/*
  Human
  homepage: <https://github.com/vladmandic/human>
  author: <https://github.com/vladmandic>'
*/

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2 or rank 1 but got rank ${d.rank}.`),u!=null&&P(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),cu(i,o,l,u,d,s)}var Ww=V({batchNorm2d_:BM});function WM(e,t,r,a,n,s){let i=$(e,"x","batchNorm"),o=$(t,"mean","batchNorm"),l=$(r,"variance","batchNorm"),d;n!=null&&(d=$(n,"scale","batchNorm"));let u;return a!=null&&(u=$(a,"offset","batchNorm")),P(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),P(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),P(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),d!=null&&P(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${d.rank}.`),u!=null&&P(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),cu(i,o,l,u,d,s)}var Vw=V({batchNorm3d_:WM});function VM(e,t,r,a,n,s){let i=$(e,"x","batchNorm"),o=$(t,"mean","batchNorm"),l=$(r,"variance","batchNorm"),d;n!=null&&(d=$(n,"scale","batchNorm"));let u;return a!=null&&(u=$(a,"offset","batchNorm")),P(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),P(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),P(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),d!=null&&P(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${d.rank}.`),u!=null&&P(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),cu(i,o,l,u,d,s)}var Uw=V({batchNorm4d_:VM});function UM(e,t,r){let a=$(e,"x","bincount"),n=$(t,"weights","bincount");P(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),P(r>=0,()=>`size must be non-negative, but got 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t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};vm.className="Adam";_i(vm);var wm=class extends Jn{constructor(e,t,r,a=null,n=0){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=a,this.decay=n,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),a==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);q(()=>{let r=he(1,this.accBeta1),a=pe(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((n,s)=>{let i=B.registeredVariables[n],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${n}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${n}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[n];if(l==null)return;let d=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=ue(L(d,this.beta1),L(l,1-this.beta1)),h=L(u,this.beta2),c=Qt(l),f=Zn(h,c);d.assign(p),u.assign(f);let m=ue(L(pe(a,r),pe(p,ue(f,this.epsilon))),i);i.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&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};wm.className="Adamax";_i(wm);var ch=class extends Jn{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let n=B.registeredVariables[t];q(()=>{let s=ue(L(this.c,a),n);n.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=dr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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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(r=>({originalName:r.name,variable:r.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)}};km.className="Momentum";_i(km);var Im=class extends Jn{constructor(e,t=.9,r=0,a=null,n=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=n,a==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let a=B.registeredVariables[t],n=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:q(()=>at(a).variable(n))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:q(()=>at(a).variable(n))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:q(()=>at(a).variable(n))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;q(()=>{let l=ue(L(i,this.decay),L(At(s),1-this.decay));if(this.centered){let d=this.accumulatedMeanGrads[r].variable,u=ue(L(d,this.decay),L(s,1-this.decay)),p=pe(L(s,this.learningRate),Tr(he(l,ue(At(u),this.epsilon)))),h=ue(L(o,this.momentum),p);i.assign(l),d.assign(u),o.assign(h);let c=he(a,h);a.assign(c)}else{let 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t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Im.className="RMSProp";_i(Im);var vs=class{static sgd(e){return new ch(e)}static momentum(e,t,r=!1){return new km(e,t,r)}static rmsprop(e,t=.9,r=0,a=null,n=!1){return new Im(e,t,r,a,n)}static adam(e=.001,t=.9,r=.999,a=null){return new vm(e,t,r,a)}static adadelta(e=.001,t=.95,r=null){return new xm(e,t,r)}static adamax(e=.002,t=.9,r=.999,a=null,n=0){return new wm(e,t,r,a,n)}static 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new en(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return sf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Z1(e?this.trainableWeights:this.weights)}setWeights(e){q(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Set),n[d.name].add(o.name),!r.has(d.name)&&s.push(d)}}return{sorted:a,recipientMap:n}}function dV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let a=0;a<e.sourceLayer.inboundNodes.length;++a)for(let n of e.sourceLayer.inboundNodes[a].outputTensors)if(n.id===e.id){r=a;break}t=e.sourceLayer.getOutputAt(r)}return t}var vn=class extends nt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Pm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Cs(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;wn(x===0,"input layer has >1 nodes"),wn(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof od))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},a={},n={},s={},i=[],o=(y,A,x,b,v,C)=>{(b==null||v==null||C==null)&&(b=y.sourceLayer,v=y.nodeIndex,C=y.tensorIndex);let T=b.inboundNodes[v];if(x.indexOf(T)!==-1)throw new en(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(T)!==-1)return;this.containerNodes.add(vn.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(T)===-1&&x.push(T);let E=T.inboundLayers.length;for(let R=0;R<E;R++){let z=T.inputTensors[R],M=T.inboundLayers[R],I=T.nodeIndices[R],D=T.tensorIndices[R];o(z,A,x,M,I,D)}for(A.push(T);x.indexOf(T)>=0;)x.splice(x.indexOf(T),1);i.push(T)},l=[],d=[];for(let y of this.outputs)o(y,l,d);let u=i.slice().reverse();for(let y of u){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];A=Math.max(A,x),a[y.outboundLayer.id]=A,n[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],C=y.nodeIndices[b],T=v.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(A+1,E),r[T.id]=T}}let p={};for(let y in t){let A=t[y];A in p||(p[A]=[]),p[A].push(r[y])}let h={};for(let y in a){let A=a[y];A in h||(h[A]=[]),h[A].push(n[y])}let c=Object.keys(h).map(y=>parseInt(y,10)).sort(Sc);this.layers=[];for(let y of c){let A=h[y];A.sort((x,b)=>{let v=s[x.id],C=s[b.id];return v<C?-1:v>C?1:0});for(let x of A)x instanceof vn&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=h,c=Object.keys(p).map(y=>parseInt(y,10)).sort(Sc);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of p[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new en(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". 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Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Om({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new H("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},a=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new H(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,a++}let n=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)n.push([r[i],e[s]]);else if(t)throw new H(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new H(`${s.length} of ${a} weights are not set: ${s}`)}J2(n)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${nA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=J1(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return q(()=>{e=It(e);let r=new ho;for(let a=0;a<this.inputs.length;++a)r.add(this.inputs[a],e[a]);return dp(this.outputs,r,t)})}computeMask(e,t){return q(()=>{e=It(e);let r;return t==null?r=ko(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=nf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],d=o.name+"_0_0";r[d]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Sc);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let d=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(d.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];u.push(x)}let p=d.computeOutputShape(ea(u)),h=nf(p),c=d.inboundNodes.indexOf(l);for(let f=0;f<h.length;f++){let m=`${d.name}_${c}_${f}`;r[m]=h[f]}}}let n=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],d=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${d}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];wn(o in r),n.push(r[o])}return ea(n)}runInternalGraph(e,t){t==null&&(t=ko(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],d=e[o],u=t[o];r[l.id]=[d,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Sc);for(let o of a){let l=this.nodesByDepth[o];for(let d of l){let u=d.outboundLayer,p=d.inputTensors,h=d.outputTensors,c=new Array;for(let f of p)f.id in r&&c.push(r[f.id]);if(c.length===p.length){let f={},m,g,y,A;if(d.callArgs!=null&&(f=d.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(u.call(x,f)),A=It(u.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(u.call(m,f)),A=It(u.computeMask(m,g));if(u.activityRegularizer)throw new Be("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<h.length;++x){let b=h[x],v=y[x],C=A[x];r[b.id]=[v,C]}}}}let n=[],s=[],i=[];for(let o of this.outputs){wn(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,d]=r[o.id];i.push(l.shape),n.push(l),s.push(d)}return[n,s,i]}buildNodeConversionMap(e){let t={},r;for(let a of this.layers){r=a instanceof vn?1:0;for(let n=0;n<a.inboundNodes.length;n++){let s=vn.nodeKey(a,n);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`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 H("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new H(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let a=vn.nodeKey(t,r);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],h=vn.nodeKey(s,u),c={};if(this.containerNodes.has(h)){if(p.callArgs)try{JSON.stringify(p.callArgs),c=p.callArgs}catch(f){console.warn(`Layer ${s.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).`),c={}}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],A=p.tensorIndices[m],x=vn.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let d={};d.name=s.name,d.className=i,d.config=o,d.inboundNodes=l,r.push(d)}e.layers=r;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=vn.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,d,u])}e.inputLayers=a;let n=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=vn.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.outputLayersTensorIndices[s];n.push([i.name,d,u])}return e.outputLayers=n,e}static fromConfig(e,t,r={},a=!1){let n={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],C=x[2];if(A=x[3]==null?{}:x[3],!(b in n)){i(m,g);return}let T=n[b];if(T.inboundNodes.length<=v){i(m,g);return}let E=T.inboundNodes[v];y.push(E.outputTensors[C])}y.length>0&&m.apply(ea(y),A)}function l(m){let g=m.name,y=nn(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),n[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let d=t.name,u=t.layers;for(let m of u)l(m);for(;!WB(s);)for(let m of u){let g=n[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let p=[],h=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];wn(g in n);let x=n[g].inboundNodes[y].outputTensors;p.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];wn(g in n);let x=n[g].inboundNodes[y].outputTensors;h.push(x[A])}return new e({inputs:p,outputs:h,name:d})}get stateful(){if(this._stateful)throw new H("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function pV(e,t,r){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(n=>null);if(a===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!==a)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let n=[];return t.forEach(s=>{s in e?n.push(e[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${a}) outputs, so ${r} must be either an array with ${a} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function b7(e,t){return pV(e,t,"classWeight")}async function v7(e,t,r,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let n=q(()=>{if(e.shape.length===1)return Pr(e);if(e.shape.length===2){if(e.shape[1]>1)return Ta(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());re(n);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function hV(e,t){return L(e,t)}var cV=32;function w7(e,t){let r,a,n=t;r=n.xs,a=n.ys,w.assert(r!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=b3("input",e.inputNames,r),i=b3("output",e.outputNames,a),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function b3(e,t,r){if(r instanceof et)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let a=[];for(let n of t){if(r[n]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${n}'.`);a.push(r[n])}return a}}function fV(e){if(e.length===3)throw new Be("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function mV(e,t,r){let a=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!a||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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Use LayersModel.compile(modelCompileArgs).");let n=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===lf?n.push(i.slice(0,i.length-1).concat([1])):n.push(i)}if(e=k3(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=k3(t,this.feedOutputNames,n,!1,"target"),wV(e,t,null),kV(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&a!=null&&a>0&&e[0].shape[0]%a!==0)throw new H(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${a}. 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if(e.metrics!=null){n={};for(let s in e.metrics)n[s]=lo(e.metrics[s])}this.compile({loss:a,metrics:n,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Ir.getSaveHandlers(e);if(i.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new H(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ir.encodeWeights(this.getNamedWeights(t)),a=!1,n=null,s={modelTopology:this.toJSON(n,a),format:SV,generatedBy:`TensorFlow.js tfjs-layers v${nA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Ir.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Ir.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(y3(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){y3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};jn.className="Model";de.registerClass(jn);var I7=class extends jn{};I7.className="Functional";de.registerClass(I7);async function TV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let a=$p(r),n=nn(a,t);if(e.weightsManifest!=null){let s=await Ir.loadWeights(e.weightsManifest,e.pathPrefix,n.weights.map(o=>o.originalName)),i={};for(let o of n.weights)i[o.originalName]=s[o.originalName];n.loadWeights(i),re(s)}return n}async function CV(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Ir.getLoadHandlers(e,t);if(r.length===0)r.push(Ir.browserHTTPRequest(e,t));else if(r.length>1)throw new H(`Found more than one (${r.length}) 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compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new en("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new en("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new en("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},a=!1){let n,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new H("Legacy serialization format not supported yet.");n=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."),n=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof ry))throw new Be(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of n){let l=nn(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new H("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 H("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},_m=ry;_m.className="Sequential";de.registerClass(_m);function RV(e){return new jn(e)}function FV(e){return new _m(e)}function MV(e,t){return t==null&&(t={}),CV(e,t)}function S7(e){return o7(e)}function $V(e,t){Q2.registerCallbackConstructor(e,t)}var na=class extends de.Serializable{getConfig(){return{}}},T7=class extends na{apply(e,t=1){return sW(e,t)}};T7.className="elu";de.registerClass(T7);var C7=class extends na{apply(e){return I2(e)}};C7.className="selu";de.registerClass(C7);var N7=class extends na{apply(e){return Fn(e)}};N7.className="relu";de.registerClass(N7);var E7=class extends na{apply(e){return q(()=>ph(6,Fn(e)))}};E7.className="relu6";de.registerClass(E7);var R7=class extends na{apply(e){return e}};R7.className="linear";de.registerClass(R7);var F7=class extends na{apply(e){return Sr(e)}};F7.className="sigmoid";de.registerClass(F7);var M7=class extends na{apply(e){return oW(e)}};M7.className="hardSigmoid";de.registerClass(M7);var $7=class extends na{apply(e){return rd(e)}};$7.className="softplus";de.registerClass($7);var P7=class extends na{apply(e){return iW(e)}};P7.className="softsign";de.registerClass(P7);var O7=class extends na{apply(e){return hu(e)}};O7.className="tanh";de.registerClass(O7);var iA=class extends na{apply(e,t=-1){return sd(e,t)}};iA.className="softmax";de.registerClass(iA);var z7=class extends na{apply(e,t=-1){return c2(e,t)}};z7.className="logSoftmax";de.registerClass(z7);var D7=class extends na{apply(e,t=1){return q(()=>L(Sr(L(e,t)),e))}};D7.className="swish";de.registerClass(D7);var _7=class extends na{apply(e){return q(()=>L(e,hu(rd(e))))}};_7.className="mish";de.registerClass(_7);function Bs(e){return e.getClassName()}function T1(e,t={}){return fh(e,de.SerializationMap.getMap().classNameMap,t,"activation")}function Ws(e){if(e==null){let t={};return t.className="linear",t.config={},T1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},T1(t)}else return e instanceof na?e:T1(e)}function oA(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 L7=class extends de.Serializable{},xh=class extends L7{constructor(e){super();oA(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 q(()=>{let t=Vt([1]);return this.hasL1&&(t=ue(t,ke(L(this.l1,Qt(e))))),this.hasL2&&(t=ue(t,ke(L(this.l2,yh(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};xh.className="L1L2";de.registerClass(xh);function PV(e){return oA(e),new xh({l1:e!=null?e.l1:null,l2:0})}function OV(e){return oA(e),new xh({l2:e!=null?e.l2:null,l1:0})}var S3={l1l2:"L1L2"};function xt(e){return D2(e)}function T3(e,t={}){return fh(e,de.SerializationMap.getMap().classNameMap,t,"regularizer")}function Mt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in S3?S3[e]:e,config:{}};return T3(t)}else return e instanceof L7?e:T3(e)}var lA=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ve(e);let r=Fn(e);return this.maxValue!=null&&(r=pa(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};lA.className="ReLU";de.registerClass(lA);var uA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ve(e);return am(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};uA.className="LeakyReLU";de.registerClass(uA);var dA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Mt(e.alphaRegularizer),this.alphaConstraint=ar(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)r[a]=e[a];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ve(e),dm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:rr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};dA.className="PReLU";de.registerClass(dA);var pA=class extends nt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Be(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ve(e);return uh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};pA.className="ELU";de.registerClass(pA);var hA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=Ve(e);return L(r,me(fa(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};hA.className="ThresholdedReLU";de.registerClass(hA);var cA=class extends nt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new iA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ve(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};cA.className="Softmax";de.registerClass(cA);function lu(e,t,r){if(typeof e=="number")return ko(e,t);if(e.length!==t)throw new H(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let n=e[a];if(!tW(n))throw new H(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${n}`)}return e}function sn(e,t,r,a,n=1){if(e==null)return e;let s=t+(t-1)*(n-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function kn(e,t,r,a){if(e==null)return null;if(a==="valid")e=e*t+Ls([r-t,0]);else if(a==="same")e=e*t;else throw new H(`Unsupport padding mode: ${a}.`);return e}function fA(e,t){return q(()=>(Ut(t),t==="channelsFirst"?rt(e,[0,2,3,1]):e))}function B7(e,t){return q(()=>(Ut(t),t==="channelsFirst"?rt(e,[0,2,3,4,1]):e))}function zV(e,t,r,a=1,n="valid",s,i=1){return q(()=>{if(s==null&&(s=un()),Ut(s),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=rt(e,[0,2,1])),n==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=s2(e,t,a,n==="same"?"same":"valid","NWC",i);return r!=null&&(o=fn(o,r)),o})}function C3(e,t,r,a=[1,1],n="valid",s,i,o=null){return q(()=>{if(s==null&&(s=un()),Ut(s),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=fA(e,s);if(n==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_s.conv2d({x:l,filter:t,strides:a,pad:n==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=rt(l,[0,3,1,2])),l})}function DV(e,t,r,a=[1,1,1],n="valid",s,i){return q(()=>{if(s==null&&(s=un()),Ut(s),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=B7(e,s);if(n==="causal")throw new Be("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=l2(o,t,a,n==="same"?"same":"valid","NDHWC",i),r!=null&&(o=fn(o,r)),s==="channelsFirst"&&(o=rt(o,[0,4,1,2,3])),o})}var mA=class extends nt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",mA.verifyArgs(t),this.rank=e,pr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Be(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=lu(t.kernelSize,e,"kernelSize"),this.strides=lu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ut(this.dataFormat),this.activation=Ws(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Mt(t.biasRegularizer),this.activityRegularizer=Mt(t.activityRegularizer),this.dilationRate=lu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new H(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(wn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_2(e.kernelSize,"number",1,3))throw new H(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Bs(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:rr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},bh=class extends mA{constructor(e,t){super(e,t);this.kernel=null,bh.verifyArgs(t),this.filters=t.filters,pr(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Mt(t.kernelRegularizer)}build(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],a=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return q(()=>{e=Ve(e);let r,a=this.bias==null?null:this.bias.read(),n=Xk(this.activation.getClassName());if(n!=null&&this.rank===2)r=C3(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,n);else{if(this.rank===1)r=zV(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=C3(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=DV(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Be("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=mt(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let n=0;n<r.length;++n){let s=sn(r[n],this.kernelSize[n],this.padding,this.strides[n],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[n]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:rr(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},W7=class extends bh{constructor(e){super(2,e);W7.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_2(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Lm=W7;Lm.className="Conv2D";de.registerClass(Lm);var V7=class extends bh{constructor(e){super(3,e);V7.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Bm=V7;Bm.className="Conv3D";de.registerClass(Bm);var gA=class extends Lm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],a=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return q(()=>{let r=Ve(e);if(r.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let a=r.shape,n=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],d=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],h=this.strides[1],c=kn(o,p,d,this.padding),f=kn(l,h,u,this.padding),m=[n,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=rt(r,[0,2,3,1]));let g=o2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=rt(g,[0,3,1,2])),this.bias!=null&&(g=fn(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,a,n;this.dataFormat==="channelsFirst"?(r=1,a=2,n=3):(r=3,a=1,n=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[a]=kn(t[a],o,s,this.padding),t[n]=kn(t[n],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};gA.className="Conv2DTranspose";de.registerClass(gA);var yA=class extends Bm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],a=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return q(()=>{let r=Ve(e);if(r.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let a=r.shape,n=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],d=a[s],u=a[i],p=this.kernelSize[0],h=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=kn(l,f,p,this.padding),A=kn(d,m,h,this.padding),x=kn(u,g,c,this.padding),b=[n,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=rt(r,[0,2,3,4,1]));let v=Zw(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=rt(v,[0,4,1,2,3])),this.bias!==null&&(v=fn(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,a,n,s;this.dataFormat==="channelsFirst"?(r=1,a=2,n=3,s=4):(r=4,a=1,n=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],d=this.strides[0],u=this.strides[1],p=this.strides[2];return t[r]=this.filters,t[a]=kn(t[a],d,i,this.padding),t[n]=kn(t[n],u,o,this.padding),t[s]=kn(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};yA.className="Conv3DTranspose";de.registerClass(yA);var U7=class extends bh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Mt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Mt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],a=this.kernelSize.concat([r,this.depthMultiplier]),n=[];for(let i=0;i<this.rank;++i)n.push(1);n.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",n,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return q(()=>{e=Ve(e);let r;if(this.rank===1)throw new Be("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=rt(e,[0,2,3,1])),r=yk(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=fn(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=rt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=rr(this.depthwiseConstraint),e.pointwiseConstraint=rr(this.pointwiseConstraint),e}};U7.className="SeparableConv";var AA=class extends U7{constructor(e){super(2,e)}};AA.className="SeparableConv2D";de.registerClass(AA);var G7=class extends bh{constructor(e){super(1,e);G7.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"&&!_2(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},xA=G7;xA.className="Conv1D";de.registerClass(xA);var bA=class extends nt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Ve(e),this.dataFormat==="channelsLast"){let r=Tc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Tc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Tc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Tc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};bA.className="Cropping2D";de.registerClass(bA);var vA=class extends nt{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,Ut(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,JB(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return q(()=>{let r=Ve(e),a=r.shape;if(this.dataFormat==="channelsFirst"){r=rt(r,[0,2,3,1]);let n=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[n,s]):Ie.resizeBilinear(r,[n,s]);return rt(i,[0,3,1,2])}else{let n=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[n,s]):Ie.resizeBilinear(r,[n,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};vA.className="UpSampling2D";de.registerClass(vA);function _V(e,t,r=[1,1],a="valid",n,s){return q(()=>{n==null&&(n=un()),Ut(n);let i=fA(e,n);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=lh(i,t,r,a==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}var wA=class extends mA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Mt(e.depthwiseRegularizer)}build(e){if(e=mt(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],a=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Ve(e);let r=_V(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=fn(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,n=sn(t,this.kernelSize[0],this.padding,this.strides[0]),s=sn(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,n,s]:[e[0],n,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=rr(this.depthwiseRegularizer),e}};wA.className="DepthwiseConv2D";de.registerClass(wA);function j7(e,t,r,a){if(Array.isArray(e)){if(t!=null||r!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");a!=null&&(r=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return t=n(t),r=n(r),{inputs:e,initialState:t,constants:r}}function H7(e,t,r,a=!1,n,s,i=!1,o=!1){return q(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let d=[1,0].concat(dn(2,l));if(t=rt(t,d),s!=null)throw new Be("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=me(me(n,"bool"),"float32"),n.rank===l-1&&(n=Ht(n,-1)),n=rt(n,d)),a&&(t=Fa(t,0),n!=null&&(n=Fa(n,0)));let u=[],p,h=r,c=t.shape[0],f=ra(t),m;n!=null&&(m=ra(n));for(let y=0;y<c;++y){let A=f[y],x=q(()=>e(A,h));if(n==null)p=x[0],h=x[1];else{let b=q(()=>{let v=m[y],C=he(Ra(v),v),T=ue(L(x[0],v),L(h[0],C)),E=h.map((R,z)=>ue(L(x[1][z],v),L(R,C)));return{output:T,newStates:E}});p=b.output,h=b.newStates}o&&u.push(p)}let g;return o&&(g=nr(u,1)),[p,g,h]})}var q7=class extends nt{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Um({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({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 dn(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){X1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],a;if(this.returnSequences?a=[e[0],e[1],r]:a=[e[0],r],this.returnState){let n=[];for(let s of t)n.push([e[0],s]);return[a].concat(n)}else return a}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(n=>null);return[r].concat(a)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Be("Constants support is not implemented in RNN yet.");X1(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let a=[e[0]].concat(e.slice(2));this.cell.build(a);let n;if(Array.isArray(this.cell.stateSize)?n=this.cell.stateSize:n=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),n))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=n.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bn("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Vt([r,a])):this.states_=[Vt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Vt([r,a])):this.states_[0]=Vt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let a=0;a<this.states_.length;++a){let n=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(n.shape,i))throw new H(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${n.shape}`);this.states_[a]=n}}this.states_=this.states_.map(a=>dr(a.clone()))})}apply(e,t){let r=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let n=j7(e,r,a,this.numConstants);e=n.inputs,r=n.initialState,a=n.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof tn){let o=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=d,u}else return super.apply(e,t)}call(e,t){return q(()=>{let r=t==null?null:t.mask,a=t==null?null:t.training,n=t==null?null:t.initialState;e=Ve(e),n==null&&(this.stateful?n=this.states_:n=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(n.length!==s)throw new H(`RNN Layer has ${s} state(s) but was passed ${n.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=H7((h,c)=>{let f=this.cell.call([h].concat(c),i);return[f[0],f.slice(1)]},e,n,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],d=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?d:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return q(()=>{let t=Vt(e.shape);return t=ke(t,[1,2]),t=gh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?q1(t,[1,r]):t):this.cell.stateSize>1?[q1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===q7.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let a=t.cell,n=nn(a,r);return new e(Object.assign(t,{cell:n}))}},Qn=q7;Qn.className="RNN";de.registerClass(Qn);var vh=class extends nt{},Wm=class extends vh{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,pr(this.units,"units"),this.activation=Ws(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=yu([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=yu([1,Ls([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 q(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vs({ones:()=>Ra(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vs({ones:()=>Ra(r),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let n,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?n=Sn(L(e,s),this.kernel.read()):n=Sn(e,this.kernel.read()),this.bias!=null&&(n=fn(n,this.bias.read())),i!=null&&(r=L(r,i));let o=ue(n,Sn(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:rr(this.kernelConstraint),recurrentConstraint:rr(this.recurrentConstraint),biasConstraint:rr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Wm.className="SimpleRNNCell";de.registerClass(Wm);var kA=class extends Qn{constructor(e){e.cell=new Wm(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,a=t==null?null:t.training,n=t==null?null:t.initialState;return super.call(e,{mask:r,training:a,initialState:n})})}static fromConfig(e,t){return new e(t)}};kA.className="SimpleRNN";de.registerClass(kA);var Vm=class extends vh{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pr(this.units,"units"),this.activation=Ws(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ws(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=yu([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=yu([1,Ls([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 q(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vs({ones:()=>Ra(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vs({ones:()=>Ra(a),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let n=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,n[0]));let d=Sn(e,this.kernel.read());this.useBias&&(d=fn(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,h]=Kt(u,[2*this.units,this.units],u.rank-1),c=Sn(a,p),[f,m,g]=Kt(d,3,d.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(ue(f,y)),o=this.recurrentActivation.apply(ue(m,A));let x=Sn(L(o,a),h);l=this.activation.apply(ue(g,x));let b=ue(L(i,a),L(ue(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:rr(this.kernelConstraint),recurrentConstraint:rr(this.recurrentConstraint),biasConstraint:rr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Vm.className="GRUCell";de.registerClass(Vm);var IA=class extends Qn{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 Vm(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,a=t==null?null:t.training,n=t==null?null:t.initialState;return super.call(e,{mask:r,training:a,initialState:n})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};IA.className="GRU";de.registerClass(IA);var wh=class extends vh{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,pr(this.units,"units"),this.activation=Ws(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ws(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=yu([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=yu([1,Ls([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=mt(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let n=this.biasInitializer,s=this.units;a=new(t=class extends qa{apply(i,o){let l=n.apply([s]),d=new Cm().apply([s]),u=n.apply([s*2]);return h3(h3(l,d),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],n=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vs({ones:()=>Ra(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vs({ones:()=>Ra(a),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,d,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let p=Sn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=ue(p,Sn(a,this.recurrentKernel.read())),this.useBias&&(p=fn(p,this.bias.read()));let[h,c,f,m]=Kt(p,4,p.rank-1);o=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(c),d=ue(L(l,n),L(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(d));return[g,g,d]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:rr(this.kernelConstraint),recurrentConstraint:rr(this.recurrentConstraint),biasConstraint:rr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};wh.className="LSTMCell";de.registerClass(wh);var SA=class extends Qn{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 wh(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,a=t==null?null:t.training,n=t==null?null:t.initialState;return super.call(e,{mask:r,training:a,initialState:n})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};SA.className="LSTM";de.registerClass(SA);var Um=class extends vh{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let r=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(r.splice(0,i.stateSize.length)):a.push(r.splice(0,1));a.reverse();let n=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=a[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),n.push(s.slice(1))}r=[];for(let i of n.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){X1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,a)=>{mo(`RNNCell_${a}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let a=[];for(let n of t.cells)a.push(nn(n,r));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Z1(e)}setWeights(e){let t=[];for(let r of this.cells){let a=r.weights.length,n=e.splice(a);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],n[s]])}J2(t)}};Um.className="StackedRNNCells";de.registerClass(Um);function Vs(e){let{ones:t,rate:r,training:a=!1,count:n=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):r7(t(),r),o=()=>Ah(i,t,a);return!n||n<=1?dr(o().clone()):Array(n).fill(void 0).map(o).map(l=>dr(l.clone()))}var K7=class extends Qn{constructor(e){if(e.unroll)throw new Be("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Be("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,a=t==null?null:t.training,n=t==null?null:t.initialState;return super.call(e,{mask:r,training:a,initialState:n})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,r=e.shape,a=this.computeSingleOutputShape(r),n=[a[0],...a.slice(2)],s=Vt(n);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bn("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,a=this.computeSingleOutputShape(r),n=[a[0],...a.slice(2)];if(r[0]==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Vt(n)):this.states_=[Vt(n)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Vt(n)):this.states_[0]=Vt(n);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=n;if(!w.arraysEqual(i.shape,o))throw new H(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>dr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:a,padding:n,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],d=e[o?4:3],u=sn(l,a[0],n,s[0],i[0]),p=sn(d,a[1],n,s[1],i[1]);return[...e.slice(0,2),...o?[r,u,p]:[u,p,r]]}};K7.className="ConvRNN2D";var Gm=class extends wh{constructor(e){let{filters:t,kernelSize:r,strides:a,padding:n,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,pr(this.filters,"filters"),this.kernelSize=lu(r,2,"kernelSize"),this.kernelSize.forEach(o=>pr(o,"kernelSize")),this.strides=lu(a||1,2,"strides"),this.strides.forEach(o=>pr(o,"strides")),this.padding=n||"valid",Pa(this.padding),this.dataFormat=s||"channelsLast",Ut(this.dataFormat),this.dilationRate=lu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>pr(o,"dilationRate"))}build(e){var t;e=mt(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[r]}`);let a=e[r],n=4,s=this.kernelSize.concat([a,this.filters*n]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*n]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,d=this.filters;o=new(t=class extends qa{apply(u,p){let h=l.apply([d]),c=da([d]),f=l.apply([d*2]);return G2([h,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*n],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,a=e[0],n=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vs({ones:()=>Ra(a),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(W,ee,Q)=>!ee||!ee[Q]?W:L(ee[Q],W),d=l(a,o,0),u=l(a,o,1),p=l(a,o,2),h=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vs({ones:()=>Ra(n),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(n,c,0),m=l(n,c,1),g=l(n,c,2),y=l(n,c,3),A=3,[x,b,v,C]=Kt(this.kernel.read(),i,A),[T,E,R,z]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];d=this.inputConv(d,x,T,this.padding),u=this.inputConv(u,b,E,this.padding),p=this.inputConv(p,v,R,this.padding),h=this.inputConv(h,C,z,this.padding);let[M,I,D,O]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,D),y=this.recurrentConv(y,O);let j=this.recurrentActivation.apply(ue(d,f)),X=this.recurrentActivation.apply(ue(u,m)),_=ue(L(X,s),L(j,this.activation.apply(ue(p,g)))),K=L(this.recurrentActivation.apply(ue(h,y)),this.activation.apply(_));return[K,K,_]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,a){let n=Os(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?fn(n,r,this.dataFormat):n}recurrentConv(e,t){return Os(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Gm.className="ConvLSTM2DCell";de.registerClass(Gm);var TA=class extends K7{constructor(e){let t=new Gm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};TA.className="ConvLSTM2D";de.registerClass(TA);var jm=class extends nt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let a=0;a<this.noiseShape.length;++a)r.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return r}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,n=this.getNoiseShape(r);return Ah(()=>r7(r,this.rate,n,this.seed),()=>r,a)}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()}};jm.className="Dropout";de.registerClass(jm);var CA=class extends jm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};CA.className="SpatialDropout1D";de.registerClass(CA);var NA=class extends nt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,pr(this.units,"units"),this.activation=Ws(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Mt(e.kernelRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.activityRegularizer=Mt(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 q(()=>{this.invokeCallHook(e,t);let r=Ve(e),a=Xk(this.activation.getClassName()),n;return a!=null?n=Sn(r,this.kernel.read(),a,this.bias?this.bias.read():null):(n=Sn(r,this.kernel.read()),this.bias!=null&&(n=fn(n,this.bias.read())),this.activation!=null&&(n=this.activation.apply(n))),n})}getConfig(){let e={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:rr(this.kernelConstraint),biasConstraint:rr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};NA.className="Dense";de.registerClass(NA);var EA=class extends nt{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 H(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ns(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let a=[0];for(let n=2;n<r.rank;++n)a.push(n);a.push(1),r=rt(r,a)}return nW(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};EA.className="Flatten";de.registerClass(EA);var RA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ws(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);return this.activation.apply(r)})}getConfig(){let e={activation:Bs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};RA.className="Activation";de.registerClass(RA);var FA=class extends nt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Ve(e),rW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};FA.className="RepeatVector";de.registerClass(FA);var MA=class extends nt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",a=t.slice(),n=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new H("Can only specifiy one unknown dimension.");else n*=l}let i=Ns(e);if(s!==null){if(n===0||i%n!==0)throw new H(r);a[s]=i/n}else if(i!==n)throw new H(r);return a}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e),a=r.shape,n=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return U(r,n)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};MA.className="Reshape";de.registerClass(MA);var $A=class extends nt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=dn(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((r,a)=>{t[a+1]=e[r]}),t}call(e,t){return rt(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};$A.className="Permute";de.registerClass($A);var PA=class extends nt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=Ve(e),a=-1;return rf(mu(r,this.maskValue),a)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e),a=-1,n=!0,s=rf(mu(r,this.maskValue),a,n);return L(r,me(s,r.dtype))})}};PA.className="Masking";de.registerClass(PA);var OA=class extends nt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,pr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pr(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Mt(e.embeddingsRegularizer),this.activityRegularizer=Mt(e.activityRegularizer),this.embeddingsConstraint=ar(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Ve(e),mu(e,at(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 H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let a=0;a<t.length;++a){let n=t[a],s=e[a+1];if(n!=null&&s!=null&&n!==s)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);n==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);r.dtype!=="int32"&&(r=Sm(r,"int32"));let a=t7(this.embeddings.read(),U(r,[r.size]));return U(a,mt(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:rr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};OA.className="Embedding";de.registerClass(OA);var Il=class extends nt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Be}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let n=e[e.length-t.length+a],s=t[a];if(n==null||s==null||n<0||s<0)r.push(null);else if(n===1)r.push(s);else if(s===1)r.push(n);else{if(n!==s)throw new H("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(n)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new H(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let n of e)n!=null&&n[0]!==null&&t.push(n[0]);if(t=Cs(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let a=e.map(n=>n.length);e.indexOf(null)===-1&&Cs(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let r=[],a=e.map(n=>n.rank);if(a.indexOf(null)===-1){let n=Ls(a);for(let s of e){let i=s.rank;for(let o=0;o<n-i;++o)s=gh(s,1);r.push(s)}return this.mergeFunction(r)}else{let n=!1;for(let o of e){let l=o.rank;if(l==null){let d=o.shape,u=d[0],p=d.slice(1).concat([u]),h=U(o,[u].concat(Ns(d.slice(1))));h=rt(h,[1,0]),h=U(h,p),r.push(h),n=!0}else if(l>1){let d=dn(1,l).concat([0]);r.push(rt(o,d)),n=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(n){if(i==null){let o=s.shape,l=o.length,d=o[l-1],u=[d].concat(o.slice(0,o.length-1));s=U(rt(U(s,[-1,d]),[1,0]),u)}else if(i>1){let o=[i-1].concat(dn(0,i-1));s=rt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let n=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,n)}let r=[];for(let a of e)a!=null&&a[0]!==null&&r.push(a[0]);return r=Cs(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Ht(a,0));let r=t[0];for(let a=1;a<t.length-1;++a)r=ln(r,t[a]);return r})}},zA=class extends Il{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=ue(t,e[r]);return t})}};zA.className="Add";de.registerClass(zA);var DA=class extends Il{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};DA.className="Multiply";de.registerClass(DA);var _A=class extends Il{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=ue(t,e[r]);return L(1/e.length,t)})}};_A.className="Average";de.registerClass(_A);var LA=class extends Il{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Zn(t,e[r]);return t})}};LA.className="Maximum";de.registerClass(LA);var BA=class extends Il{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=ph(t,e[r]);return t})}};BA.className="Minimum";de.registerClass(BA);var WA=class extends Il{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 H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let r=[];for(let a=0;a<e.length;++a){let n=e[a].slice();n.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,n)){s=!0;break}s||r.push(n)}if(r.