b]:h[A]*d[b]}}let g=n.makeTensorInfo(u.shape,p,d);if(l!=null){let y=C.getUndoAxesPermutation(l),x=ar({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),x}return g}var RK={kernelName:qi,backendName:"cpu",kernelFunc:EK};function _K(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;Ce(s,"cumsum");let l=C.getAxesPermutation([a],s.shape.length),u=s;l!=null&&(u=ar({inputs:{x:s},backend:n,attrs:{perm:l}}));let c=C.getInnerMostAxes(1,s.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=Nn(u.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(u.shape),p),h=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(y,x)=>y+f-x-1:(y,x)=>y+x;for(let y=0;y<h.length;y+=f)for(let x=0;x<f;x++){let A=m(y,x);if(x===0)d[A]=o?0:h[A];else{let b=m(y,x-1);d[A]=o?h[b]+d[b]:h[A]+d[b]}}let g=n.makeTensorInfo(u.shape,p,d);if(l!=null){let y=C.getUndoAxesPermutation(l),x=ar({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),x}return g}var $K={kernelName:oo,backendName:"cpu",kernelFunc:_K};function DK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let l=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,c=Yx(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let l=n.bufferSync(s),u=n.bufferSync(a),c=rS(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var PK={kernelName:sm,backendName:"cpu",kernelFunc:DK};function FK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=s.shape[0],l=s.shape[1],u=s.shape[2],c=s.shape[3],p=l*a,d=u*a,h=c/(a*a),f=n.data.get(s.dataId).values,m=new Float32Array(i*p*d*h),g=0;for(let y=0;y<i;++y)for(let x=0;x<p;++x){let A=Math.floor(x/a),b=x%a;for(let v=0;v<d;++v){let S=Math.floor(v/a),I=v%a,E=(b*a+I)*h;for(let R=0;R<h;++R){let _=R+E+c*(S+u*(A+l*y));m[g++]=f[_]}}}return n.makeTensorInfo([i,p,d,h],s.dtype,m)}var OK={kernelName:Ki,backendName:"cpu",kernelFunc:FK};function XS(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=r;Ce([s,a],"depthwiseConv2DNative");let c=w.computeStrides(s.shape),p=w.computeStrides(a.shape),d=l;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);let h=C.computeConv2DInfo(s.shape,a.shape,o,d,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,v=h.outChannels/h.inChannels,S=new on(h.outShape,s.dtype),I=n.data.get(s.dataId).values,E=n.data.get(a.dataId).values,R=S.values;for(let P=0;P<h.batchSize;++P){let _=P*c[0],D=P*S.strides[0];for(let T=0;T<h.outHeight;++T){let F=D+T*S.strides[1],U=T*h.strideHeight-b;for(let X=0;X<f;++X){let z=U+X*g;if(z<0||z>=h.inHeight)continue;let Z=X*p[0],W=_+z*c[1];for(let ee=0;ee<h.outWidth;++ee){let Q=F+ee*S.strides[2],ae=ee*h.strideWidth-A;for(let J=0;J<m;++J){let se=ae+J*y;if(se<0||se>=h.inWidth)continue;let ie=Z+J*p[1],me=W+se*h.inChannels,be=Q,Ee=ie;for(let Re=0;Re<h.inChannels;++Re){let ze=I[me+Re];for(let Be=0;Be<v;++Be)R[be+Be]+=ze*E[Ee+Be];be+=v,Ee+=v}}}}}}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var MK={kernelName:io,backendName:"cpu",kernelFunc:XS};function zK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=r;Ce([s,a],"depthwiseConv2dNativeBackpropFilter");let p=C.computeConv2DInfo(s.shape,c,o,i,l,u,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new on(p.filterShape,"float32"),y=p.padInfo.left,x=p.padInfo.top,A=p.outChannels/p.inChannels,b=n.data.get(s.dataId).values,v=new on(s.shape,s.dtype,b),S=n.data.get(a.dataId).values,I=new on(a.shape,a.dtype,S);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((x-E)/d)),P=Math.min(p.outHeight,(p.inHeight+x-E)/d);for(let _=0;_<m;++_){let D=Math.max(0,Math.ceil((y-_)/h)),T=Math.min(p.outWidth,(p.inWidth+y-_)/h);for(let F=0;F<p.outChannels;++F){let U=Math.trunc(F/A),X=F%A,z=0;for(let Z=0;Z<p.batchSize;++Z)for(let W=R;W<P;++W){let ee=E+W*d-x;for(let Q=D;Q<T;++Q){let ae=_+Q*h-y;z+=v.get(Z,ee,ae,U)*I.get(Z,W,Q,F)}}g.set(z,E,_,U,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var 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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),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#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",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=`
#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)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Ml(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function w0(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function TQ(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function NQ(e,t,n="index"){let r=e.map((a,o)=>o),s=TQ(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,l=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${l};`}).join("")}function c3(e){let t=w.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function d3(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var TI=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:NI}=C;function EQ(e,t,n){let r=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=p3(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:r.push(`uniform int ${h.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}r.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break;default:break}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{r.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let s=r.join(`
`),a=e.map(h=>RQ(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Kn(),l=DQ(i),u,c,p=OQ(i);return t.isPacked?(u=_Q(t.logicalShape,o,n.enableShapeUniforms),c=FQ(i)):(u=$Q(t.logicalShape,o,n.enableShapeUniforms),c=PQ(i)),n.packedInputs&&(p+=BQ),[p,l,c,s,u,a,n.userCode].join(`
`)}function Ic(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return JQ(e,t);case 1:return eee(e,t);case 2:return nee(e,t);case 3:return see(e,t);case 4:return oee(e,t);case 5:return iee(e);case 6:return lee(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function EI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return YQ(e);case 1:return QQ(e,t);case 2:return tee(e,t);case 3:return ree(e,t);default:return aee(e,t)}}function RQ(e,t,n=!1,r){let s="";n?s+=EI(e,r):s+=Ic(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=uee(e,t):s+=cee(e,t)),s}function _Q(e,t,n){switch(e.length){case 0:return RI();case 1:return WQ(e,t,n);case 2:return KQ(e,t,n);case 3:return UQ(e,t,n);default:return HQ(e,t,n)}}function $Q(e,t,n){switch(e.length){case 0:return RI();case 1:return VQ(e,t,n);case 2:return ZQ(e,t,n);case 3:return GQ(e,t,n);case 4:return jQ(e,t,n);case 5:return qQ(e,t);case 6:return XQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function DQ(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function PQ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function FQ(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function OQ(e){return`${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);
}
${MQ}
${zQ}
${LQ}
`}var MQ=`
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);
}
`,zQ=`
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);
}
`,LQ=`
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);
}
`,BQ=`
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 RI(){return`
int getOutputCoords() {
return 0;
}
`}function WQ(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${r[1]}.0);
}
`:r[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${r[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
}
`}function VQ(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function UQ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function GQ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${w0(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let r=Ml(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec3(r, c, d);
}
`}function HQ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),a=s*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
int b${u} = index / ${o};
index -= b${u} * ${o};
`+i,l=`b${u}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${e.length}(${l});
}
`}function jQ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${w0(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let r=Ml(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec4(r, c, d, d2);
}
`}function qQ(e,t){let n=Ml(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function XQ(e,t){let n=Ml(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function KQ(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));
}
`;let s=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function ZQ(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function zl(e){return`offset${e}`}function YQ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Kn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function JQ(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[s,a]=e.shapeInfo.texShape;if(s===1&&a===1)return`
float ${r}() {
return sampleTexture(${n}, halfCR);
}
`;let o=zl(n);if(t)return`
float ${r}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,l]=e.shapeInfo.texShape;return`
float ${r}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
return sampleTexture(${n}, uv);
}
`}function QQ(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=Kn();if(t)return`
vec4 ${r}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
vec4 ${r}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function eee(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${r}(int index) {
${Cc(e)}
}
`;let s=e.shapeInfo.texShape,a=s[0],o=s[1];if(o===1&&a===1)return`
float ${r}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=zl(n);return o===1?t?`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${r}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function tee(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Kn();if(a!=null&&w.arraysEqual(n,a))return t?`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return ${l.texture2D}(${r}, uv);
}
`:`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${l.texture2D}(${r}, uv);
}
`;if(t)return`
vec4 ${s}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${r}, uv);
}
`;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${r}, uv);
}
`}function nee(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`;let d=a[0],h=a[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`}let{newShape:o,keptDims:i}=w.squeezeShape(n),l=o;if(l.length<n.length){let d=Tc(e,l),h=["row","col"];return`
${Ic(d,t)}
float ${s}(int row, int col) {
return ${s}(${Nc(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${Cc(e)}
}
`;let u=a[0],c=a[1],p=zl(r);return c===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${r}, uv);
}
`:u===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:t?`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${p};
vec2 uv = uvFromFlat(${u}, ${c}, index);
return sampleTexture(${r}, uv);
}
`}function ree(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=Tc(e,d),m=["b","row","col"];return`
${EI(f,t)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Nc(m,h)});
}
`}let i=Kn();if(t)return`
vec4 ${s}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`;let l=o[0],u=o[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${p}, ${c}, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`}function see(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=Tc(e,u),g=["row","col","depth"];return`
${Ic(m,t)}
float ${s}(int row, int col, int depth) {
return ${s}(${Nc(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${Cc(e)}
}
`;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
float ${s}(int row, int col, int depth) {
int stride1 = ${r}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(d===o&&h==null)return t?`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;let f=zl(r);return t?`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${r}Shape[1] * ${r}Shape[2];
int stride1 = ${r}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${p}, ${d}, index);
return sampleTexture(${r}, uv);
}
`}function aee(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=Kn();if(t)return`
vec4 ${r}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${s.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,d*=a[o-m-1],f=`b${m} * ${d} + `+f;return`
vec4 ${r}(${h}) {
int index = ${f};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
return ${s.texture2D}(${n}, uv);
}
`}function oee(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(n);if(l.length<n.length){let x=Tc(e,l),A=["row","col","depth","depth2"];return`
${Ic(x,t)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Nc(A,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${Cc(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;if(h===a&&c==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;let y=zl(r);return t?`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${y});
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${d}, ${h}, index + ${y});
return sampleTexture(${r}, uv);
}
`}function iee(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=Tc(e,l),g=["row","col","depth","depth2","depth3"];return`
${Ic(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Nc(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${s})) +
depth3;
${Cc(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&c==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let f=zl(n);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${s} + depth3 + ${f};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function lee(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=w.squeezeShape(t);if(s.length<t.length){let g=Tc(e,s),y=["row","col","depth","depth2","depth3","depth4"];return`
${Ic(g)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Nc(y,a)});
}
`}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${Cc(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&p==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=zl(n);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function Cc(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function uee(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=NI(e.shapeInfo.logicalShape,t.logicalShape),l=yt(o),u=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(x=>`coords.${p[x+u]} = 0;`).join(`
`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((x,A)=>`coords.${p[A+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,y=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!y)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let x=a-2,A=a-1;i.indexOf(x)>-1&&i.indexOf(A)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(A)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${s}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${r}(${d});
${h}
}
`}function cee(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
float ${s}() {
return sampleTexture(${n}, resultUV);
}
`;let u=yt(l),c=NI(e.shapeInfo.logicalShape,t.logicalShape),p=l-i,d,h=["x","y","z","w","u","v"];i===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
float ${s}() {
${u} coords = getOutputCoords();
${d}
return get${r}(${f});
}
`}function yt(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 ${e} is not yet supported`)}function p3(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,l=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:s}}function Tc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Nc(e,t){return t.map(n=>e[n]).join(", ")}function dee(e,t,n,r){let s=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=s.map(c=>c.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=EQ(s,o,t),l=oI(e.gl,i),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,..._I(e,t,u)}}function _I(e,t,n){let r={},s={},a={},o=[],i,l,u,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];r[f]=e.getUniformLocation(n,f,d),r[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(s[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),u=e.getUniformLocation(n,"outShapeStrides",d),l=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:r,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:s,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}}function Dv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!w.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function pee(e,t,n,r,s){t.program.enableShapeUniforms||(Dv(t.inShapeInfos,n),Dv([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=p3(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,p,u)}});let i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(r.shape);switch(r.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,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],p=s[u];if(l.type==="float")e.gl.uniform1fv(c,p);else if(l.type==="vec2")e.gl.uniform2fv(c,p);else if(l.type==="vec3")e.gl.uniform3fv(c,p);else if(l.type==="vec4")e.gl.uniform4fv(c,p);else if(l.type==="int")e.gl.uniform1iv(c,p);else if(l.type==="ivec2")e.gl.uniform2iv(c,p);else if(l.type==="ivec3")e.gl.uniform3iv(c,p);else if(l.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function hee(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=p3(e.packedInputs,o.shape,l),d="",h="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),y=w.sizeFromShape(o.shape)===1,x=C.getBroadcastDims(o.shape,n.shape),A=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;r+=`${m}_${A}_${u?p:""}_${c.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${l}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${Y().getNumber("WEBGL_VERSION")}`,a}function lr(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var fee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Kn();this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?w0(["r","c","d"],e):Ml(["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;
}
`}},mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Kn();this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?w0(["r","c","d"],e):Ml(["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;
}
`}},gee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Kn();this.outputShape=e,this.userCode=`
${TI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},yee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Kn();this.outputShape=e,this.userCode=`
${TI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},Aee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Kn();this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?d3():c3(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${r}, 0., 0., 0.);
}
`}},xee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Kn();this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?d3():c3(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${r}
${n.output} = ${s};
}
`}},$I={};Le($I,{bindVertexProgramAttributeStreams:()=>WI,createBufferFromOutputTexture:()=>GI,createFloat16MatrixTexture:()=>MI,createFloat16PackedMatrixTexture:()=>BI,createFloat32MatrixTexture:()=>OI,createIndexBuffer:()=>FI,createPackedMatrixTexture:()=>LI,createUnsignedBytesMatrixTexture:()=>zI,createVertexBuffer:()=>PI,createVertexShader:()=>DI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>jI,downloadFloat32MatrixFromBuffer:()=>HI,downloadMatrixFromPackedOutputTexture:()=>XI,downloadPackedMatrixFromBuffer:()=>qI,getInternalFormatForFloat16MatrixTexture:()=>f3,getInternalFormatForFloat16PackedMatrixTexture:()=>y3,getInternalFormatForFloat32MatrixTexture:()=>h3,getInternalFormatForPackedMatrixTexture:()=>g3,getInternalFormatForUnsignedBytesMatrixTexture:()=>m3,uploadDenseMatrixToTexture:()=>VI,uploadPixelDataToTexture:()=>UI});function DI(e){let t=Kn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return aI(e,n)}function PI(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 uI(e,t)}function FI(e){let t=new Uint16Array([0,1,2,2,1,3]);return cI(e,t)}function Lp(e,t,n,r,s,a){pI(t,n);let o=dI(e),i=e.TEXTURE_2D;return we(e,()=>e.bindTexture(i,o)),we(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)):we(e,()=>e.texStorage2D(i,1,r,t,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function h3(e){return e.internalFormatFloat}function OI(e,t,n,r){let[s,a]=zp(t,n);return Lp(e,s,a,h3(r),r.textureFormatFloat,e.FLOAT)}function f3(e){return e.internalFormatHalfFloat}function MI(e,t,n,r){let[s,a]=zp(t,n);return 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t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,v0(t,e)):this.gl=As(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),Y().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Nd(this.gl,s),Er(this.gl,a))this.textureHalfFloatExtension=Nd(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Er(this.gl,r))this.colorBufferHalfFloatExtension=Nd(this.gl,r);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Er(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Er(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=PI(this.gl),this.indexBuffer=FI(this.gl),this.framebuffer=hI(this.gl),this.textureConfig=l3(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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t-1}var{addImpl:vee,bincountImpl:KI,bincountReduceImpl:wee,ceilImpl:kee,concatImpl:See,equalImpl:Iee,expImpl:Cee,expm1Impl:Tee,floorImpl:Nee,gatherNdImpl:Eee,gatherV2Impl:Ree,greaterImpl:_ee,greaterEqualImpl:$ee,lessImpl:Dee,lessEqualImpl:Pee,linSpaceImpl:Fee,logImpl:Oee,maxImpl:Mee,maximumImpl:zee,minimumImpl:Lee,multiplyImpl:Bee,negImpl:Wee,notEqualImpl:Vee,prodImpl:Uee,rangeImpl:Gee,rsqrtImpl:Hee,sigmoidImpl:jee,simpleAbsImpl:ZI,sliceImpl:qee,sparseFillEmptyRowsImpl:Xee,sparseReshapeImpl:Kee,sparseSegmentReductionImpl:YI,sqrtImpl:Zee,stridedSliceImpl:Yee,stringNGramsImpl:Jee,stringSplitImpl:Qee,stringToHashBucketFastImpl:ete,subImpl:tte,tileImpl:nte,topKImpl:rte,transposeImpl:A3,uniqueImpl:ste}=x0;function JI(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Un(e,t){return t===1?[e]:JI(e,t)}function ate(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var ote=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=lr(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Un("rc",this.rank),n=yt(this.rank),r=this.getOutOfBoundsCondition(t),s=this.getSetup(t),a=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${a}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let s=`${n===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],r=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${r};
`}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]})`}},QI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length);let n="";for(let r=0;r<4;r++){let s="thisRC = rc;";r%2===1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=`
${s}
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${r}] =
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${r>0?"}":""}
`}this.userCode=`
${ite(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?d3():c3(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function ite(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?NQ(["r","c","d"],"inputShape"):Ml(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var lte=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Fv(t,n),s=Ov(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=Pv(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===3?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===4?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===1?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===0?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===2&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=Fv(n,r),a=Ov(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=Pv(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.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 ute(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Pv(e,t,n,r,s){let a=cte(t,r),o;if(s){let[l,u]=kc(e[0],e[1]);o=l*u}else{let[l,u]=zp(e[0],e[1]);o=l*u}let i=ute(n,a);return o*i}function cte(e,t){switch(e){case 3:return g3(t);case 4:return y3(t);case 1:return h3(t);case 0:return f3(t);case 2:return m3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function dte(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function Fv(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 ${e}`)}function Ov(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Zs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Yr="if (isnan(x)) return x;",pte="return x;",Mv="return abs(x);",hte="return (x >= 0.0) ? x : (exp(x) - 1.0);",fte=Yr+`
return (x < 0.0) ? 0.0 : x;
`,mte=Yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,cu="return x;",gte="return 1.0 / (1.0 + exp(-1.0 * x));",yte="return x;",Ate=`
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;
`,xte=`
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;
`,bte=`
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;
`,vte="return 1.0 / (1.0 + exp(-1.0 * x));",Ci=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},wte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lr(this.outputShape.length);let t=e.length,n=Un("rc",t),r=yt(t),s=ate(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${o}));
}
`}},kte=Kr.whereImpl,Ste=1e-7,Ite=1e-4,tf={};function Cte(e){return e in tf||(tf[e]={}),tf[e]}var Tte=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Nte=600;function Ete(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*Nte/1024/1024}var e8=class extends Ou{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,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof vu)t=e;else{let n=As(Y().getNumber("WEBGL_VERSION"),e);t=new vu(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=As(Y().getNumber("WEBGL_VERSION"));t=new vu(n),this.binaryCache=Cte(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new lte(this.gpgpu),this.numMBBeforeWarning=Ete(),this.texData=new Zd(this,sn())}nextDataId(){return e8.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:1,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,s){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:1,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new Ci(o,cu):p=new Zs(o,cu);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:r}],r),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(r==="complex64"){let p=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let h;i?h=new Ci(r,cu):h=new Zs(r,cu);let f=this.runWebGLProgram(h,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Qh(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;we(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sn().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:r,shape:s,slice:a,dtype:o,isPacked:i,texture:l}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new Ci(s,cu):d=new Zs(s,cu);let h=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),c=sn().makeTensorFromDataId(u.dataId,u.shape,u.dtype),p=this.texData.get(u.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!rI(n))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Qh(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(p),h}let a=Y().getBool("WEBGL_PACK")&&r===!0,o=a?cf(t):t,i=a?new yee(o):new gee(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Tte){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return kte(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ci(e.shape,t),s=this.compileAndRun(r,[e],n);return sn().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=ZI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Mv,e.dtype);let t=new Zs(e.shape,Mv),n=this.compileAndRun(t,[e]);return sn().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return sn().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new wte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ote(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Li(e.shape),...Bi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Li(t),...Bi(t)],a=new QI(s,n),o=!0,i=[n],l=this.runWebGLProgram(a,[r],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:r,shape:s,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(s),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=cf(s),i;r?i=new mee(o):i=new fee(o);let l=!0,u=[t!=null?t:Qh(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,u,l,t);return{dtype:a,shape:s,dataId:c.dataId}}runWebGLProgram(e,t,n,r,s=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Qh(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(o.shape)===0)return i.values=w.getTypedArrayFromDType(o.dtype,0),o;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)<=Y().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&&!Xd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=hee(e,u,c),d=this.getAndSaveBinary(p,()=>dee(this.gpgpu,e,u,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||pee(this.gpgpu,d,u,c,r),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Ste:Ite}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=bI(n,i),t.texShape=c),s!=null){let p=cf(n),d,h=c[1],f=c[0],m=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(i||!m)&&([h,f]=kc(c[0],c[1])),i?d=new xee(p,m):d=new Aee(p,m);let g=m?[f,h]:c,y=this.makeTensorInfo(g,r),x=this.texData.get(y.dataId);m?x.usage=2:x.usage=1,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,s);let A=[[f,h]],b=!0,v=this.runWebGLProgram(d,[y],r,A,b),S=this.texData.get(v.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,Y().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 p=this.acquireTexture(c,o,r,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=Rte(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}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 n=new Promise(r=>{try{this.checkCompletion_(t),r(!0)}catch(s){throw s}});e.push(n)}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?(u3(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:n,infLoc:r,nanLoc:s,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}=_I(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Bp=e8;Bp.nextDataId=0;function Rte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var _te="0.0.0";function t8(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}yp.isBrowser()&&Rl("webgl",()=>new Bp,2);var $te={forceHalfFloat:t8},n8=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Fu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=lr(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},k0=`
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;
`,Wp=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=lr(s);let a="";if(r)if(s===0||w.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${yt(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Un("coords",s);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${i[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${i[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function or(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Dte={kernelName:go,backendName:"webgl",kernelFunc:or};function Xo(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=or({inputs:{x:r},backend:n}),l=or({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Pte={kernelName:Jd,backendName:"webgl",kernelFunc:Xo},r8="return (a < 0.) ? b * a : a;",s8=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Fte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Wp(s8,s.shape,o.shape):new Fu(r8,s.shape,o.shape),l=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var Ote={kernelName:yo,backendName:"webgl",kernelFunc:Fte},a8="return (a < 0.) ? b * a : a;",o8=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Mte(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Wp(o8,r.shape,s.shape):new Fu(a8,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var zte={kernelName:Eo,backendName:"webgl",kernelFunc:Mte},Ec="if (isnan(x)) return x;",Lte=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Bte=`
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 ot({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,l=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,l);return i.makeTensorInfo(o.shape,l,d)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Ci(o.shape,t):c=new Zs(o.shape,e),i.runWebGLProgram(c,[o],l)}}function vn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,v]=A,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Fu(e,l.shape,u.shape);return c.runWebGLProgram(E,[S,I],Nn(b.dtype,v.dtype))}),x=Xo({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),x}let p=a||Nn(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&s!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(f):f,y=l.dtype==="string"?C.fromUint8ToStringArray(m):m,[x,A]=s(l.shape,u.shape,g,y,p),b=c.makeTensorInfo(A,p),v=c.texData.get(b.dataId);return v.values=x,b}let d=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Wp(t,l.shape,u.shape,n):h=new Fu(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],p)}}function S0(e,t=!1){if(e==="linear")return t?yte:pte;if(e==="relu")return t?xte:fte;if(e==="elu")return t?Ate:hte;if(e==="relu6")return t?bte:mte;if(e==="prelu")return t?o8:a8;if(e==="leakyrelu")return t?s8:r8;if(e==="sigmoid")return t?vte:gte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var i8=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=lr(this.outputShape.length);let u=r?e[1]:e[2],c=Math.ceil(u/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",A="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(A=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${c}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${c}; i++) {
int batchA = ${x};
int batchB = ${A};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},zv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Lv=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},Bv="return a * b;";function x3(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=C.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),l=n.texData.get(s.dataId),u=new Lv(zv.REAL,r.shape,s.shape),c=new Lv(zv.IMAG,r.shape,s.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:s.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:s.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Xo({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),l=n.texData.get(s.dataId),[u,c]=Bee(r.shape,s.shape,i.values,l.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=u,p}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Wp(Bv,r.shape,s.shape):o=new Fu(Bv,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var Wte={kernelName:Co,backendName:"webgl",kernelFunc:x3};function Vte(e,t,n){let r=[Li(e.shape),...Bi(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Li(t),...Bi(t)],o=new QI(a,r),i=!0,l=[r],u=n.runWebGLProgram(o,[s],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(s.dataId);return c.isPacked&&!Xd(s.shape,l)&&!(c.texture!==null&&Xd(c.shape,l))?Vte(s,l,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:l,dtype:s.dtype})}var Ute={kernelName:hl,backendName:"webgl",kernelFunc:ve},Wv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";s%n>0&&(u=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${u}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; 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 + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},Gte=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,p=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,d="vec4";t==="all"?(o="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(o="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
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;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${u};
if (${c===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${c===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${c===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function Hte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ll(e,t,n,r){let s=Hte(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:l,outSize:u}=s[o],c,p;n==="mean"?c=o===0?new Wv({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new Wv({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new Gte({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),p=a,a=r.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(p)}return a}var jte=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=yt(this.rank),s=qte(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function qte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var Xte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=yt(this.rank),s=JI("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=s[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${i}) {
result[1] = ${l};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${i}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function I0(e,t,n){let r=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xte(e.shape,t):new jte(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function Kte(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,l=C.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=I0(e,l,r),i=C.getInnerMostAxes(i.length,a)),C.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,y=ve({inputs:{x:c},attrs:{shape:[g,f]},backend:r}),x=gp(e.dtype),A=Ll(y,x,"sum",r),b=ve({inputs:{x:A},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(A),u&&r.disposeIntermediateTensorInfo(c),b}function C0(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return Kte(s,a,o,n)}var Zte={kernelName:zo,backendName:"webgl",kernelFunc:C0};function bn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=s.shape[a[c]];let u;if(o.shouldExecuteOnCPU([s])){let p=o.texData.get(s.dataId).values,d=A3(p,s.shape,s.dtype,a,l);u=o.makeTensorInfo(l,s.dtype);let h=o.texData.get(u.dataId);h.values=d}else u=I0(s,a,o);return u}var Yte={kernelName:Uo,backendName:"webgl",kernelFunc:bn},l8=1e3;function Uf({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),x=w.sizeFromShape(g),b=El.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let v=n?[y,p,h]:[y,h,p],S=r?[x,f,d]:[x,d,f],I=ve({inputs:{x:e},backend:s,attrs:{shape:v}}),E=ve({inputs:{x:t},backend:s,attrs:{shape:S}}),R=[I,E],P=Math.max(y,x),_=n?I.shape[1]:I.shape[2],D=a!=null,T=o!=null,F=l==="leakyrelu",U=l!=null?S0(l,!0):null,X=D||T||F||U!=null,z;if((h===1||f===1)&&_>l8&&X===!1){let W=I,ee=E;n&&(W=bn({inputs:{x:I},backend:s,attrs:{perm:[0,2,1]}}),R.push(W)),r&&(ee=bn({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),R.push(ee));let Q=f!==1,ae=f===1,J=W;Q&&(J=ve({inputs:{x:W},backend:s,attrs:{shape:[P,_,1]}}),R.push(J));let se=f===1?2:1,ie=ee;ae&&(ie=ve({inputs:{x:ee},backend:s,attrs:{shape:[P,1,_]}}),R.push(ie));let me=x3({inputs:{a:J,b:ie},backend:s});z=C0({inputs:{x:me},backend:s,attrs:{axis:se,keepDims:!0}}),R.push(me)}else{let W=Nn(e.dtype,t.dtype),ee=new i8(v,S,[P,h,f],n,r,D,U,T,F),Q=[I,E];if(a!=null&&Q.push(a),T&&Q.push(o),F){let ae=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));Q.push(ae),R.push(ae)}z=s.runWebGLProgram(ee,Q,W)}let Z=ve({inputs:{x:z},backend:s,attrs:{shape:b}});R.push(z);for(let W of R)s.disposeIntermediateTensorInfo(W);return Z}function Jte(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=r;return Uf({a:s,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var Qte={kernelName:Pa,backendName:"webgl",kernelFunc:Jte},Vv="return abs(x);";function ene(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=ZI(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Ci(r.shape,Vv):s=new Zs(r.shape,Vv),n.runWebGLProgram(s,[r],r.dtype)}var tne={kernelName:Gi,backendName:"webgl",kernelFunc:ene},nne=Yr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,rne=ot({opSnippet:nne}),sne={kernelName:zu,backendName:"webgl",kernelFunc:rne},ane=Yr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,one=ot({opSnippet:ane}),ine={kernelName:Lu,backendName:"webgl",kernelFunc:one},Uv="return a + b;",lne=vn({opSnippet:Uv,packedOpSnippet:Uv,supportsComplex:!0,cpuKernelImpl:vee}),une={kernelName:ea,backendName:"webgl",kernelFunc:lne},cne=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},dne=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function hf(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return or({inputs:{x:r[0]},backend:n});if(r.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(r.length/2),u=hf({inputs:r.slice(0,l),backend:n}),c=hf({inputs:r.slice(l),backend:n});return hf({inputs:[u,c],backend:n})}let s=r.map(l=>l.dtype).reduce((l,u)=>Nn(l,u)),a=r.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new dne(r[0].shape,a):new cne(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var pne={kernelName:Za,backendName:"webgl",kernelFunc:hf};function hne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=C.getAxesPermutation(u,i),p=s;c!=null&&(p=bn({inputs:{x:s},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("all",u,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ll(m,m.dtype,"all",n),y;if(o){let x=C.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var fne={kernelName:Bu,backendName:"webgl",kernelFunc:hne};function mne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=C.getAxesPermutation(u,i),p=s;c!=null&&(p=bn({inputs:{x:s},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("any",u,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ll(m,m.dtype,"any",n),y;if(o){let x=C.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var gne={kernelName:Wu,backendName:"webgl",kernelFunc:mne},yne=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},Ane=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=yt(i),u=Un("coords",i),c,p;if(a===1){p=i+1;let I=yt(p);c=`