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return q(()=>G2(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),a=this.axis<0?r.length+this.axis:this.axis;for(let n of t.slice(1)){if(r[a]==null||n[a]==null){r[a]=null;break}r[a]+=n[a]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return q(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(me(Ra(e[s]),"bool")):t[s].rank<e[s].rank?a.push(Ht(t[s],-1)):a.push(t[s]);let n=kt(a,this.axis);return t2(n,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Concatenate";de.registerClass(WA);function np(e,t){for(;e<0;)e+=t;return e}function LV(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Be("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Be("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,n=t.shape.length;r==null&&(r=[a-1,n-2]);let s=r;return q(()=>{let i;if(a>n){i=a-n;let l=[];for(let d=0;d<i;++d)l.push(1);t=U(t,t.shape.concat(l))}else if(n>a){i=n-a;let l=[];for(let d=0;d<i;++d)l.push(1);e=U(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(rt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,d=s[1]===t.shape.length-1;o=Ke(e,t,l,d)}if(i>0){let l;a>n?l=a+n-3:l=a-1;let d=[];for(let u=l;u<l+i;++u)d.push(u);o=Ye(o,d)}return o.shape.length===1&&(o=Ht(o,1)),o})}var VA=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Be("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,r);if(t[a[0]]!==r[a[1]])throw new H(`Dimension incompatibility: ${t[a[0]]} !== ${r[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((n,s)=>np(n,e[s].shape.length)):a=[np(this.axes,t.shape.length),np(this.axes,r.shape.length)],this.normalize&&(t=of(t,a[0]),r=of(r,a[1])),LV(t,r,a)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[np(this.axes,e.length),np(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Be("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,r);t.splice(a[0],1),r.splice(a[1],1),r.splice(0,1);let n=t.concat(r);return n.length===1&&n.push(1),n}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};VA.className="Dot";de.registerClass(VA);var UA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);return Ah(()=>ue(Tm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};UA.className="GaussianNoise";de.registerClass(UA);var GA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let r=Ve(e);return this.rate>0&&this.rate<1?Ah(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return L(r,Tm(r.shape,1,a))},()=>r,t.training||!1):r})}};GA.className="GaussianDropout";de.registerClass(GA);var jA=class extends nt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ve(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Ah(()=>{let a=Ve(e),n=1.6732632423543772,s=1.0507009873554805,i=-n*s,o=xl(nd(r),this.rate);o=Sm(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,d=-l*i*this.rate,u=ue(L(a,o),L(ue(o,-1),i));return ue(L(u,l),d)},()=>Ve(e),t.training||!1)}return e})}};jA.className="AlphaDropout";de.registerClass(jA);function Pp(e,t,r,a,n,s=.001){let i;if(e.rank===2)i=Ww(e,t,r,a,n,s);else if(e.rank===3)i=Vw(e,t,r,a,n,s);else if(e.rank===4)i=Uw(e,t,r,a,n,s);else throw new Be(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function BV(e,t,r,a,n=.001){return q(()=>{let s=lm(e,a),i=s.mean,o=s.variance;return[Pp(e,i,o,r,t,n),i,o]})}function WV(e,t,r,a,n=.001){return q(()=>{let s=lm(e,a),i=s.mean,o=s.variance,l=[];for(let c of dn(0,e.rank))a.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let d=U(i,l),u=U(o,l),p=t==null?null:U(t,l),h=r==null?null:U(r,l);return[Pp(e,d,u,h,p,n),i,o]})}function VV(e,t,r,a,n=.001){return w.arraysEqual(a.slice().sort(),dn(0,e.rank-1))?BV(e,t,r,a,n):WV(e,t,r,a,n)}var HA=class extends nt{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=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Mt(e.betaRegularizer),this.gammaRegularizer=Mt(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let a=[r];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let r=t.training==null?!1:t.training,a=Ve(e),n=a.shape,s=n.length,i=dn(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ko(1,s);l[o]=n[o];let d=i.slice();d.sort();let u=!w.arraysEqual(d,dn(0,s).slice(0,s-1)),p=()=>{if(u){let g=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),A=this.center?U(this.beta.read(),l):null,x=this.scale?U(this.gamma.read(),l):null;return Pp(a,g,y,A,x,this.epsilon)}else return Pp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return p();let[h,c,f]=VV(a,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{q(()=>{let x=1-A,b=g.read(),v=L(he(b,y),x);g.write(he(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),h})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:rr(this.betaConstraint),gammaConstraint:rr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};HA.className="BatchNormalization";de.registerClass(HA);var qA=class extends nt{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=Mt(e.betaRegularizer),this.gammaRegularizer=Mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let n=0;n<this.axis.length;++n)this.axis[n]<0&&(this.axis[n]+=t);for(let n of this.axis)if(n<0||n>=t)throw new Error(`Invalid axis: ${n}`);if(this.axis.length!==Cs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(n=>e[n]),a=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let r=Ve(e),a=r.shape,n=a.length;return q(()=>{let{mean:s,variance:i}=lm(r,this.axis,!0),o=ko(1,n);for(let c of this.axis)o[c]=a[c];let l=c=>c!=null&&c.shape.length!==n?U(c,o):c,d=l(this.gamma.read()),u=l(this.beta.read()),p=[],h=[];for(let c=0;c<n;++c)this.axis.indexOf(c)!==-1?(p.push(a[c]),h.push(1)):(p.push(1),h.push(a[c]));return s=Wa(s,p),i=Wa(i,p),d=Wa(d,h),u=Wa(u,h),Pp(r,s,i,u,d,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="LayerNormalization";de.registerClass(qA);function UV(e,t,r){return q(()=>{if(e.rank!==4)throw new H(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=un()),r!=="channelsLast"&&r!=="channelsFirst")throw new H(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return r==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ja(e,a)})}var KA=class extends nt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?un():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=mt(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return q(()=>UV(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};KA.className="ZeroPadding2D";de.registerClass(KA);function Hm(e,t,r,a,n,s){return q(()=>{Ut(n),Yk(s),Pa(a),r==null&&(r=[1,1]),a==null&&(a="valid"),n==null&&(n=un()),s==null&&(s="max"),e=fA(e,n);let i,o=a==="same"?"same":"valid";return s==="max"?i=om(e,t,r,o):i=Qf(e,t,r,o),n==="channelsFirst"&&(i=rt(i,[0,3,1,2])),i})}function X7(e,t,r,a,n,s){return q(()=>{Ut(n),Yk(s),Pa(a),r==null&&(r=[1,1,1]),a==null&&(a="valid"),n==null&&(n=un()),s==null&&(s="max"),e=B7(e,n);let i,o=a==="same"?"same":"valid";return s==="max"?i=y2(e,t,r,o):i=a2(e,t,r,o),n==="channelsFirst"&&(i=rt(i,[0,4,1,2,3])),i})}var Z7=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pa(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=sn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=gh(Ve(e),2);let r=this.poolingFunction(Ve(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ye(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},XA=class extends Z7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),Hm(e,t,r,a,n,"max")}};XA.className="MaxPooling1D";de.registerClass(XA);var ZA=class extends Z7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),Hm(e,t,r,a,n,"avg")}};ZA.className="AveragePooling1D";de.registerClass(ZA);var Y7=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new H(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];pr(this.poolSize,"poolSize"),pr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pa(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=sn(t,this.poolSize[0],this.padding,this.strides[0]),r=sn(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},YA=class extends Y7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),Hm(e,t,r,a,n,"max")}};YA.className="MaxPooling2D";de.registerClass(YA);var JA=class extends Y7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),Hm(e,t,r,a,n,"avg")}};JA.className="AveragePooling2D";de.registerClass(JA);var J7=class extends nt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new H(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];pr(this.poolSize,"poolSize"),pr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pa(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=sn(t,this.poolSize[0],this.padding,this.strides[0]),r=sn(r,this.poolSize[1],this.padding,this.strides[1]),a=sn(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,a]:[e[0],t,r,a,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},QA=class extends J7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),X7(e,t,r,a,n,"max")}};QA.className="MaxPooling3D";de.registerClass(QA);var ex=class extends J7{constructor(e){super(e)}poolingFunction(e,t,r,a,n){return Ut(n),Pa(a),X7(e,t,r,a,n,"avg")}};ex.className="AveragePooling3D";de.registerClass(ex);var Q7=class extends nt{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Be}},tx=class extends Q7{constructor(e){super(e||{})}call(e,t){return q(()=>{let r=Ve(e);return Wt(r,1)})}};tx.className="GlobalAveragePooling1D";de.registerClass(tx);var rx=class extends Q7{constructor(e){super(e||{})}call(e,t){return q(()=>{let r=Ve(e);return hr(r,1)})}};rx.className="GlobalMaxPooling1D";de.registerClass(rx);var e4=class extends nt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Be}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ax=class extends e4{call(e,t){return q(()=>{let r=Ve(e);return this.dataFormat==="channelsLast"?Wt(r,[1,2]):Wt(r,[2,3])})}};ax.className="GlobalAveragePooling2D";de.registerClass(ax);var nx=class extends e4{call(e,t){return q(()=>{let r=Ve(e);return this.dataFormat==="channelsLast"?hr(r,[1,2]):hr(r,[2,3])})}};nx.className="GlobalMaxPooling2D";de.registerClass(nx);var t4=class extends nt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let a=t.layer,n=nn(a,r);delete t.layer;let s={layer:n};return Object.assign(s,t),new e(s)}},sx=class extends t4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new H(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=mt(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),a=e[1];return[r[0],a].concat(r.slice(1))}call(e,t){return q(()=>(e=Ve(e),H7((r,a)=>[Ve(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};sx.className="TimeDistributed";de.registerClass(sx);function GV(e){wl(YB,"BidirectionalMergeMode",e)}var jV="concat",ix=class extends t4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=nn(r),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=nn(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?jV:e.mergeMode,GV(this.mergeMode),e.weights)throw new Be("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,a,n;return this.returnState&&(n=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,a=[r]):this.mergeMode==null?a=[r,r.slice()]:a=[r],this.returnState?this.mergeMode==null?a.concat(n).concat(n.slice()):[r].concat(n).concat(n.slice()):ea(a)}apply(e,t){let r=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let n=j7(e,r,a,this.numConstants);if(e=n.inputs,r=n.initialState,a=n.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let d=r.map(u=>new qt({shape:u.shape}));this.forwardLayer.stateSpec=d.slice(0,l/2),this.backwardLayer.stateSpec=d.slice(l/2),i.push(...d)}if(a!=null)throw new Be("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof tn;for(let l of s)if(l instanceof tn!==o)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),d=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=d;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return q(()=>{let r=t.initialState,a,n;if(r==null)a=this.forwardLayer.call(e,t),n=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),n=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(n.slice(1))),a=a[0],n=n[0]),this.returnSequences&&(n=Fa(n,1));let 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implemented`)}},dj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ke(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[tk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[rt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[a,n]=k("fusedOps",e,t,r),s=a==="biasadd",i=n==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[d,u]=k("args",e,t,r);return[_s.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:d,activation:n,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[cu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[cu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[ik(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[sd(k("x",e,t,r))];case"LogSoftmax":return[c2(k("x",e,t,r))];case"SparseToDense":return[M2(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[hr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Wt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[zs(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[t2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[rf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Ta(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[Mw(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[A2(k("x",e,t,r),i,o)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[d2(k("x",e,t,r),i,o,l)]}case"Bincount":let a=k("x",e,t,r),n=k("weights",e,t,r),s=k("size",e,t,r);return[n2(a,n,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),d=k("binaryOutput",e,t,r);return[Yw(i,o,l,d)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cj=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let a=k("n",e,t,r),n=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,a),[kt(s,n)]}case"Gather":{let a=k("x",e,t,r),n=k("indices",e,t,r);return[fu(a,me(n,"int32"),0)]}case"GatherV2":{let a=k("axis",e,t,r),n=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[fu(s,me(i,"int32"),a,n)]}case"Reverse":{let a=k("dims",e,t,r),n=[];for(let i=0;i<a.length;i++)a[i]&&n.push(i);let s=k("x",e,t,r);return[Fa(s,n)]}case"ReverseV2":{let a=k("axis",e,t,r),n=k("x",e,t,r);return[Fa(n,a)]}case"Slice":{let a=k("begin",e,t,r),n=k("size",e,t,r);return[Oe(k("x",e,t,r),a,n)]}case"StridedSlice":{let a=k("begin",e,t,r),n=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),d=k("newAxisMask",e,t,r),u=k("shrinkAxisMask",e,t,r),p=k("x",e,t,r);return[bk(p,a,n,s,i,o,l,d,u)]}case"Pack":return q(()=>{let a=k("axis",e,t,r),n=k("tensors",e,t,r),s=n[0].shape,i=Ye(n[0]).shape,o=n.map(l=>{let d=w.arraysEqual(l.shape,s);if(!d&&!w.arraysEqual(Ye(l).shape,i))throw new Error("the input tensors shape does not match");return d?l:U(l,s)});return[nr(o,a)]});case"Unpack":{let a=k("axis",e,t,r),n=k("tensor",e,t,r);return ra(n,a)}case"Tile":{let a=k("reps",e,t,r);return[Wa(k("x",e,t,r),a)]}case"Split":case"SplitV":{let a=k("axis",e,t,r),n=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,n,a)}case"ScatterNd":{let a=k("indices",e,t,r),n=k("values",e,t,r),s=k("shape",e,t,r);return[Ck(a,n,s)]}case"GatherNd":{let a=k("x",e,t,r),n=k("indices",e,t,r);return[Nk(a,n)]}case"SparseToDense":{let a=k("sparseIndices",e,t,r),n=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[M2(a,s,n,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:n,emptyRowIndicator:s,reverseIndexMap:i}=up.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[a,n,s,i]}case"SparseReshape":{let{outputIndices:a,outputShape:n}=up.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[a,n]}case"SparseSegmentMean":return[up.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[up.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mj=(e,t,r)=>{switch(e.op){case"FFT":return[hm(k("x",e,t,r))];case"IFFT":return[Fp(k("x",e,t,r))];case"RFFT":return[cm(k("x",e,t,r))];case"IRFFT":return[N2(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gj=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:n}=Dc.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[a,n]}case"StringSplit":{let{indices:a,values:n,shape:s}=Dc.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[a,n,s]}case"StringToHashBucketFast":return[Dc.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yj=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let a=k("axis",e,t,r);return[Ht(k("x",e,t,r),a)]}case"Squeeze":{let a=k("axis",e,t,r);return[Ye(k("x",e,t,r),a)]}case"Reshape":return[U(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[ck(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[ja(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let a=k("blockShape",e,t,r),n=k("paddings",e,t,r);return[um(k("x",e,t,r),a,n)]}case"BatchToSpaceND":{let a=k("blockShape",e,t,r),n=k("crops",e,t,r);return[em(k("x",e,t,r),a,n)]}case"DepthToSpace":{let a=k("blockSize",e,t,r),n=k("dataFormat",e,t,r).toUpperCase();return[Jw(k("x",e,t,r),a,n)]}case"BroadcastTo":return[xp(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[Gw(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function P3(e,t,r,a){let n=((s,i,o)=>{switch(s.category){case"arithmetic":return q(()=>qG(s,i,o));case"basic_math":return q(()=>KG(s,i,o));case"control":return ej(s,i,o);case"convolution":return q(()=>tj(s,i,o));case"creation":return q(()=>rj(s,i,o));case"dynamic":return aj(s,i,o);case"evaluation":return q(()=>nj(s,i,o));case"image":return q(()=>lj(s,i,o));case"graph":return q(()=>sj(s,i,o));case"logical":return q(()=>uj(s,i,o));case"matrices":return q(()=>dj(s,i,o));case"normalization":return q(()=>pj(s,i,o));case"reduction":return q(()=>hj(s,i,o));case"slice_join":return q(()=>cj(s,i,o));case"sparse":return q(()=>fj(s,i,o));case"spectral":return q(()=>mj(s,i,o));case"string":return q(()=>gj(s,i,o));case"transformation":return q(()=>yj(s,i,o));case"hash_table":return oj(s,i,o,a);case"custom":let l=h4(s.op);if(l&&l.customExecutor)return l.customExecutor(new HG(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,r);return w.isPromise(n)?n.then(s=>[].concat(s)):[].concat(n)}var O3=class{constructor(e={},t={},r={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function z3(e,t,r,a){let n=new Set,s=[],i=null,o=null,l=new Set,d=Object.keys(e).map(h=>la(h)[0]),u=[];a!=null&&(u=a.map(h=>la(h.name)[0]));let p=[...t];for(;p.length>0;){let h=p.pop();if((P4(h)||wj(h)||kj(h))&&i==null&&(i=h,o=i.children.map(c=>c.name).filter(c=>n.has(c))),n.add(h.name),r[h.name]==null&&d.indexOf(h.name)===-1&&u.indexOf(h.name)===-1){if(h.inputs.length===0){s.push(h.name);continue}h.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),p.push(c))})}}return{inputs:e,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:i,syncInputs:o}}function Aj(e,t,r){let{usedNodes:a,inputs:n}=r,s=[],i=Object.keys(n).map(u=>la(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,d=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||d.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(h=>l.has(h.name))&&s.push(p)})}return d}var xj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],bj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],vj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function P4(e){return xj.indexOf(e.op)>=0}function wj(e){return bj.indexOf(e.op)>=0}function kj(e){return vj.indexOf(e.op)>=0}var fy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new fy(e.functions[r],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(r=>e[r].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(n=>n.name).sort(),a=t.map(n=>n.name).sort();return r.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let r=z3(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:n,syncInputs:s}=r;if(n!=null)throw new Error(`This execution contains the node '${n.name}', which has the dynamic op '${n.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return Aj(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=r.map(u=>this.graph.nodes[la(u)[0]]),n=t.map(u=>la(u)[0]),s=n.map(u=>this.graph.nodes[u]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},d={};return q(()=>{let u=new O3(this.weightMap,l,d,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=la(f),y=[];y[g]=e[f],p[m]=y});let h=this.getFrozenTensorIds(p),c={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=P3(m,p,u,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,u,h,n,c)}}return this.parent==null&&u.dispose(h),t.map(f=>Mr(f,p,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,r,a,n,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=SG(o.name,r,a);l!=null&&l.forEach(d=>{if(d&&!d.kept&&!n.has(d.id)){let u=i[d.id];if(u===1){if(!this.keepTensorForDebug)d.dispose();else{let[p,h]=In(t.name,a);this.intermediateTensors[p]?this.intermediateTensors[p][h]=d:(this.intermediateTensors[p]=[],this.intermediateTensors[p][h]=d)}delete i[d.id]}else u!=null&&i[d.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,r=!1,a={},n={}){r||(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(d){console.warn(d.message)}this.resetIntermediateTensors();let s=new O3(this.weightMap,a,n,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(d=>Mr(d,this.tensorsMap,s)),o=i.map(d=>d.id),l=Object.keys(e).map(d=>e[d].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let a=e.reduce((n,s,i)=>(n[this.inputs[i].name]=s,n),{});return this._executeAsync(a,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,a){let n=Object.keys(e),s=n.map(A=>this.graph.nodes[la(A)[0]]),i=r.map(A=>la(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:d,dynamicNode:u,syncInputs:p}=z3(e,o,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=la(A),v=[];v[b]=e[A],c[x]=v});let f={},m=this.getFrozenTensorIds(c),g={};for(;h.length>0;){let A=this.processStack(s,h,t,c,g,m,i,f,l);await Promise.all(A)}u==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!P4(A)&&!Mr(A.name,c,t)).map(A=>A.name);if(y.length>0){let A="";throw u!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${n}]. Consider providing the following inputs: [${d}]. ${A}`)}return c}processStack(e,t,r,a,n,s,i,o,l){let d=[];for(;t.length>0;){let u=t.pop();r.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&k("isConstant",u.node,a,r)&&([p]=In(u.node.name,r)),a[u.node.name]==null){let h=P3(u.node,a,r,this._resourceManager);p||([p]=In(u.node.name,r));let c=r.currentContext;w.isPromise(h)?d.push(h.then(f=>(a[p]=f,r.currentContext=c,this.checkTensorForDisposal(p,u.node,a,r,s,i,o),this.processChildNodes(u.node,t,r,a,n,l),f))):(a[p]=h,this.checkTensorForDisposal(p,u.node,a,r,s,i,o),this.processChildNodes(u.node,t,r,a,n,l))}else this.processChildNodes(u.node,t,r,a,n,l)}return d}processChildNodes(e,t,r,a,n,s){e.children.forEach(i=>{let[o]=In(i.name,r);n[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Mr(l,a,r))&&(n[o]=!0,t.push({contexts:r.currentContext,node:i})):i.inputNames.every(l=>!!Mr(l,a,r))&&(n[o]=!0,t.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let r=e[t],[a]=la(t),n=this.graph.nodes[a];if(n.attrParams.shape&&n.attrParams.shape.value){let s=n.attrParams.shape.value,i=s.length===r.shape.length&&r.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${n.name}'] provided in model.execute(dict) must be [${s}], but was [${r.shape}]`)}n.attrParams.dtype&&n.attrParams.dtype.value&&w.assert(r.dtype===n.attrParams.dtype.value,()=>`The dtype of dict['${n.name}'] provided in model.execute(dict) must be ${n.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(e){let t={};for(let r in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let a=this._signature.inputs[r];t[a.name]=e[r]}else t[r]=e[r];return t}checkInputs(e){let t=Object.keys(e).filter(r=>{let[a]=la(r);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[r]=la(t);if(!this.graph.nodes[r])throw new Error(`The output '${t}' is not found in the graph`)})}},Ij=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Sj="?tfjs-format=file",Tj="model.json",qm=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ij}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ir.browserHTTPRequest(e,this.loadOptions);else{let t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new fy(R3.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let n=R3.Instance.transformGraph(e.modelInitializer);this.initializer=new fy(n),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Ir.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,r,a)=>(t[r]=e[a],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 r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Cj(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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TextDecoder;else{let{StringDecoder:r}=wv();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof et)&&!(e instanceof Promise)&&!t)}function $j(e){return e==null||Pj(e)||Array.isArray(e)||typeof e=="object"&&e instanceof et||w.isTypedArray(e)}function Pj(e){return e===null||typeof e!="object"&&typeof e!="function"}function Oj(e){return Fj(e,zj)}function zj(e){return e instanceof et?{value:e.clone(),recurse:!1}:xu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var L4=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},B4=class extends L4{constructor(){super(B4.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let a=0;a<r;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},W4=B4;W4.INITIAL_CAPACITY=32;function V4(e){return new Lj(e)}function hx(e){return new Bj(e)}function Dj(e,t){return new U4(e,t)}function _j(e,t=G4.FAIL){return new Xj(e,t)}var fr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new qj(this,e)}filter(e){return new jj(this,e)}map(e){return new Hj(this,e)}mapAsync(e){return new D3(this,e)}serialMapAsync(e){return new D3(this,e).serial()}flatmap(e){return new Kj(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Gj(this,e,t)}columnMajorBatch(e,t=!0,r=D4){return this.rowMajorBatch(e,t).map(a=>Mj(a,r))}concatenate(e,t){return new U4(V4([this,e]),t)}take(e){return e<0||e==null?this:new Uj(this,e)}skip(e){return e<0||e==null?this:new Vj(this,e)}prefetch(e){return new j4(this,e)}shuffle(e,t){return new Zj(this,e,t)}serial(){return new Wj(this)}},Lj=class extends fr{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Oj(e),done:!1}}},Bj=class extends fr{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},Wj=class extends fr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Vj=class extends fr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},Uj=class extends fr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Gj=class extends fr{constructor(e,t,r=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},jj=class extends fr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},Hj=class extends fr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=rn.getTensorsInContainer(e.value),r=this.transform(e.value),a=rn.getTensorsInContainer(r);for(let n of t)rn.isTensorInList(n,a)||n.dispose();return{value:r,done:!1}}},qj=class extends fr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},D3=class extends fr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=rn.getTensorsInContainer(e.value),r=await this.transform(e.value),a=rn.getTensorsInContainer(r);for(let n of t)rn.isTensorInList(n,a)||n.dispose();return{value:r,done:!1}}},cx=class extends fr{constructor(){super();this.outputQueue=new W4,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Kj=class extends cx{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=rn.getTensorsInContainer(e.value),r=this.transform(e.value),a=rn.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let n of t)rn.isTensorInList(n,a)||n.dispose();return!0}},U4=class extends fr{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},G4=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(G4||{}),Xj=class extends fr{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function a(s){return s instanceof fr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let n=await _4(this.iterators,a);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:n,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},j4=class extends fr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new L4(t)}summary(){return`${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()}},Zj=class extends j4{constructor(e,t,r){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Rj.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},ud=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),oa(async()=>(await r.iterator()).columnMajorBatch(e,t,Qj),a)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,oa(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,oa(async()=>(await t.iterator()).filter(a=>q(()=>e(a))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return oa(async()=>(await t.iterator()).map(r=>q(()=>e(r))),this.size)}mapAsync(e){let t=this;return oa(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return oa(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,oa(async()=>{let a=hx(async()=>({value:await t.iterator(),done:!1}));return Dj(a.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,oa(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,n=Ej.alea(t||w.now().toString());return oa(async()=>{let s=n.int32();return r&&(s+=n.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,oa(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ud.MAX_BUFFER_SIZE=1e4;function oa(e,t=null){return new class extends ud{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Yj(e){return oa(async()=>V4(e),e.length)}function Jj(e){if(!xu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return oa(async()=>{let r=await _4(e,a=>{if(a instanceof ud)return{value:a.iterator(),recurse:!1};if(xu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return _j(r,1)},t)}function Qj(e){if(e===null)return null;let t=e[0];return $j(t)?{value:eH(e),recurse:!1}:{value:null,recurse:!0}}function eH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?nr(e):pt(e)}var H4=class extends ud{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Rc='"',ip=Symbol("out"),_3=Symbol("field"),Fc=Symbol("quote"),N1=Symbol("quoteafterquote"),L3=Symbol("quoteinquote"),q4=class extends ud{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 H4(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((a,n)=>(a[n]=a[n]+1||1,a),{}),r=Object.keys(t).filter(a=>t[a]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},a={};for(let n=0;n<this.fullColumnNames.length;n++){let s=this.fullColumnNames[n],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[n],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let d=Number(o);if(isNaN(d))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=d;else switch(i.dtype){case"float32":l=d;break;case"int32":l=Math.floor(d);break;case"bool":l=this.getBoolean(o);break;default:l=d}}i&&i.isLabel?a[s]=l:r[s]=l}}return Object.keys(a).length===0?r:{xs:r,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],a=0,n=e.length,s=ip;for(let i=0;i<n;i++)switch(s){case ip:switch(e.charAt(i)){case Rc:a=i+1,s=Fc;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=ip;break;default:s=_3,a=i;break}break;case _3:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(a,i)),s=ip,a=i+1;break;default:}break;case Fc:switch(e.charAt(i)){case Rc:s=N1;break;default:}break;case N1:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(a,i-1)),s=ip,a=i+1;break;case Rc:s=Fc;break;default:s=L3;break}break;case L3:switch(e.charAt(i)){case Rc:s=Fc;break;default:}break;default:}if(s===N1?r.push(e.substring(a,n-1)):r.push(e.substring(a)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},K4=class extends fr{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_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new K4(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(a=>{let n=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(n),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((a,n)=>r.set(a,n*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),pt(r,t)}},X4=class extends fr{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,n=(1-r)/2,s=(1-a)/2,i=n+r,o=a+s;this.cropBox=an([s,n,o,i],[1,4])}else this.cropBox=an([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new X4(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$a.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=r.shape;return U(r,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Z4=class{},Y4=class extends fr{split(e){return new tH(this,e)}},tH=class extends Y4{constructor(e,t){super();this.upstream=e,this.impl=new rH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},rH=class extends cx{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},aH=class extends fr{decodeUTF8(){return new nH(this)}},nH=class extends Y4{constructor(e){super();this.upstream=e,this.impl=new sH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},sH=class extends cx{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=wv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Y().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},J4=class extends aH{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let n=this.file.slice(this.offset,r);a.readAsArrayBuffer(n)}this.offset=r}),done:!1}}};async function iH(e,t={},r){let a,n;typeof e=="string"?a=e:(a=e.url,n=oH(e));let s=await(r||w.fetch)(a,n);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new J4(i,t)}else throw new Error(s.statusText)}var oH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Q4(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var e6=class extends Z4{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Q4(this.input)&&Y().get("IS_NODE")){let e=Hc();this.input=e.readFileSync(this.input.substr(7))}return new J4(this.input,this.options)}},t6=class extends Z4{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Q4(this.url)?new e6(this.url,this.fileOptions).iterator():iH(this.url,this.fileOptions)}};function lH(e,t={}){return new q4(new t6(e),t)}function uH(e){let t=hx(e);return oa(async()=>t)}function dH(e){return oa(async()=>{let t=await e();return hx(()=>t.next())})}async function pH(e,t){return X4.create(e,t)}async function hH(e){return K4.create(e)}var cH="0.0.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var fH=Ha.whereImpl,r6=class extends Iu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Dp(this,kr())}nextDataId(){return r6.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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u=N.computePool3DInfo(s.shape,i,o,1,l,d),p=u.strideDepth,h=u.strideHeight,c=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,A=u.dilationHeight,x=u.dilationWidth,b=u.effectiveFilterDepth,v=u.effectiveFilterHeight,C=u.effectiveFilterWidth,T=b-1-u.padInfo.front,E=C-1-u.padInfo.left,R=v-1-u.padInfo.top,z=Le(s.shape,"float32"),M=1/(f*m*g),I=r.bufferSync(n);for(let D=0;D<u.batchSize;++D)for(let O=0;O<u.inChannels;++O)for(let j=0;j<u.inDepth;++j)for(let X=0;X<u.inHeight;++X)for(let _=0;_<u.inWidth;++_){let K=j-T,W=X-R,ee=_-E,Q=0;for(let ne=0;ne<b;ne+=y){let Z=(K+ne)/p;if(!(Z<0||Z>=u.outDepth||Math.floor(Z)!==Z))for(let ae=0;ae<v;ae+=A){let ie=(W+ae)/h;if(!(ie<0||ie>=u.outHeight||Math.floor(ie)!==ie))for(let xe=0;xe<C;xe+=x){let be=(ee+xe)/c;be<0||be>=u.outWidth||Math.floor(be)!==be||(Q+=I.get(D,Z,ie,be,O))}}}z.set(Q*M,D,j,X,_,O)}return r.makeTensorInfo(z.shape,z.dtype,z.values)}var rK={kernelName:Cf,backendName:"cpu",kernelFunc:tK};function aK(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,input:s}=t,i=s;Ne([n,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=N.computePool2DInfo(i.shape,o,l,1,d),p=u.strideHeight,h=u.strideWidth,c=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,A=u.effectiveFilterWidth,x=A-1-u.padInfo.left,b=y-1-u.padInfo.top,v=Le(i.shape,"float32"),C=1/(c*f),T=r.data.get(n.dataId).values,E=Le(n.shape,"float32",T);for(let R=0;R<u.batchSize;++R)for(let z=0;z<u.inChannels;++z)for(let M=0;M<u.inHeight;++M)for(let I=0;I<u.inWidth;++I){let D=M-b,O=I-x,j=0;for(let X=0;X<y;X+=m){let _=(D+X)/p;if(!(_<0||_>=u.outHeight||Math.floor(_)!==_))for(let K=0;K<A;K+=g){let W=(O+K)/h;W<0||W>=u.outWidth||Math.floor(W)!==W||(j+=E.get(R,_,W,z))}}v.set(j*C,R,M,I,z)}return r.makeTensorInfo(v.shape,v.dtype,v.values)}var nK={kernelName:Tf,backendName:"cpu",kernelFunc:aK};function 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r.makeTensorInfo(n.shape,n.dtype,m)}var iK={kernelName:oi,backendName:"cpu",kernelFunc:sK};function oK(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,crops:i}=a;Ne([n],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(n.shape,s,o),d=N.getPermuted(l.length,s.length),u=N.getReshapedPermuted(n.shape,s,o),p=N.getSliceBeginCoords(i,s.length),h=N.getSliceSize(u,i,s.length),c=$t({inputs:{x:n},backend:r,attrs:{shape:l}}),f=Ma({inputs:{x:c},backend:r,attrs:{perm:d}}),m=$t({inputs:{x:f},backend:r,attrs:{shape:u}}),g=So({inputs:{x:m},backend:r,attrs:{begin:p,size:h}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var lK={kernelName:Mo,backendName:"cpu",kernelFunc:oK};function uK(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,weights:s}=t,{size:i}=a,o=r.data.get(n.dataId).values,l=r.data.get(s.dataId).values,d=gx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}var dK={kernelName:Nf,backendName:"cpu",kernelFunc:uK};function pK(e){let{inputs:t,backend:r}=e,{s0:a,s1:n}=t,s=r.data.get(a.dataId).values,i=r.data.get(n.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var hK={kernelName:Ef,backendName:"cpu",kernelFunc:pK},cK=gt(Kn,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),fK={kernelName:Kn,backendName:"cpu",kernelFunc:cK},mK=e=>{let{x:t}=e.inputs,r=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),n=r.data.get(t.dataId),s=n.complexTensorInfos.real,i=n.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let d=0;d<o.length;d++){let u=o[d],p=l[d];a[d]=Math.hypot(u,p)}return r.makeOutput(a,t.shape,"float32")},gK={kernelName:Bp,backendName:"cpu",kernelFunc:mK};function bu(e){let{inputs:t,backend:r}=e,{input:a}=t,n=r.data.get(a.dataId).complexTensorInfos.imag,s=r.data.get(n.dataId).values;return r.makeTensorInfo(n.shape,n.dtype,s)}var yK={kernelName:Gp,backendName:"cpu",kernelFunc:bu};function vu(e){let{inputs:t,backend:r,attrs:a}=e,{axis:n}=a,s=w.parseAxisParam(n,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return En({inputs:{x:o[0]},backend:r});let l=o.map(m=>m.shape);if(N.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>Io({inputs:{input:b},backend:r})),g=o.map(b=>bu({inputs:{input:b},backend:r})),y=vu({inputs:m,backend:r,attrs:{axis:s}}),A=vu({inputs:g,backend:r,attrs:{axis:s}}),x=ua({inputs:{real:y,imag:A},backend:r});return 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tr(h.outShape,n.dtype),v=w.computeStrides(n.shape),C=w.computeStrides(s.shape),T=v[0],E=x?v[1]:v[2],R=x?v[2]:1,z=x?1:v[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],D=x?b.strides[2]:1,O=x?1:b.strides[1],j=r.data.get(n.dataId).values,X=r.data.get(s.dataId).values,_=b.values;for(let K=0;K<h.batchSize;++K){let W=K*T,ee=K*M;for(let Q=0;Q<h.outHeight;++Q){let ne=ee+Q*I,Z=Q*h.strideHeight-A;for(let ae=0;ae<c;++ae){let ie=Z+ae*m;if(ie<0||ie>=h.inHeight)continue;let xe=ae*C[0],be=W+ie*E;for(let Te=0;Te<h.outWidth;++Te){let Re=ne+Te*D,$e=Te*h.strideWidth-y;for(let _e=0;_e<f;++_e){let qe=$e+_e*g;if(qe<0||qe>=h.inWidth)continue;let Ze=xe+_e*C[1],st=be+qe*R,ht=Ze;for(let ct=0;ct<h.inChannels;++ct){let yt=j[st+ct*z];for(let Et=0;Et<h.outChannels;++Et)_[Re+Et*O]+=yt*X[ht+Et];ht+=h.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,_)}var xK={kernelName:Ys,backendName:"cpu",kernelFunc:X6};function 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r.makeTensorInfo(A.shape,A.dtype,A.values)}var vK={kernelName:Rf,backendName:"cpu",kernelFunc:bK};function wK(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a;Ne([n,s],"conv2dBackpropInput");let p=w.computeStrides(s.shape),h=w.computeStrides(n.shape),c=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(i,s.shape,o,1,l,u,!1,c),m=new tr(f.inShape,"float32"),g=m.values,y=r.data.get(n.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=p,{batchSize:C,filterHeight:T,filterWidth:E,inChannels:R,inHeight:z,inWidth:M,outChannels:I,outHeight:D,outWidth:O,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let _=T-1-f.padInfo.top,K=E-1-f.padInfo.left,W=c==="channelsLast",ee=m.strides[0],Q=W?m.strides[1]:m.strides[2],ne=W?m.strides[2]:1,Z=W?1:m.strides[1],ae=h[0],ie=W?h[1]:h[2],xe=W?h[2]:1,be=W?1:h[1];for(let Te=0;Te<C;++Te)for(let Re=0;Re<R;++Re)for(let $e=0;$e<z;++$e){let _e=$e-_,qe=Math.max(0,Math.ceil(_e/j)),Ze=Math.min(D,(T+_e)/j);for(let st=0;st<M;++st){let ht=st-K,ct=Math.max(0,Math.ceil(ht/X)),yt=Math.min(O,(E+ht)/X),Et=0;for(let ut=qe;ut<Ze;++ut){let qr=ut*j-_e;for(let gr=ct;gr<yt;++gr){let Kr=gr*X-ht,za=ae*Te+ie*ut+xe*gr,Xr=x*(T-1-qr)+b*(E-1-Kr)+v*Re;for(let Rr=0;Rr<I;++Rr){let Da=y[za+be*Rr],xn=A[Xr+Rr];Et+=Da*xn}}}let Hr=ee*Te+Q*$e+ne*st+Z*Re;g[Hr]=Et}}return r.makeTensorInfo(m.shape,m.dtype,m.values)}var kK={kernelName:Js,backendName:"cpu",kernelFunc:wK};function IK(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dilations:l}=a;Ne([n,s],"conv3d");let d=N.computeConv3DInfo(n.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:h,dilationDepth:c,dilationHeight:f,dilationWidth:m,padInfo:g}=d,y=g.front,A=g.left,x=g.top,b=new tr(d.outShape,n.dtype),v=r.data.get(n.dataId).values,C=r.data.get(s.dataId).values,T=b.values,E=w.computeStrides(n.shape),R=w.computeStrides(s.shape);for(let z=0;z<d.batchSize;++z){let M=z*E[0],I=z*b.strides[0];for(let D=0;D<d.outDepth;++D){let O=I+D*b.strides[1],j=D*d.strideDepth-y;for(let X=0;X<u;++X){let _=j+X*c;if(_<0||_>=d.inDepth)continue;let K=X*R[0],W=M+_*E[1];for(let ee=0;ee<d.outHeight;++ee){let Q=O+ee*b.strides[2],ne=ee*d.strideHeight-x;for(let Z=0;Z<p;++Z){let ae=ne+Z*f;if(ae<0||ae>=d.inHeight)continue;let ie=K+Z*R[1],xe=W+ae*E[2];for(let be=0;be<d.outWidth;++be){let Te=Q+be*d.outChannels,Re=be*d.strideWidth-A;for(let $e=0;$e<h;++$e){let _e=Re+$e*m;if(_e<0||_e>=d.inWidth)continue;let qe=ie+$e*R[2],Ze=xe+_e*d.inChannels,st=qe;for(let ht=0;ht<d.inChannels;++ht){let ct=v[Ze+ht];for(let yt=0;yt<d.outChannels;++yt)T[Te+yt]+=ct*C[st+yt];st+=d.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var SK={kernelName:Wp,backendName:"cpu",kernelFunc:IK};function TK(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,dy:s}=t,{strides:i,pad:o,filterShape:l}=a;Ne([n,s],"conv3dBackpropFilterV2");let d=w.computeStrides(n.shape),u=w.computeStrides(s.shape),p=N.computeConv3DInfo(n.shape,l,i,1,o),h=p.strideDepth,c=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,y=p.filterWidth,A=new tr(p.filterShape,"float32"),x=A.values,[b,v,C,T]=A.strides,E=r.data.get(s.dataId).values,[R,z,M,I]=u,D=r.data.get(n.dataId).values,[O,j,X,_]=d,K=p.padInfo.front,W=p.padInfo.left,ee=p.padInfo.top;for(let Q=0;Q<m;++Q){let ne=Math.max(0,Math.ceil((K-Q)/h)),Z=Math.min(p.outDepth,(p.inDepth+K-Q)/h),ae=Q*b;for(let ie=0;ie<g;++ie){let xe=Math.max(0,Math.ceil((ee-ie)/c)),be=Math.min(p.outHeight,(p.inHeight+ee-ie)/c),Te=ie*v+ae;for(let Re=0;Re<y;++Re){let $e=Math.max(0,Math.ceil((W-Re)/f)),_e=Math.min(p.outWidth,(p.inWidth+W-Re)/f),qe=Re*C+Te;for(let Ze=0;Ze<p.inChannels;++Ze){let st=Ze*T+qe;for(let ht=0;ht<p.outChannels;++ht){let ct=0;for(let yt=0;yt<p.batchSize;++yt){let Et=yt*O,Hr=yt*R;for(let ut=ne;ut<Z;++ut){let qr=(Q+ut*h-K)*j+Et,gr=ut*z+Hr;for(let Kr=xe;Kr<be;++Kr){let za=(ie+Kr*c-ee)*X+qr,Xr=Kr*M+gr;for(let Rr=$e;Rr<_e;++Rr){let Da=(Re+Rr*f-W)*_+za,xn=Rr*I+Xr;ct+=D[Da+Ze]*E[xn+ht]}}}}x[st+ht]=ct}}}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var CK={kernelName:Ff,backendName:"cpu",kernelFunc:TK};function NK(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;Ne([n],"conv3dBackpropInputV2");let d=w.computeStrides(n.shape),u=w.computeStrides(s.shape),p=N.computeConv3DInfo(l,s.shape,o,1,i),h=new tr(p.inShape,"float32"),c=h.values,[f,m,g,y]=h.strides,A=r.data.get(n.dataId).values,[x,b,v,C]=d,T=r.data.get(s.dataId).values,[E,R,z,M]=u,{batchSize:I,filterDepth:D,filterHeight:O,filterWidth:j,inChannels:X,inDepth:_,inHeight:K,inWidth:W,outChannels:ee,outDepth:Q,outHeight:ne,outWidth:Z,strideDepth:ae,strideHeight:ie,strideWidth:xe}=p,be=D-1-p.padInfo.front,Te=O-1-p.padInfo.top,Re=j-1-p.padInfo.left;for(let $e=0;$e<I;++$e)for(let _e=0;_e<X;++_e)for(let qe=0;qe<_;++qe){let 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PK(e){let{inputs:t,backend:r,attrs:a}=e,{image:n,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:d}=a,[u,p,h,c]=n.shape,f=s.shape[0],[m,g]=o,y=Le([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(n.dataId).values,v=w.computeStrides(n.shape),C=w.computeStrides(y.shape);for(let T=0;T<f;T++){let E=T*4,R=A[E],z=A[E+1],M=A[E+2],I=A[E+3],D=x[T];if(D>=u)continue;let O=m>1?(M-R)*(p-1)/(m-1):0,j=g>1?(I-z)*(h-1)/(g-1):0;for(let X=0;X<m;X++){let _=m>1?R*(p-1)+X*O:.5*(R+M)*(p-1);if(_<0||_>p-1){for(let K=0;K<g;K++)for(let W=0;W<c;W++){let ee=W+K*C[2]+X*C[1]+T*C[0];y.values[ee]=d}continue}if(l==="bilinear"){let K=Math.floor(_),W=Math.ceil(_),ee=_-K;for(let Q=0;Q<g;Q++){let ne=g>1?z*(h-1)+Q*j:.5*(z+I)*(h-1);if(ne<0||ne>h-1){for(let xe=0;xe<c;xe++){let be=xe+Q*C[2]+X*C[1]+T*C[0];y.values[be]=d}continue}let Z=Math.floor(ne),ae=Math.ceil(ne),ie=ne-Z;for(let xe=0;xe<c;xe++){let be=xe+Z*v[2]+K*v[1]+D*v[0],Te=b[be];be=xe+ae*v[2]+K*v[1]+D*v[0];let 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p=Or(d.dtype,"int32"),h=w.makeZerosTypedArray(w.sizeFromShape(d.shape),p),c=r.data.get(d.dataId).values,f=d.shape[d.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)h[x]=i?0:c[x];else{let b=m(y,A-1);h[x]=i?c[b]+h[b]:c[x]+h[b]}}let g=r.makeTensorInfo(d.shape,p,h);if(l!=null){let y=N.getUndoAxesPermutation(l),A=Ma({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(d),A}return g}var DK={kernelName:Po,backendName:"cpu",kernelFunc:zK};function _K(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,weights:s}=t,{size:i,binaryOutput:o}=a;if(n.shape.length===1){let l=r.data.get(n.dataId).values,d=r.data.get(s.dataId).values,u=gx(l,d,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}else if(n.shape.length===2){let l=r.bufferSync(n),d=r.bufferSync(s),u=s6(l,d,i,o);return r.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be 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a=1,n=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,n,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(s),i}function MJ(e,t){let r=Cx(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let n=1,s=1;e.texImage2D(e.TEXTURE_2D,0,r.internalFormatHalfFloat,n,s,0,r.textureFormatFloat,r.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function NI(e){return e!==2?!1:pn(e).fenceSync!=null}function hd(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Pe=Y();Pe.registerFlag("HAS_WEBGL",()=>Pe.getNumber("WEBGL_VERSION")>0);Pe.registerFlag("WEBGL_VERSION",()=>xy(2)?2:xy(1)?1:0);Pe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Pe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Pe.get("WEBGL_VERSION")===2);Pe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Pe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Pe.registerFlag("WEBGL_PACK",()=>Pe.getBool("HAS_WEBGL"));Pe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_CLIP",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_REDUCE",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_CONV_IM2COL",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>kI(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>II(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Pe.getNumber("WEBGL_VERSION");return e===0?0:SI(e)});Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Pe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!nh.isMobile());Pe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>TI(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Pe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Pe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Pe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>CI(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>NI(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Pe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Pe.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Pe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>nh.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Pe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Pe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Pe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Pe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Br(){let e,t,r,a,n,s,i,o,l,d;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",r="out",a="in",n="texture",s="outputColor",i="out vec4 outputColor;",o=`
      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

      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="",d=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(e="",t="attribute",r="varying",a="varying",n="texture2D",s="gl_FragColor",i="",o=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,d=`
      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:r,varyingFs:a,texture2D:n,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:d}}function Sl(e,t,r="index"){let a=w.computeStrides(t);return a.map((n,s)=>{let i=`int ${e[s]} = ${r} / ${n}`,o=s===a.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${n}`:`index -= ${e[s]} * ${n}`;return`${i}; ${o};`}).join("")}function Ym(e,t,r="index"){let a=w.computeStrides(t);return a.map((n,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function $J(e,t){let r=e.length,a=e.map(s=>`${t}[${s}]`),n=new Array(r-1);n[r-2]=a[r-1];for(let s=r-3;s>=0;--s)n[s]=`(${n[s+1]} * ${a[s+1]})`;return n}function PJ(e,t,r="index"){let a=e.map((s,i)=>i),n=$J(a,t);return n.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${n[i]}`,l=i===n.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${n[i]}`:`index -= ${e[i]} * ${n[i]}`;return`${o}; ${l};`}).join("")}function Nx(e){let t=w.computeStrides(e).map(r=>r.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function Ex(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var EI=`
  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:RI}=N;function OJ(e,t,r){let a=[];if(e.forEach(h=>{let c=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?a.push(`uniform float ${h.name}${c>1?`[${c}]`:""};`):(a.push(`uniform sampler2D ${h.name};`),a.push(`uniform int offset${h.name};`)),r.enableShapeUniforms){let{uniformShape:f}=Rx(r.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(f.length){case 1:a.push(`uniform int ${h.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${h.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(h=>{a.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let n=a.join(`
`),s=e.map(h=>zJ(h,t,r.packedInputs,r.enableShapeUniforms)).join(`
`),i=t.texShape,o=Br(),l=LJ(o),d,u,p=VJ(o);return t.isPacked?(d=DJ(t.logicalShape,i,r.enableShapeUniforms),u=WJ(o)):(d=_J(t.logicalShape,i,r.enableShapeUniforms),u=BJ(o)),r.packedInputs&&(p+=HJ),[p,l,u,n,d,s,r.userCode].join(`
`)}function cd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return nQ(e,t);case 1:return iQ(e,t);case 2:return lQ(e,t);case 3:return dQ(e,t);case 4:return hQ(e,t);case 5:return cQ(e);case 6:return fQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function FI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return aQ(e);case 1:return sQ(e,t);case 2:return oQ(e,t);case 3:return uQ(e,t);default:return pQ(e,t)}}function zJ(e,t,r=!1,a){let n="";r?n+=FI(e,a):n+=cd(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?n+=mQ(e,t):n+=gQ(e,t)),n}function DJ(e,t,r){switch(e.length){case 0:return MI();case 1:return qJ(e,t,r);case 2:return tQ(e,t,r);case 3:return XJ(e,t,r);default:return YJ(e,t,r)}}function _J(e,t,r){switch(e.length){case 0:return MI();case 1:return KJ(e,t,r);case 2:return rQ(e,t,r);case 3:return ZJ(e,t,r);case 4:return JJ(e,t,r);case 5:return QJ(e,t);case 6:return eQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function LJ(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function BJ(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function WJ(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function VJ(e){return`${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);
    }