${I} sourceLocR = ${I}(${u.join()}, 0);
++${u[i-1]};
${I} sourceLocG = ${I}(${u.join()}, 0);
++${u[i-2]};
${I} sourceLocA = ${I}(${u.join()}, 0);
--${u[i-1]};
${I} sourceLocB = ${I}(${u.join()}, 0);
--${u[i-2]};`}else p=i,c=`
${l} sourceLocR = coords;
++${u[i-1]};
${l} sourceLocG = coords;
++${u[i-2]};
${l} sourceLocA = coords;
--${u[i-1]};
${l} sourceLocB = coords;
--${u[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map(I=>"int "+I),m=Un("sourceLocR",p-1).concat("inIdx.r"),g=Un("sourceLocG",p-1).concat("inIdx.g"),y=Un("sourceLocB",p-1).concat("inIdx.b"),x=Un("sourceLocA",p-1).concat("inIdx.a"),A=n==="max"?"greaterThan":"lessThan",b=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${x.join()})));`,v=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,S=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${S}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
${c}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${v};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${v};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${A}(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 u8(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=C.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},l=new yne(i,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=u8(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function c8(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=C.computeOptimalWindowSize(a),i=new Ane(s,o,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=c8(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function d8(e,t,n,r){let s=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=C.computeOutAndReduceShapes(l.shape,s),p=w.sizeFromShape(c),d=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=u8(e,d,r);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return c8(e,t,r)}function xne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=C.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=bn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=C.getInnerMostAxes(o.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=d8(n,l,o[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var bne={kernelName:Ya,backendName:"webgl",kernelFunc:xne};function vne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=C.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=bn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=C.getInnerMostAxes(o.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=d8(n,l,o[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var wne={kernelName:Vu,backendName:"webgl",kernelFunc:vne},kne=Yr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,Sne=ot({opSnippet:kne}),Ine={kernelName:Uu,backendName:"webgl",kernelFunc:Sne},Cne=Yr+"return log(x + sqrt(x * x + 1.0));",Tne=ot({opSnippet:Cne}),Nne={kernelName:Gu,backendName:"webgl",kernelFunc:Tne},Ene=Yr+`
return atan(x);
`,Rne=ot({opSnippet:Ene}),_ne={kernelName:Hu,backendName:"webgl",kernelFunc:Rne},$ne=Lte+`
return atan(a, b);
`,Dne=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bte+`
return result;
`,Pne=vn({opSnippet:$ne,packedOpSnippet:Dne}),Fne={kernelName:qu,backendName:"webgl",kernelFunc:Pne},One=Yr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Mne=ot({opSnippet:One}),zne={kernelName:ju,backendName:"webgl",kernelFunc:Mne},Kd=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let I=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${d}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${c};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${u}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${I} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?s?m:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,S=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${c};
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
);
${S}
}
}
setOutput(${A});
}
`}},b3=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${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 < ${d};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${p}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let S=Math.floor(a/4)*4,I=a%4,E=`
if (${x}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${y});
const float initializationValue = ${A};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${A});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${S}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${E}
}
int xC = xCCorner + ${S};
if (${I===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${I===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${I===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${E}
}
}
setOutput(${v});
}
}
`}};function Lne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Sc(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1;w.assert(C.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=C.computePool2DInfo(s.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return or({inputs:{x:s},backend:n});let p=new Kd(c,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var Bne={kernelName:Ja,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],p=C.computePool3DInfo(s.shape,a,o,c,i,l,u),d=new b3(p,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var Vne={kernelName:Yd,backendName:"webgl",kernelFunc:Wne},Une=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${c});
const float avgMultiplier = float(${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${a}) {
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 < ${l};
wC+= ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Gne=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${c};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${u}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function Hne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=r,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,l,p,u,c),h=new Gne(d);return n.runWebGLProgram(h,[s],o.dtype)}var jne={kernelName:Jf,backendName:"webgl",kernelFunc:Hne};function qne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Sc([s,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=r,c=C.computePool2DInfo(o.shape,i,l,1,u),p=new Une(c);return n.runWebGLProgram(p,[s],o.dtype)}var Xne={kernelName:Yf,backendName:"webgl",kernelFunc:qne};function Kne(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Uf({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var Zne={kernelName:Qa,backendName:"webgl",kernelFunc:Kne},Yne=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},Jne=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},Qne=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,s,a],c=null;o!=null&&(c=o.shape,u.push(o));let p=null;i!=null&&(p=i.shape,u.push(i));let d=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Jne(r.shape,s.shape,a.shape,c,p,l):new Yne(r.shape,s.shape,a.shape,c,p,l);return t.runWebGLProgram(d,u,u[0].dtype)},ere={kernelName:fo,backendName:"webgl",kernelFunc:Qne},tre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=nre(this.rank),r,s=e.map((a,o)=>`sourceLoc.${ey[o]} = start[${o}] + coords.${ey[o]};`);r=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${r}
setOutput(getSource(${n}));
}
`}},ey=["x","y","z","w","u","v"];function nre(e){if(e===1)return"sourceLoc";if(e<=6)return ey.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=Un("coords",this.rank),r=Un("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${a};
--${r[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${a};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function sre(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Mt.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let l=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,l+1),a}function Rc(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,l]=Mt.parseSliceParams(s,a,o);if(Mt.assertParamsValid(s,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let p=n.texData.get(s.dataId),d=qee(p.values,i,l,s.shape,s.dtype);return n.makeTensorInfo(l,s.dtype,d)}let{isPacked:u}=n.texData.get(s.dataId),c=Mt.isSliceContinous(s.shape,i,l);if(u||!c){let p=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rre(l):new tre(l),d=[i];return n.runWebGLProgram(p,[s],s.dtype,d)}return n.uploadToGPU(s.dataId),sre(s,i,l,n)}var are={kernelName:Al,backendName:"webgl",kernelFunc:Rc},ore=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=C.getReshaped(s.shape,a,i),u=C.getPermuted(l.length,a.length),c=C.getReshapedPermuted(s.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=ve({inputs:{x:s},backend:n,attrs:{shape:l}}),m=bn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:c}}),y=Rc({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},ire={kernelName:Hi,backendName:"webgl",kernelFunc:ore};function lre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),l=n.readSync(a.dataId),u=KI(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var ure={kernelName:Qf,backendName:"webgl",kernelFunc:lre};function cre(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var dre={kernelName:em,backendName:"webgl",kernelFunc:cre},pre="return float(a != b);",p8=vn({opSnippet:pre,cpuKernelImpl:Vee,dtype:"bool"}),hre={kernelName:il,backendName:"webgl",kernelFunc:p8};function Vp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return or({inputs:{x:s.complexTensorInfos.real},backend:n})}var fre={kernelName:ip,backendName:"webgl",kernelFunc:Vp},mre="return float(int(x));";function gre(e,t){let n=new Zs(e.shape,mre),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function ty(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return or({inputs:{x:s},backend:n});let o=Ft(s.shape),i=ty({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),l=Xo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(s.dtype==="complex64"){let o=Vp({inputs:{input:s},backend:n}),i=ty({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=or({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return gre(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=p8({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var yre={kernelName:eo,backendName:"webgl",kernelFunc:ty},Gv="return ceil(x);",Are=ot({opSnippet:Gv,packedOpSnippet:Gv,cpuKernelImpl:kee}),xre={kernelName:to,backendName:"webgl",kernelFunc:Are},bre=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));
}
`}},vre=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 wre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;Y().getBool("WEBGL_PACK_CLIP")?i=new vre(s.shape):i=new bre(s.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,l)}var kre={kernelName:ta,backendName:"webgl",kernelFunc:wre},Sre=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 Hv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Ire(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new Sre(r.shape),o=[Hv(r,s.complexTensorInfos.real),Hv(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var Cre={kernelName:Qd,backendName:"webgl",kernelFunc:Ire},Tre=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},Nre=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,s=yt(r),a=Un("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),p=`if (${l} < ${i[0]}) {
return getChannel(
getT0(${c}), vec2(${u.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
return getChannel(
getT${f}(${nf(o,l,m)}),
vec2(${nf(u,l,m)}));
}`}let d=i.length,h=i[i.length-1];p+=`
return getChannel(
getT${d}(${nf(o,l,h)}),
vec2(${nf(u,l,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${p}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[r-1]} = ${a[r-1]} + 1;
if (${a[r-1]} < ${n[r-1]}) {
result.g = getValue(${a});
}
${a[r-2]} = ${a[r-2]} + 1;
if (${a[r-2]} < ${n[r-2]}) {
result.a = getValue(${a});
}
${a[r-1]} = ${a[r-1]} - 1;
if (${a[r-2]} < ${n[r-2]} &&
${a[r-1]} < ${n[r-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function nf(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function T0(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return or({inputs:{x:s.complexTensorInfos.imag},backend:n})}var Ere={kernelName:rp,backendName:"webgl",kernelFunc:T0};function gu(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(m=>Vp({inputs:{input:m},backend:n})),p=e.map(m=>T0({inputs:{input:m},backend:n})),d=gu(c,t,n),h=gu(p,t,n),f=Xo({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let c=e.map(y=>{let x=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:n,attrs:{shape:[-1,x]}})}),p=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=C.computeOutShape(c.map(y=>y.shape),1),h=c[0].shape[0]===1,f=See(p,d,r,h),m=C.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,r,f);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=gu(e.slice(0,c),t,n),d=gu(e.slice(c),t,n),h=gu([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new Nre(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:o}=Rre(e,t,n),i=new Tre(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function Rre(e,t,n){let r=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:n})),outShape:r}}function h8(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=C.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return or({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return C.assertParamsConsistent(l,a),gu(i,a,n)}var _re={kernelName:ji,backendName:"webgl",kernelFunc:h8},f8=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";n&&(r?A=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?A=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:A=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${A}
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${x}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${v}
${b}
setOutput(result);
}
`}},$re=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${c}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},Dre=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=lr(this.outputShape.length);let{dataFormat:n}=t,r=Kn(),s=n==="channelsLast",a=s?0:1,o=s?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
blockIndex = rc.y + ${c};
pos = rc.x + ${u};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${u*2+c}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+c}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${r.output} = result;
}
`}};function m8({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((p===1||d===1)&&c>l8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},S=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Xd(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let I=ve({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let E=Uf({a:v,b:I,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=r.texData.get(E.dataId);w.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=S,R.shape=n.outShape,g=or({inputs:{x:E},backend:r}),g.shape=n.outShape,y.push(E)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ve({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),S=ve({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Uf({a:v,b:S,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:I},backend:r,attrs:{shape:n.outShape}}),y.push(v),y.push(S),y.push(I)}for(let b of y)r.disposeIntermediateTensorInfo(b);return g}function g8({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=d*p,y=[m,g],x=!0,A=!1,b=[],v=ve({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),S=ve({inputs:{x:t},backend:r,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(S);let I=new Dre(y,n),E=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=r.runWebGLProgram(I,[v],"float32",E),P=ve({inputs:{x:R},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let _=s!=null,D=a!=null,T=i==="leakyrelu",F=i?S0(i,!0):null,U=new i8(P.shape,S.shape,[1,g,n.outChannels],x,A,_,F,D,T),X=[P,S];if(s&&X.push(s),D&&X.push(a),T){let ee=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));X.push(ee),b.push(ee)}let z=r.runWebGLProgram(U,X,"float32"),Z=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],W=ve({inputs:{x:z},backend:r,attrs:{shape:Z}});b.push(z);for(let ee of b)r.disposeIntermediateTensorInfo(ee);return W}function Pre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=r,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(s.shape,a.shape,o,u,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=m8({x:s,filter:a,convInfo:d,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)h=g8({x:s,filter:a,convInfo:d,backend:n});else{let m=new f8(d);h=n.runWebGLProgram(m,[s,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Fre={kernelName:no,backendName:"webgl",kernelFunc:Pre},Ore=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Mre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${c}];
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) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},zre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Lre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${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) / ${s}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 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 Bre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(s.shape,c,o,1,i,u,!1,p),h=new Ore(d);return n.runWebGLProgram(h,[s,a],"float32")}var Wre={kernelName:tm,backendName:"webgl",kernelFunc:Bre};function Vre(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=r,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=new Mre(d);return n.runWebGLProgram(h,[s,a],"float32")}var Ure={kernelName:ro,backendName:"webgl",kernelFunc:Vre};function Gre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r,u=C.computeConv3DInfo(s.shape,a.shape,o,l,i),c=new $re(u);return n.runWebGLProgram(c,[s,a],"float32")}var Hre={kernelName:ep,backendName:"webgl",kernelFunc:Gre};function jre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:l}=r,u=C.computeConv3DInfo(s.shape,l,o,1,i),c=new zre(u);return n.runWebGLProgram(c,[s,a],"float32")}var qre={kernelName:nm,backendName:"webgl",kernelFunc:jre};function Xre(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:l}=r,u=C.computeConv3DInfo(l,a.shape,i,1,o),c=new Lre(u);return n.runWebGLProgram(c,[s,a],"float32")}var Kre={kernelName:rm,backendName:"webgl",kernelFunc:Xre},Zre=Ec+`
return cos(x);
`,Yre=ot({opSnippet:Zre}),Jre={kernelName:so,backendName:"webgl",kernelFunc:Yre},Qre=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,ese=ot({opSnippet:Qre}),tse={kernelName:ao,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,p]=n;this.outputShape=[u,c,p,l];let d=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${x});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${A};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${s}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},rse=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=r,c=new nse(s.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[s,a,o],"float32")},sse={kernelName:Xi,backendName:"webgl",kernelFunc:rse},jv=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"1.0":`getX(${qv(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${yt(r)} coords = getOutputCoords();
int end = ${Xv(r,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${Xv(r,"coords")} = idx;
val *= getX(${qv(r,"coords")});
}
setOutput(val);
}
`}};function qv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Xv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function ase(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length,u=C.getAxesPermutation([a],l),c=s;u!=null&&(c=bn({inputs:{x:s},backend:n,attrs:{perm:u}}));let p=C.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=or({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new jv(c.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new jv(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=C.getUndoAxesPermutation(u),m=bn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var ose={kernelName:qi,backendName:"webgl",kernelFunc:ase},Kv=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${Zv(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${yt(r)} coords = getOutputCoords();
int end = ${Yv(r,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${Yv(r,"coords")} = idx;
val += getX(${Zv(r,"coords")});
}
setOutput(val);
}
`}};function Zv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Yv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function ise(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length,u=C.getAxesPermutation([a],l),c=s;u!=null&&(c=bn({inputs:{x:s},backend:n,attrs:{perm:u}}));let p=C.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=or({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new Kv(c.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new Kv(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=C.getUndoAxesPermutation(u),m=bn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var lse={kernelName:oo,backendName:"webgl",kernelFunc:ise};function use(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let l=n.readSync(s.dataId),u=n.readSync(a.dataId),c=KI(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(s.shape.length===2){let l=n.bufferSync(s),u=n.bufferSync(a),c=wee(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var cse={kernelName:sm,backendName:"webgl",kernelFunc:use},dse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function pse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],l=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new dse(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var hse={kernelName:Ki,backendName:"webgl",kernelFunc:pse},y8=class{constructor(e,t=!1,n=null,r=!1,s=!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=lr(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(r?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,u="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${c}
${u}
setOutput(result);
}
`}},A8=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=lr(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,p=c,d=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;d+=`
for (int r = 0; r < ${u}; r++) {
`;for(let g=0;g<c;g++)d+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;d+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(p+1)/2;g++){let y=g*2;if(d+=`
xC = xCCorner + ${y*l};
`,i===1){if(y<c&&(o%2===1?(d+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${y}Ready = 1;
}
`,l===1&&y>0?d+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:d+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):d+=`
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<c)){let x=o%2===0?w.nearestLargerEven(l):l;l%2===0&&o%2===1||l%2!==0&&o%2!==1?(d+=`
xCOffset = xC + imod(pads[1], 2) + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${y+1}Ready = 1;
}
`,l>1&&(d+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),d+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):x===1?d+=`
xC${y+1} = xTexelC${y};
`:d+=`
xCOffset = xC + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${y+1};
`}}else y<c&&(o%2===1?(d+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<c&&(d+=`
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);
`)):(d+=`
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${y+1}.xy);
`,y+1<c&&(d+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<c&&(d+=`
wTexel = getW(r, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<c&&(d+=`
wTexel = getW(r, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}d+=`
}
`,d+=`
}
`;let h="",f="";n&&(r?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${d}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function fse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=C.computeConv2DInfo(s.shape,a.shape,o,c,i,u,!0),d;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new A8(p):d=new y8(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[s,a],"float32",h)}var mse={kernelName:io,backendName:"webgl",kernelFunc:fse},gse=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},yse=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Ase(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=r,p=C.computeConv2DInfo(s.shape,c,o,i,l,u,!0),d=new gse(p);return n.runWebGLProgram(d,[s,a],"float32")}var xse={kernelName:am,backendName:"webgl",kernelFunc:Ase};function bse(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=r,p=C.computeConv2DInfo(c,a.shape,o,i,l,u,!0),d=new yse(p);return n.runWebGLProgram(d,[s,a],"float32")}var vse={kernelName:om,backendName:"webgl",kernelFunc:bse},wse=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function kse(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=ve({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new wse(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ve({inputs:{x:l},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var Sse={kernelName:im,backendName:"webgl",kernelFunc:kse},Ise=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:p}=r;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${c}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${o}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${u};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Cse(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l}=r,u=C.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",l),c,p=new Ise(u);c=n.runWebGLProgram(p,[s,a],"float32");let d=ve({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var Tse={kernelName:tp,backendName:"webgl",kernelFunc:Cse};function Nse(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:l}=C.decodeEinsumEquation(s,a.length);C.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=C.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=C.getEinsumPermutation(h,l[g]),A;C.isIdentityPermutation(y)?A=a[g]:(A=bn({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let v=0;v<x.length;++v)b.splice(x[v],0,1);w.arraysEqual(A.shape,b)||(A=ve({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=x3({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=C0({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var Ese={kernelName:np,backendName:"webgl",kernelFunc:Nse},Rse="return (x >= 0.0) ? x : (exp(x) - 1.0);",_se=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,$se=ot({opSnippet:Rse,packedOpSnippet:_se}),Dse={kernelName:uo,backendName:"webgl",kernelFunc:$se},Pse="return (b >= 1.0) ? a : a * (b + 1.0);",Fse=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Ose=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Wp(Fse,r.shape,s.shape):new Fu(Pse,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Mse={kernelName:lm,backendName:"webgl",kernelFunc:Ose},zse=`
return vec4(equal(a, b));
`,Lse="return float(a == b);",Bse=vn({opSnippet:Lse,packedOpSnippet:zse,dtype:"bool",cpuKernelImpl:Iee}),Wse={kernelName:Zi,backendName:"webgl",kernelFunc:Bse},Vse=`
// 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 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Use=ot({opSnippet:Vse}),Gse={kernelName:Xu,backendName:"webgl",kernelFunc:Use},Hse=Ec+`
return exp(x);
`,jse=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,x8=ot({opSnippet:Hse,packedOpSnippet:jse,cpuKernelImpl:Cee,dtype:"float32"}),qse={kernelName:co,backendName:"webgl",kernelFunc:x8};function ny(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+s+1),i.splice(l,0,1),ve({inputs:{x:a},backend:r,attrs:{shape:i}})}var Xse={kernelName:Yi,backendName:"webgl",kernelFunc:ny},Jv="return exp(x) - 1.0;",Kse=ot({opSnippet:Jv,packedOpSnippet:Jv,cpuKernelImpl:Tee}),Zse={kernelName:Ji,backendName:"webgl",kernelFunc:Kse},Qv=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function b8(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new Qv("real",l,t),c=new Qv("imag",l,t),p=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Xo({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Yse(e){let{inputs:t,backend:n}=e,{input:r}=t;return b8(r,!1,n)}var Jse={kernelName:um,backendName:"webgl",kernelFunc:Yse},Qse=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 Up(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Qse(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var eae={kernelName:Ku,backendName:"webgl",kernelFunc:Up},tae=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},nae={kernelName:Qi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new tae(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},e4="return floor(x);",rae=ot({opSnippet:e4,packedOpSnippet:e4,cpuKernelImpl:Nee}),sae={kernelName:po,backendName:"webgl",kernelFunc:rae},aae=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,oae=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,iae=vn({opSnippet:aae,packedOpSnippet:oae,dtype:"int32"}),lae={kernelName:ho,backendName:"webgl",kernelFunc:iae},uae=class{constructor(e){this.variableNames=["A"];let t=Kn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},cae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Kn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},dae={kernelName:Md,backendName:"webgl",kernelFunc:pae},du;function pae(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[l,u]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],c=[u,l],p=[u,l,a];(i||o)&&(du==null&&(du=document.createElement("canvas").getContext("2d")),du.canvas.width=l,du.canvas.height=u,du.drawImage(s,0,0,l,u),s=du.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),s);let h=Y().getBool("WEBGL_PACK")?new cae(p):new uae(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function hae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(s.shape,a.shape,l,p,u,d,!1,m),y,x=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=m8({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)y=g8({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,S=h==="leakyrelu",I=h?S0(h,!1):null,E=new f8(g,b,I,v,S),R=[s,a];if(o&&R.push(o),i&&R.push(i),S){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(P),x.push(P)}y=n.runWebGLProgram(E,R,"float32")}let A=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var fae={kernelName:Fa,backendName:"webgl",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=[],m=c;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 g=C.computeConv2DInfo(s.shape,a.shape,l,m,u,p,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?S0(d,y):null,A=[s,a],b=o!=null,v=i!=null,S=d==="leakyrelu";if(b&&A.push(o),v&&A.push(i),S){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));A.push(P),f.push(P)}let I;y?I=new A8(g,b,x,v,S):I=new y8(g,b,x,v,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(I,A,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),R}var gae={kernelName:Oa,backendName:"webgl",kernelFunc:mae},yae=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=yt(t.length),s=yt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function Aae(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[l,u,c,p]=C.prepareAndValidate(r,s),d=ve({inputs:{x:s},backend:n,attrs:{shape:[u,o]}}),h=ve({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.readSync(s.dataId),x=n.bufferSync(r),A=Eee(y,x,r.dtype,u,o,c,p,r.shape,i);return n.makeTensorInfo(l,r.dtype,A.values)}let f=new yae(o,p,[u,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var xae={kernelName:tl,backendName:"webgl",kernelFunc:Aae},bae=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),r=vae(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${r}));
}
`}};function vae(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("index"):r.push(`${n[s]}`);return r.join()}function v8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,l=w.parseAxisParam(o,s.shape)[0];if(Y().get("DEBUG")){let x=n.readSync(a.dataId),A=s.shape[l];for(let b=0;b<x.length;++b){let v=x[b];w.assert(v<=A-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${A-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(s,a,l,i),c=w.sizeFromShape(a.shape),p=[],d=ve({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ve({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let x=n.bufferSync(h),A=n.bufferSync(d),b=Ree(A,x,f);return p.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new bae(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let y=ve({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}var wae={kernelName:el,backendName:"webgl",kernelFunc:v8},kae="return float(a > b);",Sae=`
return vec4(greaterThan(a, b));
`,Iae=vn({opSnippet:kae,packedOpSnippet:Sae,cpuKernelImpl:_ee,dtype:"bool"}),Cae={kernelName:nl,backendName:"webgl",kernelFunc:Iae},Tae="return float(a >= b);",Nae=`
return vec4(greaterThanEqual(a, b));
`,Eae=vn({opSnippet:Tae,packedOpSnippet:Nae,dtype:"bool",cpuKernelImpl:$ee}),Rae={kernelName:mo,backendName:"webgl",kernelFunc:Eae};function _ae(e){let{inputs:t,backend:n}=e,{input:r}=t;return b8(r,!0,n)}var $ae={kernelName:cm,backendName:"webgl",kernelFunc:_ae},Dae="return float(!isnan(x) && !isinf(x));",Pae=ot({opSnippet:Dae,dtype:"bool"}),Fae={kernelName:Zu,backendName:"webgl",kernelFunc:Pae},Oae="return float(isinf(x));",Mae=ot({opSnippet:Oae,dtype:"bool"}),zae={kernelName:Yu,backendName:"webgl",kernelFunc:Mae},Lae="return float(isnan(x));",Bae=ot({opSnippet:Lae,dtype:"bool"}),Wae={kernelName:Ju,backendName:"webgl",kernelFunc:Bae},Vae="return float(a < b);",Uae=`
return vec4(lessThan(a, b));
`,Gae=vn({opSnippet:Vae,packedOpSnippet:Uae,cpuKernelImpl:Dee,dtype:"bool"}),Hae={kernelName:rl,backendName:"webgl",kernelFunc:Gae},jae="return float(a <= b);",qae=`
return vec4(lessThanEqual(a, b));
`,Xae=vn({opSnippet:jae,packedOpSnippet:qae,cpuKernelImpl:Pee,dtype:"bool"}),Kae={kernelName:sl,backendName:"webgl",kernelFunc:Xae};function Zae(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=Fee(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Yae={kernelName:dm,backendName:"webgl",kernelFunc:Zae},Jae=Ec+`
return x < 0.0 ? 0./0. : log(x);
`,Qae=`
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;
`,eoe=ot({opSnippet:Jae,packedOpSnippet:Qae,cpuKernelImpl:Oee}),toe={kernelName:Ao,backendName:"webgl",kernelFunc:eoe},noe=Ec+`
return log(1.0 + x);
`,roe=ot({opSnippet:noe}),soe={kernelName:Qu,backendName:"webgl",kernelFunc:roe},aoe="return float(a >= 1.0 && b >= 1.0);",ooe=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,ioe=vn({opSnippet:aoe,packedOpSnippet:ooe,dtype:"bool"}),loe={kernelName:al,backendName:"webgl",kernelFunc:ioe},uoe="return float(!(x >= 1.0));",coe=ot({opSnippet:uoe}),doe={kernelName:ec,backendName:"webgl",kernelFunc:coe},poe="return float(a >= 1.0 || b >= 1.0);",hoe=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,foe=vn({opSnippet:poe,packedOpSnippet:hoe,dtype:"bool"}),moe={kernelName:sp,backendName:"webgl",kernelFunc:foe},goe=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${l})`:s===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},yoe=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${l})`:s===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${i};
setOutput(result);
}
`}},Aoe=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=r,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new yoe(s.shape,a,o,i,l):new goe(s.shape,a,o,i,l);return n.runWebGLProgram(u,[s],s.dtype)},xoe={kernelName:ap,backendName:"webgl",kernelFunc:Aoe},boe=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=s,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},voe=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=r,p=new boe(s.shape,i,l,u,c);return n.runWebGLProgram(p,[s,a,o],s.dtype)},woe={kernelName:pm,backendName:"webgl",kernelFunc:voe};function koe(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=ve({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),l=Ll(i,e.dtype,"max",r),u=ve({inputs:{x:l},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}function w8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=C.getAxesPermutation(u,i),p=c!=null,d=n.shouldExecuteOnCPU([s]),h=s;if(p){if(d){let A=n.texData.get(h.dataId).values,b=new Array(i);for(let I=0;I<b.length;I++)b[I]=s.shape[c[I]];let v=A3(A,s.shape,s.dtype,c,b);h=n.makeTensorInfo(b,s.dtype);let S=n.texData.get(h.dataId);S.values=v}else h=I0(s,c,n);u=C.getInnerMostAxes(u.length,i)}C.assertAxesAreInnerMostDims("max",u,i);let[f,m]=C.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=C.expandShapeToKeepDim(f,l));let y;if(d){let A=n.texData.get(h.dataId).values,b=Mee(A,w.sizeFromShape(m),g,s.dtype);y=n.makeTensorInfo(g,s.dtype);let v=n.texData.get(y.dataId);v.values=b}else y=koe(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var Soe={kernelName:xo,backendName:"webgl",kernelFunc:w8},Ioe=n8+`
return max(a, b);
`,Coe=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+k0+`
return result;
`,Toe=vn({opSnippet:Ioe,packedOpSnippet:Coe,cpuKernelImpl:zee}),Noe={kernelName:bo,backendName:"webgl",kernelFunc:Toe};function Eoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Sc(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1;w.assert(C.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=C.computePool2DInfo(s.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return or({inputs:{x:s},backend:n});let p=new Kd(c,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var Roe={kernelName:vo,backendName:"webgl",kernelFunc:Eoe};function _oe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],p=C.computePool3DInfo(s.shape,a,o,c,i,u,l),d=new b3(p,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var $oe={kernelName:op,backendName:"webgl",kernelFunc:_oe},Doe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,l=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${r}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Poe=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${c}, ${p}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${u} +