    ${UJ}
    ${GJ}
    ${jJ}
  `}var UJ=`
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);
}
`,GJ=`
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);
}
`,jJ=`
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);
}
`,HJ=`
  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 MI(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function qJ(e,t,r){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?r?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${a[1]}.0);
      }
    `:a[1]===1?r?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${a[0]}.0);
      }
    `:r?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
    }
  `}function KJ(e,t,r){return t[0]===1?r?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?r?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:r?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function XJ(e,t,r){if(r)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[2]/2),s=n*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec3(b, r, c);
    }
  `}function ZJ(e,t,r){if(r)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Ym(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let a=Sl(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec3(r, c, d);
    }
  `}function YJ(e,t,r){if(r)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[e.length-1]/2),s=n*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let d=2;d<e.length-1;d++)i*=e[e.length-d-1],o=`
      int b${d} = index / ${i};
      index -= b${d} * ${i};
    `+o,l=`b${d}, `+l;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

      ${o}

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec${e.length}(${l});
    }
  `}function JJ(e,t,r){if(r)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Ym(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let a=Sl(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec4(r, c, d, d2);
    }
  `}function QJ(e,t){let r=Sl(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${r}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function eQ(e,t){let r=Sl(["r","c","d","d2","d3","d4"],e);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${r}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function tQ(e,t,r){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
      }
    `;let n=Math.ceil(e[1]/2);return r?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));

      int index = resTexRC.x * ${a[1]} + resTexRC.y;
      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec2(r, c);
    }
  `}function rQ(e,t,r){return w.arraysEqual(e,t)?r?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?r?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?r?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:r?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function Tl(e){return`offset${e}`}function aQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Br();return`
    vec4 ${r}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function nQ(e,t){let r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${r};}`;let[n,s]=e.shapeInfo.texShape;if(n===1&&s===1)return`
      float ${a}() {
        return sampleTexture(${r}, halfCR);
      }
    `;let i=Tl(r);if(t)return`
    float ${a}() {
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
      return sampleTexture(${r}, uv);
    }
  `;let[o,l]=e.shapeInfo.texShape;return`
    float ${a}() {
      vec2 uv = uvFromFlat(${o}, ${l}, ${i});
      return sampleTexture(${r}, uv);
    }
  `}function sQ(e,t){let r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),n=e.shapeInfo.texShape,s=Br();if(t)return`
    vec4 ${a}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${r}, uv);
    }
  `;let i=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];return`
    vec4 ${a}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${r}, uv);
    }
  `}function iQ(e,t){let r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
      float ${a}(int index) {
        ${fd(e)}
      }
    `;let n=e.shapeInfo.texShape,s=n[0],i=n[1];if(i===1&&s===1)return`
      float ${a}(int index) {
        return sampleTexture(${r}, halfCR);
      }
    `;let o=Tl(r);return i===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
        return sampleTexture(${r}, uv);
      }
    `:s===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${r}TexShape[1]), 0.5);
        return sampleTexture(${r}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${r}, uv);
      }
    `:t?`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${o});
      return sampleTexture(${r}, uv);
    }
  `:`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
      return sampleTexture(${r}, uv);
    }
  `}function oQ(e,t){let r=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Br();if(s!=null&&w.arraysEqual(r,s))return t?`
      vec4 ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);

        return ${l.texture2D}(${a}, uv);
      }
    `:`
      vec4 ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);