wR * ${u} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Foe(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=r,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,l,p,u,c),h=new b3(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Poe(d),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Ooe={kernelName:fm,backendName:"webgl",kernelFunc:Foe};function Moe(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Sc([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=r,d=C.computePool2DInfo(i.shape,l,u,1,c,p),h=!0,f=new Kd(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Doe(d),y=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var zoe={kernelName:hm,backendName:"webgl",kernelFunc:Moe};function Loe(e,t,n,r){let s=new Kd(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new Kd(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var Boe={kernelName:mm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,s,a,u,o),[p,d]=Loe(r,i,c,l);return[p,d]}};function Woe(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=ve({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),l=Ll(i,"float32","mean",r),u=ve({inputs:{x:l},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Voe={kernelName:wo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=C.getAxesPermutation(u,i),p=c!=null,d=o.shouldExecuteOnCPU([r]),h=[],f=r;if(p){if(d){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let E=0;E<v.length;E++)v[E]=r.shape[c[E]];let S=A3(b,r.shape,r.dtype,c,v);f=o.makeTensorInfo(v,r.dtype);let I=o.texData.get(f.dataId);I.values=S}else f=I0(r,c,o);h.push(f),u=C.getInnerMostAxes(u.length,i)}C.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=C.computeOutAndReduceShapes(f.shape,u),y=m;s&&(y=C.expandShapeToKeepDim(m,l));let x=Woe(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function Uoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=C.getAxesPermutation(u,i),p=s;c!=null&&(p=bn({inputs:{x:s},backend:n,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,s.shape.length)),C.assertAxesAreInnerMostDims("min",u,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ll(m,m.dtype,"min",n),y;if(o){let x=C.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Goe={kernelName:ko,backendName:"webgl",kernelFunc:Uoe},Hoe=n8+`
return min(a, b);
`,joe=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+k0+`
return result;
`,qoe=vn({opSnippet:Hoe,packedOpSnippet:joe,cpuKernelImpl:Lee}),Xoe={kernelName:So,backendName:"webgl",kernelFunc:qoe},Koe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,s=yt(r),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${r}; 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};
}
}
${s} coords = outC - start;
setOutput(getX(${i}));
}
`}},Zoe=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let r=e.length,s=yt(r),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Un("rc",r),l=Un("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(r===1){let h=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;d=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${c});
${i[r-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${c});
}
`}else{let h=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;d=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${c});
${i[r-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${c});
}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${c});
${i[r-1]} += 1;
if(${u}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${c});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},Yoe=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zoe(r.shape,s,a):new Koe(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Joe={kernelName:Io,backendName:"webgl",kernelFunc:Yoe},Qoe=`if (b == 0.0) return NAN;
return mod(a, b);`,eie=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+k0+`
return result;
`,tie=vn({opSnippet:Qoe,packedOpSnippet:eie}),nie={kernelName:tc,backendName:"webgl",kernelFunc:tie},rie=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},sie=`
if (a == b) {
return 1.0;
};
return a / b;`,aie=`
// 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;
`,k8=vn({opSnippet:sie,packedOpSnippet:aie,checkOutOfBounds:!0}),oie={kernelName:lo,backendName:"webgl",kernelFunc:k8},t4="return a - b;",S8=vn({opSnippet:t4,packedOpSnippet:t4,supportsComplex:!0,cpuKernelImpl:tte}),iie={kernelName:Wo,backendName:"webgl",kernelFunc:S8};function I8(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=w8({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=C.expandShapeToKeepDim(i.shape,o),u=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),c=S8({inputs:{a:s,b:u},backend:n}),p=x8({inputs:{x:c},backend:n}),d=C0({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:d},backend:n,attrs:{shape:l}}),f=k8({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var lie={kernelName:Lo,backendName:"webgl",kernelFunc:I8};function uie(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,l=i?s:I8({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new rie(u,c,a),d=[[o]],h=n.runWebGLProgram(p,[l],"int32",d);return i||n.disposeIntermediateTensorInfo(l),h}var cie={kernelName:gm,backendName:"webgl",kernelFunc:uie},die=Yr+`
return -x;
`,pie=`
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 hie(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=Wee(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Ci(r.shape,pie):s=new Zs(r.shape,die),n.runWebGLProgram(s,[r],r.dtype)}var fie={kernelName:ol,backendName:"webgl",kernelFunc:hie},mie=Kr.nonMaxSuppressionV3Impl;function gie(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=r,u=n.readSync(s.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=mie(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var yie={kernelName:ll,backendName:"webgl",kernelFunc:gie},Aie=Kr.nonMaxSuppressionV4Impl;function xie(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(s.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=Aie(c,p,o,i,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var bie={kernelName:nc,backendName:"webgl",kernelFunc:xie},vie=Kr.nonMaxSuppressionV5Impl;function wie(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(s.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=vie(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var kie={kernelName:ul,backendName:"webgl",kernelFunc:wie},Sie=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},Iie=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,l=w.sizeFromShape(s.shape),u=new Sie(l,a,o,i),c=ve({inputs:{x:s},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[c],s.dtype);n.disposeIntermediateTensorInfo(c);let d=[...s.shape,a],h=ve({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},Cie={kernelName:dl,backendName:"webgl",kernelFunc:Iie};function Gf(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=Vp({inputs:{input:r},backend:n}),a=Gf({inputs:{x:s},backend:n}),o=T0({inputs:{input:r},backend:n}),i=Gf({inputs:{x:o},backend:n}),l=Xo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Up({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Tie={kernelName:Tl,backendName:"webgl",kernelFunc:Gf};function C8(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=Vp({inputs:{input:r},backend:n}),a=C8({inputs:{x:s},backend:n}),o=T0({inputs:{input:r},backend:n}),i=Gf({inputs:{x:o},backend:n}),l=Xo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Up({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Nie={kernelName:cl,backendName:"webgl",kernelFunc:C8};function Eie(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return ny({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=ny({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(p),p}),u=h8({inputs:l,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Rie={kernelName:pl,backendName:"webgl",kernelFunc:Eie},_ie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,s=yt(r),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},$ie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,s=yt(r),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Un("rc",r),l=Un("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
if(${u}) {
`,r===1?"":`}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${i[r-1]} += 1;
if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=r===1?2:4;f<m;f++)h+=`
${p[f]}
if (${d}) {
result[${f}] = float(value);
} else {
${s} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${c});
}
`;h+=r===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},T8=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let u=a.map((c,p)=>c[0]+s.shape[p]+c[1]);return Up({backend:n,attrs:{shape:u,value:o,dtype:s.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $ie(s.shape,a,o):new _ie(s.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[s],s.dtype,l)},Die={kernelName:To,backendName:"webgl",kernelFunc:T8},Pie=`
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);
`,Fie=`
// 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));
`+k0+`
return result;
`,Oie=vn({opSnippet:Pie,packedOpSnippet:Fie}),Mie={kernelName:No,backendName:"webgl",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,l=[],u=w.parseAxisParam(a,s.shape),c=u,p=C.getAxesPermutation(c,i),d=s;p!=null&&(d=bn({inputs:{x:s},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,i),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=Uee(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),y=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),x=gp(s.dtype),A=Ll(y,x,"prod",n);h=ve({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=C.expandShapeToKeepDim(h.shape,u);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Lie={kernelName:Ro,backendName:"webgl",kernelFunc:zie},N8=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=Gee(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Bie={kernelName:rc,backendName:"webgl",kernelFunc:N8},Wie="return 1.0 / x;",Vie=ot({opSnippet:Wie}),Uie={kernelName:sc,backendName:"webgl",kernelFunc:Vie},Gie=Yr+`
return (x < 0.0) ? 0.0 : x;
`,Hie=`
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;
`,jie=ot({opSnippet:Gie,packedOpSnippet:Hie}),qie={kernelName:_o,backendName:"webgl",kernelFunc:jie},Xie=Yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Kie=`
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;
`,Zie=ot({opSnippet:Xie,packedOpSnippet:Kie}),Yie={kernelName:Do,backendName:"webgl",kernelFunc:Zie},Jie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/c[0]},
${u[1]/c[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the 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);
}
`}},Qie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/c[0]},
${u[1]/c[1]},
${u[1]/c[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function ele(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qie(s.shape,l,u,a,o):new Jie(s.shape,l,u,a,o);return n.runWebGLProgram(c,[s],"float32")}var tle={kernelName:$o,backendName:"webgl",kernelFunc:ele},nle=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-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), ${s-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:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new nle(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var sle={kernelName:Am,backendName:"webgl",kernelFunc:rle},ale=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/c[0]},
${u[1]/c[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},ole=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/c[0]},
${u[1]/c[1]},
${u[1]/c[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function ile(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ole(s.shape,l,u,a,o):new ale(s.shape,l,u,a,o);return n.runWebGLProgram(c,[s],s.dtype)}var lle={kernelName:ac,backendName:"webgl",kernelFunc:ile},ule=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function cle(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new ule(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var dle={kernelName:ym,backendName:"webgl",kernelFunc:cle},ple=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=yt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}},hle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=Un("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(r.slice())};
if(${s}){
result.g = ${l(r.slice())};
}
if(${a}) {
result.b = ${u(r.slice())};
if(${s}) {
result.a = ${c(r.slice())};
}
}
setOutput(result);
}
`;function i(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function fle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return or({inputs:{x:s},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hle(s.shape,i):new ple(s.shape,i);return n.runWebGLProgram(l,[s],s.dtype)}var mle={kernelName:fl,backendName:"webgl",kernelFunc:fle},gle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
vec3 fill = vec3(${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]));
${s}
if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},yle={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,l=new gle(r.shape,a),[u,c]=C.getImageCenter(o,r.shape[1],r.shape[2]),p=[[u,c,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(l,[r],r.dtype,p)}},Ale=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,xle=ot({opSnippet:Ale}),ble={kernelName:ml,backendName:"webgl",kernelFunc:xle},vle="return inversesqrt(x);",wle=ot({opSnippet:vle,cpuKernelImpl:Hee}),kle={kernelName:Po,backendName:"webgl",kernelFunc:wle},E8=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(s.length),l=yt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,p="";r===1?p="i":r===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${s});
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 < ${t}; j++) {
int index = round(${c});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Sle(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=C.calculateShapes(a,s,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,s.dtype);let h=ve({inputs:{x:s},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new E8(l,i,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var Ile={kernelName:gl,backendName:"webgl",kernelFunc:Sle},Cle=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);r=i.join(),s=l.join()}let a=yt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function Tle(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Cle(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],Nn(s.dtype,a.dtype))}var Nle={kernelName:yl,backendName:"webgl",kernelFunc:Tle},Ele=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Rle=ot({opSnippet:Ele}),_le={kernelName:oc,backendName:"webgl",kernelFunc:Rle},$le=Ec+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Dle=`
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;
`,Ple=ot({opSnippet:$le,packedOpSnippet:Dle,cpuKernelImpl:jee}),Fle={kernelName:Oo,backendName:"webgl",kernelFunc:Ple},Ole=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Mle=ot({opSnippet:Ole}),zle={kernelName:ic,backendName:"webgl",kernelFunc:Mle},Lle=Ec+`
return sin(x);
`,Ble=ot({opSnippet:Lle}),Wle={kernelName:Fo,backendName:"webgl",kernelFunc:Ble},Vle=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Ule=ot({opSnippet:Vle}),Gle={kernelName:xl,backendName:"webgl",kernelFunc:Ule},Hle=`
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=ot({opSnippet:Hle}),qle={kernelName:lc,backendName:"webgl",kernelFunc:jle},Xle=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<s.shape.length;++y)l.push([0,0]);let u=[],c=T8({inputs:{x:s},backend:n,attrs:{paddings:l,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=ve({inputs:{x:c},backend:n,attrs:{shape:p}}),m=bn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Kle={kernelName:bl,backendName:"webgl",kernelFunc:Xle};function Zle(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(r.dataId),l=n.readSync(s.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,d,h,f,m]=Xee(i,r.shape,r.dtype,l,s.dtype,u,c);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],s.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var Yle={kernelName:lp,backendName:"webgl",kernelFunc:Zle};function Jle(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,p]=Kee(i,r.shape,r.dtype,o,l);return[n.makeTensorInfo(c,r.dtype,u),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Qle={kernelName:uc,backendName:"webgl",kernelFunc:Jle};function eue(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),l=n.readSync(a.dataId),[u,c]=YI(o,r.shape,r.dtype,i,l,!0);return n.makeTensorInfo(c,r.dtype,u)}var tue={kernelName:up,backendName:"webgl",kernelFunc:eue};function nue(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),l=n.readSync(a.dataId),[u,c]=YI(o,r.shape,r.dtype,i,l);return n.makeTensorInfo(c,r.dtype,u)}var rue={kernelName:cp,backendName:"webgl",kernelFunc:nue};function sue(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=C.calculateShapes(a,s,i),d=!1,h=new E8(u,l,s.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,s,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var aue={kernelName:dp,backendName:"webgl",kernelFunc:sue};function oue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],l=C.prepareSplitSize(s,a,i),u=s.shape.length,c=new Array(u).fill(0),p=s.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=Rc({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var iue={kernelName:vl,backendName:"webgl",kernelFunc:oue},n4="return sqrt(x);",lue=ot({opSnippet:n4,packedOpSnippet:n4,cpuKernelImpl:Zee}),uue={kernelName:Mo,backendName:"webgl",kernelFunc:lue},cue="return x * x;",due=ot({opSnippet:cue}),pue={kernelName:cc,backendName:"webgl",kernelFunc:due},r4="return (a - b) * (a - b);",hue=vn({opSnippet:r4,packedOpSnippet:r4}),fue={kernelName:Bo,backendName:"webgl",kernelFunc:hue};function mue({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=Yr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Zs(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var gue={kernelName:Go,backendName:"webgl",kernelFunc:mue},yue=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=yt(n.length),a=yt(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function Aue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(s.shape,a,o,i,l,u,c,p,d),v;if(m)v=ve({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||y){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let I=Mt.computeOutShape(x,A,b),E=Rc({inputs:{x:s},backend:n,attrs:{begin:x,size:I}});v=ve({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([s])){let E=n.readSync(s.dataId),R=We(s.shape,s.dtype,E),P=Yee(h,R,b,x);v=n.makeTensorInfo(f,s.dtype,P.values)}else{let E=new yue(x,b,h);v=n.runWebGLProgram(E,[s],s.dtype)}let S=ve({inputs:{x:v},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(v),S}var xue={kernelName:wl,backendName:"webgl",kernelFunc:Aue};function bue(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=r,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Jee(d,h,s,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var vue={kernelName:pp,backendName:"webgl",kernelFunc:bue};function wue(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,p]=Qee(i,l,s),d=c.length;return[n.makeTensorInfo([d,2],"int32",u),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var kue={kernelName:xm,backendName:"webgl",kernelFunc:wue};function Sue(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=ete(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var Iue={kernelName:bm,backendName:"webgl",kernelFunc:Sue},Cue="return tan(x);",Tue=ot({opSnippet:Cue}),Nue={kernelName:kl,backendName:"webgl",kernelFunc:Tue},Eue=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Rue=ot({opSnippet:Eue}),_ue={kernelName:Vo,backendName:"webgl",kernelFunc:Rue},$ue=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=yt(this.rank),s=Due(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Due(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function R8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let l=n.readSync(s.dataId),u=s.dtype==="string"?l.map(d=>w.decodeString(d)):l,c=We(s.shape,s.dtype,u),p=nte(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new $ue(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var Pue={kernelName:na,backendName:"webgl",kernelFunc:R8},Fue=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));
}
}
`}},Oue=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 gi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function s4(e){let t=1;for(;t<e;)t*=2;return t}function Mue(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=s.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([s])||c<i||a>l){let P=n.readSync(s.dataId),[_,D]=rte(P,u,s.dtype,a,o);return[n.makeTensorInfo(_.shape,_.dtype,_.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,s.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[s,Up({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(s.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(s):s,m=w.sizeFromShape(u)/c,g=ve({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&gi(n,h);let y=s4(a),x=s4(c),A=null,b=()=>A===null?[g,g]:[g,A],v=(P,_,D)=>{let T=b(),F=new Fue(D),X=[[c],[A===null?1:0],[Number.NEGATIVE_INFINITY],[P],[_]],z=A;A=n.runWebGLProgram(F,T,"int32",X),gi(n,z)};for(let P=1;P<y;P*=2){let _=P*2;for(let D=P;D>=1;D/=2)v(_,D,[m,x])}for(let P=x;P>y;P/=2){let _=b(),D=new Oue([m,P/2]),F=[[c],[A===null?1:0],[y]],U=A;A=n.runWebGLProgram(D,_,"int32",F),gi(n,U);let X=y/2,z=X*2;for(let Z=X;Z>=1;Z/=2)v(z,Z,A.shape)}let S=A;A=Rc({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),gi(n,S);let I=v8({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});gi(n,g);let E=u.slice(0,-1);E.push(a),S=A,A=ve({inputs:{x:A},attrs:{shape:E},backend:n}),gi(n,S);let R=I;return I=ve({inputs:{x:I},attrs:{shape:E},backend:n}),gi(n,R),[I,A]}var zue={kernelName:Sl,backendName:"webgl",kernelFunc:Mue},Lue=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Bue(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=r,[c,p,d,h]=s.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new Lue(p,d,o,i,l,g);return n.runWebGLProgram(y,[s,a],"float32")}var Wue={kernelName:Il,backendName:"webgl",kernelFunc:Bue};function Vue(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;Sc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=ste(o,s,a.shape,a.dtype);return[r.makeTensorInfo(l,a.dtype,i),r.makeTensorInfo([u.length],"int32",u)]}var Uue={kernelName:vm,backendName:"webgl",kernelFunc:Vue};function Gue(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,l=s.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=Rc({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=ve({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Hue={kernelName:Cl,backendName:"webgl",kernelFunc:Gue},jue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,p=`
sumValue += dot(values, segFilter);
`,d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${p}
}
int inIdx = inOffset + ${u};
if (${c===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${p}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${p}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${p}
}
setOutput(${l});
}
`}};function que(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,l=[],u=0,c=C.getAxesPermutation([u],i),p=s;c!=null&&(p=bn({inputs:{x:s},backend:n,attrs:{perm:c}}),l.push(p),u=C.getInnerMostAxes(1,i)[0]);let d=C.segment_util.computeOutShape(p.shape,u,o),h=w.sizeFromShape([p.shape[u]]),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=gp(s.dtype),g=(b,v,S,I,E)=>{let R=b.shape[0],P=b.shape[1],_=C.segment_util.segOpComputeOptimalWindowSize(P,E),D={windowSize:_,inSize:P,batchSize:R,numSegments:E},T=new jue(D,v),F=n.compileAndRun(T,[b,S],I);if(l.push(F),F.shape[1]===E)return F;let U=N8({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=R8({inputs:{x:U},backend:n,attrs:{reps:[P/_]}});return l.push(U),l.push(X),g(F,v,X,I,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:y},backend:n,attrs:{shape:d}}),A=x;if(c!=null){l.push(x);let b=C.getUndoAxesPermutation(c);A=bn({inputs:{x:A},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Xue={kernelName:hp,backendName:"webgl",kernelFunc:que},Kue=[Qte,tne,sne,ine,une,pne,fne,gne,bne,wne,Ine,Nne,_ne,Fne,zne,Bne,Vne,jne,Xne,Zne,ere,ire,ure,dre,yre,xre,kre,Pte,Cre,_re,Fre,Wre,Ure,Hre,qre,Kre,Jre,tse,sse,ose,lse,cse,hse,mse,xse,vse,Sse,Tse,Ese,Dse,Mse,Wse,Gse,qse,Xse,Zse,Jse,eae,nae,sae,lae,dae,fae,gae,xae,wae,Cae,Rae,Dte,$ae,Ere,Fae,zae,Wae,Ote,Hae,Kae,Yae,toe,soe,loe,doe,moe,xoe,woe,Soe,Noe,Roe,$oe,Ooe,zoe,Boe,Voe,Goe,Xoe,Joe,nie,cie,Wte,fie,yie,bie,kie,hre,Cie,Nie,Rie,Die,Mie,zte,Lie,Bie,fre,oie,Uie,qie,Yie,Ute,tle,sle,lle,dle,mle,yle,ble,kle,Ile,Nle,_le,Fle,zle,Wle,Gle,are,lie,qle,Kle,Yle,Qle,tue,rue,aue,iue,uue,pue,fue,gue,xue,vue,kue,Iue,iie,Zte,Nue,_ue,Pue,zue,Wue,Yte,Uue,Hue,Xue,Tie];for(let e of Kue)qr(e);var Ms=Y();Ms.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Ms.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Ms.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Ms.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Ms.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Ms.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Ms.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Ms.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Zue="return a + b;",Yue="return areal * breal - aimag * bimag;",Jue="return areal * bimag + aimag * breal;",Que="return a / b;",ece="return a * b;",tce="return (a - b) * (a - b);",nce="return a - b;",rce="return f32(a == b);",sce="return vec4<f32>(a == b);",ace="return f32(a > b);",oce="return vec4<f32>(a > b);",ice="return f32(a >= b);",lce="return vec4<f32>(a >= b);",uce="return f32(a < b);",cce="return vec4<f32>(a < b);",dce="return f32(a <= b);",pce="return vec4<f32>(a <= b);",hce="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",fce=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,mce=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,_8=`
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;
}
`,gce=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,yce=`
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);
`,Ace="return f32(a != b);",xce="return vec4<f32>(a != b);",bce=`
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);
`,vce=`
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;
${_8}
return resultTemp;
`,wce="if (a < 0.0) { return b * a; } return a;",kce=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function a4(e,t){let n=t?_8:mce;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function Gp(e,t){switch(e){case 0:return ece;case 1:return Zue;case 2:return nce;case 3:return Que;case 4:return t?sce:rce;case 5:return t?oce:ace;case 6:return t?lce:ice;case 7:return t?cce:uce;case 8:return t?pce:dce;case 9:return t?fce:hce;case 10:return t?xce:Ace;case 11:return tce;case 12:return t?yce:gce;case 14:return t?kce:wce;case 15:return a4("max",t);case 16:return a4("min",t);case 13:return t?vce:bce;case 17:return Yue;case 18:return Jue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Sce="return abs(a);",Ice="return ceil(a);",Cce="return cos(a);",Tce=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Nce="return exp(a) - 1.0;",Ece="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Rce=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,_ce="return exp(a);",$ce="return floor(a);",Dce="return a;",Pce=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Fce="return f32(!(a >= 1.0));",Oce="return -a;",Mce="if (a < 0.0) { return uniforms.alpha * a; } return a;",zce=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`,Lce="if(a < 0.0) { return 0.0; } return a;",Bce="return clamp(a, 0.0, 6.0);",Wce="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Vce=`
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;
`,Uce="return 1.0/sqrt(a);",Gce="return 1.0 / (1.0 + exp(-1.0 * a));",Hce="return sin(a);",jce=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,qce="return sqrt(a);",Xce="return a * a;",Kce=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Zce="return f32(i32((a)));";function xi(e,t){switch(e){case 0:return Sce;case 2:return Cce;case 3:return Tce;case 1:return Ice;case 4:return t?Rce:Ece;case 5:return _ce;case 6:return Nce;case 7:return $ce;case 8:return Dce;case 9:return Pce;case 10:return Fce;case 11:return Oce;case 14:return t?zce:Mce;case 12:return t?Vce:Lce;case 13:return t?Wce:Bce;case 15:return Uce;case 18:return Gce;case 16:return Hce;case 17:return jce;case 19:return qce;case 20:return Xce;case 21:return Kce;case 22:return Zce;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function la(e,t=!1){if(e===null)return null;if(e==="linear")return xi(8);if(e==="relu")return xi(12,t);if(e==="elu")return xi(4,t);if(e==="relu6")return xi(13,t);if(e==="prelu")return Gp(14,t);if(e==="sigmoid")return xi(18,t);if(e==="leakyrelu")return xi(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Yce(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function yn(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 ff(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function v3(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Ko(){return`
${v3()}
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 Qe(){return`
${Ko()}
let index = getGlobalIndex();
`}function Jce(e,t,n,r=!1){let s=[];if(s.push(`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),r===!0)return s.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
dispatchSize : vec3<u32>,
};
@group(0) @binding(0) var<storage, write> result: array<${ff(t.dtype,n.isVec4)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[o4,s.join(`
`),i4(t.shape),n.getUserCode()].join(`
`);let a="struct Uniforms { NAN : f32, ";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${yn(e[d].shape.length)}, `}),a+=`outShape : ${yn(t.shape.length)}, `;let o=t.shape.length-1;a+=`
outShapeStrides: ${yn(o)}, `,n.size&&(a+="size : i32, "),n.uniforms&&(a+=n.uniforms),a+="};",s.push(a),n.atomic?s.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`):s.push(`
@group(0) @binding(0) var<storage, write> result: array<${ff(t.dtype,n.isVec4)}>;
`),n.variableNames.forEach((p,d)=>{s.push(`
@group(0) @binding(${1+d}) var<storage, read> ${p}: array<${ff(e[d].dtype,n.isVec4)}>;
`)}),a!==""&&s.push(`
@group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
`);let[i,l]=sde(t.shape,n.dispatchLayout),u=[o4,s.join(`
`),i4(t.shape),i,Qce(t.shape.length)];if(n.atomic||u.push(ede(t.shape,t.dtype,n.isVec4)),l===t.shape.length){let p=e.map(d=>tde(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);u.push(p)}return u.push(n.getUserCode()),u.join(`
`)}var o4=`
// 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 Qce(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 ede(e,t,n){let r=e.length,s=ff(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result[flatIndex] = ${s}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result[flatIndex] = ${s}(value);
}`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result[flatIndex] = ${s}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result[flatIndex] = ${s}(value);
}`,r>=2){let o=["d0","d1","d2","d3"].slice(0,r),i=yn(r);n?a+=`
fn setOutputAtCoords(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:a+=`
fn setOutputAtCoords(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return a}function tde(e,t,n,r){let s=nde(e,n);return e.shape.length<=t.length&&(s+=rde(e,t,n,r)),s}function nde(e,t){let n=e.name,r=e.shape.length,s=yn(r),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,r),i=o.map(c=>`${c} : i32`).join(", ");if(r<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}[0]);
}
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,u=`${r}D`;return r===0&&(u="1D"),t?`
fn ${a}(${i}) -> vec4<f32> {
return vec4<f32>(${n}[getIndexFromCoords${u}(${s}(${o.join(",")}),
${l}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}[getIndexFromCoords${u}(${s}(${o.join(",")}),
${l})]);
}
`}function rde(e,t,n,r){let s=e.name,a=s.charAt(0).toUpperCase()+s.slice(1),o="get"+a+"ByOutput",i=e.shape.length,l=t.length,u=yn(l);if(w.arraysEqual(e.shape,t)&&r)return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${s}[globalIndex]);
}
fn ${o}Coords(coords : ${u}) -> vec4<f32> {
return vec4<f32>(${s}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${o}Index(globalIndex : i32) -> f32 {
return f32(${s}[globalIndex]);
}
fn ${o}Coords(coords : ${u}) -> f32 {
return f32(${s}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let c=C.getBroadcastDims(e.shape,t),p=l-i,d="";if(i===0)return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}Coords(coords : ${u}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}Index(globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}Coords(coords : ${u}) -> f32{
return get${a}();
}
`;l<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords[${g+p}] = 0;`).join(`
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=yn(i),y=e.shape.map((x,A)=>`coords[${A+p}]`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${s.charAt(0).toLowerCase()+s.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${d}
return ${s}[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
fn ${o}Coords(coordsIn : ${u}) -> vec4<f32> {
var coords = coordsIn;
${d}
return ${s}[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${d}
return f32(${s}[getIndexFromCoords${m}(${h}, ${f})]);
}
fn ${o}Coords(coordsIn : ${u}) -> f32 {
var coords = coordsIn;
${d}
return f32(${s}[getIndexFromCoords${m}(${h}, ${f})]);
}
`}function sde(e,t){let{x:n,y:r=[],z:s=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${yn(a)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,a];let o="",i=[n,r,s],l=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Yce(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let u=[];for(let d=0;d<l;d++)u.push(`d${d}`);let c=yn(l),p=`fn getOutputCoords() -> ${c} {
${o}
`;return u.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${u.join(",")}); }`,[p,l]}function i4(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),r=yn(t),s=[];for(let o=0;o<t;o++)s.push(`d${o}`);if(n.length===1)return` fn getCoordsFromIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${s[i]} = index2 / uniforms.outShapeStrides[${i}]`,u=i===n.length-1?`let ${s[i+1]} = index2 - ${s[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${s[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${u};`}).join("");return`
fn getCoordsFromIndex(index : i32) -> ${r} {
${a}
return ${r}(${s.join(",")});
}
`}var $8={};Le($8,{ArrayBufferToTypedArray:()=>P8,GPUBytesPerElement:()=>ry,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>w3,computeWorkGroupSizeForMatMul:()=>D8,computeWorkPerThreadForConv2d:()=>k3,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>S3,tilesFitEvenlyIntoShape:()=>Js});var Ri=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Js(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,r)=>n%e[r]===0)}function Oe(e,t,n=[1,1,1],r=[1,1,1]){let[s,a,o]=[Math.ceil(Ri(e.x.map(i=>t[i]))/(n[0]*r[0])),e.y?Math.ceil(Ri(e.y.map(i=>t[i]))/(n[1]*r[1])):1,e.z?Math.ceil(Ri(e.z.map(i=>t[i]))/(n[2]*r[2])):1];return[s,a,o]}function w3(e,t){let n=Ri(e.x.map(s=>t[s])),r=Ri(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function D8(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function k3(e,t){let n=Ri(e.x.map(s=>t[s])),r=Ri(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,n)=>n)}}function ry(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function P8(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 S3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function F8(e,t,n,r){return w.assert(r%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${r/e[0]}>, ${t}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${r}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${r};
${Ko()}
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 ade=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&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=o,this.activation=s,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],r=[this.tileAOuter,this.tileInner],s=[this.tileInner,this.tileBOuter];return[Js(r,this.aShape.slice(1)),Js(s,n.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)`,n="",r="";if(this.activation){let o=la(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${s}
${r}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${F8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function I3(e,t){let n=t[1]*e[1],r=t[0]*e[0],s=n>r?n:r;return`
var<workgroup> mm_Asub : array<array<f32, ${s}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${r}>, ${s}>;
${Ko()}
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) / ${s} + 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 = ${s} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${s} / ${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 * ${s} + 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 * ${s} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; 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 ode(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Ko()}
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 O8=class{constructor(e,t,n,r=!1,s=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=r?e[1]:e[2];this.workGroupSize=D8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let u=a!=null,c=i!=null;u&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=r,this.transposeB=s,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,l]:[this.outputShape[0],l,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${r}_${s}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,r=t>n?t:n;this.outputShape[1]===1&&(r*=4),w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[t,r],a=[r,n];return[Js(s,this.aShape.slice(1)),Js(a,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 n="",r="";if(this.activation){let o=la(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${s}
${r}
setOutputAtCoords(batch, row, col, value);
}
${this.outputShape[1]>1?I3([this.workPerThread,this.workPerThread,1],this.workGroupSize):ode(this.workGroupSize)}
`}};function ide(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Ko()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var lde=class{constructor(e,t=!1,n=!1,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=r!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}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 n="",r="";if(this.activation){let o=la(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
${e}
}
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${s}
${r}
setOutputAtCoords(batch, row, col, value);
}
${ide()}
`}};function ude(e){let t=e[1]/2,n=e[0],r=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${r}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${r}>;
var<workgroup> mm_Asub2 : array<array<f32, ${r}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${r}>;
// 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.