        return ${l.texture2D}(${a}, uv);
      }
    `;if(t)return`
    vec4 ${n}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `;let d=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],u=Math.ceil(r[1]/2);return`
    vec4 ${n}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${d[0]}, ${d[1]}, row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `}function lQ(e,t){let r=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
      float ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `;let h=s[0],c=s[1];return`
    float ${n}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${h}.0);
      return sampleTexture(${a}, uv);
    }
  `}let{newShape:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let h=md(e,l),c=["row","col"];return`
      ${cd(h,t)}
      float ${n}(int row, int col) {
        return ${n}(${gd(c,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
        ${fd(e)}
      }
    `;let d=s[0],u=s[1],p=Tl(a);return u===1?t?`
      float ${n}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${r[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${d}.0);
      return sampleTexture(${a}, uv);
    }
  `:d===1?t?`
      float ${n}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${r[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
      return sampleTexture(${a}, uv);
    }
  `:t?`
      float ${n}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
        return sampleTexture(${a}, uv);
      }
    `:`
  float ${n}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${r[1]} + col + ${p};
    vec2 uv = uvFromFlat(${d}, ${u}, index);
    return sampleTexture(${a}, uv);
  }
`}function uQ(e,t){let r=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let h=r.slice(1),c=[1,2],f=md(e,h),m=["b","row","col"];return`
        ${FI(f,t)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${gd(m,c)});
        }
      `}let o=Br();if(t)return`
    vec4 ${n}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `;let l=i[0],d=i[1],u=Math.ceil(r[2]/2),p=u*Math.ceil(r[1]/2);return`
    vec4 ${n}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${l}, ${d}, ${p}, ${u}, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `}function dQ(e,t){let r=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),d=o;if(d.length<r.length){let m=md(e,d),g=["row","col","depth"];return`
        ${cd(m,t)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${gd(g,l)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${fd(e)}
      }
    `;let u=e.shapeInfo.texShape,p=u[0],h=u[1],c=e.shapeInfo.flatOffset;if(h===s&&c==null)return t?`
      float ${n}(int row, int col, int depth) {
        int stride1 = ${a}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
        float ${n}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${h}.0, ${p}.0);
          return sampleTexture(${a}, uv);
        }
      `;if(h===i&&c==null)return t?`
      float ${n}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${n}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${r[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${p}.0);
      return sampleTexture(${a}, uv);
    }
  `;let f=Tl(a);return t?`
    float ${n}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${a}Shape[1] * ${a}Shape[2];
      int stride1 = ${a}Shape[2];
      int index = row * ${s} + col * ${i} + depth + ${f};
      vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
      return sampleTexture(${a}, uv);
    }
    `:`
      float ${n}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${f};
        vec2 uv = uvFromFlat(${p}, ${h}, index);
        return sampleTexture(${a}, uv);
      }
  `}function pQ(e,t){let r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),n=Br();if(t)return`
    vec4 ${a}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${r}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${n.texture2D}(${r}, uv);
    }
  `;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],d=l[0],u=l[1],p=Math.ceil(s[i-1]/2),h=p*Math.ceil(s[i-2]/2),c="int b, int row, int col",f=`b * ${h} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)c=`int b${m}, `+c,h*=s[i-m-1],f=`b${m} * ${h} + `+f;return`
    vec4 ${a}(${c}) {
      int index = ${f};
      int texR = index / ${u};
      int texC = index - texR * ${u};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${d});
      return ${n.texture2D}(${r}, uv);
    }
  `}function hQ(e,t){let r=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:d}=w.squeezeShape(r);if(l.length<r.length){let A=md(e,l),x=["row","col","depth","depth2"];return`
      ${cd(A,t)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${gd(x,d)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${i}, ${s}, 1)));
        ${fd(e)}
      }
    `;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],c=p[1],f=`int stride2 = ${a}Shape[3];`,m=`int stride1 = ${a}Shape[2] * stride2;`,g=`int stride0 = ${a}Shape[1] * stride1;`;if(c===o&&u==null)return t?`
      float ${n}(int row, int col, int depth, int depth2) {
        ${f}
        ${m}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${c}.0, ${h}.0);
        return sampleTexture(${a}, uv);
      }
    `;if(c===s&&u==null)return t?`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${r[1]*r[2]}, ${r[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
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        return sampleTexture(${a}, uv);
      }
    `;let y=Tl(a);return t?`
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${m}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
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      return sampleTexture(${a}, uv);
    }
  `:`
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} +
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    }
  `}function cQ(e){let t=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),n=t[4],s=t[3]*n,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:d}=w.squeezeShape(t);if(l.length<t.length){let m=md(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${cd(m)}
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        return ${a}(${gd(g,d)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
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        ${fd(e)}
      }
    `;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],c=p[1];if(c===o&&u==null)return`
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        int texR = row;
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                         vec4(${i}, ${s}, ${n}, 1));
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        return sampleTexture(${r}, uv);
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      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
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          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
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    float ${a}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
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    }
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    `}let i=t[5],o=t[4]*i,l=t[3]*o,d=t[2]*l,u=t[1]*d;if(e.shapeInfo.isUniform)return`
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        int index = round(dot(
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          vec4(${u}, ${d}, ${l}, ${o})) +
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      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
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               float(depth4);
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    `;if(f===i&&p==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
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               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
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        return sampleTexture(${r}, uv);
      }
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    float ${a}(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 * ${u} + col * ${d} + depth * ${l} +
          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
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      return sampleTexture(${r}, uv);
    }
  `}function fd(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
    for (int i = 0; i < ${r}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
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`);let h="";i<2&&s>0?h="coords":h=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+d]}`).join(", ");let c="return outputValue;",f=w.sizeFromShape(e.shapeInfo.logicalShape)===1,m=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)c=`
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      ${l} coords = getOutputCoords();
      ${u}
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      ${c}
    }
  `}function gQ(e,t){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),n="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
      float ${n}() {
        return sampleTexture(${r}, resultUV);
      }
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    }
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Shape ${o} and ${l} must match`)})}function AQ(e,t,r,a,n){t.program.enableShapeUniforms||(V3(t.inShapeInfos,r),V3([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,d)=>{let u=t.program.variableNames[d],p=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`],c=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(c){let{uniformShape:m}=Rx(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,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&&h!=null&&e.gl.uniform1i(h,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,p,d)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(a.shape);switch(a.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,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&n&&t.program.customUniforms.forEach((l,d)=>{let u=t.customUniformLocations[d],p=n[d];if(l.type==="float")e.gl.uniform1fv(u,p);else if(l.type==="vec2")e.gl.uniform2fv(u,p);else if(l.type==="vec3")e.gl.uniform3fv(u,p);else if(l.type==="vec4")e.gl.uniform4fv(u,p);else if(l.type==="int")e.gl.uniform1iv(u,p);else if(l.type==="ivec2")e.gl.uniform2iv(u,p);else if(l.type==="ivec3")e.gl.uniform3iv(u,p);else if(l.type==="ivec4")e.gl.uniform4iv(u,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function xQ(e,t,r){let a="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:d,uniformShape:u,keptDims:p}=Rx(e.packedInputs,i.shape,l),h="",c="",f="";if(u.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];h=`${v[0]>1}_${v[1]>1}`}else if(u.length===2&&!e.packedInputs)c=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let v=w.computeStrides(u);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=u.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${m}_${x}_${d?p:""}_${u.length}_${y}_${A}_${g}_${h}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let n=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+n+`${Y().getNumber("WEBGL_VERSION")}`,s}function sa(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var bQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Br();this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Ym(["r","c","d"],e):Sl(["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;
      }
    `}},vQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Br();this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Ym(["r","c","d"],e):Sl(["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;
      }
    `}},wQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Br();this.outputShape=e,this.userCode=`
      ${EI}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},kQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Br();this.outputShape=e,this.userCode=`
      ${EI}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},IQ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Br();this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
      ${this.enableShapeUniforms?Ex():Nx(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / texShape[1];
        int c = imod(flatIndex, texShape[1]);
        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
        vec4 values = ${r.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${r.output} = vec4(${a}, 0., 0., 0.);
      }
    `}},SQ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Br();this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length);let a="",n="result";t&&(n="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
          localCoords = coords;
          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${i};
          if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${s};

            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);

            flatIndex = idiv(flatIndex, 4, 1.);

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${r.texture2D}(A, uv);

            if (offset == 0) {
              result[${o}] = values[0];
            } else if (offset == 1) {
              result[${o}] = values[1];
            } else if (offset == 2) {
              result[${o}] = values[2];
            } else {
              result[${o}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?Ex():Nx(e)}

        void main() {
          ivec3 coords = getOutputCoords();

          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;

          ${a}

          ${r.output} = ${n};
        }
    `}},$I={};De($I,{bindVertexProgramAttributeStreams:()=>VI,createBufferFromOutputTexture:()=>jI,createFloat16MatrixTexture:()=>_I,createFloat16PackedMatrixTexture:()=>WI,createFloat32MatrixTexture:()=>DI,createIndexBuffer:()=>zI,createPackedMatrixTexture:()=>BI,createUnsignedBytesMatrixTexture:()=>LI,createVertexBuffer:()=>OI,createVertexShader:()=>PI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>qI,downloadFloat32MatrixFromBuffer:()=>HI,downloadMatrixFromPackedOutputTexture:()=>XI,downloadPackedMatrixFromBuffer:()=>KI,getInternalFormatForFloat16MatrixTexture:()=>Mx,getInternalFormatForFloat16PackedMatrixTexture:()=>Ox,getInternalFormatForFloat32MatrixTexture:()=>Fx,getInternalFormatForPackedMatrixTexture:()=>Px,getInternalFormatForUnsignedBytesMatrixTexture:()=>$x,uploadDenseMatrixToTexture:()=>UI,uploadPixelDataToTexture:()=>GI});function PI(e){let t=Br(),r=`${t.version}
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          setOutput(vec4(getA(), 0., 0., 0.));
        }
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          if(${a}) {
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          } else {
            ${n}

            setOutput(vec4(${s}));
          }
        }
      `}}getSourceCoordsArr(e){let t=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let n=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)n=`${e[e.length-1-s]},`+n;t.push(n)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let r=this.rank-2;r<this.rank;r++)t+=`${e[r]} >= ${this.enableShapeUniforms?`outShape[${r}]`:this.outputShape[r]}`,r<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),r=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],a=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${t[0]};
      int c = ${t[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${r};
      bool rEdge = rp1 >= ${a};
    `}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]}),
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            rEdge || cEdge ? 0. : getA(${t[3]})`}},e8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length);let r="";for(let a=0;a<4;a++){let n="thisRC = rc;";a%2===1&&(n+="thisRC.z += 1;"),a>1&&(n+="thisRC.y += 1;"),r+=`
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      `}this.userCode=`
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      ${this.enableShapeUniforms?Ex():Nx(e)}

      void main() {
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        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};

        ${r}

        setOutput(result);
      }
    `}};function cee(e,t){return`
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      return ivec3(r, c, d);
    }
  `}var fee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let a=G3(t,r),n=j3(e,a,r);n in this.freeTextures||(this.freeTextures[n]=[]),n in this.usedTextures||(this.usedTextures[n]=[]);let s=U3(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[n].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[n].shift();return this.usedTextures[n].push(o),o}let i;return a===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[n].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,a){if(this.freeTextures==null)return;let n=G3(r,a),s=j3(t,n,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=U3(t,n,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],d=l.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(d,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 mee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function U3(e,t,r,a,n){let s=gee(t,a),i;if(n){let[l,d]=pd(e[0],e[1]);i=l*d}else{let[l,d]=Th(e[0],e[1]);i=l*d}let o=mee(r,s);return i*o}function gee(e,t){switch(e){case 3:return Px(t);case 4:return Ox(t);case 1:return Fx(t);case 0:return Mx(t);case 2:return $x(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function yee(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function G3(e,t){if(e===1)return 3;if(e===0||e==null)return yee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function j3(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Gn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}},Ka="if (isnan(x)) return x;",Aee="return x;",H3="return abs(x);",xee="return (x >= 0.0) ? x : (exp(x) - 1.0);",bee=Ka+`
  return (x < 0.0) ? 0.0 : x;
`,vee=Ka+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Zl="return x;",wee="return 1.0 / (1.0 + exp(-1.0 * x));",kee="return x;",Iee=`
  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;
`,See=`
  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;
`,Tee=`
  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;
`,Cee="return 1.0 / (1.0 + exp(-1.0 * x));",co=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
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    `}},Nee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=sa(this.outputShape.length);let t=e.length,r=$r("rc",t),a=vt(t),n=pee(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
      void main() {
        ${a} rc = getOutputCoords();
        vec4 packedInput = getA(${n});

        setOutput(getChannel(packedInput, ${i}));
      }
    `}},Eee=Ha.whereImpl,Ree=1e-7,Fee=1e-4,R1={};function Mee(e){return e in R1||(R1[e]={}),R1[e]}var $ee=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Pee=600;function Oee(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*Pee/1024/1024}var t8=class extends Iu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof uu)t=e;else{let r=pn(Y().getNumber("WEBGL_VERSION"),e);t=new uu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=pn(Y().getNumber("WEBGL_VERSION"));t=new uu(r),this.binaryCache=Mee(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new fee(this.gpgpu),this.numMBBeforeWarning=Oee(),this.texData=new Dp(this,kr())}nextDataId(){return t8.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:r,values:e,usage:1,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,a,n){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:a,values:t,usage:1,refCount:n})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:a,complexTensorInfos:n,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new co(i,Zl):p=new Gn(i,Zl);let h=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),c=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return r;let l=this.activeTimers!=null,d;l&&(d=w.now());let u;if(a==="complex64"){let p=this.readSync(n.real.dataId),h=this.readSync(n.imag.dataId);u=N.mergeRealAndImagArrays(p,h)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-d),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:a,slice:n,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(n!=null){let c;o?c=new co(a,Zl):c=new Gn(a,Zl);let f=this.runWebGLProgram(c,[{dataId:e,shape:a,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=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,d;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);let c=this.texData.get(d.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Mc(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];u=N.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(d!=null&&this.disposeIntermediateTensorInfo(d),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,u),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(c=>c(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kr().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let r=this.texData.get(e),{values:a,shape:n,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let h;o?h=new co(n,Zl):h=new Gn(n,Zl);let c=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let d=this.decode(e,t.customTexShape),u=kr().makeTensorFromDataId(d.dataId,d.shape,d.dtype),p=this.texData.get(d.dataId);return{tensorRef:u,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(a=>w.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!sI(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:a}=this.texData.get(e),n=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),h=this.texData.get(p.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(h.texture.texture,...Mc(t)).subarray(0,n);return this.disposeIntermediateTensorInfo(p),c}let s=Y().getBool("WEBGL_PACK")&&a===!0,i=s?Wc(t):t,o=s?new kQ(i):new wQ(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),d=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture.texture,d.texShape[0],d.texShape[1]).subarray(0,n);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],a=!1;this.programTimersStack==null?(this.programTimersStack=r,a=!0):this.activeTimers.push(r),this.activeTimers=r,e();let n=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(n);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,d)=>({name:s[d],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:a,usage:n,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,r),this.textureManager.releaseTexture(t,a,n,s)));let d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=$ee){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Eee(e.shape,t)}packedUnaryOp(e,t,r){let a=new co(e.shape,t),n=this.compileAndRun(a,[e],r);return kr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=YI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,H3,e.dtype);let t=new Gn(e.shape,H3),r=this.compileAndRun(t,[e]);return kr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let a;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let n=r.map(s=>w.encodeString(s));a=this.write(n,e,t)}else a=this.write(r,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:a}=this.makeTensorInfo(e,t,r);return kr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new Nee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new hee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[To(e.shape),...Co(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},n=[To(t),...Co(t)],s=new e8(n,r),i=!0,o=[r],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:a,shape:n,dtype:s}=r;if(t!=null){let p=w.sizeFromShape(n),h=t[0]*t[1]*4;w.assert(p<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Wc(n),o;a?o=new vQ(i):o=new bQ(i);let l=!0,d=[t!=null?t:Mc(i)],u=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,d,l,t);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,r,a,n=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Mc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],d=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&&!Op(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let u={shape:i.shape,texData:o,isUniform:!1},p=xQ(e,d,u),h=this.getAndSaveBinary(p,()=>yQ(this.gpgpu,e,d,u)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),AQ(this.gpgpu,h,d,u,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(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")&&o.isPacked&&n===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,a,n=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,a,n)}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(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=q(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Ree:Fee}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:a,values:n,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,d;l&&(d=w.now());let u=t.texShape;if(u==null&&(u=wI(r,o),t.texShape=u),n!=null){let p=Wc(r),h,c=u[1],f=u[0],m=n instanceof Uint8Array||n instanceof Uint8ClampedArray;(o||!m)&&([c,f]=pd(u[0],u[1])),o?h=new SQ(p,m):h=new IQ(p,m);let g=m?[f,c]:u,y=this.makeTensorInfo(g,a),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,n);let x=[[f,c]],b=!0,v=this.runWebGLProgram(h,[y],a,x,b),C=this.texData.get(v.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(v.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-d)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:a}=r;return this.releaseGPUData(e),t!=null&&(r.values=zee(t,a)),r.values}acquireTexture(e,t,r,a){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let n=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${n} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},Nh=t8;Nh.nextDataId=0;function zee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<r.length;++a)r[a]=Math.round(e[a]);return r}else throw new Error(`Unknown dtype ${t}`)}var Dee="0.0.0";function r8(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}nh.isBrowser()&&Al("webgl",()=>new Nh,2);var _ee={forceHalfFloat:r8},a8=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,wu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=sa(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},Jm=`
  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;
`,Eh=class{constructor(e,t,r,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r);let n=this.outputShape.length;this.enableShapeUniforms=sa(n);let s="";if(a)if(n===0||w.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${vt(n)} coords = getOutputCoords();
        `,n===1)this.enableShapeUniforms?s+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=$r("coords",n);this.enableShapeUniforms?s+=`
            bool nextRowOutOfBounds =
              (${i[n-2]} + 1) >= outShape[${n} - 2];
            bool nextColOutOfBounds =
              (${i[n-1]} + 1) >= outShape[${n} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:s+=`
            bool nextRowOutOfBounds =
              (${i[n-2]} + 1) >= ${this.outputShape[n-2]};
            bool nextColOutOfBounds =
              (${i[n-1]} + 1) >= ${this.outputShape[n-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${s}

        setOutput(result);
      }
    `}};function ha(e){let{inputs:t,backend:r}=e,{x:a}=t;return r.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Lee={kernelName:ui,backendName:"webgl",kernelFunc:ha};function Bi(e){let{inputs:t,backend:r}=e,{real:a,imag:n}=t,s=r.makeTensorInfo(a.shape,"complex64"),i=r.texData.get(s.dataId),o=ha({inputs:{x:a},backend:r}),l=ha({inputs:{x:n},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Bee={kernelName:Lp,backendName:"webgl",kernelFunc:Bi},n8="return (a < 0.) ? b * a : a;",s8=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Wee(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{alpha:s}=a,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(s8,n.shape,i.shape):new wu(n8,n.shape,i.shape),l=r.runWebGLProgram(o,[n,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var Vee={kernelName:di,backendName:"webgl",kernelFunc:Wee},i8="return (a < 0.) ? b * a : a;",o8=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Uee(e){let{inputs:t,backend:r}=e,{x:a,alpha:n}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(o8,a.shape,n.shape):new wu(i8,a.shape,n.shape);return r.runWebGLProgram(s,[a,n],"float32")}var Gee={kernelName:wi,backendName:"webgl",kernelFunc:Uee},yd="if (isnan(x)) return x;",jee=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,Hee=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:a}){return({inputs:n,backend:s})=>{let{x:i}=n,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let p=o.texData.get(i.dataId),h=r(p.values,l);return o.makeTensorInfo(i.shape,l,h)}let d=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return d?u=new co(i.shape,t):u=new Gn(i.shape,e),o.runWebGLProgram(u,[i],l)}}function Ar({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:a=!1,cpuKernelImpl:n,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:d}=i,u=o;if(a&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(d.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:v.dataId,dtype:v.dtype,shape:d.shape},E=new wu(e,l.shape,d.shape);return u.runWebGLProgram(E,[C,T],Or(b.dtype,v.dtype))}),A=Bi({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),A}let p=s||Or(l.dtype,d.dtype);if((l.dtype==="string"||d.dtype==="string"||u.shouldExecuteOnCPU([l,d]))&&n!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(d.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=n(l.shape,d.shape,g,y,p),b=u.makeTensorInfo(x,p),v=u.texData.get(b.dataId);return v.values=A,b}let h=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return h?c=new Eh(t,l.shape,d.shape,r):c=new wu(e,l.shape,d.shape),u.runWebGLProgram(c,[l,d],p)}}function Qm(e,t=!1){if(e==="linear")return t?kee:Aee;if(e==="relu")return t?See:bee;if(e==="elu")return t?Iee:xee;if(e==="relu6")return t?Tee:vee;if(e==="prelu")return t?o8:i8;if(e==="leakyrelu")return t?s8:n8;if(e==="sigmoid")return t?Cee:wee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var l8=class{constructor(e,t,r,a=!1,n=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=sa(this.outputShape.length);let d=a?e[1]:e[2],u=Math.ceil(d/2),p=a?"i * 2, rc.y":"rc.y, i * 2",h=n?"rc.z, i * 2":"i * 2, rc.z",c=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=n?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:l?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:m=`vec4 activation(vec4 x) {
          ${i}
        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${m}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${u}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${u}; i++) {
          int batchA = ${A};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${p});
          vec4 b = getMatrixB(batchB, ${h});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${c[0]} * ${f[0]});
          result += (${c[1]} * ${f[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${y}

        ${g}

        setOutput(result);
      }
    `}},q3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},K3=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},X3="return a * b;";function Dx(e){let{inputs:t,backend:r}=e,{a,b:n}=t,s=N.upcastType(a.dtype,n.dtype);if(a.dtype==="complex64"){let o=r.texData.get(a.dataId),l=r.texData.get(n.dataId),d=new K3(q3.REAL,a.shape,n.shape),u=new K3(q3.IMAG,a.shape,n.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],h=r.runWebGLProgram(d,p,"float32"),c=r.runWebGLProgram(u,p,"float32"),f=Bi({inputs:{real:h,imag:c},backend:r});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([a,n])){let o=r.texData.get(a.dataId),l=r.texData.get(n.dataId),[d,u]=HQ(a.shape,n.shape,o.values,l.values,s),p=r.makeTensorInfo(u,s),h=r.texData.get(p.dataId);return h.values=d,p}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Eh(X3,a.shape,n.shape):i=new wu(X3,a.shape,n.shape),r.runWebGLProgram(i,[a,n],s)}var qee={kernelName:xi,backendName:"webgl",kernelFunc:Dx};function Kee(e,t,r){let a=[To(e.shape),...Co(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},s=[To(t),...Co(t)],i=new e8(s,a),o=!0,l=[a],d=r.runWebGLProgram(i,[n],e.dtype,l,o);return{dataId:d.dataId,shape:t,dtype:d.dtype}}function ve(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{shape:s}=a,i=r,o=w.sizeFromShape(n.shape),l=w.inferFromImplicitShape(s,o),d=w.sizeFromShape(l);w.assert(o===d,()=>`The new shape (${l}) has ${d} elements and the old shape (${n.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(n.dataId);return u.isPacked&&!Op(n.shape,l)&&!(u.texture!==null&&Op(u.shape,l))?Kee(n,l,i):(i.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var Xee={kernelName:tl,backendName:"webgl",kernelFunc:ve},Z3=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:a,inSize:n,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${w.isInt(u)?u.toPrecision(2):u}, ones);`}let d="";n%r>0&&(d=`
        if (inIdx < 0 || inIdx >= ${n}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${d}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

        float sumValue = 0.0;

        for (int i = 0; i < ${i}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${l}
        }

        int inIdx = inOffset + ${i};
        if (${o===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${l}
        } else if (${o===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${l}
        } else if (${o===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},Zee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:a,inSize:n,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let d=Math.floor(r/4)*4,u=r%4,p=`
      if (${t==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${t==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${o}(values, minMaxValue);
        if (${t==="min"} || ${t==="max"}) {
          minMaxValue = ${o}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,h="vec4";t==="all"?(i="1.0",p=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,h="bvec4"):t==="any"&&(i="0.0",p=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,h="bvec4");let c="";n%r>0&&(c=`
        if (inIdx < 0 || inIdx >= ${n}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${i};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${c}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

        vec4 minMaxValue = vec4(${i});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${d}; i += 4) {
          int inIdx = inOffset + i;
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${p}
        }