${Ko()}
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) / ${r} + 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 + ${r};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${r};
}
} 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 + ${r};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${r};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; 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 + ${r};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${r};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; 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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var cde=class{constructor(e,t,n,r=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=r!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=s,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}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;`,n="",r="";if(this.activation){let o=la(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,r="value = activation(value, outCoord);"}let s=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${s}
${r}
setOutputAtCoords(batch, row, col, value);
}
}
${ude(this.workGroupSize)}
`}};function qe(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:s}=n,a=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${r.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:o,dtype:r.dtype}}var dde={kernelName:hl,backendName:"webgpu",kernelFunc:qe};function C3({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),x=w.sizeFromShape(g),b=El.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let v=n?[y,p,h]:[y,h,p],S=r?[x,f,d]:[x,d,f],I=qe({inputs:{x:e},backend:s,attrs:{shape:v}}),E=qe({inputs:{x:t},backend:s,attrs:{shape:S}}),R=[I,E],P=Math.max(y,x),_=p%4===0&&f%4===0&&!n&&!r&&f>=32,D;h*f<=32?D=new lde([P,h,f],n,r,a,l,o):!n&&!r&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?D=new cde(v,S,[P,h,f],a,l,o):_?D=new ade(v,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):D=new O8(v,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,r,a,l,o);let T=[I,E];a&&T.push(a),o&&T.push(o);let F=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];l==="leakyrelu"&&(F.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let U=s.runWebGPUProgram(D,T,e.dtype,F),X=qe({inputs:{x:U},backend:s,attrs:{shape:b}});R.push(U);for(let z of R)s.disposeData(z.dataId);return X}function pde(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=r;return C3({a:s,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var hde={kernelName:Pa,backendName:"webgpu",kernelFunc:pde},l4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${Gp(this.op,!1)}
}
${Qe()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},fde=class{constructor(e,t,n,r){this.variableNames=["A","B"],this.size=!0;let s=256;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=r?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=r,this.op=e,this.shaderKey=`binaryShared_${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 {
${Gp(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Qe()}
// 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);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},mde=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${Gp(this.op,this.isVec4)}
}
${Qe()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},M8=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${Gp(this.op,!1)}
}
${Qe()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function u4(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new mde(e,t,n);let s=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return s||a?new fde(e,t,n,a):new M8(e,t,n)}function Or(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var gde={kernelName:go,backendName:"webgpu",kernelFunc:Or};function _c(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Or({inputs:{x:r},backend:n}),l=Or({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var yde={kernelName:Jd,backendName:"webgpu",kernelFunc:_c},Hp=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${xi(this.op,!1)}
}
${Qe()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function wn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:r,backend:s})=>{let{x:a}=r,o=s,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let u=o.tensorMap.get(a.dataId),c=t(u.values,i);return o.makeTensorInfo(a.shape,i,c)}let l=new Hp(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Zn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:r}){return({inputs:s,backend:a})=>{let{a:o,b:i}=s,l=a;if(n&&o.dtype==="complex64"){let p=l.tensorMap.get(o.dataId),d=l.tensorMap.get(i.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},v=u4(e,o.shape,i.shape);return l.runWebGPUProgram(v,[A,b],Nn(y.dtype,x.dtype))});else{let g=new l4(17,o.shape,i.shape),y=new l4(18,o.shape,i.shape),x=[{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},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=_c({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=r||Nn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let p=l.tensorMap.get(o.dataId).values,d=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?C.fromUint8ToStringArray(p):p,f=o.dtype==="string"?C.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let c=u4(e,o.shape,i.shape);return l.runWebGPUProgram(c,[o,i],u)}}var{addImpl:Ade,ceilImpl:xde,concatImpl:bde,equalImpl:vde,expImpl:wde,expm1Impl:kde,floorImpl:Sde,gatherNdImpl:Ide,gatherV2Impl:Cde,greaterEqualImpl:Tde,greaterImpl:Nde,lessEqualImpl:Ede,lessImpl:Rde,logImpl:_de,maxImpl:$de,maximumImpl:Dde,minimumImpl:Pde,multiplyImpl:Fde,negImpl:Ode,notEqualImpl:Mde,prodImpl:zde,rangeImpl:Lde,rsqrtImpl:Bde,simpleAbsImpl:Wde,sliceImpl:Vde,stridedSliceImpl:Ude,stringNGramsImpl:Gde,subImpl:Hde,tileImpl:jde,topKImpl:qde,transposeImpl:Xde,uniqueImpl:Axe}=x0,Kde=wn({opType:0,cpuKernelImpl:Wde}),Zde={kernelName:Gi,backendName:"webgpu",kernelFunc:Kde},Yde=Zn({opSnippet:1,cpuKernelImpl:Ade,supportsComplex:!0}),Jde={kernelName:ea,backendName:"webgpu",kernelFunc:Yde},Qde=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
${Qe()}
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, ${t});
}
}
}
`}};function epe(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Or({inputs:{x:r[0]},backend:n});let s=r.map(i=>i.dtype).reduce((i,l)=>Nn(i,l)),a=r.map(i=>i.shape),o=new Qde(a);return n.runWebGPUProgram(o,r,s)}var tpe={kernelName:Za,backendName:"webgpu",kernelFunc:epe},z8=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let r=[t];C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,e.length),this.op=n==="min"?"<":">";let[s]=C.computeOutAndReduceShapes(e,r);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=(s,a)=>this.outputShape.length===1?s:`${s}[${a}]`,n=s=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${s}]`;return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
let outputCoords = getCoordsFromIndex(outputIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${n(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${t("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Qe()}
let outputIndex = index / i32(workGroupSizeX);
let coordInfo = getInputCoordInfo(outputIndex);
let Length = ${n("uniforms.axis")};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x[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]);
}
}
`}},npe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(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]}>;
${v3()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = A[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},rpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=yn(this.outputShape.length),t=spe(this.newDim);return`
${Qe()}
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 spe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let r=0;r<e.length;r++)n[e[r]]=`resRC[${r}]`;return n.join()}function Qs(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=s.shape[a[c]];if(n.shouldExecuteOnCPU([s])){let p=o.tensorMap.get(s.dataId).values,d=Xde(p,s.shape,s.dtype,a,l);return n.makeTensorInfo(l,s.dtype,d)}if(s.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new npe(s.shape,a);return o.runWebGPUProgram(c,[s],s.dtype)}let u=new rpe(s.shape,a);return o.runWebGPUProgram(u,[s],s.dtype)}var ape={kernelName:Uo,backendName:"webgpu",kernelFunc:Qs};function ope(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=C.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Qs({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=C.getInnerMostAxes(o.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=new z8(l.shape,o[0],"max"),p=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var ipe={kernelName:Ya,backendName:"webgpu",kernelFunc:ope};function lpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=C.getAxesPermutation(o,s.shape.length),l=s,u=[];i!=null&&(l=Qs({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(l),o=C.getInnerMostAxes(o.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=new z8(l.shape,o[0],"min"),p=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var upe={kernelName:Vu,backendName:"webgpu",kernelFunc:lpe},L8=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=Xe(this.outputShape),this.dispatch=Oe(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"),`
${Qe()}
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});
}
}
`}},B8=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=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Qe()}
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 cpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1,c=C.computePool2DInfo(s.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Or({inputs:{x:s},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new B8(c):(p=new L8(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[s],s.dtype,d)}var dpe={kernelName:Ja,backendName:"webgpu",kernelFunc:cpe};function ppe(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return C3({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var hpe={kernelName:Qa,backendName:"webgpu",kernelFunc:ppe},fpe=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=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${yn(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=yn(this.rank),t=mpe(this.rank),n;return this.start.length===1?n=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((s,a)=>`sourceLoc.${sy[a]} = uniforms.start[${a}] + coords.${sy[a]};`),`
${Qe()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${n.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},sy=["x","y","z","w","u","v"];function mpe(e){if(e===1)return"sourceLoc";if(e<=6)return sy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function $c(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,l]=Mt.parseSliceParams(s,a,o);if(Mt.assertParamsValid(s,i,l),n.shouldExecuteOnCPU([s])||s.dtype==="string"){let p=n.tensorMap.get(s.dataId),d=Vde(p.values,i,l,s.shape,s.dtype);return n.makeTensorInfo(l,s.dtype,d)}if(w.sizeFromShape(l)===0)return n.makeTensorInfo(l,s.dtype,[]);let u=new fpe(i,l),c=[{type:"int32",data:i}];return n.runWebGPUProgram(u,[s],s.dtype,c)}var gpe={kernelName:Al,backendName:"webgpu",kernelFunc:$c},ype=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=C.getReshaped(s.shape,a,i),u=C.getPermuted(l.length,a.length),c=C.getReshapedPermuted(s.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=qe({inputs:{x:s},backend:n,attrs:{shape:l}}),m=Qs({inputs:{x:f},backend:n,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:n,attrs:{shape:c}}),y=$c({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),y},Ape={kernelName:Hi,backendName:"webgpu",kernelFunc:ype},W8=Zn({opSnippet:10,dtype:"bool",cpuKernelImpl:Mde}),xpe={kernelName:il,backendName:"webgpu",kernelFunc:W8};function jp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.tensorMap.get(r.dataId);return Or({inputs:{x:s.complexTensorInfos.real},backend:n})}var bpe={kernelName:ip,backendName:"webgpu",kernelFunc:jp};function vpe(e,t){let n=new Hp(e.shape,22),r=t.runWebGPUProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function ay(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return Or({inputs:{x:s},backend:n});let o=Ft(s.shape),i=ay({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),l=_c({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(s.dtype==="complex64"){let o=jp({inputs:{input:s},backend:n}),i=ay({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=Or({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return vpe(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=W8({inputs:{a:s,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var wpe={kernelName:eo,backendName:"webgpu",kernelFunc:ay},kpe=wn({opType:1,cpuKernelImpl:xde}),Spe={kernelName:to,backendName:"webgpu",kernelFunc:kpe},Ipe=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=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${Qe()}
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);
}
}
`}},Cpe=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=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${Qe()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Tpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return w.sizeFromShape(s.shape)%4===0?i=new Ipe(s.shape):i=new Cpe(s.shape),n.runWebGPUProgram(i,[s],s.dtype,l)}var Npe={kernelName:ta,backendName:"webgpu",kernelFunc:Tpe},Epe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(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${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;s<this.offsetLength;s++)e.push(`else if (yC < uniforms.offset${[s]}){ setOutputAtCoords(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s-1})); }`);let n=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${Qe()}
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 N0(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.tensorMap.get(r.dataId);return Or({inputs:{x:s.complexTensorInfos.imag},backend:n})}var Rpe={kernelName:rp,backendName:"webgpu",kernelFunc:N0};function oy(e,t,n){let r=e[0].dtype;if(r==="complex64"){let h=e.map(x=>jp({inputs:{input:x},backend:n})),f=e.map(x=>N0({inputs:{input:x},backend:n})),m=oy(h,t,n),g=oy(f,t,n),y=_c({inputs:{real:m,imag:g},backend:n});return h.forEach(x=>n.disposeData(x.dataId)),f.forEach(x=>n.disposeData(x.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),y}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let h=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:n,attrs:{shape:[-1,v]}})}),f=h.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),m=C.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=bde(f,m,r,g),x=C.computeOutShape(e.map(b=>b.shape),t),A=n.makeTensorInfo(x,r,y);return h.forEach(b=>n.disposeData(b.dataId)),A}let{tensors2D:a,outShape:o}=_pe(e,t,n),i=a.map(h=>h.shape),l=new Epe(i),u=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],u.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],u.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(l,a,a[0].dtype,u);a.forEach(h=>n.disposeData(h.dataId));let d=qe({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeData(p.dataId),d}function _pe(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:r}}function V8(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=C.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return Or({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return C.assertParamsConsistent(l,a),oy(i,a,n)}var $pe={kernelName:ji,backendName:"webgpu",kernelFunc:V8},Dpe=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,this.hasLeakyreluAlpha=s,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],r=this.outputShape[3],s=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Js(e,[n,s]),Js(t,[s,r])]}getSampleAWithRemainder(e){return`let flatIndex${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=F8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),r=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<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);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} else if (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,s=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${r}
}
return vec4<f32>(0.0);
`,a=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);
`,o="",i="";if(this.activation){let c=la(this.activation,this.isVec4);if(this.hasPreluActivationWeights)o=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${c}
}`;else{if(this.hasLeakyreluAlpha)throw o=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaByOutputCoords(outCoord);
${c}
}`,new Error("Leakyrelu is not supported.");o=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${c}
}`}i="value = activation(value, outCoord);"}let l=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${o}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${s}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${a}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${l}
${i}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${e}
`}},Ppe=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=w3(this.dispatchLayout,this.outputShape),this.elementsPerThread=k3(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[e,n],s=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Js(r,[a,i]),Js(s,[i,o])]}getUserCode(){let e=I3(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,r=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;
`,s="",a="";if(this.activation){let l=la(this.activation,!1);this.hasPreluActivationWeights?s=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${l}
}`:s=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${l}
}
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${s}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${r}
}
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);
${o}
${a}
result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},Fpe=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=r,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let s=la(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${s}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Ko()}
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}},Ope=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Qe()}
for(var i = 0; i<${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 Mpe({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let y=n.inHeight*n.inWidth*n.inChannels;h=qe({inputs:{x:e},backend:r,attrs:{shape:[1,n.batchSize,y]}}),f=qe({inputs:{x:t},backend:r,attrs:{shape:[1,y,n.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];h=qe({inputs:{x:e},backend:r,attrs:{shape:[1,y,n.inChannels]}}),f=qe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=C3({a:h,b:f,transposeA:c,transposeB:p,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=qe({inputs:{x:m},backend:r,attrs:{shape:n.outShape}});return r.disposeData(h.dataId),r.disposeData(f.dataId),r.disposeData(m.dataId),g}function zpe({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=n,A=x==="channelsLast",b=l*u*c,v=m*f,S=[v,b],I=!1,E=!1,R=[],P=qe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),_=qe({inputs:{x:t},backend:r,attrs:{shape:[1,b,-1]}});R.push(P),R.push(_);let D=new Ope(S,A),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[c*l]},{type:"int32",data:[c]}],F=r.runWebGPUProgram(D,[P],P.dtype,T),U=qe({inputs:{x:F},backend:r,attrs:{shape:[1,S[0],S[1]]}});R.push(F),R.push(U);let X=[1,S[0],S[1]],z=new O8(X,[1,v,n.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),I,E,s,i,a),Z=X[1],W=X[2],ee=n.outChannels,Q=[{type:"int32",data:[Z]},{type:"int32",data:[ee]},{type:"int32",data:[W]}],ae=[U,_];s&&ae.push(s),a&&ae.push(a),i==="leakyrelu"&&(T.push({type:"float32",data:[o]}),z.uniforms+=" alpha : f32,");let J=r.runWebGPUProgram(z,ae,U.dtype,Q),se=A?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],ie=qe({inputs:{x:J},backend:r,attrs:{shape:se}});R.push(J);for(let me of R)r.disposeData(me.dataId);return ie}function U8({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=s!=null,u=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Mpe({x:e,filter:t,convInfo:n,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return zpe({x:e,filter:t,convInfo:n,backend:r,bias:s,preluActivationWeights:a,leakyreluAlpha:o,activation:i});let d=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Fpe(n,l,i,u);else{h?c=new Dpe(n,l,i,u):c=new Ppe(n,l,i,u);let y=n.outShape[1]*n.outShape[2],x=n.outShape[3],A=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[y]},{type:"int32",data:[x]},{type:"int32",data:[A]})}let g=[e,t];return l&&g.push(s),u&&g.push(a),i==="leakyrelu"&&(m.push({type:"float32",data:[o]}),c.uniforms+=" alpha : f32,"),r.runWebGPUProgram(c,g,e.dtype,m)}function Lpe(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(s.shape,a.shape,o,u,i,c,!1,p);return U8({x:s,filter:a,convInfo:d,backend:r})}var Bpe={kernelName:no,backendName:"webgpu",kernelFunc:Lpe},Wpe=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=w3(this.dispatchLayout,this.outputShape),this.elementsPerThread=k3(this.dispatchLayout,this.outputShape),this.dispatch=Oe(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;
}
${I3(this.elementsPerThread,this.workGroupSize)}
`}},Vpe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Qe()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function Upe(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=r,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Vpe(d);else{f=new Wpe(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[s,a],"float32",h)}var Gpe={kernelName:ro,backendName:"webgpu",kernelFunc:Upe},Hpe=wn({opType:2}),jpe={kernelName:so,backendName:"webgpu",kernelFunc:Hpe},qpe=wn({opType:3}),Xpe={kernelName:ao,backendName:"webgpu",kernelFunc:qpe},Kpe=class{constructor(e,t,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,n[0],n[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=r==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,r,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
${Qe()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${r};
let width_scale = ${o};
let in_y = ${s};
if( in_y < 0.0 || in_y > ${e} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let in_x = ${i};
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);
}
}
}
`}},Zpe=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=r,c=new Kpe(s.shape[3],a.shape,i,l),p=[{type:"float32",data:[u]}];return n.runWebGPUProgram(c,[s,a,o],"float32",p)},Ype={kernelName:Xi,backendName:"webgpu",kernelFunc:Zpe},c4=class{constructor(e,t,n,r){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=r,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",n=this.exclusive?t:`getX(${d4(e,"coords",this.op)})`,r=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${r-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${r}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
${Qe()}
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${p4(e,"coords",this.op)};
var val = ${n};
let pow2 = i32(pow(2.0, uniforms.index));
if (${s}) {
let idx = ${a};
${p4(e,"coords",this.op)} = idx;
val ${this.op}= getX(${d4(e,"coords",this.op)});
}
setOutputAtIndex(index, val);
}
}
`}};function d4(e,t,n){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 ${n} for rank ${e} is not yet supported`)}function p4(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function G8(e,t,n,r,s,a){let o=t.shape.length,i=C.getAxesPermutation([r],o),l=t;i!=null&&(l=Qs({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);let c=l.shape[u],p=Or({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new c4(e,l.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(s){let d=new c4(e,l.shape,s,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=Qs({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(l.dataId),h}return p}function Jpe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return G8("*",s,n,a,o,i)}var Qpe={kernelName:qi,backendName:"webgpu",kernelFunc:Jpe};function ehe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return G8("+",s,n,a,o,i)}var the={kernelName:oo,backendName:"webgpu",kernelFunc:ehe},nhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${Qe()}
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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function rhe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],l=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new nhe(f,o);return n.runWebGPUProgram(g,[s],s.dtype,m)}var she={kernelName:Ki,backendName:"webgpu",kernelFunc:rhe},H8=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let s=la(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${s}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${v3()}
fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},j8=class{constructor(e,t=!1,n=null,r=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=r,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let s=la(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${s}
}
`,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
${e}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Ko()}
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / uniforms.channelMul;
let q = d2 - d1 * uniforms.channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + uniforms.filterHeight *
uniforms.dilation[0];
let inputColEnd = inputColStart + uniforms.filterWidth *
uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] &&
inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${n}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function ahe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(s.shape,a.shape,o,c,i,u,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new H8(p):(h=new j8(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[s,a],s.dtype,d)}var ohe={kernelName:io,backendName:"webgpu",kernelFunc:ahe},q8=Zn({opSnippet:0,cpuKernelImpl:Fde,supportsComplex:!0}),ihe={kernelName:Co,backendName:"webgpu",kernelFunc:q8},lhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${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 n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`}
fn getOffset(outputIndex : i32) -> i32 {
let outputCoords = getCoordsFromIndex(outputIndex);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Qe()}
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) {
${n}
}
}
`}};function qp(e,t,n,r,s){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),l=i,u=C.getAxesPermutation(l,a),c=e;u!=null&&(c=Qs({inputs:{x:e},attrs:{perm:u},backend:s}),l=C.getInnerMostAxes(l.length,a),o.push(c)),C.assertAxesAreInnerMostDims(r,l,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,l),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f;if((r==="max"||r==="prod")&&s.shouldExecuteOnCPU([c])){let m=s.tensorMap.get(c.dataId).values;switch(r){case"max":let g=$de(m,w.sizeFromShape(d),h,e.dtype);f=s.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=zde(c.shape,c.dtype,m,l);f=s.makeTensorInfo(x,A,y);break;default:throw new Error(`${r} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),y=w.sizeFromShape(c.shape)/m,x={windowSize:m,inSize:m,batchSize:y,outSize:1},A=r==="mean"?"float32":gp(e.dtype),b=[{type:"int32",data:[m]}],v=new lhe(x,r),S=s.runWebGPUProgram(v,[c],A,b);o.push(S),f=qe({inputs:{x:S},attrs:{shape:h},backend:s})}return o.forEach(m=>s.disposeData(m.dataId)),f}function T3(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return qp(s,a,o,"sum",n)}var uhe={kernelName:zo,backendName:"webgpu",kernelFunc:T3};function che(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:l}=C.decodeEinsumEquation(s,a.length);C.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=C.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=C.getEinsumPermutation(h,l[g]),A;C.isIdentityPermutation(y)?A=a[g]:(A=Qs({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let v=0;v<x.length;++v)b.splice(x[v],0,1);w.arraysEqual(A.shape,b)||(A=qe({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=q8({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=T3({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var dhe={kernelName:np,backendName:"webgpu",kernelFunc:che},phe=wn({opType:4}),hhe={kernelName:uo,backendName:"webgpu",kernelFunc:phe},fhe=Zn({opSnippet:4,dtype:"bool",cpuKernelImpl:vde}),mhe={kernelName:Zi,backendName:"webgpu",kernelFunc:fhe},X8=wn({opType:5,cpuKernelImpl:wde,dtype:"float32"}),ghe={kernelName:co,backendName:"webgpu",kernelFunc:X8};function iy(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+s+1),i.splice(l,0,1),qe({inputs:{x:a},backend:r,attrs:{shape:i}})}var yhe={kernelName:Yi,backendName:"webgpu",kernelFunc:iy},Ahe=wn({opType:6,cpuKernelImpl:kde}),xhe={kernelName:Ji,backendName:"webgpu",kernelFunc:Ahe},bhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${Qe()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function Dc(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new bhe(r),i=[{type:"float32",data:[s]}];return t.runWebGPUProgram(o,[],a,i)}}var vhe={kernelName:Ku,backendName:"webgpu",kernelFunc:Dc},whe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${Qe()}