        int inIdx = inOffset + ${d};
        if (${u===1}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${p}
        } else if (${u===2}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${p}
        } else if (${u===3}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${p}
        }
        setOutput(${l});
      }
    `}};function Yee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],a=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:a,outSize:Math.ceil(r/a)})}return t}function Cl(e,t,r,a){let n=Yee(e.shape),s=e;for(let i=0;i<n.length;i++){let{inSize:o,windowSize:l,outSize:d}=n[i],u,p;r==="mean"?u=i===0?new Z3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},o):new Z3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d}):u=new Zee({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},r),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var Jee=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let a=vt(this.rank),n=Qee(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${n}));
    }
    `}};function Qee(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let n=0;n<e.length;n++)a[e[n]]=r[n];return a.join()}var ete=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let d=0;d<r.length;d++)r[d]=e[t[d]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=vt(this.rank),n=QI("rc",this.rank),s=new Array(this.rank);for(let d=0;d<t.length;d++)s[t[d]]=n[d];let i=`vec2(${s.slice(-2).join()})`,o=`++${n[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
    void main() {
      ${a} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${o}) {
        result[1] = ${l};
      }
      --${n[this.rank-1]};
      if(++${n[this.rank-2]} < ${r[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function e0(e,t,r){let a=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ete(e.shape,t):new Jee(e.shape,t);return r.runWebGLProgram(a,[e],e.dtype)}function tte(e,t,r,a){let n=t,s=e.shape.length,i=w.parseAxisParam(n,e.shape),o=i,l=N.getAxesPermutation(o,s),d=l!=null,u=e;d&&(u=e0(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[p,h]=N.computeOutAndReduceShapes(u.shape,o),c=p;r&&(c=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(h),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:u},attrs:{shape:[m,f]},backend:a}),y=ah(e.dtype),A=Cl(g,y,"sum",a),x=ve({inputs:{x:A},attrs:{shape:c},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),d&&a.disposeIntermediateTensorInfo(u),x}function t0(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a;return tte(n,s,i,r)}var rte={kernelName:Ri,backendName:"webgl",kernelFunc:t0};function Dr(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{perm:s}=a,i=r,o=n.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=n.shape[s[u]];let d;if(i.shouldExecuteOnCPU([n])){let u=i.texData.get(n.dataId).values,p=zx(u,n.shape,n.dtype,s,l);d=i.makeTensorInfo(l,n.dtype);let h=i.texData.get(d.dataId);h.values=p}else d=e0(n,s,i);return d}var ate={kernelName:Oi,backendName:"webgl",kernelFunc:Dr},u8=1e3;function Af({a:e,b:t,transposeA:r,transposeB:a,backend:n,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let d=e.shape.length,u=t.shape.length,p=r?e.shape[d-2]:e.shape[d-1],h=a?t.shape[u-1]:t.shape[u-2],c=r?e.shape[d-1]:e.shape[d-2],f=a?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(p===h,()=>`Error in matMul: inner shapes (${p}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${a} must match.`);let b=r?[y,p,c]:[y,c,p],v=a?[A,f,h]:[A,h,f],C=ve({inputs:{x:e},backend:n,attrs:{shape:b}}),T=ve({inputs:{x:t},backend:n,attrs:{shape:v}}),E=[C,T],R=Math.max(y,A),z=r?C.shape[1]:C.shape[2],M=s!=null,I=i!=null,D=l==="leakyrelu",O=l!=null?Qm(l,!0):null,j=M||I||D||O!=null,X;if((c===1||f===1)&&z>u8&&j===!1){let K=C,W=T;r&&(K=Dr({inputs:{x:C},backend:n,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(W=Dr({inputs:{x:T},backend:n,attrs:{perm:[0,2,1]}}),E.push(W));let ee=f!==1,Q=f===1,ne=K;ee&&(ne=ve({inputs:{x:K},backend:n,attrs:{shape:[R,z,1]}}),E.push(ne));let Z=f===1?2:1,ae=W;Q&&(ae=ve({inputs:{x:W},backend:n,attrs:{shape:[R,1,z]}}),E.push(ae));let ie=Dx({inputs:{a:ne,b:ae},backend:n});X=t0({inputs:{x:ie},backend:n,attrs:{axis:Z,keepDims:!0}}),E.push(ie)}else{let K=Or(e.dtype,t.dtype),W=new l8(b,v,[R,c,f],r,a,M,O,I,D),ee=[C,T];if(s!=null&&ee.push(s),I&&ee.push(i),D){let Q=n.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(Q),E.push(Q)}X=n.runWebGLProgram(W,ee,K)}let _=ve({inputs:{x:X},backend:n,attrs:{shape:x}});E.push(X);for(let K of E)n.disposeIntermediateTensorInfo(K);return _}function nte(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a;return Af({a:n,b:s,transposeA:l,transposeB:d,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var ste={kernelName:Rs,backendName:"webgl",kernelFunc:nte},Y3="return abs(x);";function ite(e){let{inputs:t,backend:r}=e,{x:a}=t;if(r.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=r.texData.get(a.dataId),i=YI(s.values);return r.makeTensorInfo(a.shape,a.dtype,i)}let n;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new co(a.shape,Y3):n=new Gn(a.shape,Y3),r.runWebGLProgram(n,[a],a.dtype)}var ote={kernelName:Fo,backendName:"webgl",kernelFunc:ite},lte=Ka+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,ute=it({opSnippet:lte}),dte={kernelName:Tu,backendName:"webgl",kernelFunc:ute},pte=Ka+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,hte=it({opSnippet:pte}),cte={kernelName:Cu,backendName:"webgl",kernelFunc:hte},J3="return a + b;",fte=Ar({opSnippet:J3,packedOpSnippet:J3,supportsComplex:!0,cpuKernelImpl:CQ}),mte={kernelName:qn,backendName:"webgl",kernelFunc:fte},gte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((n,s)=>`T${s}`);let r=[];this.variableNames.forEach(n=>{r.push(`float v${n} = get${n}AtOutCoords();`)});let a=this.variableNames.map(n=>`v${n}`).join(" + ");this.userCode=`
      void main() {
        ${r.join(`
        `)}

        float result = ${a};
        setOutput(result);
      }
    `}},yte=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((n,s)=>`T${s}`);let r=[];this.variableNames.forEach(n=>{r.push(`vec4 v${n} = get${n}AtOutCoords();`)});let a=this.variableNames.map(n=>`v${n}`).join(" + ");this.userCode=`
      void main() {
        ${r.join(`
        `)}

        vec4 result = ${a};
        setOutput(result);
      }
    `}};function Gc(e){let{inputs:t,backend:r}=e,a=t;if(a.length===1)return ha({inputs:{x:a[0]},backend:r});if(a.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Gc({inputs:a.slice(0,o),backend:r}),d=Gc({inputs:a.slice(o),backend:r});return Gc({inputs:[l,d],backend:r})}let n=a.map(o=>o.dtype).reduce((o,l)=>Or(o,l)),s=a.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new yte(a[0].shape,s):new gte(a[0].shape,s);return r.runWebGLProgram(i,a,n)}var Ate={kernelName:js,backendName:"webgl",kernelFunc:Gc};function xte(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a,o=n.shape.length,l=w.parseAxisParam(s,n.shape),d=l,u=N.getAxesPermutation(d,o),p=n;u!=null&&(p=Dr({inputs:{x:n},backend:r,attrs:{perm:u}}),d=N.getInnerMostAxes(d.length,o)),N.assertAxesAreInnerMostDims("all",d,o);let[h,c]=N.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(c),m=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,f]}}),g=Cl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:h}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),u!=null&&r.disposeIntermediateTensorInfo(p),y}var bte={kernelName:Nu,backendName:"webgl",kernelFunc:xte};function vte(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a,o=n.shape.length,l=w.parseAxisParam(s,n.shape),d=l,u=N.getAxesPermutation(d,o),p=n;u!=null&&(p=Dr({inputs:{x:n},backend:r,attrs:{perm:u}}),d=N.getInnerMostAxes(d.length,o)),N.assertAxesAreInnerMostDims("any",d,o);let[h,c]=N.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(c),m=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,f]}}),g=Cl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:h}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),u!=null&&r.disposeIntermediateTensorInfo(p),y}var wte={kernelName:Eu,backendName:"webgl",kernelFunc:vte},kte=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:a,batchSize:n,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[n,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${a};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${a}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},Ite=class{constructor(e,t,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let n=e[e.length-1],s=Math.ceil(n/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=vt(o),d=$r("coords",o),u,p;if(s===1){p=o+1;let T=vt(p);u=`
        ${T} sourceLocR = ${T}(${d.join()}, 0);
        ++${d[o-1]};
        ${T} sourceLocG = ${T}(${d.join()}, 0);
        ++${d[o-2]};
        ${T} sourceLocA = ${T}(${d.join()}, 0);
        --${d[o-1]};
        ${T} sourceLocB = ${T}(${d.join()}, 0);
        --${d[o-2]};`}else p=o,u=`
        ${l} sourceLocR = coords;
        ++${d[o-1]};
        ${l} sourceLocG = coords;
        ++${d[o-2]};
        ${l} sourceLocA = coords;
        --${d[o-1]};
        ${l} sourceLocB = coords;
        --${d[o-2]};`;let h=["x","y","z","w","u","v"].slice(0,p),c="."+h[p-1],f=h.map(T=>"int "+T),m=$r("sourceLocR",p-1).concat("inIdx.r"),g=$r("sourceLocG",p-1).concat("inIdx.g"),y=$r("sourceLocB",p-1).concat("inIdx.b"),A=$r("sourceLocA",p-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=a?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${A.join()})));`,v=`vec4(
            getAChannel(${m.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,C=a?"":`
      float getBestIndicesAChannel(${f.join()}) {
        return getChannel(getBestIndicesA(${h.join()}),
                                          vec2(${h.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${f.join()}) {
        return getChannel(getA(${h.join()}),
                               vec2(${h.slice(-2).join()}));
      }
      ${C}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${d[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${d[o-2]} < ${i[o-2]-1};
        ${u}
        ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},
          sourceLocB${c}, sourceLocA${c}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${v};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${b}
          vec4 candidate = ${v};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function d8(e,t,r,a=null){let n=t.shape[0],s=t.shape[1];a!=null&&(n=a.shape[0],s=a.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:n,outSize:Math.ceil(s/i)},l=new kte(o,r,a==null),d=[t];a!=null&&d.push(a);let u=e.runWebGLProgram(l,d,"int32");if(u.shape[1]===1)return u;let p=d8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),p}function p8(e,t,r,a=null){let n=a!=null?a.shape:t.shape,s=n[n.length-1],i=N.computeOptimalWindowSize(s),o=new Ite(n,i,r,a==null),l=a==null?[t]:[t,a],d=e.runWebGLProgram(o,l,"int32");if(d.shape.length===t.shape.length){let u=p8(e,t,r,d);return e.disposeIntermediateTensorInfo(d),u}return d}function h8(e,t,r,a){let n=[r];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),n,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[d,u]=N.computeOutAndReduceShapes(l.shape,n),p=w.sizeFromShape(u),h=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});s.push(h);let c=d8(e,h,a);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:d}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return p8(e,t,a)}function Ste(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s}=a,i=w.parseAxisParam(s,n.shape),o=N.getAxesPermutation(i,n.shape.length),l=n,d=[];o!=null&&(l=Dr({inputs:{x:n},backend:r,attrs:{perm:o}}),d.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=h8(r,l,i[0],"max");return d.forEach(p=>r.disposeIntermediateTensorInfo(p)),u}var Tte={kernelName:Hs,backendName:"webgl",kernelFunc:Ste};function Cte(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s}=a,i=w.parseAxisParam(s,n.shape),o=N.getAxesPermutation(i,n.shape.length),l=n,d=[];o!=null&&(l=Dr({inputs:{x:n},backend:r,attrs:{perm:o}}),d.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=h8(r,l,i[0],"min");return d.forEach(p=>r.disposeIntermediateTensorInfo(p)),u}var Nte={kernelName:Ru,backendName:"webgl",kernelFunc:Cte},Ete=Ka+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,Rte=it({opSnippet:Ete}),Fte={kernelName:Fu,backendName:"webgl",kernelFunc:Rte},Mte=Ka+"return log(x + sqrt(x * x + 1.0));",$te=it({opSnippet:Mte}),Pte={kernelName:Mu,backendName:"webgl",kernelFunc:$te},Ote=Ka+`
  return atan(x);
`,zte=it({opSnippet:Ote}),Dte={kernelName:$u,backendName:"webgl",kernelFunc:zte},_te=jee+`
  return atan(a, b);
`,Lte=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Hee+`
  return result;
`,Bte=Ar({opSnippet:_te,packedOpSnippet:Lte}),Wte={kernelName:Ou,backendName:"webgl",kernelFunc:Bte},Vte=Ka+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Ute=it({opSnippet:Vte}),Gte={kernelName:Pu,backendName:"webgl",kernelFunc:Ute},zp=class{constructor(e,t,r,a=!1,n=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=e.padInfo.top,c=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${h}, ${c});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${u};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${p};
                wC += ${d}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${T} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?n?m:g:`wR * ${p} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,v=s%4,C=`
      if (${f}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${A}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${h}, ${c});
      const float initializationValue = ${y};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${y});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${u};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${b}; wC += 4) {
            int xC = xCCorner + wC * ${d};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${d}, d),
              getValue(batch, xR, xC + 2 * ${d}, d),
              getValue(batch, xR, xC + 3 * ${d}, d)
            );

            ${C}
          }

          int xC = xCCorner + ${b};
          if (${v===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${C}
          } else if (${v===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${d}, d),
              initializationValue,
              initializationValue
            );

            ${C}
          } else if (${v===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${d}, d),
              getValue(batch, xR, xC + 2 * ${d}, d),
              initializationValue
            );

            ${C}
          }
        }
        setOutput(${x});
      }
    `}},_x=class{constructor(e,t,r,a=!1,n=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,d=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,h=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${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 < ${h};
              wD += ${d}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${c};
                wR += ${u}) {
              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 = ${a?n?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${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 C=Math.floor(s/4)*4,T=s%4,E=`
      if (${A}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${m}, ${g}, ${y});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${h};
            wD += ${d}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${c};
            wR += ${u}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${C}; wC += 4) {
              int xC = xCCorner + wC * ${p};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
                getValue(batch, xD, xR, xC + 3 * ${p}, ch)
              );

              ${E}
            }

            int xC = xCCorner + ${C};
            if (${T===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${T===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${T===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
                initializationValue
              );

              ${E}
            }
          }
          setOutput(${v});
        }
      }
    `}};function jte(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t;hd(n,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=N.computePool2DInfo(n.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return ha({inputs:{x:n},backend:r});let p=new zp(u,"avg",!1);return r.runWebGLProgram(p,[n],"float32")}var Hte={kernelName:qs,backendName:"webgl",kernelFunc:jte};function qte(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:d}=a,u=[1,1,1],p=N.computePool3DInfo(n.shape,s,i,u,o,l,d),h=new _x(p,"avg",!1);return r.runWebGLProgram(h,[n],"float32")}var Kte={kernelName:_p,backendName:"webgl",kernelFunc:qte},Xte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,a=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*r);this.userCode=`
      const ivec2 pads = ivec2(${d}, ${u});
      const float avgMultiplier = float(${p});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${o};
            wR += ${s}) {
          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 < ${l};
            wC+= ${i}) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},Zte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,a=e.filterWidth,n=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,h=e.effectiveFilterWidth,c=u-1-e.padInfo.front,f=p-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*r*a);this.userCode=`
      const ivec3 pads = ivec3(${c}, ${f}, ${m});
      const float avgMultiplier = float(${g});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${u};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${n}.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) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${h};
                wC += ${d}) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Yte(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],h=N.computePool3DInfo(i.shape,o,l,p,d,u),c=new Zte(h);return r.runWebGLProgram(c,[n],i.dtype)}var Jte={kernelName:Cf,backendName:"webgl",kernelFunc:Yte};function Qte(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,input:s}=t,i=s;hd([n,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=N.computePool2DInfo(i.shape,o,l,1,d),p=new Xte(u);return r.runWebGLProgram(p,[n],i.dtype)}var ere={kernelName:Tf,backendName:"webgl",kernelFunc:Qte};function tre(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s}=t,{transposeA:i,transposeB:o}=a;return Af({a:n,b:s,transposeA:i,transposeB:o,backend:r})}var rre={kernelName:Ks,backendName:"webgl",kernelFunc:tre},are=class{constructor(e,t,r,a,n,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${o};
        float inv = scale * inversesqrt(variance + float(${s}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},nre=class{constructor(e,t,r,a,n,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${o};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${s}));

        setOutput((x - mean) * inv + offset);
      }
    `}},sre=({inputs:e,backend:t,attrs:r})=>{let{x:a,mean:n,variance:s,offset:i,scale:o}=e;w.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||n.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let d=[a,n,s],u=null;i!=null&&(u=i.shape,d.push(i));let p=null;o!=null&&(p=o.shape,d.push(o));let h=Y().getBool("WEBGL_PACK_NORMALIZATION")?new nre(a.shape,n.shape,s.shape,u,p,l):new are(a.shape,n.shape,s.shape,u,p,l);return t.runWebGLProgram(h,d,d[0].dtype)},ire={kernelName:oi,backendName:"webgl",kernelFunc:sre},ore=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=lre(this.rank),a,n=e.map((s,i)=>`sourceLoc.${vy[i]} = start[${i}] + coords.${vy[i]};`);a=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${n.join(`
`)}
      `,this.userCode=`
      void main() {
        ${a}
        setOutput(getSource(${r}));
      }
    `}},vy=["x","y","z","w","u","v"];function lre(e){if(e===1)return"sourceLoc";if(e<=6)return vy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var ure=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),r=$r("coords",this.rank),a=$r("sourceLoc",this.rank),n=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${n})`,i=`
      result.x = ${s};
      if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
        ++${a[this.rank-1]};
        result.y = ${s};
        --${a[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${r[this.rank-1]};
      if (++${r[this.rank-2]} < ${e[this.rank-2]}) {
        ++${a[this.rank-2]};
        result.z = ${s};
        if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
          ++${a[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((d,u)=>`start[${u}]`).join()});`:e.map((d,u)=>`${a[u]} = ${r[u]} + start[${u}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `}};function dre(e,t,r,a){let n=a.texData.get(e.dataId),s=a.makeTensorInfo(r,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,n),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=Ot.computeFlatOffset(t,w.computeStrides(e.shape));n.slice&&(o+=n.slice.flatOffset),i.slice={flatOffset:o,origDataId:n.slice&&n.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Ad(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{begin:s,size:i}=a,[o,l]=Ot.parseSliceParams(n,s,i);if(Ot.assertParamsValid(n,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,n.dtype,[]);if(r.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=r.texData.get(n.dataId),h=QQ(p.values,o,l,n.shape,n.dtype);return r.makeTensorInfo(l,n.dtype,h)}let{isPacked:d}=r.texData.get(n.dataId),u=Ot.isSliceContinous(n.shape,o,l);if(d||!u){let p=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ure(l):new ore(l),h=[o];return r.runWebGLProgram(p,[n],n.dtype,h)}return r.uploadToGPU(n.dataId),dre(n,o,l,r)}var pre={kernelName:il,backendName:"webgl",kernelFunc:Ad},hre=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,crops:i}=a;w.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(n.shape,s,o),d=N.getPermuted(l.length,s.length),u=N.getReshapedPermuted(n.shape,s,o),p=N.getSliceBeginCoords(i,s.length),h=N.getSliceSize(u,i,s.length),c=[],f=ve({inputs:{x:n},backend:r,attrs:{shape:l}}),m=Dr({inputs:{x:f},backend:r,attrs:{perm:d}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:u}}),y=Ad({inputs:{x:g},backend:r,attrs:{begin:p,size:h}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},cre={kernelName:Mo,backendName:"webgl",kernelFunc:hre};function fre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,weights:s}=t,{size:i}=a,o=r.readSync(n.dataId),l=r.readSync(s.dataId),d=ZI(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}var mre={kernelName:Nf,backendName:"webgl",kernelFunc:fre};function gre(e){let{inputs:t,backend:r}=e,{s0:a,s1:n}=t,s=r.readSync(a.dataId),i=r.readSync(n.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var yre={kernelName:Ef,backendName:"webgl",kernelFunc:gre},Are="return float(a != b);",c8=Ar({opSnippet:Are,cpuKernelImpl:KQ,dtype:"bool"}),xre={kernelName:Ko,backendName:"webgl",kernelFunc:c8};function Rh(e){let{inputs:t,backend:r}=e,{input:a}=t,n=r.texData.get(a.dataId);return ha({inputs:{x:n.complexTensorInfos.real},backend:r})}var bre={kernelName:Kp,backendName:"webgl",kernelFunc:Rh},vre="return float(int(x));";function wre(e,t){let r=new Gn(e.shape,vre),a=t.runWebGLProgram(r,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function wy(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{dtype:s}=a;if(s==="complex64"){if(n.dtype==="complex64")return ha({inputs:{x:n},backend:r});let i=Vt(n.shape),o=wy({inputs:{x:n},backend:r,attrs:{dtype:"float32"}}),l=Bi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(n.dtype==="complex64"){let i=Rh({inputs:{input:n},backend:r}),o=wy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(n.dtype,s)){let i=ha({inputs:{x:n},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return wre(n,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=c8({inputs:{a:n,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var kre={kernelName:Xs,backendName:"webgl",kernelFunc:wy},Q3="return ceil(x);",Ire=it({opSnippet:Q3,packedOpSnippet:Q3,cpuKernelImpl:EQ}),Sre={kernelName:Zs,backendName:"webgl",kernelFunc:Ire},Tre=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));
      }
    `}},Cre=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 Nre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{clipValueMin:s,clipValueMax:i}=a,o;Y().getBool("WEBGL_PACK_CLIP")?o=new Cre(n.shape):o=new Tre(n.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[n],n.dtype,l)}var Ere={kernelName:Kn,backendName:"webgl",kernelFunc:Nre},Rre=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 ev(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Fre(e){let{inputs:t,backend:r}=e,{x:a}=t,n=r.texData.get(a.dataId),s=new Rre(a.shape),i=[ev(a,n.complexTensorInfos.real),ev(a,n.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Mre={kernelName:Bp,backendName:"webgl",kernelFunc:Fre},$re=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,n=t[t.length-1];r.push(`else setOutput(getT${a}(yR, yC-${n}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${r.join(`
        `)}
      }
    `}},Pre=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let r=this.outputShape,a=r.length,n=vt(a),s=$r("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],d=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
        return getChannel(
            getT0(${u}), vec2(${d.join()}));
        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
        if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
          return getChannel(
            getT${f}(${Pc(i,l,m)}),
            vec2(${Pc(d,l,m)}));
        }`}let h=o.length,c=o[o.length-1];p+=`
        return getChannel(
          getT${h}(${Pc(i,l,c)}),
          vec2(${Pc(d,l,c)}));`,this.userCode=`
      float getValue(${i.map(f=>"int "+f)}) {
        ${p}
      }

      void main() {
        ${n} coords = getOutputCoords();
        vec4 result = vec4(getValue(${s}), 0., 0., 0.);

        ${s[a-1]} = ${s[a-1]} + 1;
        if (${s[a-1]} < ${r[a-1]}) {
          result.g = getValue(${s});
        }

        ${s[a-2]} = ${s[a-2]} + 1;
        if (${s[a-2]} < ${r[a-2]}) {
          result.a = getValue(${s});
        }