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);
}
}
`}},khe={kernelName:Qi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new whe(n.shape);return r.runWebGPUProgram(s,[n],n.dtype)}},She=wn({opType:7,cpuKernelImpl:Sde}),Ihe={kernelName:po,backendName:"webgpu",kernelFunc:She},Che=Zn({opSnippet:12,dtype:"int32"}),The={kernelName:ho,backendName:"webgpu",kernelFunc:Che},Nhe=(e,t,n,r,s)=>{let a=[r,...n];return s&&a.push(s),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},K8=(e,t,n,r,s,a=!1)=>{let o={dtype:s.dtype,shape:s.shape},i=Jce(r,o,t,a),l=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function Z8(e,t,n,r="",s=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+r+s}function h4(e){let{externalImage:t,backend:n,attrs:r,outShape:s,useImport:a}=e,{numChannels:o}=r,i=w.sizeFromShape(s),l=w.computeStrides(s),u=n.makeTensorInfo(s,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(s);let p=[u.shape],d=[u.dtype,a?"import":"copyExternal"],h=Z8(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>K8(n.device,c,f.pipelineLayout,[],u,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,s[1],s[0])},[s[1],s[0]]);let g=n.tensorMap.get(u.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let y=[i,o,...l,...c.dispatch];c.setUniform(n.device,y);let x;if(a){let A={source:t};x=n.device.importExternalTexture(A)}else x=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,x,u.dataId),u}var Ehe={kernelName:Md,backendName:"webgpu",kernelFunc:Rhe},pu;function Rhe(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r;if(s==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&s instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&s instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&s instanceof ImageBitmap,[c,p]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],d=[p,c,a];if(Y().getBool("WEBGPU_USE_IMPORT")&&o)return h4({externalImage:s,backend:n,attrs:r,outShape:d,useImport:!0});if((o||i)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=c,pu.canvas.height=p,pu.drawImage(s,0,0,c,p),s=pu.canvas),u||l||o||i)return h4({externalImage:s,backend:n,attrs:r,outShape:d,useImport:!1});let h=s.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(s.width*s.height*a);let y=h.length,x=0;for(let A=0;A<y;A++)A%4<a&&(f[x++]=h[A])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var _he=class{constructor(e,t,n,r,s){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset")),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale")),this.offsetShape=r,this.scaleShape=s,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)"),`
${Qe()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},$he={kernelName:fo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r,scale:s,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,u=n,c=[r,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;s!=null&&(d=s.shape,c.push(s));let h=new _he(r.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,c,r.dtype,f)}};function Dhe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(s.shape,a.shape,l,p,u,d,!1,m);return U8({x:s,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var Phe={kernelName:Fa,backendName:"webgpu",kernelFunc:Dhe};function Fhe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=c;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 m=C.computeConv2DInfo(s.shape,a.shape,l,f,u,p,!0),g=[s,a],y=o!=null,x=i!=null;y&&g.push(o),x&&g.push(i);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new H8(m,y,d,x):(b=new j8(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),n.runWebGPUProgram(b,g,"float32",A)}var Ohe={kernelName:Oa,backendName:"webgpu",kernelFunc:Fhe},Mhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${yn(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Qe()}
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 zhe(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[l,u,c,p]=C.prepareAndValidate(r,s),d=qe({inputs:{x:s},backend:n,attrs:{shape:[u,o]}}),h=qe({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let x=n.readSync(s.dataId),A=n.bufferSync(r),b=Ide(x,A,r.dtype,u,o,c,p,r.shape,i);return n.makeTensorInfo(l,r.dtype,b.values)}let f=new Mhe(o,[u,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),y=qe({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var Lhe={kernelName:tl,backendName:"webgpu",kernelFunc:zhe},Bhe=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=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Whe(this.aShape);return`
${Qe()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`}};function Whe(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e.length;r++)r===2?n.push("indexZ"):n.push(`${t[r]}`);return n.join()}function Y8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,l=w.parseAxisParam(o,s.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(s,a,l,i),c=w.sizeFromShape(a.shape),p=[],d=qe({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=qe({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([s,a])){let A=n.tensorMap.get(h.dataId).values,b=We(h.shape,h.dtype,A),S=n.tensorMap.get(d.dataId).values,I=We(d.shape,d.dtype,S),E=Cde(I,b,f);return p.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(u.outputShape,E.dtype,E.values)}let m=new Bhe(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let y=qe({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeData(x.dataId)),y}var Vhe={kernelName:el,backendName:"webgpu",kernelFunc:Y8},Uhe=Zn({opSnippet:5,cpuKernelImpl:Nde,dtype:"bool"}),Ghe={kernelName:nl,backendName:"webgpu",kernelFunc:Uhe},Hhe=Zn({opSnippet:6,dtype:"bool",cpuKernelImpl:Tde}),jhe={kernelName:mo,backendName:"webgpu",kernelFunc:Hhe};function qhe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=[{type:"float32",data:[a]}],i=new Hp(s.shape,14);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[s],"float32",o)}var Xhe={kernelName:yo,backendName:"webgpu",kernelFunc:qhe},Khe=Zn({opSnippet:7,dtype:"bool",cpuKernelImpl:Rde}),Zhe={kernelName:rl,backendName:"webgpu",kernelFunc:Khe},Yhe=Zn({opSnippet:8,dtype:"bool",cpuKernelImpl:Ede}),Jhe={kernelName:sl,backendName:"webgpu",kernelFunc:Yhe},Qhe=wn({opType:9,cpuKernelImpl:_de}),efe={kernelName:Ao,backendName:"webgpu",kernelFunc:Qhe},tfe=Zn({opSnippet:9,dtype:"bool"}),nfe={kernelName:al,backendName:"webgpu",kernelFunc:tfe},rfe=wn({opType:10}),sfe={kernelName:ec,backendName:"webgpu",kernelFunc:rfe};function J8(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r;return qp(s,a,o,"max",n)}var afe={kernelName:xo,backendName:"webgpu",kernelFunc:J8},ofe=Zn({opSnippet:15,cpuKernelImpl:Dde}),ife={kernelName:bo,backendName:"webgpu",kernelFunc:ofe};function lfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=r,u=1,c=C.computePool2DInfo(s.shape,a,o,u,i,l),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Or({inputs:{x:s},backend:n});p=new B8(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new L8(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[s],s.dtype,d)}var ufe={kernelName:vo,backendName:"webgpu",kernelFunc:lfe};function cfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{keepDims:a,axis:o}=r;return qp(s,o,a,"mean",n)}var dfe={kernelName:wo,backendName:"webgpu",kernelFunc:cfe};function pfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return qp(s,a,o,"min",n)}var hfe={kernelName:ko,backendName:"webgpu",kernelFunc:pfe},ffe=Zn({opSnippet:16,cpuKernelImpl:Pde}),mfe={kernelName:So,backendName:"webgpu",kernelFunc:ffe},gfe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,s)=>r[0]+e[s]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((r,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),n=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),r=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=yn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Qe()}
if (index < uniforms.size) {
let start = ${o}(${t});
let end = ${o}(${n});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${r}) {
${a} = ${r} * 2 - ${a} - ${this.offset};
} else if(${a} >= ${s}) {
${a} = (${s} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${i}));
}
}
`}},yfe={kernelName:Io,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{paddings:s,mode:a}=t,o=n,i=s.map(c=>({type:"int32",data:[c[0],c[1]]})),l=new gfe(r.shape,s,a);return o.runWebGPUProgram(l,[r],r.dtype,i)}};function Afe(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.tensorMap.get(r.dataId),[o,i]=Ode(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s=new Hp(r.shape,11);return n.runWebGPUProgram(s,[r],r.dtype)}var xfe={kernelName:ol,backendName:"webgpu",kernelFunc:Afe};function bfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=r,u=n.readSync(s.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=Kr.nonMaxSuppressionV3Impl(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var vfe={kernelName:ll,backendName:"webgpu",kernelFunc:bfe};function wfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(s.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Kr.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var kfe={kernelName:ul,backendName:"webgpu",kernelFunc:wfe};function Hf(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=jp({inputs:{input:r},backend:n}),a=Hf({inputs:{x:s},backend:n}),o=N0({inputs:{input:r},backend:n}),i=Hf({inputs:{x:o},backend:n}),l=_c({inputs:{real:a,imag:i},backend:n});return n.disposeData(s.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return Dc({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Sfe={kernelName:Tl,backendName:"webgpu",kernelFunc:Hf};function Q8(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=jp({inputs:{input:r},backend:n}),a=Q8({inputs:{x:s},backend:n}),o=N0({inputs:{input:r},backend:n}),i=Hf({inputs:{x:o},backend:n}),l=_c({inputs:{real:a,imag:i},backend:n});return n.disposeData(s.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return Dc({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Ife={kernelName:cl,backendName:"webgpu",kernelFunc:Q8};function Cfe(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return iy({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=iy({inputs:{input:c},backend:n,attrs:{dim:s}});return i.push(p),p}),u=V8({inputs:l,backend:n,attrs:{axis:s}});return i.forEach(c=>n.disposeData(c.dataId)),u}var Tfe={kernelName:pl,backendName:"webgpu",kernelFunc:Cfe},Nfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=yn(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),r=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),s=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${r})`:`${r}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Qe()}
if (index < uniforms.size) {
let start = ${s};
let end = ${a};
let outC = getCoordsFromIndex(index);
if (${o} || ${i}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${l}));
}
}
}
`}},eC=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(a.every(u=>w.arraysEqual(u,[0,0])))return Or({inputs:{x:s},backend:n});if(w.sizeFromShape(s.shape)===0){let u=a.map((c,p)=>c[0]+s.shape[p]+c[1]);return Dc({backend:n,attrs:{shape:u,value:o,dtype:s.dtype}})}let i=[{type:"float32",data:[o]}];a.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let l=new Nfe(s.shape,a);return n.runWebGPUProgram(l,[s],s.dtype,i)},Efe={kernelName:To,backendName:"webgpu",kernelFunc:eC},Rfe=Zn({opSnippet:13}),_fe={kernelName:No,backendName:"webgpu",kernelFunc:Rfe};function $fe(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=new M8(14,r.shape,s.shape);return n.runWebGPUProgram(a,[r,s],"float32")}var Dfe={kernelName:Eo,backendName:"webgpu",kernelFunc:$fe};function Pfe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return qp(s,a,o,"prod",n)}var Ffe={kernelName:Ro,backendName:"webgpu",kernelFunc:Pfe},Ofe=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=Lde(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Mfe={kernelName:rc,backendName:"webgpu",kernelFunc:Ofe},tC=Zn({opSnippet:3}),zfe={kernelName:lo,backendName:"webgpu",kernelFunc:tC},Lfe=wn({opType:12}),Bfe={kernelName:_o,backendName:"webgpu",kernelFunc:Lfe},Wfe=wn({opType:13}),Vfe={kernelName:Do,backendName:"webgpu",kernelFunc:Wfe},Ufe=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${Qe()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`}};function Gfe(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,size:o,halfPixelCenters:i}=r,[l,u]=o,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new Ufe(s.shape,l,u);return n.runWebGPUProgram(f,[s],"float32",h)}var Hfe={kernelName:$o,backendName:"webgpu",kernelFunc:Gfe},jfe=class{constructor(e,t,n,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${r}`}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",`
${Qe()}
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 qfe(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[l,u]=i,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new jfe(s.shape,l,u,o);return n.runWebGPUProgram(f,[s],s.dtype,h)}var Xfe={kernelName:ac,backendName:"webgpu",kernelFunc:qfe},Kfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(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`
${Qe()}
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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},Zfe={kernelName:Nl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,l=new Kfe(r.shape,a),[u,c]=C.getImageCenter(o,r.shape[1],r.shape[2]),p=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(s)]},{type:"float32",data:[Math.cos(s)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(l,[r],r.dtype,p)}},Yfe=wn({opType:15,cpuKernelImpl:Bde}),Jfe={kernelName:Po,backendName:"webgpu",kernelFunc:Yfe},Qfe=class{constructor(e,t,n,r,s,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Xe(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${r}_${this.sliceDimGreaterThanOne}_${o}`;let i=yn(s.length);this.uniforms=`sliceDim : i32, strides: ${i}, size: i32,`,this.updatesRank=r,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",r="",s="",a="";this.updatesRank===1?(r="coords[0]",s="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(r="coords[0], coords[1]",s="vec2<i32>(flattenedIndex, coords[1])",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let o=`getUpdates(${r})`,i=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`
${a}
${Qe()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${o};
let flatIndex = getOutputIndexFromCoords(${s});
${i}
}
}`}};function eme(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=C.calculateShapes(a,s,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,s.dtype);let h=qe({inputs:{x:s},backend:n,attrs:{shape:[l,i]}}),f=qe({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=f.dtype,g=Dc({backend:n,attrs:{shape:d,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[y]}],A=new Qfe(f.shape,i,h.shape.length,f.shape.length,c,d,m),b=n.runWebGPUProgram(A,[f,h],m,x,g),v=qe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),v}var tme={kernelName:gl,backendName:"webgpu",kernelFunc:eme},nme=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${r[o]}`),o<this.cRank&&s.push(`${r[o]}`);e=s.join(),t=a.join()}return`
${Qe()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function rme(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new nme(r.shape.length,s.shape,s.shape.length);return n.runWebGPUProgram(o,[r,s,a],Nn(s.dtype,a.dtype))}var sme={kernelName:yl,backendName:"webgpu",kernelFunc:rme},ame=wn({opType:18}),ome={kernelName:Oo,backendName:"webgpu",kernelFunc:ame},ime=wn({opType:16}),lme={kernelName:Fo,backendName:"webgpu",kernelFunc:ime},ume=wn({opType:17}),cme={kernelName:xl,backendName:"webgpu",kernelFunc:ume},nC=Zn({opSnippet:2,cpuKernelImpl:Hde,supportsComplex:!0}),dme={kernelName:Wo,backendName:"webgpu",kernelFunc:nC};function pme(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=J8({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=C.expandShapeToKeepDim(i.shape,o),u=qe({inputs:{x:i},backend:n,attrs:{shape:l}}),c=nC({inputs:{a:s,b:u},backend:n}),p=X8({inputs:{x:c},backend:n}),d=T3({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=qe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=tC({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(u.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var hme={kernelName:Lo,backendName:"webgpu",kernelFunc:pme},fme=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<s.shape.length;++y)l.push([0,0]);let u=[],c=eC({inputs:{x:s},backend:n,attrs:{paddings:l,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=qe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Qs({inputs:{x:f},backend:n,attrs:{perm:d}}),g=qe({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeData(y.dataId)),g},mme={kernelName:bl,backendName:"webgpu",kernelFunc:fme},gme=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${r}_${i}`;let l=yn(s.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";n===1?u="i":n===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let c="";r===1?c="i":r===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Qe()}
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 < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function yme(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=C.calculateShapes(a,s,i),d=!1,h=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:c}],f=new gme(u,l,s.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,s,o],a.dtype,h),g=qe({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var Ame={kernelName:dp,backendName:"webgpu",kernelFunc:yme};function xme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],l=C.prepareSplitSize(s,a,i),u=s.shape.length,c=new Array(u).fill(0),p=s.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=$c({inputs:{x:s},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var bme={kernelName:vl,backendName:"webgpu",kernelFunc:xme},vme=wn({opType:19}),wme={kernelName:Mo,backendName:"webgpu",kernelFunc:vme},kme={kernelName:cc,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t,s=new Hp(n.shape,20);return r.runWebGPUProgram(s,[n],n.dtype)}},Sme=Zn({opSnippet:11}),Ime={kernelName:Bo,backendName:"webgpu",kernelFunc:Sme},Cme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=yn(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((s,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Qe()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function Tme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(s.shape,a,o,i,l,u,c,p,d),v;if(m)v=qe({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||y){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let S=Mt.computeOutShape(x,A,b),I=$c({inputs:{x:s},backend:n,attrs:{begin:x,size:S}});v=qe({inputs:{x:I},backend:n,attrs:{shape:f}}),n.disposeData(I.dataId)}else if(n.shouldExecuteOnCPU([s])){let I=n.readSync(s.dataId),E=We(s.shape,s.dtype,I),R=Ude(h,E,b,x);v=n.makeTensorInfo(f,s.dtype,R.values)}else{let I=new Cme(h),E=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(I,[s],s.dtype,E);v=qe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return v}var Nme={kernelName:wl,backendName:"webgpu",kernelFunc:Tme};function Eme(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=r,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Gde(d,h,s,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Rme={kernelName:pp,backendName:"webgpu",kernelFunc:Eme},_me=wn({opType:21}),$me={kernelName:Vo,backendName:"webgpu",kernelFunc:_me},Dme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Pme(this.rank,"uniforms.");return`
${Qe()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Pme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e;s++)r.push(`(${n[s]} % ${t}aShape[${s}])`);return r.join()}function Fme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(n.shouldExecuteOnCPU([s])||s.dtype==="string"||s.shape.length>=5){let l=n.readSync(s.dataId),u=s.dtype==="string"?l.map(d=>w.decodeString(d)):l,c=We(s.shape,s.dtype,u),p=jde(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Dme(s.shape,a);return n.runWebGPUProgram(o,[s],s.dtype)}var Ome={kernelName:na,backendName:"webgpu",kernelFunc:Fme},Mme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
${Qe()}
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));
}
}
}
`}},zme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
${Qe()}
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 f4(e){let t=1;for(;t<e;)t*=2;return t}function Lme(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=s.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([s])){let v=n.readSync(s.dataId),[S,I]=qde(v,i,s.dtype,a,o);return[n.makeTensorInfo(S.shape,S.dtype,S.values),n.makeTensorInfo(I.shape,I.dtype,I.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,s.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[s,Dc({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(i)/l,p=qe({inputs:{x:s},attrs:{shape:[c,l]},backend:n}),d=f4(a),h=f4(l),f=null,m=()=>f===null?[p,p]:[p,f],g=(v,S,I)=>{let E=m(),R=new Mme(I),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[v]},{type:"int32",data:[S]}],D=f;f=n.runWebGPUProgram(R,E,"int32",_),hu(n,D)};for(let v=1;v<d;v*=2){let S=v*2;for(let I=v;I>=1;I/=2)g(S,I,[c,h])}for(let v=h;v>d;v/=2){let S=m(),I=new zme([c,v/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(I,S,"int32",R),hu(n,P);let _=d/2,D=_*2;for(let T=_;T>=1;T/=2)g(D,T,f.shape)}let y=f;f=$c({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),hu(n,y);let x=Y8({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});hu(n,p);let A=i.slice(0,-1);A.push(a),y=f,f=qe({inputs:{x:f},attrs:{shape:A},backend:n}),hu(n,y);let b=x;return x=qe({inputs:{x},attrs:{shape:A},backend:n}),hu(n,b),[x,f]}var Bme={kernelName:Sl,backendName:"webgpu",kernelFunc:Lme},Wme=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=Xe(this.outputShape),this.dispatch=Oe(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;
}
${Qe()}
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 Vme(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=r,[c,p,d,h]=s.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new Wme(g),x=o==="nearest"?1:2,A;switch(i){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[s,a],"float32",b)}var Ume={kernelName:Il,backendName:"webgpu",kernelFunc:Vme};function Gme(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,l=s.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=$c({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=qe({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Hme={kernelName:Cl,backendName:"webgpu",kernelFunc:Gme},jme=[hde,Zde,Jde,tpe,ipe,upe,dpe,hpe,Ape,wpe,Spe,Npe,yde,$pe,Bpe,Gpe,jpe,Xpe,Ype,Qpe,the,she,ohe,dhe,hhe,mhe,ghe,yhe,xhe,vhe,khe,Ehe,Ihe,The,$he,Phe,Ohe,Lhe,Vhe,Ghe,jhe,gde,Rpe,Xhe,Zhe,Jhe,efe,nfe,sfe,afe,ife,ufe,dfe,hfe,mfe,yfe,ihe,xfe,vfe,kfe,xpe,Ife,Tfe,Efe,_fe,Dfe,Ffe,Mfe,bpe,zfe,Bfe,Vfe,dde,Hfe,Xfe,Zfe,Jfe,tme,sme,ome,lme,cme,gpe,Nme,Rme,hme,mme,Ame,bme,wme,kme,Ime,dme,uhe,$me,Ome,Bme,Ume,ape,Hme,Sfe];for(let e of jme)qr(e);var qme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let r=m4(e,t);if(this.freeBuffers.has(r)||this.freeBuffers.set(r,[]),this.usedBuffers.has(r)||this.usedBuffers.set(r,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(r).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(r).shift();return this.usedBuffers.get(r).push(a),a}this.numBytesAllocated+=e;let s=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(r).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let r=m4(t,n);this.freeBuffers.has(r)||this.freeBuffers.set(r,[]),this.freeBuffers.get(r).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(r),a=s.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");s.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},r=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function m4(e,t){return`${e}_${t}`}var rC=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
@binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Qe()}
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]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,r)=>n===this.lastUniformData[r])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),r=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}},Xme=class extends rC{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),r=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}},Kme=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),g4=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,r=t.dispatchLayout,s=t.dispatch;if(s.every(o=>o<=n))return s;w.assert(s[0]>n&&r.y===void 0&&r.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(s[0]));return a>n?(a=Math.ceil(Math.cbrt(s[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},sC=class extends Ou{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!S3())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 qme(this.device),this.tensorMap=new Zd(this,sn()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().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 sC.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:r}=this.tensorMap.get(e);r!=null&&(this.disposeData(r.real.dataId,!0),this.disposeData(r.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()},s=w.sizeFromShape(t)*ry(n);return this.tensorMap.set(r,{dtype:n,values:e,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:1}),r}move(e,t,n,r,s){if(r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*ry(r);this.tensorMap.set(e,{dtype:r,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new rC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Xme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}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 n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let r;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],o=s[1];r=C.mergeRealAndImagArrays(a,o)}else{let s=await this.getBufferData(t);r=P8(s,t.dtype)}return this.convertAndCacheOnCPU(e,r),r}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(s);return o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),r=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(r).set(t.values):new Float32Array(r).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let s={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(s)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let o;switch(a.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 ${a.data.length}D shape`)}t=Math.ceil(t/o)*o,n.push(t),t+=a.data.length*4});let r=new ArrayBuffer(t);e.forEach((a,o)=>{let i=n[o];a.type==="int32"?new Int32Array(r,i,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(r,i,a.data.length).set(a.data):new Float32Array(r,i,a.data.length).set(a.data)});let s=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(s,0,r,0,t),{offset:0,size:t,buffer:s}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let s=0;s<e;s++)t.push({binding:s+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),r=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:r}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,r,s){if(!s){if(s=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(s.shape)===0){let I=this.tensorMap.get(s.dataId);return I.values=w.getTypedArrayFromDType(s.dtype,0),s}this.uploadToGPU(s.dataId)}e.dispatch=g4(this.device,e);let a=[{type:"float32",data:[NaN]}],o=t.concat(s).map(I=>I.shape),i="int32";o.map(I=>{a.push({type:i,data:I})});let l=w.computeStrides(s.shape);if(a.push({type:i,data:l}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?I/4:I]})}r&&(a=[...a,...r]);let u=this.makeUniforms(a),c=t.map((I,E)=>{if(I.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(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[E]}}),p=c.map(I=>I.dtype).concat(s.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,s.shape)),h=c.map(I=>w.arraysEqual(I.shape,s.shape)).join("_"),f=d.map(I=>I.join("_")).join(";"),m=Z8(e,o,p,f,h),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),x=this.getAndSavePipeline(m,()=>K8(this.device,e,y,c,s)),A=this.activeTimers!=null,b=Nhe(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(s),u);this.ensureCommandEncoderReady();let v=this.getComputePass();A&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(x),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),A&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(s.dataId);let S={byteSize:u.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};return this.uniformDisposalQueue.push(S),Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),A&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}runFromPixelsProgram(e,t,n,r,s){e.dispatch=g4(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:r},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(s),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let r=new BigUint64Array(n.getMappedRange()),s=Number(r[1]-r[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=Kme){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},N3=sC;N3.nextDataId=0;var aC={};Le(aC,{WebGPUBackend:()=>N3,webgpu_util:()=>$8});S3()&&Rl("webgpu",async()=>{Y().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Y().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,r={},s=t.features.has("timestamp-query");r.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},s?r.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(r);return new N3(a,s)},3);var Ut=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ut||{}),E0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(E0||{}),oC;function Zme(e){oC=e.wasm.cwrap(Pa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Yme(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet 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L0e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(s.shape,a.shape,l,u,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,x=f.padInfo.right,A=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,S=f.dilationWidth,I=f.strideHeight,E=f.strideWidth,R=f.inChannels,P=f.outChannels,_=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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V0e(e){let{backend:t,inputs:n,attrs:r}=e,{dy:s,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=r,p=1,d=C.convertConv2DDataFormat(l),h=C.computeConv2DInfo(c,a.shape,o,p,i,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:v,outWidth:S,strideHeight:I,strideWidth:E}=h,R=m-1-h.padInfo.top,P=g-1-h.padInfo.left,_=h.dataFormat==="channelsLast",D=w.computeStrides(h.inShape),T=w.computeStrides(s.shape),[F,U,X]=w.computeStrides(a.shape),z=D[0],Z=_?D[1]:D[2],W=_?D[2]:1,ee=_?1:D[1],Q=T[0],ae=_?T[1]:T[2],J=_?T[2]:1,se=_?1:T[1],ie=t.makeOutput(h.inShape,"float32"),me=t.dataIdMap.get(ie.dataId).id,be=t.dataIdMap.get(s.dataId).id,Ee=t.dataIdMap.get(a.dataId).id;return yC(be,Ee,f,m,g,x,A,y,v,S,b,I,E,R,P,F,U,X,z,Z,W,ee,Q,ae,J,se,me),ie}var U0e={kernelName:ro,backendName:"wasm",setupFunc:W0e,kernelFunc:V0e},G0e=kn(so),H0e=kn(ao),AC=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(AC||{}),xC;function j0e(e){xC=e.wasm.cwrap(Xi,null,["number","number","number","number","array","number","number","number","number","number"])}function q0e(e){let{backend:t,inputs:n,attrs:r}=e,{method:s,extrapolationValue:a,cropSize:o}=r,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Xp({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(u.dataId).id,A=t.makeOutput(h,"float32"),b=t.dataIdMap.get(A.dataId).id,v=new Uint8Array(new Int32Array(i.shape).buffer);return xC(g,y,x,c,v,p,d,AC[s],a,b),m!=null&&t.disposeData(m.dataId),A}var X0e={kernelName:Xi,backendName:"wasm",setupFunc:j0e,kernelFunc:q0e},bC;function K0e(e){bC=e.wasm.cwrap(qi,null,["number","number","number","number","number","number"])}function Z0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumprod does not support ${s.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([a],l),c=s;u!==null&&(c=Xa({inputs:{x:s},attrs:{perm:u},backend:n}));let p=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;bC(f,o?1:0,i?1:0,h,m,Ut[s.dtype]);let g=d;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xa({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Y0e={kernelName:qi,backendName:"wasm",setupFunc:K0e,kernelFunc:Z0e},vC;function J0e(e){vC=e.wasm.cwrap(oo,null,["number","number","number","number","number","number"])}function Q0e(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,l=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([a],l),c=s;u!==null&&(c=Xa({inputs:{x:s},attrs:{perm:u},backend:n}));let p=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;vC(f,o?1:0,i?1:0,h,m,Ut[s.dtype]);let g=d;if(u!==null){let y=C.getUndoAxesPermutation(u);g=Xa({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var e2e={kernelName:oo,backendName:"wasm",setupFunc:J0e,kernelFunc:Q0e},wC;function t2e(e){wC=e.wasm.cwrap(Ki,null,["number","number","number","array","number","array","array","number","number"])}function n2e(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r,i=s.shape[0],l=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],c=o==="NHWC"?s.shape[3]:s.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(w.computeStrides(s.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),v=t.dataIdMap.get(m.dataId).id;return wC(y,a,o==="NHWC"?1:0,x,s.shape.length-1,A,b,f.length,v),m}var r2e={kernelName:Ki,backendName:"wasm",setupFunc:t2e,kernelFunc:n2e},kC;function s2e(e){kC=e.wasm.cwrap(io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function a2e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=C.computeConv2DInfo(s.shape,a.shape,l,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,v=h.dilationWidth,S=h.strideHeight,I=h.strideWidth,E=h.inChannels,R=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let _=r.makeOutput(h.outShape,"float32"),D=r.dataIdMap.get(_.dataId).id;return kC(o,s.shape[0],s.shape[1],s.shape[2],i,f,m,g,y,x,A,P,b,v,S,I,E,R,D),_}var o2e={kernelName:io,backendName:"wasm",setupFunc:s2e,kernelFunc:a2e},i2e=kn(uo),l2e=!1,u2e=Yn(Zi,l2e,"bool"),c2e=kn(co,"float32");function ly(e){let{inputs:t,attrs:n,backend:r}=e,{input:s}=t,{dim:a}=n,o=s.shape.length,i=s.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),nr({inputs:{x:s},backend:r,attrs:{shape:i}})}var d2e={kernelName:Yi,backendName:"wasm",kernelFunc:ly};function SC(e){let{attrs:{shape:t,value:n,dtype:r},backend:s}=e,a=s.makeOutput(t,r);return s.typedArrayFromHeap(a).fill(n),a}var p2e={kernelName:Ku,backendName:"wasm",kernelFunc:SC},IC;function h2e(e){IC=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number"])}function f2e(e){let{inputs:t,backend:n}=e,{image:r}=t,s=n.makeOutput(r.shape,r.dtype),a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,[i,l,u,c]=r.shape;return IC(a,i,l,u,c,o),s}var m2e={kernelName:Qi,backendName:"wasm",kernelFunc:f2e,setupFunc:h2e},g2e=kn(po),y2e=!1,A2e=Yn(ho,y2e),CC;function x2e(e){CC=e.wasm.cwrap(fo,null,["number","number","number","number","number","number","number"])}function b2e(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:s}=r,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return CC(c,p,d,h,f,s,g),m}var v2e={kernelName:fo,backendName:"wasm",setupFunc:x2e,kernelFunc:b2e},TC;function w2e(e){TC=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function k2e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(s.shape,a.shape,l,c,u,d),g=E0[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(s.dataId).id,x=r.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let J=r.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${A})`);b=J.id}let v=m.filterHeight,S=m.filterWidth,I=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,_=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,z=m.batchSize,Z=m.inHeight,W=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ee=r.makeOutput(m.outShape,"float32"),Q=r.dataIdMap.get(ee.dataId).id,ae=i==null?0:r.dataIdMap.get(i.dataId).id;return TC(y,z,Z,W,x,v,S,b,I,E,R,P,X,_,D,T,F,U,A,g,ae,f||0,Q),ee}var S2e={kernelName:Fa,backendName:"wasm",setupFunc:w2e,kernelFunc:k2e},NC;function I2e(e){NC=e.wasm.cwrap(Oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function C2e(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(s.shape,a.shape,l,c,u,d,!0),g=E0[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(s.dataId).id,x=r.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let J=r.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${A})`);b=J.id}let v=m.filterHeight,S=m.filterWidth,I=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,_=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,F=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,z=m.batchSize,Z=m.inHeight,W=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ee=r.makeOutput(m.outShape,"float32"),Q=r.dataIdMap.get(ee.dataId).id,ae=i==null?0:r.dataIdMap.get(i.dataId).id;return NC(y,z,Z,W,x,v,S,b,I,E,R,P,X,_,D,T,F,U,A,g,ae,f||0,Q),ee}var T2e={kernelName:Oa,backendName:"wasm",setupFunc:I2e,kernelFunc:C2e},EC;function N2e(e){EC=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function E2e(e){let{backend:t,inputs:n}=e,{params:r,indices:s}=n,[a,o,i,l]=Sy.prepareAndValidate(r,s),u=t.makeOutput(a,r.dtype);if(o===0)return u;let c=s.shape,p=c[c.length-1],h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=t.dataIdMap.get(u.dataId).id;return EC(h,Ut[r.dtype],m,o,p,i,g,y),u}var R2e={kernelName:tl,backendName:"wasm",setupFunc:N2e,kernelFunc:E2e},RC;function _2e(e){RC=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $2e(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,indices:a}=n,{axis:o,batchDims:i}=r,l=w.parseAxisParam(o,s.shape)[0],u=t.readSync(a.dataId),c=s.shape[l];for(let R=0;R<u.length;++R){let P=u[R];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(s,a,l,i),d=nr({inputs:{x:s},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=nr({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,s.dtype);if(w.sizeFromShape(s.shape)===0)return g;let y=d.shape.length-1,A=t.dataIdMap.get(d.dataId).id,v=t.dataIdMap.get(f.dataId).id,S=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return RC(A,Ut[s.dtype],I,y,v,p.batchSize,E,S),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var D2e={kernelName:el,backendName:"wasm",setupFunc:_2e,kernelFunc:$2e},P2e=!1,F2e=Yn(nl,P2e,"bool"),O2e=!1,M2e=Yn(mo,O2e,"bool"),_C;function z2e(e){_C=e.wasm.cwrap(yo,null,["number","number","number","number"])}function L2e(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,s=r.dataIdMap.get(t.dataId).id,a=r.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let o=r.dataIdMap.get(a.dataId).id;_C(s,Ut[t.dtype],n,o)}return a}var B2e={kernelName:yo,backendName:"wasm",setupFunc:z2e,kernelFunc:L2e},W2e=!1,V2e=Yn(rl,W2e,"bool"),U2e=!1,G2e=Yn(sl,U2e,"bool"),H2e=kn(Ao),j2e=!1,q2e=Yn(al,j2e,"bool"),$C;function X2e(e){$C=e.wasm.cwrap(xo,null,["number","number","number","number"])}function K2e(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:s,keepDims:a}=r,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Zo(o,s,t);if(h){let A=t.dataIdMap.get(c.dataId).id;u=c,l=A}let f=u.shape.length;C.assertAxesAreInnerMostDims("max",p,f);let[m,g]=C.computeOutAndReduceShapes(u.shape,p),y=w.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;$C(l,Ut[o.dtype],y,A)}if(h&&t.disposeData(c.dataId),a){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Z2e={kernelName:xo,backendName:"wasm",setupFunc:X2e,kernelFunc:K2e},Y2e=!1,J2e=Yn(bo,Y2e),DC;function Q2e(e){DC=e.wasm.cwrap(vo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function e1e(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id;w.assert(s.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${s.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=C.computePool2DInfo(s.shape,o,i,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,x=c.dilationWidth,A=c.strideHeight,b=c.strideWidth,v=c.inChannels,S=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let I=r.makeOutput(c.outShape,"float32"),E=r.dataIdMap.get(I.dataId).id;return DC(a,s.shape[0],s.shape[1],s.shape[2],p,d,h,f,m,g,y,x,A,b,v,S,E),I}var t1e={kernelName:vo,backendName:"wasm",setupFunc:Q2e,kernelFunc:e1e},PC;function n1e(e){PC=e.wasm.cwrap(wo,null,["number, number, number"])}function r1e(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Zo(o,s,t),f=p;if(h){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,f=C.getInnerMostAxes(f.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=C.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=Xp({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(m,"float32");if(w.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;PC(l,y,b)}if(h&&t.disposeData(c.dataId),a){let b=C.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var s1e={kernelName:wo,backendName:"wasm",setupFunc:n1e,kernelFunc:r1e},FC;function a1e(e){FC=e.wasm.cwrap(ko,null,["number","number","number","number"])}function o1e(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Zo(o,s,t);if(h){let A=t.dataIdMap.get(c.dataId).id;A!==i&&(u=c,l=A)}let f=u.shape.length;C.assertAxesAreInnerMostDims("min",p,f);let[m,g]=C.computeOutAndReduceShapes(u.shape,p),y=w.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;FC(l,Ut[o.dtype],y,A)}if(h&&t.disposeData(c.dataId),a){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var i1e={kernelName:ko,backendName:"wasm",setupFunc:a1e,kernelFunc:o1e},l1e=!1,u1e=Yn(So,l1e),OC=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(OC||{}),MC;function c1e(e){MC=e.wasm.cwrap(Io,null,["number","array","number","number","array","array","number","number"])}function d1e(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:s}}=e,a=r.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=r.map(f=>f[0]),p=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return MC(o,u,t.shape.length,Ut[t.dtype],d,h,OC[s],l),i}var p1e={kernelName:Io,backendName:"wasm",kernelFunc:d1e,setupFunc:c1e},h1e=!0,f1e=Yn(Co,h1e),m1e=kn(ol);function E3(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],s=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:s,pSelectedScores:a,pValidOutputs:o}}var zC;function g1e(e){zC=e.wasm.cwrap(ll,"number",["number","number","number","number","number"])}function y1e(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o}=r,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=zC(u,c,a,s,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=E3(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var A1e={kernelName:ll,backendName:"wasm",setupFunc:g1e,kernelFunc:y1e},LC;function x1e(e){LC=e.wasm.cwrap(nc,"number",["number","number","number","number","number","bool"])}function b1e(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=LC(c,p,a,s,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=E3(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var v1e={kernelName:nc,backendName:"wasm",setupFunc:x1e,kernelFunc:b1e},BC;function w1e(e){BC=e.wasm.cwrap(ul,"number",["number","number","number","number","number","number"])}function k1e(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:s,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=BC(c,p,a,s,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=E3(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var S1e={kernelName:ul,backendName:"wasm",setupFunc:w1e,kernelFunc:k1e},I1e=!1,C1e=Yn(il,I1e,"bool"),WC;function T1e(e){WC=e.wasm.cwrap(dl,null,["number","number","number","number","number"])}function 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D1e={kernelName:pl,backendName:"wasm",kernelFunc:$1e},VC;function P1e(e){VC=e.wasm.cwrap(To,null,["number","array","number","number","array","array","number","number"])}function F1e(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:s}}=e,a=r.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return SC({backend:n,attrs:{shape:a,value:s,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=r.map(m=>m[0]),d=r.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return VC(o,c,t.shape.length,Ut[t.dtype],h,f,s,u),i}var UC={kernelName:To,backendName:"wasm",kernelFunc:F1e,setupFunc:P1e},O1e=!1,M1e=Yn(No,O1e),GC;function z1e(e){GC=e.wasm.cwrap(Eo,null,["number","number","number"])}function L1e(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,i=a,l=r,u=l;l.dtype!=="float32"&&(u=Xp({backend:n,inputs:{x:r},attrs:{dtype:"float32"}}),i=n.dataIdMap.get(u.dataId).id);let c=n.makeOutput(r.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return GC(i,o,p),l.dtype!=="float32"&&n.disposeData(u.dataId),c}var B1e={kernelName:Eo,backendName:"wasm",setupFunc:z1e,kernelFunc:L1e},HC;function W1e(e){HC=e.wasm.cwrap(Ro,null,["number","number","number","number"])}function V1e(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Zo(o,s,t),f=p;if(h){let A=t.dataIdMap.get(c.dataId).id;A!==i&&(u=c,l=A,f=C.getInnerMostAxes(f.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=C.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let 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A=t.dataIdMap.get(x.dataId).id;return jC(y,c,p,d,h,l,u,a?1:0,o?1:0,A),g!=null&&t.disposeData(g.dataId),x}var J1e={kernelName:$o,backendName:"wasm",setupFunc:Z1e,kernelFunc:Y1e},qC;function Q1e(e){qC=e.wasm.cwrap(fl,null,["number","array","number","array","number","number"])}function ege(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=w.parseAxisParam(a,s.shape);if(s.shape.length===0)return R0({inputs:{x:s},backend:n});let i=n.makeOutput(s.shape,s.dtype),l=n.dataIdMap.get(s.dataId).id,u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(o).buffer),p=new Uint8Array(new Int32Array(s.shape).buffer);qC(l,c,o.length,p,s.shape.length,u);let d=nr({inputs:{x:i},attrs:{shape:s.shape},backend:n});return n.disposeData(i.dataId),d}var tge={kernelName:fl,backendName:"wasm",kernelFunc:ege,setupFunc:Q1e},XC;function nge(e){XC=e.wasm.cwrap(Nl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rge(e){let{inputs:t,backend:n,attrs:r}=e,{image:s}=t,{radians:a,fillValue:o,center:i}=r,l=n.makeOutput(s.shape,s.dtype),u=n.dataIdMap.get(s.dataId).id,c=n.dataIdMap.get(l.dataId).id,[p,d,h,f]=s.shape,[m,g]=C.getImageCenter(i,d,h),y=o===0,x=255,A=typeof o=="number"?[o,o,o,y?0:x]:[...o,x],b=new Uint8Array(new Int32Array(A).buffer);return XC(u,p,d,h,f,a,m,g,b,A.length,c),l}var sge={kernelName:Nl,backendName:"wasm",kernelFunc:rge,setupFunc:nge},age=kn(ml),oge=kn(Po),KC;function ige(e){KC=e.wasm.cwrap(gl,null,["number","number","number","number","number","number","array","number","number"])}function lge(e){let{backend:t,inputs:n,attrs:r}=e,{indices:s,updates:a}=n,{shape:o}=r,i=t.makeOutput(o,a.dtype);if(w.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=Iy.calculateShapes(a,s,o),f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(p).buffer),x=t.dataIdMap.get(i.dataId).id;return KC(f,g,Ut[a.dtype],l,u,c,y,d,x),i}var uge={kernelName:gl,backendName:"wasm",setupFunc:ige,kernelFunc:lge},ZC;function cge(e){ZC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function dge(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(s.dataId).id,l=n.dataIdMap.get(a.dataId).id,u=n.makeOutput(s.shape,s.dtype),c=n.dataIdMap.get(u.dataId).id,p=r.shape.length,d=s.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(s.shape.slice(1));return ZC(o,i,l,h,c),u}var pge={kernelName:yl,backendName:"wasm",kernelFunc:dge,setupFunc:cge},YC;function hge(e){YC=e.wasm.cwrap(Oo,null,["number","number"])}function fge(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(s.dataId).id;return w.sizeFromShape(s.shape)===0||YC(r,a),s}var mge={kernelName:"Sigmoid",backendName:"wasm",setupFunc:hge,kernelFunc:fge},gge=kn(Fo),JC;function yge(e){JC=e.wasm.cwrap(Lo,null,["number","number","number","number"])}function Age(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,s=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[r],l=w.sizeFromShape(n.shape)/i;return w.sizeFromShape(a.shape)===0||JC(s,o,i,l),a}var xge={kernelName:Lo,backendName:"wasm",setupFunc:yge,kernelFunc:Age};function bge(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r,i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let S=1+a.length;S<s.shape.length;++S)l.push([0,0]);let u=UC.kernelFunc({inputs:{x:s},backend:n,attrs:{paddings:l,constantValue:0}}),c=C.getReshaped(u.shape,a,i,!1),p=C.getPermuted(c.length,a.length,!1),d=C.getReshapedPermuted(u.shape,a,i,!1),m=nr({inputs:{x:u},backend:n,attrs:{shape:c}}),x=Xa({inputs:{x:m},backend:n,attrs:{perm:p}}),v=nr({inputs:{x},backend:n,attrs:{shape:d}});return n.disposeData(u.dataId),n.disposeData(m.dataId),n.disposeData(x.dataId),v}var vge={kernelName:bl,backendName:"wasm",kernelFunc:bge},QC;function wge(e){QC=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function kge(e){let{backend:t,inputs:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=n,i=r.shape[0],l=r.shape[1],u=t.readSync(a.dataId)[0],c=[i+u,l],p=t.dataIdMap.get(r.dataId).id,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(o.dataId).id,f=t.makeOutput(c,r.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),s.dtype),y=t.dataIdMap.get(g.dataId).id,x=t.makeOutput([u],"bool"),A=t.dataIdMap.get(x.dataId).id,b=t.makeOutput([i],r.dtype),v=t.dataIdMap.get(b.dataId).id,S=t.makeOutput([4],"int32"),I=t.dataIdMap.get(S.dataId).id,E=QC(p,d,Ut[s.dtype],i,u,l,h,m,y,A,v,I),R=t.readSync(S.dataId),P;switch(R[0]){case 1:{P=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{P=C.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:P=C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:P=""}if(t.disposeData(S.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(x.dataId),t.disposeData(b.dataId),new Error(P);let _=f,D=g;return E!==c[0]&&(_=Wi({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),D=Wi({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[_,D,x,b]}var Sge={kernelName:lp,backendName:"wasm",setupFunc:wge,kernelFunc:kge},e9;function Ige(e){e9=e.wasm.cwrap(uc,null,["number","number","number","number","number","number","number"])}function Cge(e){let{backend:t,inputs:n}=e,{inputIndices:r,inputShape:s,newShape:a}=n;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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A=Array.from(t.readSync(s.dataId)),b=Array.from(t.readSync(h.dataId));x=C.getSparseReshapeInputOutputMismatchErrorMessage(A,b);break}default:x=""}if(t.disposeData(m.dataId),x)throw t.disposeData(p.dataId),t.disposeData(h.dataId),new Error(x);return[p,h]}var Tge={kernelName:uc,backendName:"wasm",setupFunc:Ige,kernelFunc:Cge},t9;function n9(e){t9=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function r9(e,t){let{backend:n,inputs:r}=e,{data:s,indices:a,segmentIds:o}=r,i=a.shape[0],l=n.readSync(o.dataId,i-1,i)[0],c=i>0?l+1:0;if(c<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=s.shape.slice();p[0]=c;let d=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=n.dataIdMap.get(o.dataId).id,m=n.makeOutput(p,s.dtype),g=n.dataIdMap.get(m.dataId).id,y=n.makeOutput([4],"int32"),x=n.dataIdMap.get(y.dataId).id;t9(d,Ut[s.dtype],s.shape[0],h,f,g,x,t,0);let A=n.readSync(y.dataId),b;switch(A[0]){case 0:{b=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{b=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:b=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(A[1],A[2]);break;case 3:b=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(A[1],A[2],A[3]);break;default:b=""}if(n.disposeData(y.dataId),b)throw n.disposeData(m.dataId),new Error(b);return m}function Nge(e){return r9(e,!0)}var Ege={kernelName:up,backendName:"wasm",setupFunc:n9,kernelFunc:Nge};function Rge(e){return r9(e,!1)}var _ge={kernelName:cp,backendName:"wasm",setupFunc:n9,kernelFunc:Rge};function $ge(e){let{inputs:t,attrs:n,backend:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=n,i=w.parseAxisParam(o,s.shape)[0],l=C.prepareSplitSize(s,a,i),u=new Array(s.shape.length).fill(0),c=s.shape.slice();return l.map(p=>{let d=[...c];d[i]=p;let h=Wi({inputs:{x:s},attrs:{begin:u,size:d},backend:r});return u[i]+=p,h})}var Dge={kernelName:vl,backendName:"wasm",kernelFunc:$ge},Pge=kn(Mo),Fge=kn(cc),Oge=!0,Mge=Yn(Bo,Oge),s9;function zge(e){s9=e.wasm.cwrap(Go,null,["number","number","number","number"])}function Lge(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return s9(o,s,Ut[a.dtype],l),i}var Bge={kernelName:Go,backendName:"wasm",setupFunc:zge,kernelFunc:Lge},a9;function Wge(e){a9=e.wasm.cwrap(wl,null,["number","array","number","array","array","array","array","array","number","number"])}function Vge(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(s.shape,a,o,i,l,u,c,p,d),v;if(m)v=nr({inputs:{x:s},backend:t,attrs:{shape:f}});else if(g||y){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, 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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 h9=`
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];
}
`,f9=`
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;
}
`,m9=`
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);
}
`,g9=`
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;
}
`,y9=`
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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ne(o),i},z0=(e,t)=>{let n=M0(e),r=Oc(e),s=[t*r[0]/2,t*r[1]/2];return{startPoint:[n[0]-s[0],n[1]-s[1]],endPoint:[n[0]+s[0],n[1]+s[1]],landmarks:e.landmarks,confidence:e.confidence}},L0=e=>{let t=M0(e),n=Oc(e),r=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-r),Math.round(t[1]-r)],endPoint:[Math.round(t[0]+r),Math.round(t[1]+r)],landmarks:e.landmarks,confidence:e.confidence}},V9=e=>{let t=e.map(r=>r[0]),n=e.map(r=>r[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Z3=[[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 L9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Wl=(e,t)=>{let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n},Gye=(e,t)=>{let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n},B9=(e,t)=>{let n=[],r=e.length;for(let s=0;s<r;s++){n.push([]);for(let a=0;a<r;a++)n[s].push(Wl(e[s],Gye(t,a)))}return n},U9=(e,t)=>{let 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s=t.landmarks.length>=j3.count?j3.symmetryLine:Yp.symmetryLine,a=0,o=Z3,i;if(e&&pe.kernels.includes("rotatewithoffset"))if(a=Uye(t.landmarks[s[0]],t.landmarks[s[1]]),a&&a!==0&&Math.abs(a)>.2){let u=M0(t),c=[u[0]/n.shape[2],u[1]/n.shape[1]],p=Se.rotateWithOffset(n,a,0,c);o=U9(-a,u),i=K3(t,p,[r,r]),ne(p)}else i=K3(t,n,[r,r]);else i=K3(t,n,[r,r]);return[a,o,i]}var qye=e=>{let t=e.map(r=>r[0]),n=e.map(r=>r[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},q9=(e,t)=>{let n=qye(e),r=Oc(t);return{startPoint:[n[0]-r[0]/2,n[1]-r[1]/2],endPoint:[n[0]+r[0]/2,n[1]+r[1]/2]}};var X9=6,Xye=1.4,zs,K9=null,Jo=0,Qp=null,B0=()=>Jo;async function Z9(e){var t;return pe.initial&&(zs=null),zs?e.debug&&oe("cached model:",zs.modelUrl):zs=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Jo=zs.inputs[0].shape?zs.inputs[0].shape[2]:0,Qp=Ie(Jo,"int32"),K9=ps(G9(Jo)),zs}function Kye(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,K9),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=de(t.boxSizes,Qp),t.centersNormalized=de(t.centers,Qp),t.halfBoxSize=de(t.boxSizesNormalized,Je.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=ue(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Qp),t.endNormalized=L(t.ends,Qp);let n=pc([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(r=>ne(t[r])),n}async function Y9(e,t){var i,l,u,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=Se.resizeBilinear(e,[Jo,Jo]),n.div=de(n.resized,Je.tf127),n.normalized=he(n.div,Je.tf05);let r=zs==null?void 0:zs.execute(n.normalized);if(Array.isArray(r)){let p=r.sort((d,h)=>d.size-h.size);n.concat384=St([p[0],p[2]],2),n.concat512=St([p[1],p[3]],2),n.concat=St([n.concat512,n.concat384],1),n.batch=tt(n.concat,0)}else n.batch=tt(r);ne(r),n.boxes=Kye(n.batch),n.logits=Pe(n.batch,[0,0],[-1,1]),n.sigmoid=Tn(n.logits),n.scores=tt(n.sigmoid),n.nms=await Se.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let s=await n.nms.array(),a=[],o=await n.scores.data();for(let p=0;p<s.length;p++){let d=o[s[p]];if(d>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let h={};h.bbox=Pe(n.boxes,[s[p],0],[1,-1]),h.slice=Pe(n.batch,[s[p],X9-1],[1,-1]),h.squeeze=tt(h.slice),h.landmarks=H(h.squeeze,[X9,-1]);let f=await h.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await h.landmarks.array(),confidence:d},g=W9(m,[(e.shape[2]||0)/Jo,(e.shape[1]||0)/Jo]),y=z0(g,t.face.scale||Xye),x=L0(y);a.push(x),Object.keys(h).forEach(A=>ne(h[A]))}}return Object.keys(n).forEach(p=>ne(n[p])),a}var W0={};wa(W0,{connected:()=>t5,kpt:()=>e5});var e5=["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"],t5={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 Q9=224,Zye,Yye=5,V0=[8,16,32,32,32];async function eT(){let e=[],t=0;for(;t<Yye;){let n=0,r=t;for(;r<V0.length&&V0[r]===V0[t];)n+=2,r++;let s=V0[t],a=Math.ceil(Q9/s),o=Math.ceil(Q9/s);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let u=0;u<n;++u)e.push({x:(l+.5)/o,y:(i+.5)/a});t=r}Zye={x:Ct(e.map(n=>n.x)),y:Ct(e.map(n=>n.y))}}function da(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],r=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],a=[r[0],r[1],s[0]-r[0],s[1]-r[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function tT(e,t=[1,1]){let n=[e.map(u=>u[0]),e.map(u=>u[1])],r=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],a=[(r[0]+s[0])/2,(r[1]+s[1])/2],o=Math.max(a[0]-r[0],a[1]-r[1],-a[0]+s[0],-a[1]+s[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function U0(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var sT={initial:!0},br={detector:null,landmarks:null},Mc={detector:[224,224],landmarks:[256,256]},n5=Number.MAX_SAFE_INTEGER,Qye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},H0=null,eh,Qo=[[0,0],[0,0],[0,0],[0,0]],nT=0,rT=e=>1-1/(1+Math.exp(e));async function aT(e){if(sT.initial&&(br.detector=null),!br.detector&&e.body.detector&&e.body.detector.modelPath){br.detector=await Ge(e.body.detector.modelPath);let t=Object.values(br.detector.modelSignature.inputs);Mc.