        ${s[a-1]} = ${s[a-1]} - 1;
        if (${s[a-2]} < ${r[a-2]} &&
            ${s[a-1]} < ${r[a-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function Pc(e,t,r){let a=e.indexOf(t);return e.map((n,s)=>s===a?`${n} - ${r}`:n).join()}function r0(e){let{inputs:t,backend:r}=e,{input:a}=t,n=r.texData.get(a.dataId);return ha({inputs:{x:n.complexTensorInfos.imag},backend:r})}var Ore={kernelName:Gp,backendName:"webgl",kernelFunc:r0};function au(e,t,r){let a=e[0].dtype;if(a==="complex64"){let u=e.map(m=>Rh({inputs:{input:m},backend:r})),p=e.map(m=>r0({inputs:{input:m},backend:r})),h=au(u,t,r),c=au(p,t,r),f=Bi({inputs:{real:h,imag:c},backend:r});return u.forEach(m=>r.disposeIntermediateTensorInfo(m)),p.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(c),f}let n=r.shouldExecuteOnCPU(e);if(a==="string"&&(n=!0),n){let u=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),p=u.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),h=N.computeOutShape(u.map(y=>y.shape),1),c=u[0].shape[0]===1,f=RQ(p,h,a,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,a,f);return u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),p=au(e.slice(0,u),t,r),h=au(e.slice(u),t,r),c=au([p,h],t,r);return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(h),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new Pre(e.map(p=>p.shape),t);return r.runWebGLProgram(u,e,a)}let{tensors2D:s,outShape:i}=zre(e,t,r),o=new $re(s.map(u=>u.shape)),l=r.runWebGLProgram(o,s,a);s.forEach(u=>r.disposeIntermediateTensorInfo(u));let d=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),d}function zre(e,t,r){let a=N.computeOutShape(e.map(n=>n.shape),t);return{tensors2D:e.map(n=>ve({inputs:{x:n},attrs:{shape:[-1,w.sizeFromShape(n.shape.slice(t))]},backend:r})),outShape:a}}function f8(e){let{inputs:t,backend:r,attrs:a}=e,{axis:n}=a,s=w.parseAxisParam(n,t[0].shape)[0],i=N.computeOutShape(t.map(d=>d.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(d=>w.sizeFromShape(d.shape)>0);if(o.length===1)return ha({inputs:{x:o[0]},backend:r});let l=o.map(d=>d.shape);return N.assertParamsConsistent(l,s),au(o,s,r)}var Dre={kernelName:$o,backendName:"webgl",kernelFunc:f8},m8=class{constructor(e,t=!1,r=null,a=!1,n=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,d=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,h=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(a?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:n?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:x=`
          float activation(float x) {
            ${r}
          }
        `,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),n&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

      const ivec2 strides = ivec2(${o}, ${l});
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${A}];

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${y}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${p}; wR++) {
          int xR = xRCorner + wR * ${d};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${h}; wC++) {
            int xC = xCCorner + wC * ${u};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${c}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${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, ${c}) *
                    getW(wR, wC, ${c}, d2);
              } else {
                dotProd +=
                    getX(batch, ${c}, xR, xC) *
                    getW(wR, wC, ${c}, d2);
              }

            } else if (${f===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2)
              );

              if (${m}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${f===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${c}, d2),
                getW(wR, wC, ${c} + 1, d2),
                getW(wR, wC, ${c} + 2, d2)
              );

              if (${m}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${c}),
                  getX(batch, xR, xC, ${c} + 1),
                  getX(batch, xR, xC, ${c} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${c}, xR, xC),
                  getX(batch, ${c} + 1, xR, xC),
                  getX(batch, ${c} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${v}
        ${b}
        setOutput(result);
      }
    `}},_re=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,a=e.padInfo.left,n=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,h=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${n}, ${s}, ${i});
      const ivec3 pads = ivec3(${t}, ${r}, ${a});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${u}; wF++) {
          int xF = xFCorner + wF * ${o};

          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 < ${h}; wC++) {
              int xC = xCCorner + wC * ${d};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${c}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${c}) *
                  getW(wF, wR, wC, ${c}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${c}),
                  getX(batch, xF, xR, xC, ${c} + 1),
                  getX(batch, xF, xR, xC, ${c} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${c}, d2),
                  getW(wF, wR, wC, ${c} + 1, d2),
                  getW(wF, wR, wC, ${c} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},Lre=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=sa(this.outputShape.length);let{dataFormat:r}=t,a=Br(),n=r==="channelsLast",s=n?0:1,i=n?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let d=0;d<=1;d++)for(let u=0;u<=1;u++)l+=`
          blockIndex = rc.y + ${u};
          pos = rc.x + ${d};

          ${o}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${s}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${i}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${n}) {
                  innerDims = vec2(d1, ch);
                  result[${d*2+u}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${d*2+u}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${l}

        ${a.output} = result;
      }
    `}};function g8({x:e,filter:t,convInfo:r,backend:a,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,d=a.texData.get(e.dataId),u=r.inChannels,p=l[0]*l[1]*l[2],h=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((p===1||h===1)&&u>u8)&&d.isPacked&&c&&d.texture!=null&&l[2]%2!==0&&w.arraysEqual(d.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=d.shape;d.shape=d.shape.slice(),d.shape[d.shape.length-2]++,w.assert(Op(d.shape,x.shape),()=>`packed reshape ${d.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:a,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let C=Af({a:x,b:v,backend:a,transposeA:f,transposeB:m,bias:n,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=a.texData.get(C.dataId);w.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),d.shape=b,T.shape=r.outShape,g=ha({inputs:{x:C},backend:a}),g.shape=r.outShape,y.push(C)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:a,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:a,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Af({a:x,b,transposeA:f,transposeB:m,backend:a,bias:n,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:a,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)a.disposeIntermediateTensorInfo(A);return g}function y8({x:e,filter:t,convInfo:r,backend:a,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:d,inChannels:u,outWidth:p,outHeight:h,dataFormat:c}=r,f=c==="channelsLast",m=l*d*u,g=h*p,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),C=ve({inputs:{x:t},backend:a,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(C);let T=new Lre(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=a.runWebGLProgram(T,[v],"float32",E),z=ve({inputs:{x:R},backend:a,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(z);let M=n!=null,I=s!=null,D=o==="leakyrelu",O=o?Qm(o,!0):null,j=new l8(z.shape,C.shape,[1,g,r.outChannels],A,x,M,O,I,D),X=[z,C];if(n&&X.push(n),I&&X.push(s),D){let ee=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let _=a.runWebGLProgram(j,X,"float32"),K=f?[1,h,p,r.outChannels]:[1,r.outChannels,h,p],W=ve({inputs:{x:_},backend:a,attrs:{shape:K}});b.push(_);for(let ee of b)a.disposeIntermediateTensorInfo(ee);return W}function Bre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:d,dimRoundingMode:u}=a,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(n.shape,s.shape,i,d,o,u,!1,p),c;if(h.filterHeight===1&&h.filterWidth===1&&h.dilationHeight===1&&h.dilationWidth===1&&h.strideHeight===1&&h.strideWidth===1&&(h.padInfo.type==="SAME"||h.padInfo.type==="VALID"))c=g8({x:n,filter:s,convInfo:h,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)c=y8({x:n,filter:s,convInfo:h,backend:r});else{let m=new m8(h);c=r.runWebGLProgram(m,[n,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:h.outShape}});return r.disposeIntermediateTensorInfo(c),f}var Wre={kernelName:Ys,backendName:"webgl",kernelFunc:Bre},Vre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,a=e.padInfo.top,n=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${n};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              if (${s}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},Ure=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,a=e.strideHeight,n=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,d=s?2:3,u=s?3:1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${u}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${d}]) - 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) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${r}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${r} - 1 - wC;

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${s}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}},Gre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,a=e.strideWidth,n=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t} - ${n};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${r} - ${s};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${a} - ${i};

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},jre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,a=e.filterWidth,n=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,d=a-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${o}, ${l}, ${d});

      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) / ${n}.0;

          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${t} - 1 - wF;

          for (int wR = 0; wR < ${r}; wR++) {
            float dyR = float(dyRCorner + wR) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${r} - 1 - wR;

            for (int wC = 0; wC < ${a}; wC++) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${a} - 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 Hre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,filterShape:u}=a,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(n.shape,u,i,1,o,d,!1,p),c=new Vre(h);return r.runWebGLProgram(c,[n,s],"float32")}var qre={kernelName:Rf,backendName:"webgl",kernelFunc:Hre};function Kre(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a,p=N.convertConv2DDataFormat(d),h=N.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),c=new Ure(h);return r.runWebGLProgram(c,[n,s],"float32")}var Xre={kernelName:Js,backendName:"webgl",kernelFunc:Kre};function Zre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=N.computeConv3DInfo(n.shape,s.shape,i,l,o),u=new _re(d);return r.runWebGLProgram(u,[n,s],"float32")}var Yre={kernelName:Wp,backendName:"webgl",kernelFunc:Zre};function Jre(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,d=N.computeConv3DInfo(n.shape,l,i,1,o),u=new Gre(d);return r.runWebGLProgram(u,[n,s],"float32")}var Qre={kernelName:Ff,backendName:"webgl",kernelFunc:Jre};function eae(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,d=N.computeConv3DInfo(l,s.shape,o,1,i),u=new jre(d);return r.runWebGLProgram(u,[n,s],"float32")}var tae={kernelName:Mf,backendName:"webgl",kernelFunc:eae},rae=yd+`
  return cos(x);
`,aae=it({opSnippet:rae}),nae={kernelName:Qs,backendName:"webgl",kernelFunc:aae},sae=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,iae=it({opSnippet:sae}),oae={kernelName:ei,backendName:"webgl",kernelFunc:iae},lae=class{constructor(e,t,r,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[d]=t,[u,p]=r;this.outputShape=[d,u,p,l];let h=a==="bilinear"?1:0,[c,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${A});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${s}) {
          return;
        }

        float height_scale = ${g};
        float width_scale = ${x};

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${c} ) {
          setOutput(float(${n}));
          return;
        }
        float in_x = ${b};
        if( in_x < 0.0 || in_x > ${f} ) {
          setOutput(float(${n}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${h} == 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);
        }
      }
    `}},uae=e=>{let{inputs:t,backend:r,attrs:a}=e,{image:n,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:d}=a,u=new lae(n.shape,s.shape,o,l,d);return r.runWebGLProgram(u,[n,s,i],"float32")},dae={kernelName:Oo,backendName:"webgl",kernelFunc:uae},tv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let a=e.length,n=t?"0.0":`getX(${rv(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${vt(a)} coords = getOutputCoords();
        int end = ${av(a,"coords")};
        float val = ${n};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${av(a,"coords")} = idx;
          val += getX(${rv(a,"coords")});
        }
        setOutput(val);
      }
    `}};function rv(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 ${e} is not yet supported`)}function av(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function pae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,exclusive:i,reverse:o}=a,l=n.shape.length,d=N.getAxesPermutation([s],l),u=n;d!=null&&(u=Dr({inputs:{x:n},backend:r,attrs:{perm:d}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let h=u.shape[p],c=ha({inputs:{x:u},backend:r});for(let f=0;f<=Math.ceil(Math.log2(h))-1;f++){let m=new tv(u.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new tv(u.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(d!=null){let f=N.getUndoAxesPermutation(d),m=Dr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(u),m}return c}var hae={kernelName:Po,backendName:"webgl",kernelFunc:pae};function cae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,weights:s}=t,{size:i,binaryOutput:o}=a;if(n.shape.length===1){let l=r.readSync(n.dataId),d=r.readSync(s.dataId),u=ZI(l,d,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}else if(n.shape.length===2){let l=r.bufferSync(n),d=r.bufferSync(s),u=NQ(l,d,i,o);return r.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var fae={kernelName:$f,backendName:"webgl",kernelFunc:cae},mae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${t};
      int offset_h = imod(h, ${t});
      int in_w = w / ${t};
      int offset_w = imod(w, ${t});
      int offset_d = (offset_h * ${t} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function gae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockSize:s,dataFormat:i}=a,o=n.shape[0],l=i==="NHWC"?n.shape[1]:n.shape[2],d=i==="NHWC"?n.shape[2]:n.shape[3],u=i==="NHWC"?n.shape[3]:n.shape[1],p=l*s,h=d*s,c=u/(s*s),f=i==="NHWC"?[o,p,h,c]:[o,c,p,h],m=new mae(f,s,i);return r.runWebGLProgram(m,[n],n.dtype)}var yae={kernelName:zo,backendName:"webgl",kernelFunc:gae},A8=class{constructor(e,t=!1,r=null,a=!1,n=!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=sa(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",d="";r&&(a?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:n?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:l=`
          float activation(float x) {
            ${r}
          }
        `,d="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),n&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${l}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${o};
        int q = d2 - d1 * ${o};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${s}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${i}; wC++) {
            int xC = xCCorner + wC * dilations[1];

            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${u}
        ${d}
        setOutput(result);
      }
    `}},x8=class{constructor(e,t=!1,r=null,a=!1,n=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=sa(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,d=e.filterHeight,u=e.filterWidth,p=u,h=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)h+=`
          vec4 xTexelC${g*2};
          int xTexelC${g*2}Ready;
          vec4 xTexelC${g*2+1};
          int xTexelC${g*2+1}Ready;
          vec4 xC${g};`;h+=`
    for (int r = 0; r < ${d}; r++) {
      `;for(let g=0;g<u;g++)h+=`
          xTexelC${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);`;h+=`
        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(h+=`
          xC = xCCorner + ${y*l};
          `,o===1){if(y<u&&(i%2===1?(h+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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?h+=`
                xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
                `:h+=`
                  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);
                  }
                  `):h+=`
                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<u)){let A=i%2===0?w.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(h+=`
                  xCOffset = xC + imod(pads[1], 2) + ${A};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                    xTexelC${y+1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${y+1}.zw = vec2(0.0);
                    }
                    xTexelC${y+1}Ready = 1;
                  }
                  `,l>1&&(h+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                      xTexelC${y} = getX(batch, xR, xCOffset, d1);
                      xTexelC${y}Ready = 1;
                    }
                    `),h+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
                  `):A===1?h+=`
                    xC${y+1} = xTexelC${y};
                    `:h+=`
                    xCOffset = xC + ${A};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                      xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${y+1}.zw = vec2(0.0);
                      }
                      xTexelC${y+1}Ready = 1;
                    }

                    xC${y+1} = xTexelC${y+1};
                    `}}else y<u&&(i%2===1?(h+=`
                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${y+1}.zw);
              `,y+1<u&&(h+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
                `)):(h+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${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${y+1}.xy);
              `,y+1<u&&(h+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
                `)));y<u&&(h+=`
            wTexel = getW(r, ${y}, d1, q);
            dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
          `,y+1<u&&(h+=`
              wTexel = getW(r, ${y+1}, d1, q);
              dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
            `))}h+=`
    }
  `,h+=`
      }
    `;let c="",f="";r&&(a?c=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:n?c=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${r}
        }`:c=`vec4 activation(vec4 x) {
          ${r}
        }`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),n&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${c}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${s};
        int q = d2 - d1 * ${s};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${h}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${m}
        ${f}
        setOutput(result);
      }
    `}};function Aae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:d}=a,u=l;u==null&&(u=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computeConv2DInfo(n.shape,s.shape,i,u,o,d,!0),h;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?h=new x8(p):h=new A8(p);let c=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return r.runWebGLProgram(h,[n,s],"float32",c)}var xae={kernelName:ti,backendName:"webgl",kernelFunc:Aae},bae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,a=e.padInfo.top,n=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${s} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${n};

              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);
      }
    `}},vae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,a=e.strideHeight,n=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${r}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${r} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${o}; dm++) {
              int d2 = d1 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function wae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,filterShape:u}=a,p=N.computeConv2DInfo(n.shape,u,i,o,l,d,!0),h=new bae(p);return r.runWebGLProgram(h,[n,s],"float32")}var kae={kernelName:Pf,backendName:"webgl",kernelFunc:wae};function Iae(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,inputShape:u}=a,p=N.computeConv2DInfo(u,s.shape,i,o,l,d,!0),h=new vae(p);return r.runWebGLProgram(h,[n,s],"float32")}var Sae={kernelName:Of,backendName:"webgl",kernelFunc:Iae},Tae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function Cae(e){let{inputs:t,backend:r}=e,{x:a}=t,n=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ve({inputs:{x:a},backend:r,attrs:{shape:[s]}}),o=new Tae(s),l=r.runWebGLProgram(o,[i],i.dtype),d=ve({inputs:{x:l},backend:r,attrs:{shape:n}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),d}var Nae={kernelName:zf,backendName:"webgl",kernelFunc:Cae},Eae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:a,strideHeight:n,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:d}=e,{top:u,left:p}=a;this.userCode=`
      const ivec2 strides = ivec2(${n}, ${s});
      const ivec2 pads = ivec2(${u}, ${p});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${i}; h++) {
          int hIn = hBeg + h * ${l};

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${o}; w++) {
              int wIn = wBeg + w * ${d};