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Mc.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&br.detector&&oe("cached model:",br.detector.modelUrl);return await eT(),br.detector}async function oT(e){if(sT.initial&&(br.landmarks=null),br.landmarks)e.debug&&oe("cached model:",br.landmarks.modelUrl);else{br.landmarks=await Ge(e.body.modelPath);let t=Object.values(br.landmarks.modelSignature.inputs);Mc.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Mc.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return br.landmarks}async function eAe(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let r;if(eh&&(n.cropped=Se.cropAndResize(e,[eh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let s=[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],a=[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];Qo=[[0,0],s,a,[0,0]],n.pad=Xr(n.cropped||e,Qo),n.resize=Se.resizeBilinear(n.pad,[t,t]),r=de(n.resize,Je.tf255)}else e.shape[1]!==t?(n.resize=Se.resizeBilinear(n.cropped||e,[t,t]),r=de(n.resize,Je.tf255)):r=de(n.cropped||e,Je.tf255);return Object.keys(n).forEach(s=>ne(n[s])),r}function tAe(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+Qo[2][0]+Qo[2][1])/t[0]-Qo[2][0]),Math.trunc(n.position[1]*(t[1]+Qo[1][0]+Qo[1][1])/t[1]-Qo[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(eh)for(let n of e)n.positionRaw=[n.positionRaw[0]+eh[1],n.positionRaw[1]+eh[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}async function nAe(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),r=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(r.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),a=e.find(i=>i.part==="rightWrist"),o=e.find(i=>i.part==="rightIndex");s.position[2]=((a.position[2]||0)+(o.position[2]||0))/2}async function rAe(e,t,n){var f;let r={};[r.ld,r.segmentation,r.heatmap,r.world,r.poseflag]=(f=br.landmarks)==null?void 0:f.execute(e,Qye.landmarks);let s=(await r.poseflag.data())[0],a=await r.ld.data(),o=await r.world.data();Object.keys(r).forEach(m=>ne(r[m]));let i=[],l=5;for(let m=0;m<a.length/l;m++){let g=rT(a[l*m+3]),y=rT(a[l*m+4]),x=Math.trunc(100*g*y*s)/100,A=[a[l*m+0]/Mc.landmarks[0],a[l*m+1]/Mc.landmarks[1],a[l*m+2]+0],b=[Math.trunc(n[0]*A[0]),Math.trunc(n[1]*A[1]),A[2]],v=[o[l*m+0],o[l*m+1],o[l*m+2]+0];i.push({part:e5[m],positionRaw:A,position:b,distance:v,score:x})}if(s<(t.body.minConfidence||0))return null;nAe(i);let u=tAe(i,n),c=u.map(m=>m.position),p=da(c,[n[0],n[1]]),d={};for(let[m,g]of Object.entries(t5)){let y=[];for(let x=0;x<g.length-1;x++){let A=u.find(v=>v.part===g[x]),b=u.find(v=>v.part===g[x+1]);A&&b&&y.push([A.position,b.position])}d[m]=y}return{id:0,score:Math.trunc(100*s)/100,box:p.box,boxRaw:p.boxRaw,keypoints:u,annotations:d}}async function r5(e,t){let n=[e.shape[2]||0,e.shape[1]||0],r=(t.body.skipTime||0)>le()-nT,s=n5<(t.body.skipFrames||0);if(t.skipAllowed&&r&&s&&H0!==null)n5++;else{let a={};a.landmarks=await eAe(e,256),H0=await rAe(a.landmarks,t,n),Object.keys(a).forEach(o=>ne(a[o])),nT=le(),n5=0}return H0?[H0]:[]}var zc=[{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 pa,Vl=0,s5=[],lT=0,a5=Number.MAX_SAFE_INTEGER;async function uT(e){if(pe.initial&&(pa=null),pa)e.debug&&oe("cached model:",pa.modelUrl);else{pa=await Ge(e.object.modelPath);let t=Object.values(pa.modelSignature.inputs);Vl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return pa}async function sAe(e,t,n){if(!e)return[];let r={},s=[],a=await e.array();r.squeeze=tt(e);let o=Yt(r.squeeze,6,1);r.stack=cn([o[1],o[0],o[3],o[2]],1),r.boxes=tt(r.stack),r.scores=tt(o[4]),r.classes=tt(o[5]),ne([e,...o]),r.nms=await Se.nonMaxSuppressionAsync(r.boxes,r.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence||0);let i=await r.nms.data(),l=0;for(let u of Array.from(i)){let c=Math.trunc(100*a[0][u][4])/100,p=a[0][u][5],d=zc[p].label,[h,f]=[a[0][u][0]/Vl,a[0][u][1]/Vl],m=[h,f,a[0][u][2]/Vl-h,a[0][u][3]/Vl-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];s.push({id:l++,score:c,class:p,label:d,box:g,boxRaw:m})}return Object.keys(r).forEach(u=>ne(r[u])),s}async function o5(e,t){let n=(t.object.skipTime||0)>le()-lT,r=a5<(t.object.skipFrames||0);return t.skipAllowed&&n&&r&&s5.length>0?(a5++,s5):(a5=0,new Promise(async s=>{let a=[e.shape[2]||0,e.shape[1]||0],o=Se.resizeBilinear(e,[Vl,Vl]),i=t.object.enabled?pa==null?void 0:pa.execute(o,["tower_0/detections"]):null;lT=le(),ne(o);let l=await sAe(i,a,t);s5=l,s(l)}))}var j0={};wa(j0,{connected:()=>l5,kpt:()=>i5});var i5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],l5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _n,dT=0,Qn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},u5=Number.MAX_SAFE_INTEGER;async function pT(e){return pe.initial&&(_n=null),_n?e.debug&&oe("cached model:",_n.modelUrl):_n=await Ge(e.body.modelPath),_n}async function aAe(e,t){let[n,r]=e.shape,s=H(e,[r*n]),a=gn(s,0),o=(await a.data())[0];if(ne([s,a]),o>t){let i=Rr(s,0),l=mc(i,n),u=(await l.data())[0],c=de(i,Ie(n,"int32")),p=(await c.data())[0];return ne([l,c]),[u,p,o]}return[0,0,o]}async function c5(e,t){let n=(t.body.skipTime||0)>le()-dT,r=u5<(t.body.skipFrames||0);return t.skipAllowed&&n&&r&&Object.keys(Qn.keypoints).length>0?(u5++,[Qn]):(u5=0,new Promise(async s=>{var p;let a=K(()=>{if(!(_n!=null&&_n.inputs[0].shape))return null;let d=Se.resizeBilinear(e,[_n.inputs[0].shape[2],_n.inputs[0].shape[1]],!1),h=L(d,Je.tf2);return he(h,Je.tf1)}),o;if(t.body.enabled&&(o=_n==null?void 0:_n.execute(a)),dT=le(),ne(a),o){Qn.keypoints.length=0;let d=o.squeeze();ne(o);let h=d.unstack(2);ne(d);for(let f=0;f<h.length;f++){let[m,g,y]=await aAe(h[f],t.body.minConfidence);y>(((p=t.body)==null?void 0:p.minConfidence)||0)&&Qn.keypoints.push({score:Math.round(100*y)/100,part:i5[f],positionRaw:[m/_n.inputs[0].shape[2],g/_n.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/_n.inputs[0].shape[2]),Math.round(e.shape[1]*g/_n.inputs[0].shape[1])]})}h.forEach(f=>ne(f))}Qn.score=Qn.keypoints.reduce((d,h)=>h.score>d?h.score:d,0);let i=Qn.keypoints.map(d=>d.position[0]),l=Qn.keypoints.map(d=>d.position[1]);Qn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let u=Qn.keypoints.map(d=>d.positionRaw[0]),c=Qn.keypoints.map(d=>d.positionRaw[1]);Qn.boxRaw=[Math.min(...u),Math.min(...c),Math.max(...u)-Math.min(...u),Math.max(...c)-Math.min(...c)];for(let[d,h]of Object.entries(l5)){let f=[];for(let m=0;m<h.length-1;m++){let g=Qn.keypoints.find(x=>x.part===h[m]),y=Qn.keypoints.find(x=>x.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Qn.annotations[d]=f}s([Qn])}))}var oAe=["angry","disgust","fear","happy","sad","surprise","neutral"],Br,q0=[],fT=0,mT=0,d5=Number.MAX_SAFE_INTEGER;async function gT(e){var t;return pe.initial&&(Br=null),Br?e.debug&&oe("cached model:",Br.modelUrl):Br=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),Br}async function p5(e,t,n,r){var o,i;if(!Br)return[];let s=d5<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>le()-mT;return t.skipAllowed&&a&&s&&fT===r&&q0[n]&&q0[n].length>0?(d5++,q0[n]):(d5=0,new Promise(async l=>{var c,p;let u=[];if((c=t.face.emotion)!=null&&c.enabled){let d={},h=Br!=null&&Br.inputs[0].shape?Br.inputs[0].shape[2]:0;d.resize=Se.resizeBilinear(e,[h,h],!1),d.channels=L(d.resize,Je.rgb),d.grayscale=ke(d.channels,3,!0),d.grayscaleSub=he(d.grayscale,Je.tf05),d.grayscaleMul=L(d.grayscaleSub,Je.tf2),d.emotion=Br==null?void 0:Br.execute(d.grayscaleMul),mT=le();let f=await d.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((p=t.face.emotion)==null?void 0:p.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:oAe[m]});u.sort((m,g)=>g.score-m.score),Object.keys(d).forEach(m=>ne(d[m]))}q0[n]=u,fT=r,l(u)}))}var vr,h5=[],AT=0,xT=0,bT=Number.MAX_SAFE_INTEGER;async function vT(e){return pe.initial&&(vr=null),vr?e.debug&&oe("cached 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0:t.modelPath),ei=ha.inputs[0].shape?ha.inputs[0].shape[2]:0,ei===-1&&(ei=64),ha}function X0(e,t,n,r){for(let s=0;s<q3.length;s++){let{key:a,indices:o}=q3[s],i=es[`${n}${a}`];if(!r||r.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var lAe=e=>{let t=e[Lc.leftBounds[0]][2],n=e[Lc.rightBounds[0]][2];return t-n},kT=(e,t,n,r,s,a=!1)=>{let o=L0(z0(V9([e[n],e[r]]),iAe)),i=Oc(o),l=Se.cropAndResize(t,[[o.startPoint[1]/s,o.startPoint[0]/s,o.endPoint[1]/s,o.endPoint[0]/s]],[0],[ei,ei]);if(a&&pe.kernels.includes("flipleftright")){let u=Se.flipLeftRight(l);ne(l),l=u}return{box:o,boxSize:i,crop:l}},ST=(e,t,n,r=!1)=>{let s=[];for(let a=0;a<Bc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];s.push([(r?1-o/ei:o/ei)*n[0]+t.startPoint[0],i/ei*n[1]+t.startPoint[1],l])}return{rawCoords:s,iris:s.slice(Bc.index)}},IT=(e,t,n)=>{let r=e[es[`${n}EyeUpper0`][Bc.upperCenter]][2],s=e[es[`${n}EyeLower0`][Bc.lowerCenter]][2],a=(r+s)/2;return t.map((o,i)=>{let l=a;return i===2?l=r:i===4&&(l=s),[o[0],o[1],l]})};async function TT(e,t,n,r){if(!ha)return n.debug&&oe("face mesh iris detection requested, but model is not loaded"),e;let{box:s,boxSize:a,crop:o}=kT(e,t,Lc.leftBounds[0],Lc.leftBounds[1],r,!0),{box:i,boxSize:l,crop:u}=kT(e,t,Lc.rightBounds[0],Lc.rightBounds[1],r,!0),c=St([o,u]);ne(o),ne(u);let p=ha.execute(c);ne(c);let d=await p.data();ne(p);let h=d.slice(0,Bc.numCoordinates*3),{rawCoords:f,iris:m}=ST(h,s,a,!0),g=d.slice(Bc.numCoordinates*3),{rawCoords:y,iris:x}=ST(g,i,l,!1),A=lAe(e);Math.abs(A)<30?(X0(e,f,"left",null),X0(e,y,"right",null)):A<1?X0(e,f,"left",["EyeUpper0","EyeLower0"]):X0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=IT(e,m,"left"),v=IT(e,x,"right");return e.concat(b).concat(v)}var ti={eyeLLower:[33,7,163,144,145,153,154,155,133],eyeRLower:[263,249,390,373,374,380,381,382,362],lips:[61,76,91,181,84,17,314,405,321,291,291,185,40,39,37,0,267,269,270,291,62,183,88,178,87,14,268,303,304,408,291,184,42,178,87,14,268,303,304,408,61,62,90,180,85,16,315,404,307,308,291,185,40,73,72,0,302,269,270,409,61,184,95,179,86,15,316,403,324,408,291,184,74,41,38,11,268,303,304,408],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 ET(e,t){let n={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},r=ti.eyeRLower.reduce((a,o)=>a+=e[o][2],0)/ti.eyeRLower.length;for(let a=0;a<n.irisR.length/2;a++)e.push([n.irisR[2*a+0],n.irisR[2*a+1],r]);let s=ti.eyeLLower.reduce((a,o)=>a+=e[o][2],0)/ti.eyeLLower.length;for(let a=0;a<n.irisL.length/2;a++)e.push([n.irisL[2*a+0],n.irisL[2*a+1],s]);for(let a=0;a<n.eyeL.length/2;a++)e[ti.eyeL[a]]=[n.eyeL[2*a+0],n.eyeL[2*a+1],e[ti.eyeL[a]][2]];for(let a=0;a<n.eyeR.length/2;a++)e[ti.eyeR[a]]=[n.eyeR[2*a+0],n.eyeR[2*a+1],e[ti.eyeR[a]][2]];return e}var Ls={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Bs=null,Wc=0;async function RT(e,t){var i,l,u,c,p,d,h,f,m,g;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>le()-Ls.timestamp,r=Ls.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!n||!r||Ls.boxes.length===0?(Ls.boxes=await Y9(e,t),Ls.timestamp=le(),Ls.skipped=0):Ls.skipped++;let s=[],a=[],o=0;for(let y=0;y<Ls.boxes.length;y++){let x=Ls.boxes[y],A=0,b,v={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,b,v.tensor]=j9((u=t.face.detector)==null?void 0:u.rotation,x,e,(c=t.face.mesh)!=null&&c.enabled?Wc:B0()),(p=t==null?void 0:t.filter)!=null&&p.equalization){let S=await _0(v.tensor);ne(v.tensor),v.tensor=S}if(v.boxScore=Math.round(100*x.confidence)/100,(d=t.face.mesh)!=null&&d.enabled)if(!Bs)t.debug&&oe("face mesh detection requested, but model is not loaded");else{let S=Bs.execute(v.tensor),I=S.find(D=>D.shape[D.shape.length-1]===1),E=S.find(D=>D.shape[D.shape.length-1]===1404),R=await I.data();v.faceScore=Math.round(100*R[0])/100;let P=H(E,[-1,3]),_=await P.array();if(v.faceScore<(((h=t.face.detector)==null?void 0:h.minConfidence)||1))x.confidence=v.faceScore;else{(f=t.face.attention)!=null&&f.enabled?_=await ET(_,S):(m=t.face.iris)!=null&&m.enabled&&(_=await TT(_,v.tensor,t,Wc)),v.mesh=H9(_,x,A,b,Wc),v.meshRaw=v.mesh.map(T=>[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/Wc]);for(let T of Object.keys(es))v.annotations[T]=es[T].map(F=>v.mesh[F]);v.score=v.faceScore;let D={...q9(v.mesh,x),confidence:x.confidence,landmarks:x.landmarks};v.box=Y3(D,e),v.boxRaw=J3(D,e),a.push(D)}ne([...S,P])}else{v.box=Y3(x,e),v.boxRaw=J3(x,e),v.score=v.boxScore,v.mesh=x.landmarks.map(S=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*S[0]/B0(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*S[1]/B0()]),v.meshRaw=v.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/Wc]);for(let S of Object.keys(Yp))v.annotations[S]=[v.mesh[Yp[S]]]}v.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?s.push(v):ne(v.tensor)}return Ls.boxes=a,s}async function _T(e){var t,n,r;return pe.initial&&(Bs=null),Bs?e.debug&&oe("cached model:",Bs.modelUrl):(t=e.face.attention)!=null&&t.enabled?Bs=await Ge((n=e.face.attention)==null?void 0:n.modelPath):Bs=await Ge((r=e.face.mesh)==null?void 0:r.modelPath),Wc=Bs.inputs[0].shape?Bs.inputs[0].shape[2]:0,Bs}var $T=Bl,DT=Jp;var wr,K0=[],PT=0,FT=0,A5=Number.MAX_SAFE_INTEGER;async function OT(e){var t;return pe.initial&&(wr=null),wr?e.debug&&oe("cached model:",wr.modelUrl):wr=await Ge((t=e.face.description)==null?void 0:t.modelPath),wr}function x5(e){let t=e.image||e.tensor||e;if(!(wr!=null&&wr.inputs[0].shape))return t;let n=Se.resizeBilinear(t,[wr.inputs[0].shape[2],wr.inputs[0].shape[1]],!1),r=L(n,Je.tf255);return ne(n),r}async function b5(e,t,n,r){var o,i,l,u;if(!wr)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let s=A5<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>le()-PT;return t.skipAllowed&&s&&a&&FT===r&&((l=K0[n])==null?void 0:l.age)&&((u=K0[n])==null?void 0:u.age)>0?(A5++,K0[n]):(A5=0,new Promise(async c=>{var d,h;let p={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((d=t.face.description)!=null&&d.enabled){let f=x5(e),m=wr==null?void 0:wr.execute(f);PT=le(),ne(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(y[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(p.gender=y[0]<=.5?"female":"male",p.genderScore=Math.min(.99,x));let A=Rr(m.find(R=>R.shape[1]===100),1),b=(await A.data())[0];ne(A);let S=await m.find(R=>R.shape[1]===100).data();p.age=Math.round(S[b-1]>S[b+1]?10*b-100*S[b-1]:10*b+100*S[b+1])/10;let I=m.find(R=>R.shape[1]===1024),E=I?await I.data():[];p.descriptor=Array.from(E),m.forEach(R=>ne(R))}K0[n]=p,FT=r,c(p)}))}function Z0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function th(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function LT(e,t,n){let 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r={};r.reshape=H(t,[-1,7,2]),r.div=de(r.reshape,this.inputSizeTensor),r.landmarks=ue(r.div,this.anchors[n]);let s=L(r.landmarks,this.inputSizeTensor);return Object.keys(r).forEach(a=>ne(r[a])),s}async predict(t,n){let r={};r.resize=Se.resizeBilinear(t,[this.inputSize,this.inputSize]),r.div=de(r.resize,Je.tf127),r.image=he(r.div,Je.tf1),r.batched=this.model.execute(r.image),r.predictions=tt(r.batched),r.slice=Pe(r.predictions,[0,0],[-1,1]),r.sigmoid=Tn(r.slice),r.scores=tt(r.sigmoid);let s=await r.scores.data();r.boxes=Pe(r.predictions,[0,1],[-1,4]),r.norm=this.normalizeBoxes(r.boxes),r.nms=await Se.nonMaxSuppressionAsync(r.norm,r.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await r.nms.array(),o=[];for(let i of a){let l={};l.box=Pe(r.norm,[i,0],[1,-1]),l.slice=Pe(r.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=H(l.norm,[-1,2]);let u=await l.box.data(),c=u.slice(0,2),p=u.slice(2,4),d=await l.palmLandmarks.array(),h={startPoint:c,endPoint:p,palmLandmarks:d,confidence:s[i]},f=BT(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ne(l[m]))}return Object.keys(r).forEach(i=>ne(r[i])),o}};var fAe=5,HT=1.65,jT=[0,5,9,13,17,1,2],mAe=0,gAe=2,qT=0,e2=class{constructor(t,n){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),r=t.map(o=>o[1]),s=[Math.min(...n),Math.min(...r)],a=[Math.max(...n),Math.max(...r)];return{startPoint:s,endPoint:a}}getBoxForPalmLandmarks(t,n){let r=t.map(a=>k5([...a,1],n)),s=this.calculateLandmarksBoundingBox(r);return Y0(J0(s),fAe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=Y0(J0(n),HT);r.palmLandmarks=[];for(let s=0;s<jT.length;s++)r.palmLandmarks.push(t[jT[s]].slice(0,2));return r}transformRawCoords(t,n,r,s){let a=Z0(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=w5(r,[0,0]),u=i.map(h=>[...k5(h,l),h[2]]),c=VT(s),p=[...th(n),1],d=[ni(p,c[0]),ni(p,c[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,n){let r=!1,s,a=(n.hand.skipTime||0)>le()-qT,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(s=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(r=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(n.hand.landmarks){let c=n.hand.rotation?WT(u.palmLandmarks[mAe],u.palmLandmarks[gAe]):0,p=th(u),d=[p[0]/t.shape[2],p[1]/t.shape[1]],h=n.hand.rotation&&pe.kernels.includes("rotatewithoffset")?Se.rotateWithOffset(t,c,0,d):t.clone(),f=w5(-c,p),m=r?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=LT(m,h,[this.inputSize,this.inputSize]),y=de(g,Je.tf255);ne(g),ne(h);let[x,A]=this.handPoseModel.execute(y);qT=le(),ne(y);let b=(await x.data())[0];if(ne(x),b>=n.hand.minConfidence/4){let v=H(A,[-1,3]),S=await v.array();ne(A),ne(v);let I=this.transformRawCoords(S,m,c,f),E=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:I,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ne(A)}else{let c=Y0(J0(u),HT),p={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};i.push(p)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var er={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=>er.nameMapping[e],getPoints:e=>er.pointsMapping[e]},si={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>si.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]},ri=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,n,r){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,r])}direction(t,n,r){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,r])}weight(t,n){this.weights[t]=n;let r=this.weights.reduce((s,a)=>s+a,0);this.weightsRelative=this.weights.map(s=>s*5/r)}matchAgainst(t,n){let r=0;for(let s in t){let a=t[s],o=this.curls[s];if(typeof o=="undefined"){r+=this.weightsRelative[s];continue}for(let[i,l]of o)if(a===i){r+=l*this.weightsRelative[s];break}}for(let s in n){let a=n[s],o=this.directions[s];if(typeof o=="undefined"){r+=this.weightsRelative[s];continue}for(let[i,l]of o)if(a===i){r+=l*this.weightsRelative[s];break}}return r/10}};var{thumb:vs,index:fa,middle:ma,ring:Ul,pinky:Gl}=er,{none:ws,half:AAe,full:ks}=si,{verticalUp:Vc,verticalDown:x6e,horizontalLeft:S5,horizontalRight:xAe,diagonalUpRight:bAe,diagonalUpLeft:Uc,diagonalDownRight:b6e,diagonalDownLeft:v6e}=Bt,ai=new ri("thumbs up");ai.curl(vs,ws,1);ai.direction(vs,Vc,1);ai.direction(vs,Uc,.25);ai.direction(vs,bAe,.25);for(let e of[er.index,er.middle,er.ring,er.pinky])ai.curl(e,ks,1),ai.direction(e,S5,1),ai.direction(e,xAe,1);var tn=new ri("victory");tn.curl(vs,AAe,.5);tn.curl(vs,ws,.5);tn.direction(vs,Vc,1);tn.direction(vs,Uc,1);tn.curl(fa,ws,1);tn.direction(fa,Vc,.75);tn.direction(fa,Uc,1);tn.curl(ma,ws,1);tn.direction(ma,Vc,1);tn.direction(ma,Uc,.75);tn.curl(Ul,ks,1);tn.direction(Ul,Vc,.2);tn.direction(Ul,Uc,1);tn.direction(Ul,S5,.2);tn.curl(Gl,ks,1);tn.direction(Gl,Vc,.2);tn.direction(Gl,Uc,1);tn.direction(Gl,S5,.2);tn.weight(fa,2);tn.weight(ma,2);var oi=new ri("point");oi.curl(vs,ks,1);oi.curl(fa,ws,.5);oi.curl(ma,ks,.5);oi.curl(Ul,ks,.5);oi.curl(Gl,ks,.5);oi.weight(fa,2);oi.weight(ma,2);var ii=new ri("middle finger");ii.curl(vs,ws,1);ii.curl(fa,ks,.5);ii.curl(ma,ks,.5);ii.curl(Ul,ks,.5);ii.curl(Gl,ks,.5);ii.weight(fa,2);ii.weight(ma,2);var Gc=new ri("open palm");Gc.curl(vs,ws,.75);Gc.curl(fa,ws,.75);Gc.curl(ma,ws,.75);Gc.curl(Ul,ws,.75);Gc.curl(Gl,ws,.75);var XT=[ai,tn,oi,ii,Gc];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,n,r){let s=(t-r)/(e-n),a=Math.atan(s)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function YT(e,t){if(!e||!t)return[0,0];let n=KT(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let r=KT(e[1],e[2],t[1],t[2]);return[n,r]}function ZT(e,t=1){let n=0,r=0,s=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?r=1*t:s=1*t,[n,r,s]}function wAe(e,t,n){let r=e[0]-t[0],s=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],u=e[2]-t[2],c=e[2]-n[2],p=t[2]-n[2],d=Math.sqrt(r*r+o*o+u*u),h=Math.sqrt(s*s+i*i+c*c),f=Math.sqrt(a*a+l*l+p*p),m=(f*f+d*d-h*h)/(2*f*d);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Hl.NO_CURL_START_LIMIT?y=si.none:g>Hl.HALF_CURL_START_LIMIT?y=si.half:y=si.full,y}function JT(e,t,n,r){let s;return r===Math.abs(e)?e>0?s=Bt.horizontalLeft:s=Bt.horizontalRight:r===Math.abs(t)?t>0?s=Bt.horizontalLeft:s=Bt.horizontalRight:n>0?s=Bt.horizontalLeft:s=Bt.horizontalRight,s}function QT(e,t,n,r){let s;return r===Math.abs(e)?e<0?s=Bt.verticalDown:s=Bt.verticalUp:r===Math.abs(t)?t<0?s=Bt.verticalDown:s=Bt.verticalUp:n<0?s=Bt.verticalDown:s=Bt.verticalUp,s}function kAe(e,t,n,r,s,a,o,i){let l,u=QT(e,t,n,r),c=JT(s,a,o,i);return u===Bt.verticalUp?c===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:c===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function SAe(e,t,n,r){let s=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],u=t[1]-n[1],c=Math.max(Math.abs(s),Math.abs(a),Math.abs(o)),p=Math.max(Math.abs(i),Math.abs(l),Math.abs(u)),d=0,h=0,f=0,m=p/(c+1e-5);m>1.5?d+=Hl.DISTANCE_VOTE_POWER:m>.66?h+=Hl.DISTANCE_VOTE_POWER:f+=Hl.DISTANCE_VOTE_POWER;let g=Math.sqrt(s*s+i*i),y=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+u*u),A=Math.max(g,y,x),b=e[0],v=e[1],S=n[0],I=n[1];A===g?(S=n[0],I=n[1]):A===x&&(b=t[0],v=t[1]);let P=YT([b,v],[S,I]),_=ZT(P,Hl.TOTAL_ANGLE_VOTE_POWER);d+=_[0],h+=_[1],f+=_[2];for(let T of r){let F=ZT(T,Hl.SINGLE_ANGLE_VOTE_POWER);d+=F[0],h+=F[1],f+=F[2]}let D;return d===Math.max(d,h,f)?D=QT(l,i,u,p):f===Math.max(h,f)?D=JT(a,s,o,c):D=kAe(l,i,u,p,a,s,o,c),D}function eN(e){let t=[],n=[],r=[],s=[];if(!e)return{curls:r,directions:s};for(let a of er.all){let o=er.getPoints(a),i=[],l=[];for(let u of o){let c=e[u[0]],p=e[u[1]],d=YT(c,p),h=d[0],f=d[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of er.all){let o=a===er.thumb?1:0,i=er.getPoints(a),l=e[i[o][0]],u=e[i[o+1][1]],c=e[i[3][1]],p=wAe(l,u,c),d=SAe(l,u,c,t[a].slice(o));r[a]=p,s[a]=d}return{curls:r,directions:s}}function t2(e){if(!e||e.length===0)return null;let t=eN(e),n={};for(let r of er.all)n[er.getName(r)]={curl:si.getName(t.curls[r]),direction:Bt.getName(t.directions[r])};return n}function tN(e){let t=[];if(!e||e.length===0)return t;let n=eN(e);for(let r of XT){let s=r.matchAgainst(n.curls,n.directions);s>=vAe&&t.push({name:r.name,confidence:s})}return t}var nN={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]},Hc,jc,rN;async function C5(e,t){let n=await rN.estimateHands(e,t);if(!n)return[];let r=[];for(let s=0;s<n.length;s++){let a={};if(n[s].landmarks)for(let c of Object.keys(nN))a[c]=nN[c].map(p=>n[s].landmarks[p]);let o=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let c of o)c[0]<i[0]&&(i[0]=c[0]),c[1]<i[1]&&(i[1]=c[1]),c[0]>i[2]&&(i[2]=c[0]),c[1]>i[3]&&(i[3]=c[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],l=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let u=t2(o);r.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:u})}return r}async function T5(e){var n,r;pe.initial&&(Hc=null,jc=null),!Hc||!jc?[Hc,jc]=await Promise.all([e.hand.enabled?Ge((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?Ge((r=e.hand.skeleton)==null?void 0:r.modelPath):null]):(e.debug&&oe("cached model:",Hc.modelUrl),e.debug&&oe("cached model:",jc.modelUrl));let t=new Q0(Hc);return rN=new e2(t,jc),[Hc,jc]}var pn=[null,null],IAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],li=[[0,0],[0,0]],CAe=["hand","fist","pinch","point","face","tip","pinchtip"],aN=4,oN=1.6,TAe=512,NAe=1.4,n2=Number.MAX_SAFE_INTEGER,N5=0,ga=[0,0],jt={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 lN(e){var t;if(pe.initial&&(pn[0]=null),pn[0])e.debug&&oe("cached model:",pn[0].modelUrl);else{r2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),pn[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let n=Object.values(pn[0].modelSignature.inputs);li[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,li[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return pn[0]}async function uN(e){var t;if(pe.initial&&(pn[1]=null),pn[1])e.debug&&oe("cached model:",pn[1].modelUrl);else{pn[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=Object.values(pn[1].modelSignature.inputs);li[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,li[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return pn[1]}async function EAe(e,t){let n=[];if(!e||!pn[0])return n;let r={},s=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,TAe),o=Math.round(a*s/8)*8;r.resize=Se.resizeBilinear(e,[a,o]),r.cast=ge(r.resize,"int32"),[r.rawScores,r.rawBoxes]=await pn[0].executeAsync(r.cast,IAe),r.boxes=tt(r.rawBoxes,[0,2]),r.scores=tt(r.rawScores,[0]);let i=rr(r.scores,1);ne(i[aN]),i.splice(aN,1),r.filtered=cn(i,1),ne(i),r.max=gn(r.filtered,1),r.argmax=Rr(r.filtered,1);let l=0;r.nms=await Se.nonMaxSuppressionAsync(r.boxes,r.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await r.nms.data(),c=await r.max.data(),p=await r.argmax.data();for(let d of Array.from(u)){let h=Pe(r.boxes,d,1),f=await h.data();ne(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=U0(m,NAe),y=[Math.trunc(m[0]*ga[0]),Math.trunc(m[1]*ga[1]),Math.trunc(m[2]*ga[0]),Math.trunc(m[3]*ga[1])],x=c[d],A=CAe[p[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};n.push(b)}return Object.keys(r).forEach(d=>ne(r[d])),n.sort((d,h)=>h.score-d.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function E5(e,t,n){let r={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&&pn[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},a=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=Se.cropAndResize(e,[a],[0],[li[1][0],li[1][1]],"bilinear"),s.div=de(s.crop,Je.tf255),[s.score,s.keypoints]=pn[1].execute(s.div,["Identity_1","Identity"]);let o=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(o))))/100;if(i>=(n.hand.minConfidence||0)){r.fingerScore=i,s.reshaped=H(s.keypoints,[-1,3]);let c=(await s.reshaped.array()).map(p=>[p[0]/li[1][1],p[1]/li[1][0],p[2]||0]).map(p=>[p[0]*t.boxRaw[2],p[1]*t.boxRaw[3],p[2]||0]);r.keypoints=c.map(p=>[ga[0]*(p[0]+t.boxRaw[0]),ga[1]*(p[1]+t.boxRaw[1]),p[2]||0]),r.landmarks=t2(r.keypoints);for(let p of Object.keys(iN))r.annotations[p]=iN[p].map(d=>r.landmarks&&r.keypoints[d]?r.keypoints[d]:null)}Object.keys(s).forEach(l=>ne(s[l]))}return r}async function R5(e,t){var s,a;if(!pn[0]||!pn[1]||!((s=pn[0])!=null&&s.inputs[0].shape)||!((a=pn[1])!=null&&a.inputs[0].shape))return[];ga=[e.shape[2]||0,e.shape[1]||0],n2++;let n=(t.hand.skipTime||0)>le()-N5,r=n2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&r?jt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>le()-N5,l=n2<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(c=>E5(e,c,t))):t.skipAllowed&&i&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(c=>E5(e,c,t))):(jt.boxes=await EAe(e,t),N5=le(),jt.hands=await Promise.all(jt.boxes.map(c=>E5(e,c,t))),n2=0);let u=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<jt.hands.length;c++){let p=tT(jt.hands[c].keypoints,ga);if(p.box[2]/(e.shape[2]||1)>.05&&p.box[3]/(e.shape[1]||1)>.05&&jt.hands[c].fingerScore&&jt.hands[c].fingerScore>(t.hand.minConfidence||0)){let d=U0(p.box,oN),h=U0(p.boxRaw,oN);jt.boxes.push({...u[c],box:d,boxRaw:h})}}for(let c=0;c<jt.hands.length;c++){let p=da(jt.hands[c].keypoints,ga);jt.hands[c].box=p.box,jt.hands[c].boxRaw=p.boxRaw}o(jt.hands)})}var $n,s2=[],_5=Number.MAX_SAFE_INTEGER,dN=0,pN=0;async function hN(e){var t;return pe.initial&&($n=null),$n?e.debug&&oe("cached model:",$n.modelUrl):$n=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),$n}async function $5(e,t,n,r){var o,i;if(!$n)return 0;let s=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>le()-pN,a=_5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&a&&dN===r&&s2[n]?(_5++,s2[n]):(_5=0,new Promise(async l=>{let u=Se.resizeBilinear(e,[$n!=null&&$n.inputs[0].shape?$n.inputs[0].shape[2]:0,$n!=null&&$n.inputs[0].shape?$n.inputs[0].shape[1]:0],!1),c=$n==null?void 0:$n.execute(u),p=(await c.data())[0];s2[n]=Math.round(100*p)/100,dN=r,pN=le(),ne([u,c]),l(s2[n])}))}var nh={};wa(nh,{connected:()=>o2,horizontal:()=>D5,kpt:()=>a2,relative:()=>F5,vertical:()=>P5});var a2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],D5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],P5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],F5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],o2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var mN=.005,kr={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function O5(e){for(let t of D5){let n=e.keypoints.findIndex(s=>s.part===t[0]),r=e.keypoints.findIndex(s=>s.part===t[1]);if(e.keypoints[n]&&e.keypoints[r]&&e.keypoints[n].position[0]<e.keypoints[r].position[0]){let s=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=s}}for(let t of P5){let n=e.keypoints.findIndex(s=>s&&s.part===t[0]),r=e.keypoints.findIndex(s=>s&&s.part===t[1]);e.keypoints[n]&&e.keypoints[r]&&e.keypoints[n].position[1]<e.keypoints[r].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of F5){let r=e.keypoints.findIndex(u=>u&&u.part===t[0]),s=e.keypoints.findIndex(u=>u&&u.part===t[1]),a=e.keypoints.findIndex(u=>u&&u.part===n[0]),o=e.keypoints.findIndex(u=>u&&u.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[r]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0])]:[0,0],l=e.keypoints[s]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let u=e.keypoints[r];e.keypoints[r]=e.keypoints[s],e.keypoints[s]=u}}}function gN(e){for(let t=0;t<e.length;t++)if(e[t]&&kr.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-kr.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-kr.keypoints[t].positionRaw[1])];n[0]<mN&&n[1]<mN?e[t]=kr.keypoints[t]:kr.keypoints[t]=e[t]}else kr.keypoints[t]=e[t];return e}function yN(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;kr.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]],n.pad=Xr(e,kr.padding),n.resize=Se.resizeBilinear(n.pad,[t,t]);let r=ge(n.resize,"int32");return Object.keys(n).forEach(s=>ne(n[s])),r}function AN(e,t){e.keypoints=e.keypoints.filter(r=>r&&r.position);for(let r of e.keypoints)r.position=[r.position[0]*(t[0]+kr.padding[2][0]+kr.padding[2][1])/t[0]-kr.padding[2][0],r.position[1]*(t[1]+kr.padding[1][0]+kr.padding[1][1])/t[1]-kr.padding[1][0]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1]];let n=da(e.keypoints.map(r=>r.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Sr,i2=0,M5=Number.MAX_SAFE_INTEGER,jl={boxes:[],bodies:[],last:0};async function xN(e){return pe.initial&&(Sr=null),Sr?e.debug&&oe("cached model:",Sr.modelUrl):(r2(["size"],e),Sr=await Ge(e.body.modelPath)),i2=Sr.inputs[0].shape?Sr.inputs[0].shape[2]:0,i2<64&&(i2=256),Sr}async function _Ae(e,t,n){let r=e[0][0],s=[],a=0;for(let c=0;c<r.length;c++)if(a=r[c][2],a>t.body.minConfidence){let p=[r[c][1],r[c][0]];s.push({score:Math.round(100*a)/100,part:a2[c],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}a=s.reduce((c,p)=>p.score>c?p.score:c,0);let o=[],i=da(s.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,p]of Object.entries(o2)){let d=[];for(let h=0;h<p.length-1;h++){let f=s.find(g=>g.part===p[h]),m=s.find(g=>g.part===p[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&d.push([f.position,m.position])}l[c]=d}let u={id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:s,annotations:l};return O5(u),o.push(u),o}async function $Ae(e,t,n){let r=[];for(let s=0;s<e[0].length;s++){let a=e[0][s],o=Math.round(100*a[51+4])/100;if(o>t.body.minConfidence){let i=[];for(let p=0;p<17;p++){let d=a[3*p+2];if(d>t.body.minConfidence){let h=[a[3*p+1],a[3*p+0]];i.push({part:a2[p],score:Math.round(100*d)/100,positionRaw:h,position:[Math.round((n.shape[2]||0)*h[0]),Math.round((n.shape[1]||0)*h[1])]})}}let l=da(i.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,d]of Object.entries(o2)){let h=[];for(let f=0;f<d.length-1;f++){let m=i.find(y=>y.part===d[f]),g=i.find(y=>y.part===d[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}u[p]=h}let c={id:s,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:u};O5(c),r.push(c)}}return r.sort((s,a)=>a.score-s.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function z5(e,t){if(!Sr||!(Sr!=null&&Sr.inputs[0].shape))return[];t.skipAllowed||(jl.boxes.length=0),M5++;let n=(t.body.skipTime||0)>le()-jl.last,r=M5<(t.body.skipFrames||0);return t.skipAllowed&&n&&r?jl.bodies:new Promise(async s=>{let a={};M5=0,a.input=yN(e,i2),a.res=Sr==null?void 0:Sr.execute(a.input),jl.last=le();let o=await a.res.array();jl.bodies=a.res.shape[2]===17?await _Ae(o,t,e):await $Ae(o,t,e);for(let i of jl.bodies)AN(i,[e.shape[2]||1,e.shape[1]||1]),gN(i.keypoints);Object.keys(a).forEach(i=>ne(a[i])),s(jl.bodies)})}var qc,l2=[],vN=0,L5=Number.MAX_SAFE_INTEGER,c2=0,u2=2.5;async function wN(e){if(!qc||pe.initial){qc=await Ge(e.object.modelPath);let t=Object.values(qc.modelSignature.inputs);c2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&oe("cached model:",qc.modelUrl);return qc}async function DAe(e,t,n){let r=0,s=[];for(let l of[1,2,4])K(async()=>{let u=l*13,c=tt(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===zc.length)),p=tt(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<zc.length)),h=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),f=await c.array();for(let m=0;m<c.shape[0];m++)for(let g=0;g<c.shape[1];g++){let y=f[m][g];if(y>(n.object.minConfidence||0)&&g!==61){let x=(.5+Math.trunc(m%u))/u,A=(.5+Math.trunc(m/u))/u,b=h[m].map(D=>D*(u/l/c2)),[v,S]=[x-u2/l*b[0],A-u2/l*b[1]],[I,E]=[x+u2/l*b[2]-v,A+u2/l*b[3]-S],R=[v,S,I,E];R=R.map(D=>Math.max(0,Math.min(D,1)));let P=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],_={id:r++,score:Math.round(100*y)/100,class:g+1,label:zc[g].label,box:P.map(D=>Math.trunc(D)),boxRaw:R};s.push(_)}}});e.forEach(l=>ne(l));let a=s.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),o=s.map(l=>l.score),i=[];if(a&&a.length>0){let l=await Se.nonMaxSuppressionAsync(a,o,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);i=await l.data(),ne(l)}return s=s.filter((l,u)=>i.includes(u)).sort((l,u)=>u.score-l.score),s}async function B5(e,t){let n=(t.object.skipTime||0)>le()-vN,r=L5<(t.object.skipFrames||0);return t.skipAllowed&&n&&r&&l2.length>0?(L5++,l2):(L5=0,!pe.kernels.includes("mod")||!pe.kernels.includes("sparsetodense")?l2:new Promise(async s=>{let a=[e.shape[2]||0,e.shape[1]||0],o=Se.resizeBilinear(e,[c2,c2],!1),i=de(o,Je.tf255),l=i.transpose([0,3,1,2]);ne(i),ne(o);let u;t.object.enabled&&(u=qc.execute(l)),vN=le(),ne(l);let c=await DAe(u,a,t);l2=c,s(c)}))}var sh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],PAe=sh.length,rh=sh.reduce((e,t,n)=>(e[t]=n,e),{}),FAe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],H6e=FAe.map(([e,t])=>[rh[e],rh[t]]),SN=[["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:n,maxY:r,minX:s,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(r,i),minX:Math.min(s,o),minY:Math.min(a,i)}),{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 CN(e,[t,n],[r,s]){let a=t/r,o=n/s,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/s,u.box[1]/r,u.box[2]/s,u.box[3]/r],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.keypoints.map(({score:p,part:d,position:h})=>({score:p,part:d,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/r,h.y/r]})),annotations:{}});return e.map((u,c)=>i(u,c))}var d2=class{constructor(t,n){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}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 n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function W5(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+PAe)}}function V5(e,t,n){let{heatmapY:r,heatmapX:s,id:a}=e,{y:o,x:i}=W5(r,s,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function U5(e,t,n){return e<t?t:e>n?n:e}function TN(e,t,n,r){let s=n-e,a=r-t;return s*s+a*a}function G5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Ss,MAe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],p2=1,Xc=16,zAe=50**2;function NN(e,t,n,r,s,a,o=2){let i=y=>({y:a.get(y.y,y.x,e),x:a.get(y.y,y.x,a.shape[2]/2+e)}),l=(y,x,A)=>({y:U5(Math.round(y.y/Xc),0,x-1),x:U5(Math.round(y.x/Xc),0,A-1)}),[u,c]=r.shape,p=l(t.position,u,c),d=i(p),f=G5(t.position,d);for(let y=0;y<o;y++){let x=l(f,u,c),A=W5(x.y,x.x,n,s);f=G5({x:x.x*Xc,y:x.y*Xc},{x:A.x,y:A.y})}let m=l(f,u,c),g=r.get(m.y,m.x,n);return{position:f,part:sh[n],score:g}}function LAe(e,t,n,r,s){let 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n=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,r=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],n,r,0,0,2*Math.PI),t.stroke(),ht.fillPolygons&&(t.fillStyle=ht.useDepth?"rgba(255, 255, 200, 0.3)":ht.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=ht.useDepth?"rgba(255, 200, 255, 0.3)":ht.color,t.beginPath();let n=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,r=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],n,r,0,0,2*Math.PI),t.stroke(),ht.fillPolygons&&(t.fillStyle=ht.useDepth?"rgba(255, 255, 200, 0.3)":ht.color,t.fill())}}function KAe(e,t){var n;if(ht.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let r=e.box[0]+e.box[2]/2-e.box[3]*ql(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*ql(e.rotation.angle.pitch)/90,a=new Path2D(`
M ${e.box[0]+e.box[2]/2} ${e.box[1]}
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${r} ${e.box[1]},
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C
${e.box[0]} ${s},
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d=[c[0],c[1],c[2],u[0],u[1],u[2],p[0],p[1],p[2]],h=a(d),f=o.length===478?nxe(e):{bearing:0,strength:0};return{angle:h,matrix:d,gaze:f}};var sb=async(e,t)=>{var h,f,m,g,y,x,A,b,v,S,I,E,R,P,_,D,T,F,U,X,z,Z;let n=le(),r,s,a,o,i,l,u,c,p=[];e.state="run:face";let d=await RT(t,e.config);if(e.performance.face=pe.perfadd?(e.performance.face||0)+Math.trunc(le()-n):Math.trunc(le()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let W=0;W<d.length;W++){if(e.analyze("Get Face"),!d[W].tensor||d[W].tensor.isDisposedInternal){oe("Face object is disposed:",d[W].tensor);continue}if((h=e.config.face.detector)!=null&&h.mask){let se=await MN(d[W]);ne(d[W].tensor),d[W].tensor=se}let ee=d[W].mesh&&d[W].mesh.length>200?zN(d[W],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=(f=e.config.face.emotion)!=null&&f.enabled?p5(d[W].tensor||pt([]),e.config,W,d.length):[]:(e.state="run:emotion",n=le(),o=(m=e.config.face.emotion)!=null&&m.enabled?await 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MobileFaceNet:"),e.config.async?i=(P=e.config.face.mobilefacenet)!=null&&P.enabled?f5(d[W].tensor||pt([]),e.config,W,d.length):null:(e.state="run:mobilefacenet",n=le(),i=(_=e.config.face.mobilefacenet)!=null&&_.enabled?await f5(d[W].tensor||pt([]),e.config,W,d.length):null,e.performance.mobilefacenet=Math.trunc(le()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(D=e.config.face.description)!=null&&D.enabled?b5(d[W].tensor||pt([]),e.config,W,d.length):null:(e.state="run:description",n=le(),c=(T=e.config.face.description)!=null&&T.enabled?await b5(d[W].tensor||pt([]),e.config,W,d.length):null,e.performance.description=pe.perfadd?(e.performance.description||0)+Math.trunc(le()-n):Math.trunc(le()-n)),e.analyze("End Description:"),e.config.async&&([r,a,o,i,c,s,l,u]=await Promise.all([r,a,o,i,c,s,l,u])),e.analyze("Finish Face:"),((F=e.config.face.ssrnet)==null?void 0:F.enabled)&&r&&a&&(c={...c,age:r.age,gender:a.gender,genderScore:a.genderScore}),((U=e.config.face.gear)==null?void 0:U.enabled)&&s&&(c={...c,age:s.age,gender:s.gender,genderScore:s.genderScore,race:s.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&i&&(c.descriptor=i),(z=e.config.face.iris)!=null&&z.enabled;let Q=d[W].annotations&&d[W].annotations.leftEyeIris&&d[W].annotations.leftEyeIris[0]&&d[W].annotations.rightEyeIris&&d[W].annotations.rightEyeIris[0]&&d[W].annotations.leftEyeIris.length>0&&d[W].annotations.rightEyeIris.length>0&&d[W].annotations.leftEyeIris[0]!==null&&d[W].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[W].annotations.leftEyeIris[3][0]-d[W].annotations.leftEyeIris[1][0]),Math.abs(d[W].annotations.rightEyeIris[4][1]-d[W].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ae=(Z=e.config.face.detector)!=null&&Z.return?tt(d[W].tensor):null;ne(d[W].tensor),d[W].tensor&&delete d[W].tensor;let J={...d[W],id:W};c!=null&&c.age&&(J.age=c.age),c!=null&&c.gender&&(J.gender=c.gender),c!=null&&c.genderScore&&(J.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(J.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(J.race=c==null?void 0:c.race),o&&(J.emotion=o),l&&(J.real=l),u&&(J.live=u),Q&&Q!==0&&(J.iris=Math.trunc(500/Q/11.7)/100),ee&&(J.rotation=ee),ae&&(J.tensor=ae),p.push(J),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),p};var LN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),s=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&r&&s&&r.position[1]<a.position[1]&&s.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&r&&r.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&s&&s.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},BN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let r=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),s=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(r/s)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${r<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},WN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],s=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(r*s),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(u=!1),p>d?p>.05&&t.push({iris:n,gesture:"looking right"}):d>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(u=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},VN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];if(e[n].annotations)for(let[s,a]of Object.entries(e[n].annotations))s!=="palmBase"&&Array.isArray(a)&&a[0]&&r.push({name:s.toLowerCase(),position:a[0]});if(r&&r.length>0){let s=r.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${s.name} forward`});let a=r.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let s=tN(e[n].keypoints);for(let a of s)t.push({hand:n,gesture:a.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},ab=0;function UN(e,t){var o,i,l,u,c,p,d,h,f,m,g,y,x,A,b,v,S,I,E,R,P,_,D,T,F,U,X;let n=le();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let r=Date.now()-e.timestamp,s=r<1e3?8-Math.log(r+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 z=0;z<e.body.length;z++){let Z=e.body[z].box.map((J,se)=>((s-1)*Ne.body[z].box[se]+J)/s),W=e.body[z].boxRaw.map((J,se)=>((s-1)*Ne.body[z].boxRaw[se]+J)/s),ee=e.body[z].keypoints.map((J,se)=>{var ie,me,be,Ee,Re,ze,Be,nt,it;return{score:J.score,part:J.part,position:[Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].position[0]||0)+(J.position[0]||0))/s:J.position[0],Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].position[1]||0)+(J.position[1]||0))/s:J.position[1],Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].position[2]||0)+(J.position[2]||0))/s:J.position[2]],positionRaw:[Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].positionRaw[0]||0)+(J.positionRaw[0]||0))/s:J.positionRaw[0],Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].positionRaw[1]||0)+(J.positionRaw[1]||0))/s:J.positionRaw[1],Ne.body[z].keypoints[se]?((s-1)*(Ne.body[z].keypoints[se].positionRaw[2]||0)+(J.positionRaw[2]||0))/s:J.positionRaw[2]],distance:[Ne.body[z].keypoints[se]?((s-1)*(((ie=Ne.body[z].keypoints[se].distance)==null?void 0:ie[0])||0)+(((me=J.distance)==null?void 0:me[0])||0))/s:(be=J.distance)==null?void 0:be[0],Ne.body[z].keypoints[se]?((s-1)*(((Ee=Ne.body[z].keypoints[se].distance)==null?void 0:Ee[1])||0)+(((Re=J.distance)==null?void 0:Re[1])||0))/s:(ze=J.distance)==null?void 0:ze[1],Ne.body[z].keypoints[se]?((s-1)*(((Be=Ne.body[z].keypoints[se].distance)==null?void 0:Be[2])||0)+(((nt=J.distance)==null?void 0:nt[2])||0))/s:(it=J.distance)==null?void 0:it[2]]}}),Q={},ae={connected:{}};(i=(o=t.body)==null?void 0:o.modelPath)!=null&&i.includes("efficientpose")?ae=j0:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?ae=W0:(p=(c=t.body)==null?void 0:c.modelPath)!=null&&p.includes("movenet")&&(ae=nh);for(let[J,se]of Object.entries(ae.connected)){let ie=[];for(let me=0;me<se.length-1;me++){let be=ee.find(Re=>Re.part===se[me]),Ee=ee.find(Re=>Re.part===se[me+1]);be&&Ee&&ie.push([be.position,Ee.position])}Q[J]=ie}Ne.body[z]={...e.body[z],box:Z,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let Z=e.hand[z].box.map((ae,J)=>((s-1)*Ne.hand[z].box[J]+ae)/s),W=e.hand[z].boxRaw.map((ae,J)=>((s-1)*Ne.hand[z].boxRaw[J]+ae)/s);Ne.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Ne.hand[z].keypoints=e.hand[z].keypoints);let ee=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ae,J)=>ae.map((se,ie)=>((s-1)*(Ne.hand[z].keypoints[J][ie]||1)+(se||0))/s)):[],Q={};if(Object.keys(Ne.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Ne.hand[z].annotations=e.hand[z].annotations,Q=Ne.hand[z].annotations;else if(e.hand[z].annotations)for(let ae of Object.keys(e.hand[z].annotations))Q[ae]=e.hand[z].annotations[ae]&&e.hand[z].annotations[ae][0]?e.hand[z].annotations[ae].map((J,se)=>J.map((ie,me)=>((s-1)*Ne.hand[z].annotations[ae][se][me]+ie)/s)):null;Ne.hand[z]={...e.hand[z],box:Z,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let Z=e.face[z].box.map((ee,Q)=>((s-1)*Ne.face[z].box[Q]+ee)/s),W=e.face[z].boxRaw.map((ee,Q)=>((s-1)*Ne.face[z].boxRaw[Q]+ee)/s);if(e.face[z].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=(d=e.face[z].rotation)==null?void 0:d.matrix,ee.angle={roll:((s-1)*(((f=(h=Ne.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/s,yaw:((s-1)*(((x=(y=Ne.face[z].rotation)==null?void 0:y.angle)==null?void 0:x.yaw)||0)+(((b=(A=e.face[z].rotation)==null?void 0:A.angle)==null?void 0:b.yaw)||0))/s,pitch:((s-1)*(((S=(v=Ne.face[z].rotation)==null?void 0:v.angle)==null?void 0:S.pitch)||0)+(((E=(I=e.face[z].rotation)==null?void 0:I.angle)==null?void 0:E.pitch)||0))/s},ee.gaze={bearing:((s-1)*(((P=(R=Ne.face[z].rotation)==null?void 0:R.gaze)==null?void 0:P.bearing)||0)+(((D=(_=e.face[z].rotation)==null?void 0:_.gaze)==null?void 0:D.bearing)||0))/s,strength:((s-1)*(((F=(T=Ne.face[z].rotation)==null?void 0:T.gaze)==null?void 0:F.strength)||0)+(((X=(U=e.face[z].rotation)==null?void 0:U.gaze)==null?void 0:X.strength)||0))/s},Ne.face[z]={...e.face[z],rotation:ee,box:Z,boxRaw:W}}Ne.face[z]={...e.face[z],box:Z,boxRaw:W}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let Z=e.object[z].box.map((ee,Q)=>((s-1)*Ne.object[z].box[Q]+ee)/s),W=e.object[z].boxRaw.map((ee,Q)=>((s-1)*Ne.object[z].boxRaw[Q]+ee)/s);Ne.object[z]={...e.object[z],box:Z,boxRaw:W}}if(e.persons){let z=e.persons;if(!Ne.persons||z.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(z));else for(let Z=0;Z<z.length;Z++)Ne.persons[Z].box=z[Z].box.map((W,ee)=>((s-1)*Ne.persons[Z].box[ee]+W)/s)}e.gesture&&(Ne.gesture=e.gesture);let a=le();return ab=pe.perfadd?ab+Math.round(a-n):Math.round(a-n),e.performance&&(Ne.performance={...e.performance,interpolate:ab}),Ne}var lb={};wa(lb,{distance:()=>oh,match:()=>ib,similarity:()=>ob});function oh(e,t,n={order:2,multiplier:25}){let r=0;for(let s=0;s<e.length;s++){let a=!n.order||n.order===2?e[s]-t[s]:Math.abs(e[s]-t[s]);r+=!n.order||n.order===2?a*a:a**n.order}return(n.multiplier||20)*r}var GN=(e,t,n,r)=>{if(e===0)return 1;let s=t===2?Math.sqrt(e):e**(1/t),a=(1-s/100-n)/(r-n);return Math.max(Math.min(a,1),0)};function ob(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let r=oh(e,t,n);return GN(r,n.order||2,n.min||0,n.max||1)}function ib(e,t,n={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 r=Number.MAX_SAFE_INTEGER,s=-1;for(let o=0;o<t.length;o++){let i=oh(e,t[o],n);if(i<r&&(r=i,s=o),r<(n.threshold||0))break}let a=GN(r,n.order||2,n.min||0,n.max||1);return{index:s,distance:r,similarity:a}}function HN(e,t,n,r,s){var i,l,u,c,p,d,h,f,m,g,y,x,A,b,v,S;let a=0,o=[];for(let I of e){let E={id:a++,face:I,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let F of t)I.box[0]>F.box[0]&&I.box[0]<F.box[0]+F.box[2]&&I.box[1]+I.box[3]>F.box[1]&&I.box[1]+I.box[3]<F.box[1]+F.box[3]&&(E.body=F);if(E.body)for(let F of n)F.box[0]+F.box[2]>E.body.box[0]&&F.box[0]+F.box[2]<E.body.box[0]+E.body.box[2]&&F.box[1]+F.box[3]>E.body.box[1]&&F.box[1]+F.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=F),F.box[0]<E.body.box[0]+E.body.box[2]&&F.box[0]>E.body.box[0]&&F.box[1]+F.box[3]>E.body.box[1]&&F.box[1]+F.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=F);for(let F of r)F.face!==void 0&&F.face===I.id?(i=E.gestures)==null||i.push(F):F.iris!==void 0&&F.iris===I.id?(l=E.gestures)==null||l.push(F):F.body!==void 0&&F.body===((u=E.body)==null?void 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0).decodeJpeg(n),a=s.expandDims(0);e.tf.dispose(s),r=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&oe("Warmup tfjs-node not loaded");return r}async function dxe(e){let t;return typeof createImageBitmap=="function"?t=await lxe(e):typeof Image!="undefined"||pe.Canvas!==void 0?t=await uxe(e):t=await cxe(e),t}async function pxe(e){let t=qn(),n=zr();if(t!=="webgl"&&t!=="humangl"||!n||!n.checkCompileCompletion)return;Y().set("ENGINE_COMPILE_ONLY",!0);let r=sn().state.numTensors,s=[];for(let[i,l]of Object.entries(e).filter(([u,c])=>u!==null&&c!==null)){let u=l.inputs&&l.inputs[0]&&l.inputs[0].shape?[...l.inputs[0].shape]:[1,64,64,3],c=l.inputs&&l.inputs[0]&&l.inputs[0].dtype?l.inputs[0].dtype:"float32";for(let d=0;d<u.length;d++)u[d]===-1&&(u[d]=d===0?1:64);let p=Ft(u,c);try{let d=l.execute(p);s.push(i),Array.isArray(d)?d.forEach(h=>ne(h)):ne(d)}catch(d){oe("compile fail model:",i)}ne(p)}let a=await n.checkCompileCompletionAsync();n.getUniformLocations(),oe("compile pass models:",s),oe("compile pass kernels:",a.length),Y().set("ENGINE_COMPILE_ONLY",!1);let o=sn().state.numTensors;o-r>0&&oe("tensor leak:",o-r)}async function jN(e,t){let n=le();return e.state="warmup",t&&(e.config=Gt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:le(),persons:[],error:null}:new Promise(async r=>{await pxe(e.models);let s=await dxe(e),a=le();e.config.debug&&oe("warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var td,ih,lh,A2,ub=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");md(this,td,void 0);md(this,ih,void 0);md(this,lh,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!fd(this,ih))return;let n=this.tf.engine().state.numTensors,r=fd(this,td);gd(this,td,n);let s=n-r;s!==0&&oe(...t,s)});md(this,A2,t=>{if(!fd(this,lh))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof rt))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});fe(this,"similarity",ob);fe(this,"distance",oh);fe(this,"match",ib);fe(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=pe,ka.wasmPath=Kp["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Ry}/dist/`,ka.modelBasePath=pe.browser?"../models/":"file://models/",ka.backend=pe.browser?"humangl":"tensorflow",this.version=F3,Object.defineProperty(this,"version",{value:F3}),this.config=JSON.parse(JSON.stringify(ka)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Gt(this.config,t)),v9(this.config),this.tf=Ue,this.state="idle",gd(this,td,0),gd(this,ih,!1),gd(this,lh,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new ah,this.draw={options:Dn,canvas:(n,r)=>eb(n,r),face:(n,r,s)=>Kc(n,r,s),body:(n,r,s)=>Zc(n,r,s),hand:(n,r,s)=>Yc(n,r,s),gesture:(n,r,s)=>Qc(n,r,s),object:(n,r,s)=>Jc(n,r,s),person:(n,r,s)=>Q5(n,r,s),all:(n,r,s)=>tb(n,r,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=$T,this.faceUVMap=DT,this.gl=Et,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ka)),this.config.backend=t}validate(t){return Z1(ka,t||this.config)}now(){return le()}image(t,n=!0){return Fc(t,this.config,n)}async segmentation(t,n){return $N(t,n,this.config)}enhance(t){return x5(t)}compare(t,n){return b9(this.config,t,n)}async init(){await m2(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=le(),r=Object.values(this.models).filter(o=>o).length;t&&(this.config=Gt(this.config,t)),this.env.initial&&(this.config.debug&&oe(`version: ${this.version}`),this.config.debug&&oe(`tfjs version: ${this.tf.version["tfjs-core"]}`),await m2(this)||oe("error: backend check failed"),await dc(),this.env.browser&&(this.config.debug&&oe("configuration:",this.config),this.config.debug&&oe("environment:",this.env),this.config.debug&&oe("tf flags:",this.tf.ENV.flags))),await X5(this),this.env.initial&&this.config.debug&&oe("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==r&&(await K5(this),this.emit("load"));let a=Math.trunc(le()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return UN(t,this.config)}async warmup(t){let n=le(),r=await jN(this,t),s=le();return this.performance.warmup=Math.trunc(s-n),r}async profile(t,n){let r=await this.tf.profile(()=>this.detect(t,n)),s={};for(let i of r.kernels)s[i.name]?s[i.name]+=i.kernelTimeMs:s[i.name]=i.kernelTimeMs;let a=[];Object.entries(s).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async r=>{var g,y,x,A,b,v,S,I,E,R,P,_,D,T,F,U,X,z,Z,W,ee,Q;this.state="config";let s;this.config=Gt(this.config,n),this.state="check";let a=fd(this,A2).call(this,t);a&&(oe(a,t),this.emit("error"),r({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:le(),persons:[],error:a}));let o=le();await m2(this),await this.load(),s=le(),this.state="image";let i=await Fc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(le()-s):Math.trunc(le()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&oe("could not convert input to tensor"),this.emit("error"),r({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:le(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),s=le(),this.config.skipAllowed=await x9(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(le()-s):Math.trunc(le()-s),this.analyze("Check Changed:");let l=[],u=[],c=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?sb(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=le(),l=this.config.face.enabled?await sb(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(le()-s):Math.trunc(le()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Gt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?H5(i.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?r5(i.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?c5(i.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?z5(i.tensor,d):[]),this.performance.body&&delete this.performance.body):(s=le(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await H5(i.tensor,d):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?u=this.config.body.enabled?await r5(i.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await c5(i.tensor,d):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(u=this.config.body.enabled?await z5(i.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(le()-s):Math.trunc(le()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=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")?c=this.config.hand.enabled?C5(i.tensor,h):[]:(_=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&_.includes("handtrack")&&(c=this.config.hand.enabled?R5(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(s=le(),(T=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&T.includes("handdetect")?c=this.config.hand.enabled?await C5(i.tensor,h):[]:(U=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&U.includes("handtrack")&&(c=this.config.hand.enabled?await R5(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(le()-s):Math.trunc(le()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?p=this.config.object.enabled?B5(i.tensor,this.config):[]:(z=this.config.object.modelPath)!=null&&z.includes("centernet")&&(p=this.config.object.enabled?o5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=le(),(Z=this.config.object.modelPath)!=null&&Z.includes("nanodet")?p=this.config.object.enabled?await B5(i.tensor,this.config):[]:(W=this.config.object.modelPath)!=null&&W.includes("centernet")&&(p=this.config.object.enabled?await o5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(le()-s):Math.trunc(le()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,c,p]=await Promise.all([l,u,c,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(s=le(),f=[...BN(l),...LN(u),...VN(c),...WN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(le()-s):Math.trunc(le()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(le()-o):Math.trunc(le()-o);let m=((Q=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:u,hand:c,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return HN(l,u,c,f,m)}},ne(i.tensor),this.emit("detect"),this.state="idle",r(this.result)})}};td=new WeakMap,ih=new WeakMap,lh=new WeakMap,A2=new WeakMap;return HE(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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