              if (wIn >= 0 && wIn < ${r}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function Rae(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=N.computeDilation2DInfo(n.shape,s.shape,i,o,"NHWC",l),u,p=new Eae(d);u=r.runWebGLProgram(p,[n,s],"float32");let h=ve({inputs:{x:u},backend:r,attrs:{shape:d.outShape}});return r.disposeIntermediateTensorInfo(u),h}var Fae={kernelName:Vp,backendName:"webgl",kernelFunc:Rae};function Mae(e){let{inputs:t,backend:r,attrs:a}=e,{equation:n}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(n,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:d,steps:u}=N.getEinsumComputePath(o,l),p=u.length,h=null,c=i.length,f=[];for(let m=0;m<p;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Dr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),h===null?h=x:(h=Dx({inputs:{a:x,b:h},backend:r}),f.push(h))}m<p-1&&(d[m]>=0&&(h=t0({inputs:{x:h},backend:r,attrs:{axis:d[m]-(i.length-c),keepDims:!1}}),f.push(h)),c--)}for(let m of f)m!==h&&r.disposeIntermediateTensorInfo(m);return h}var $ae={kernelName:Up,backendName:"webgl",kernelFunc:Mae},Pae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Oae=`
  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;
`,zae=it({opSnippet:Pae,packedOpSnippet:Oae}),Dae={kernelName:ai,backendName:"webgl",kernelFunc:zae},_ae="return (b >= 1.0) ? a : a * (b + 1.0);",Lae=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Bae=e=>{let{inputs:t,backend:r}=e,{dy:a,y:n}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(Lae,a.shape,n.shape):new wu(_ae,a.shape,n.shape);return r.runWebGLProgram(s,[a,n],a.dtype)},Wae={kernelName:Df,backendName:"webgl",kernelFunc:Bae},Vae=`
  return vec4(equal(a, b));
`,Uae="return float(a == b);",Gae=Ar({opSnippet:Uae,packedOpSnippet:Vae,dtype:"bool",cpuKernelImpl:FQ}),jae={kernelName:Do,backendName:"webgl",kernelFunc:Gae},Hae=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,qae=it({opSnippet:Hae}),Kae={kernelName:zu,backendName:"webgl",kernelFunc:qae},Xae=yd+`
  return exp(x);
`,Zae=`
  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;
`,b8=it({opSnippet:Xae,packedOpSnippet:Zae,cpuKernelImpl:MQ,dtype:"float32"}),Yae={kernelName:ni,backendName:"webgl",kernelFunc:b8};function ky(e){let{inputs:t,attrs:r,backend:a}=e,{dim:n}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=n;return n<0&&(w.assert(-(i+1)<=n,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+n+1),o.splice(l,0,1),ve({inputs:{x:s},backend:a,attrs:{shape:o}})}var Jae={kernelName:_o,backendName:"webgl",kernelFunc:ky},nv="return exp(x) - 1.0;",Qae=it({opSnippet:nv,packedOpSnippet:nv,cpuKernelImpl:$Q}),ene={kernelName:Lo,backendName:"webgl",kernelFunc:Qae},sv=class{constructor(e,t,r){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let n=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${n};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${i}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${a});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${a}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${s};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function v8(e,t,r){let a=r.texData.get(e.dataId),n=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=n/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,d=new sv("real",l,t),u=new sv("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],h=r.runWebGLProgram(d,p,"float32"),c=r.runWebGLProgram(u,p,"float32"),f=Bi({inputs:{real:h,imag:c},backend:r});r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function tne(e){let{inputs:t,backend:r}=e,{input:a}=t;return v8(a,!1,r)}var rne={kernelName:_f,backendName:"webgl",kernelFunc:tne},ane=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 Fh(e){let{backend:t,attrs:r}=e,{shape:a,value:n}=r,{dtype:s}=r;if(s=s||w.inferDtype(n),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(n),t.makeTensorInfo(a,s,i)}else{let i=new ane(a,n),o=[[n]];return t.runWebGLProgram(i,[],s,o)}}var nne={kernelName:Du,backendName:"webgl",kernelFunc:Fh},sne=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);
        }
    `}},ine={kernelName:Bo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,a=t,n=new sne(r.shape);return a.runWebGLProgram(n,[r],r.dtype)}},iv="return floor(x);",one=it({opSnippet:iv,packedOpSnippet:iv,cpuKernelImpl:PQ}),lne={kernelName:si,backendName:"webgl",kernelFunc:one},une=`
  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;
  }
`,dne=`
  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);
`,pne=Ar({opSnippet:une,packedOpSnippet:dne,dtype:"int32"}),hne={kernelName:ii,backendName:"webgl",kernelFunc:pne},cne=class{constructor(e){this.variableNames=["A"];let t=Br(),[r,a]=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(${a}.0, ${r}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},fne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Br(),[r,a]=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(${a}.0, ${r}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}},mne={kernelName:Ip,backendName:"webgl",kernelFunc:gne},Yl;function gne(e){let{inputs:t,backend:r,attrs:a}=e,{pixels:n}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,[l,d]=i?[n.videoWidth,n.videoHeight]:[n.width,n.height],u=[d,l],p=[d,l,s];(o||i)&&(Yl==null&&(Yl=document.createElement("canvas").getContext("2d")),Yl.canvas.width=l,Yl.canvas.height=d,Yl.drawImage(n,0,0,l,d),n=Yl.canvas);let h=r.makeTensorInfo(u,"int32");r.texData.get(h.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(h.dataId),n);let c=Y().getBool("WEBGL_PACK")?new fne(p):new cne(p),f=r.runWebGLProgram(c,[h],"int32");return r.disposeData(h.dataId),f}function yne(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dataFormat:u,dilations:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=a,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(n.shape,s.shape,l,p,d,h,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=g8({x:n,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)y=y8({x:n,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,C=c==="leakyrelu",T=c?Qm(c,!1):null,E=new m8(g,b,T,v,C),R=[n,s];if(i&&R.push(i),o&&R.push(o),C){let z=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(z),A.push(z)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Ane={kernelName:Fs,backendName:"webgl",kernelFunc:yne};function xne(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dimRoundingMode:p,activation:h,leakyreluAlpha:c}=a,f=[],m=u;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,m,d,p,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=h?Qm(h,y):null,x=[n,s],b=i!=null,v=o!=null,C=h==="leakyrelu";if(b&&x.push(i),v&&x.push(o),C){let z=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(z),f.push(z)}let T;y?T=new x8(g,b,A,v,C):T=new A8(g,b,A,v,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return f.forEach(z=>r.disposeIntermediateTensorInfo(z)),R}var bne={kernelName:Ms,backendName:"webgl",kernelFunc:xne},vne=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let a=vt(t.length),n=vt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${a} strides = ${a}(${this.strides});
         void main() {
          ${n} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${s};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function wne(e){let{inputs:t,backend:r}=e,{params:a,indices:n}=t,s=n.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,d,u,p]=N.prepareAndValidate(a,n),h=ve({inputs:{x:n},backend:r,attrs:{shape:[d,i]}}),c=ve({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape)/u,u]}});if(r.shouldExecuteOnCPU([a,n])||a.dtype==="string"){let y=r.readSync(n.dataId),A=r.bufferSync(a),x=OQ(y,A,a.dtype,d,i,u,p,a.shape,o);return r.makeTensorInfo(l,a.dtype,x.values)}let f=new vne(i,p,[d,u]),m=r.runWebGLProgram(f,[c,h],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var kne={kernelName:Vo,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=vt(this.rank),a=Sne(e,2);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${a}));
      }
    `}};function Sne(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e.length;n++)n===2?a.push("index"):a.push(`${r[n]}`);return a.join()}function w8(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,n.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=n.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let d=N.segment_util.collectGatherOpShapeInfo(n,s,l,o),u=w.sizeFromShape(s.shape),p=[],h=ve({inputs:{x:n},backend:r,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[d.batchSize,u/d.batchSize]}});p.push(h),p.push(c);let f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize];if(r.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(h),b=zQ(x,A,f);return p.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(d.outputShape,b.dtype,b.values)}let m=new Ine(h.shape,f),g=r.runWebGLProgram(m,[h,c],h.dtype);p.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:d.outputShape}});return p.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Tne={kernelName:Wo,backendName:"webgl",kernelFunc:w8},Cne="return float(a > b);",Nne=`
  return vec4(greaterThan(a, b));
`,Ene=Ar({opSnippet:Cne,packedOpSnippet:Nne,cpuKernelImpl:DQ,dtype:"bool"}),Rne={kernelName:Uo,backendName:"webgl",kernelFunc:Ene},Fne="return float(a >= b);",Mne=`
  return vec4(greaterThanEqual(a, b));
`,$ne=Ar({opSnippet:Fne,packedOpSnippet:Mne,dtype:"bool",cpuKernelImpl:_Q}),Pne={kernelName:li,backendName:"webgl",kernelFunc:$ne};function One(e){let{inputs:t,backend:r}=e,{input:a}=t;return v8(a,!0,r)}var zne={kernelName:Lf,backendName:"webgl",kernelFunc:One},Dne="return float(!isnan(x) && !isinf(x));",_ne=it({opSnippet:Dne,dtype:"bool"}),Lne={kernelName:_u,backendName:"webgl",kernelFunc:_ne},Bne="return float(isinf(x));",Wne=it({opSnippet:Bne,dtype:"bool"}),Vne={kernelName:Lu,backendName:"webgl",kernelFunc:Wne},Une="return float(isnan(x));",Gne=it({opSnippet:Une,dtype:"bool"}),jne={kernelName:Bu,backendName:"webgl",kernelFunc:Gne},Hne="return float(a < b);",qne=`
  return vec4(lessThan(a, b));
`,Kne=Ar({opSnippet:Hne,packedOpSnippet:qne,cpuKernelImpl:LQ,dtype:"bool"}),Xne={kernelName:Go,backendName:"webgl",kernelFunc:Kne},Zne="return float(a <= b);",Yne=`
  return vec4(lessThanEqual(a, b));
`,Jne=Ar({opSnippet:Zne,packedOpSnippet:Yne,cpuKernelImpl:BQ,dtype:"bool"}),Qne={kernelName:jo,backendName:"webgl",kernelFunc:Jne};function ese(e){let{backend:t,attrs:r}=e,{start:a,stop:n,num:s}=r,i=WQ(a,n,s);return t.makeTensorInfo([i.length],"float32",i)}var tse={kernelName:Bf,backendName:"webgl",kernelFunc:ese},rse=yd+`
  return x < 0.0 ? 0./0. : log(x);
`,ase=`
  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;
`,nse=it({opSnippet:rse,packedOpSnippet:ase,cpuKernelImpl:VQ}),sse={kernelName:pi,backendName:"webgl",kernelFunc:nse},ise=yd+`
  return log(1.0 + x);
`,ose=it({opSnippet:ise}),lse={kernelName:Wu,backendName:"webgl",kernelFunc:ose},use="return float(a >= 1.0 && b >= 1.0);",dse=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,pse=Ar({opSnippet:use,packedOpSnippet:dse,dtype:"bool"}),hse={kernelName:Ho,backendName:"webgl",kernelFunc:pse},cse="return float(!(x >= 1.0));",fse=it({opSnippet:cse}),mse={kernelName:Vu,backendName:"webgl",kernelFunc:fse},gse="return float(a >= 1.0 || b >= 1.0);",yse=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Ase=Ar({opSnippet:gse,packedOpSnippet:yse,dtype:"bool"}),xse={kernelName:jp,backendName:"webgl",kernelFunc:Ase},bse=class{constructor(e,t,r,a,n){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${a}) * sum`;n===.5?o=`inversesqrt(${l})`:n===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${n}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${s}; j <= ${s}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${o};
        setOutput(val);
      }
    `}},vse=class{constructor(e,t,r,a,n){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${a}) * sum`;n===.5?o=`inversesqrt(${l})`:n===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${n}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${s};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${s}; j <= ${s}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${o};
        setOutput(result);
      }
    `}},wse=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,d=Y().getBool("WEBGL_PACK_NORMALIZATION")?new vse(n.shape,s,i,o,l):new bse(n.shape,s,i,o,l);return r.runWebGLProgram(d,[n],n.dtype)},kse={kernelName:Hp,backendName:"webgl",kernelFunc:wse},Ise=class{constructor(e,t,r,a,n){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=a,this.beta=n,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(${a}) * norm + float(${r});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${a})
                * float(${n})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${n});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},Sse=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:d,beta:u}=a,p=new Ise(n.shape,o,l,d,u);return r.runWebGLProgram(p,[n,s,i],n.dtype)},Tse={kernelName:Wf,backendName:"webgl",kernelFunc:Sse};function Cse(e,t,r,a){let n=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/n,i=ve({inputs:{x:e},attrs:{shape:[s,n]},backend:a}),o=Cl(i,e.dtype,"max",a),l=ve({inputs:{x:o},attrs:{shape:r},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function k8(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{reductionIndices:s,keepDims:i}=a,o=n.shape.length,l=w.parseAxisParam(s,n.shape),d=l,u=N.getAxesPermutation(d,o),p=u!=null,h=r.shouldExecuteOnCPU([n]),c=n;if(p){if(h){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=n.shape[u[C]];let b=zx(A,n.shape,n.dtype,u,x);c=r.makeTensorInfo(x,n.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=e0(n,u,r);d=N.getInnerMostAxes(d.length,o)}N.assertAxesAreInnerMostDims("max",d,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,d),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(h){let A=r.texData.get(c.dataId).values,x=UQ(A,w.sizeFromShape(m),g,n.dtype);y=r.makeTensorInfo(g,n.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=Cse(c,m,g,r);return p&&r.disposeIntermediateTensorInfo(c),y}var Nse={kernelName:hi,backendName:"webgl",kernelFunc:k8},Ese=a8+`
  return max(a, b);
`,Rse=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Jm+`
  return result;
`,Fse=Ar({opSnippet:Ese,packedOpSnippet:Rse,cpuKernelImpl:GQ}),Mse={kernelName:ci,backendName:"webgl",kernelFunc:Fse};function $se(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t;hd(n,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=N.computePool2DInfo(n.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return ha({inputs:{x:n},backend:r});let p=new zp(u,"max",!1);return r.runWebGLProgram(p,[n],n.dtype)}var Pse={kernelName:fi,backendName:"webgl",kernelFunc:$se};function Ose(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:d}=a,u=[1,1,1],p=N.computePool3DInfo(n.shape,s,i,u,o,d,l),h=new _x(p,"max",!1);return r.runWebGLProgram(h,[n],n.dtype)}var zse={kernelName:qp,backendName:"webgl",kernelFunc:Ose},Dse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,n=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=n-1-e.padInfo.top,o=s-1-e.padInfo.left,l=n*s-1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${n};
          wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${s}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${s} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},_se=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,a=e.strideWidth,n=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,d=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,h=d-1-e.padInfo.left,c=o*l*d-1;this.userCode=`
      const ivec3 pads = ivec3(${u}, ${p}, ${h});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${o};
           wD += ${n}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${s}) {
            float dyR = float(dyRCorner + wR) / ${r}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${d};
                wC += ${i}) {
              float dyC = float(dyCCorner + wC) / ${a}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${c} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${d} +
                  wR * ${d} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function Lse(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],h=N.computePool3DInfo(i.shape,o,l,p,d,u),c=new _x(h,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new _se(h),g=r.runWebGLProgram(m,[n,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var Bse={kernelName:Uf,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,input:s,output:i}=t,o=s;hd([s,i],"maxPoolGrad");let{filterSize:l,strides:d,pad:u,dimRoundingMode:p}=a,h=N.computePool2DInfo(o.shape,l,d,1,u,p),c=!0,f=new zp(h,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new Dse(h),y=r.runWebGLProgram(g,[n,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var Vse={kernelName:Vf,backendName:"webgl",kernelFunc:Wse};function Use(e,t,r,a){let n=new zp(r,"max",!1),s=a.runWebGLProgram(n,[e],"float32");n=new zp(r,"max",!0,!0,t);let i=a.runWebGLProgram(n,[e],"float32");return[s,i]}var Gse={kernelName:Gf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:a}=e,{filterSize:n,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let d=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${d}'`);let u=N.computePool2DInfo(a.shape,n,s,d,i),[p,h]=Use(a,o,u,l);return[p,h]}};function jse(e,t,r,a){let n=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/n,i=ve({inputs:{x:e},attrs:{shape:[s,n]},backend:a}),o=Cl(i,"float32","mean",a),l=ve({inputs:{x:o},attrs:{shape:r},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Hse={kernelName:mi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:a}=e,{keepDims:n,axis:s}=t,i=r,o=a.shape.length,l=w.parseAxisParam(s,a.shape),d=l,u=N.getAxesPermutation(d,o),p=u!=null,h=i.shouldExecuteOnCPU([a]),c=[],f=a;if(p){if(h){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=a.shape[u[T]];let v=zx(x,a.shape,a.dtype,u,b);f=i.makeTensorInfo(b,a.dtype);let C=i.texData.get(f.dataId);C.values=v}else f=e0(a,u,i);c.push(f),d=N.getInnerMostAxes(d.length,o)}N.assertAxesAreInnerMostDims("sum",d,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,d),y=m;n&&(y=N.expandShapeToKeepDim(m,l));let A=jse(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function qse(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a,o=n.shape.length,l=w.parseAxisParam(s,n.shape),d=l,u=N.getAxesPermutation(d,o),p=n;u!=null&&(p=Dr({inputs:{x:n},backend:r,attrs:{perm:u}}),d=N.getInnerMostAxes(d.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",d,o);let[h,c]=N.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(c),m=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,f]}}),g=Cl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(h,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:h}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),u!=null&&r.disposeIntermediateTensorInfo(p),y}var Kse={kernelName:gi,backendName:"webgl",kernelFunc:qse},Xse=a8+`
  return min(a, b);
`,Zse=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Jm+`
  return result;
`,Yse=Ar({opSnippet:Xse,packedOpSnippet:Zse,cpuKernelImpl:jQ}),Jse={kernelName:yi,backendName:"webgl",kernelFunc:Yse},Qse=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((d,u)=>d[0]+e[u]+d[1]);let a=e.length,n=vt(a),s=t.map(d=>d[0]).join(","),i=t.map((d,u)=>d[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=r==="reflect"?0:1;if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${l};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${l};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${n} start = ${n}(${s});
      ${n} end = ${n}(${i});

      void main() {
        ${n} outC = getOutputCoords();
        for (int i = 0; i < ${a}; 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};
          }
        }
        ${n} coords = outC - start;
        setOutput(getX(${o}));
      }
    `}},eie=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,f)=>c[0]+e[f]+c[1]);let a=e.length,n=vt(a),s=t.map(c=>c[0]).join(","),i=t.map((c,f)=>c[0]+e[f]).join(","),o=$r("rc",a),l=$r("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=r==="reflect"?0:1,h="";if(a===1){let c=`
        ${n} source = rc;
        if (source < start) {
          source = start * 2 - source - ${p};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${p};
        }
        source -= start;
      `;h=`
        ${n} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${u});
        ${o[a-1]} += 1;
        if(${d}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${u});
        }
      `}else{let c=`
        ${n} source = rc;
        ${n} lt = ${n}(lessThan(source, start));
        ${n} gte = ${n}(greaterThanEqual(source, end));
        ${n} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${p}) +
                gte * ((end - 1) * 2 - source + ${p});
        source -= start;
      `;h=`
        ${n} rc = outputLoc;
        ${c}
        result[0] = getChannel(getX(${l.join()}), ${u});
        ${o[a-1]} += 1;
        if(${d}) {
          ${c}
          result[1] = getChannel(getX(${l.join()}), ${u});
        }
        rc = outputLoc;
        ${o[a-2]} += 1;
        if(${o[a-2]} < ${this.outputShape[a-2]}) {
          ${c}
          result[2] = getChannel(getX(${l.join()}), ${u});
          ${o[a-1]} += 1;
          if(${d}) {
            ${c}
            result[3] = getChannel(getX(${l.join()}), ${u});
          }
        }
      `}this.userCode=`
      const ${n} start = ${n}(${s});
      const ${n} end = ${n}(${i});

      void main() {
        ${n} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},tie=({inputs:e,backend:t,attrs:r})=>{let{x:a}=e,{paddings:n,mode:s}=r,i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eie(a.shape,n,s):new Qse(a.shape,n,s);return t.runWebGLProgram(i,[a],a.dtype)},rie={kernelName:Ai,backendName:"webgl",kernelFunc:tie},aie=`if (b == 0.0) return NAN;
  return mod(a, b);`,nie=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Jm+`
  return result;
`,sie=Ar({opSnippet:aie,packedOpSnippet:nie}),iie={kernelName:Uu,backendName:"webgl",kernelFunc:sie},oie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${t-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}},lie=`
if (a == b) {
  return 1.0;
};
return a / b;`,uie=`
  // 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;
`,I8=Ar({opSnippet:lie,packedOpSnippet:uie,checkOutOfBounds:!0}),die={kernelName:ri,backendName:"webgl",kernelFunc:I8},ov="return a - b;",S8=Ar({opSnippet:ov,packedOpSnippet:ov,supportsComplex:!0,cpuKernelImpl:oee}),pie={kernelName:$i,backendName:"webgl",kernelFunc:S8};function T8(e){let{inputs:t,backend:r,attrs:a}=e,{logits:n}=t,{dim:s}=a,i=w.parseAxisParam([s],n.shape),o=k8({inputs:{x:n},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),d=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),u=S8({inputs:{a:n,b:d},backend:r}),p=b8({inputs:{x:u},backend:r}),h=t0({inputs:{x:p},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:h},backend:r,attrs:{shape:l}}),f=I8({inputs:{a:p,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(c),f}var hie={kernelName:Fi,backendName:"webgl",kernelFunc:T8};function cie(e){let{inputs:t,backend:r,attrs:a}=e,{logits:n}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?n:T8({inputs:{logits:n},backend:r,attrs:{dim:n.shape.length-1}}),d=l.shape[0],u=l.shape[1],p=new oie(d,u,s),h=[[i]],c=r.runWebGLProgram(p,[l],"int32",h);return o||r.disposeIntermediateTensorInfo(l),c}var fie={kernelName:jf,backendName:"webgl",kernelFunc:cie},mie=Ka+`
  return -x;
`,gie=`
  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 yie(e){let{inputs:t,backend:r}=e,{x:a}=t;if(r.shouldExecuteOnCPU([a])){let s=r.texData.get(a.dataId),[i,o]=qQ(s.values,a.shape,a.dtype);return r.makeTensorInfo(o,a.dtype,i)}let n;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new co(a.shape,gie):n=new Gn(a.shape,mie),r.runWebGLProgram(n,[a],a.dtype)}var Aie={kernelName:qo,backendName:"webgl",kernelFunc:yie},xie=Ha.nonMaxSuppressionV3Impl;function bie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:a}=e,{boxes:n,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,d=r.readSync(n.dataId),u=r.readSync(s.dataId),{selectedIndices:p}=xie(d,u,i,o,l);return r.makeTensorInfo([p.length],"int32",new Int32Array(p))}var vie={kernelName:Xo,backendName:"webgl",kernelFunc:bie},wie=Ha.nonMaxSuppressionV4Impl;function kie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:a}=e,{boxes:n,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:d}=a,u=r.readSync(n.dataId),p=r.readSync(s.dataId),{selectedIndices:h,validOutputs:c}=wie(u,p,i,o,l,d);return[r.makeTensorInfo([h.length],"int32",new Int32Array(h)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Iie={kernelName:Gu,backendName:"webgl",kernelFunc:kie},Sie=Ha.nonMaxSuppressionV5Impl;function Tie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:a}=e,{boxes:n,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:d}=a,u=r.readSync(n.dataId),p=r.readSync(s.dataId),h=i,c=o,f=l,m=d,{selectedIndices:g,selectedScores:y}=Sie(u,p,h,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Cie={kernelName:Zo,backendName:"webgl",kernelFunc:Tie},Nie=class{constructor(e,t,r,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${a}), float(${r}),
                      float(index == coords.y)));
      }
    `}},Eie=e=>{let{inputs:t,backend:r,attrs:a}=e,{indices:n}=t,{depth:s,onValue:i,offValue:o}=a,l=w.sizeFromShape(n.shape),d=new Nie(l,s,i,o),u=ve({inputs:{x:n},backend:r,attrs:{shape:[l]}}),p=r.runWebGLProgram(d,[u],n.dtype);r.disposeIntermediateTensorInfo(u);let h=[...n.shape,s],c=ve({inputs:{x:p},backend:r,attrs:{shape:h}});return r.disposeIntermediateTensorInfo(p),c},Rie={kernelName:Jo,backendName:"webgl",kernelFunc:Eie};function xf(e){let{inputs:t,backend:r}=e,{x:a}=t;if(a.dtype==="complex64"){let n=Rh({inputs:{input:a},backend:r}),s=xf({inputs:{x:n},backend:r}),i=r0({inputs:{input:a},backend:r}),o=xf({inputs:{x:i},backend:r}),l=Bi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Fh({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:r})}var Fie={kernelName:ml,backendName:"webgl",kernelFunc:xf};function C8(e){let{inputs:t,backend:r}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let n=Rh({inputs:{input:a},backend:r}),s=C8({inputs:{x:n},backend:r}),i=r0({inputs:{input:a},backend:r}),o=xf({inputs:{x:i},backend:r}),l=Bi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Fh({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}var Mie={kernelName:Yo,backendName:"webgl",kernelFunc:C8};function $ie(e){let{inputs:t,backend:r,attrs:a}=e,{axis:n}=a;if(t.length===1)return ky({inputs:{input:t[0]},backend:r,attrs:{dim:n}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=ky({inputs:{input:u},backend:r,attrs:{dim:n}});return o.push(p),p}),d=f8({inputs:l,backend:r,attrs:{axis:n}});return o.forEach(u=>r.disposeIntermediateTensorInfo(u)),d}var Pie={kernelName:Qo,backendName:"webgl",kernelFunc:$ie},Oie=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,n=vt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${n} start = ${n}(${s});
      ${n} end = ${n}(${i});

      void main() {
        ${n} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${n} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}},zie=class{constructor(e,t,r){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 a=e.length,n=vt(a),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=$r("rc",a),l=$r("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${n} rc = outputLoc;`,`${o[a-1]} += 1;
       if(${d}) {
      `,a===1?"":`}
       rc = outputLoc;
       ${o[a-2]} += 1;
       if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":`  ${o[a-1]} += 1;
         if(${d}) {`],h=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let f=0,m=a===1?2:4;f<m;f++)c+=`
        ${p[f]}
        if (${h}) {
          result[${f}] = float(value);
        } else {
          ${n} source = rc - start;
          result[${f}] = getChannel(getX(${l.join()}), ${u});
        }
      `;c+=a===1?"} ":"}}",this.userCode=`
      const ${n} start = ${n}(${s});
      const ${n} end = ${n}(${i});

      void main() {
        ${n} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${c}
        setOutput(result);
      }
    `}},N8=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(n.shape)===0){let d=s.map((u,p)=>u[0]+n.shape[p]+u[1]);return Fh({backend:r,attrs:{shape:d,value:i,dtype:n.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zie(n.shape,s,i):new Oie(n.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[n],n.dtype,l)},Die={kernelName:bi,backendName:"webgl",kernelFunc:N8},_ie=`
  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);
`,Lie=`
  // 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));
  `+Jm+`
  return result;
`,Bie=Ar({opSnippet:_ie,packedOpSnippet:Lie}),Wie={kernelName:vi,backendName:"webgl",kernelFunc:Bie};function Vie(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a,o=n.shape.length,l=[],d=w.parseAxisParam(s,n.shape),u=d,p=N.getAxesPermutation(u,o),h=n;p!=null&&(h=Dr({inputs:{x:n},backend:r,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o),l.push(h)),N.assertAxesAreInnerMostDims("prod",u,o);let c;if(r.shouldExecuteOnCPU([h])){let f=r.texData.get(h.dataId).values,{outVals:m,outShape:g,outDtype:y}=XQ(h.shape,h.dtype,f,u);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(h.shape,u),g=w.sizeFromShape(m),y=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,g]}}),A=ah(n.dtype),x=Cl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,d);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var Uie={kernelName:el,backendName:"webgl",kernelFunc:Vie},E8=e=>{let{backend:t,attrs:r}=e,{start:a,stop:n,step:s,dtype:i}=r,o=ZQ(a,n,s,i);return t.makeTensorInfo([o.length],i,o)},Gie={kernelName:ju,backendName:"webgl",kernelFunc:E8},jie="return 1.0 / x;",Hie=it({opSnippet:jie}),qie={kernelName:Hu,backendName:"webgl",kernelFunc:Hie},Kie=Ka+`
  return (x < 0.0) ? 0.0 : x;
`,Xie=`
  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;
`,Zie=it({opSnippet:Kie,packedOpSnippet:Xie}),Yie={kernelName:ki,backendName:"webgl",kernelFunc:Zie},Jie=Ka+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Qie=`
  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;
`,eoe=it({opSnippet:Jie,packedOpSnippet:Qie}),toe={kernelName:Si,backendName:"webgl",kernelFunc:eoe},roe=class{constructor(e,t,r,a,n){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let d=[a&&t>1?i-1:i,a&&r>1?o-1:o],u=[a&&t>1?t-1:t,a&&r>1?r-1:r],p;n?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${d[0]/u[0]},
          ${d[1]/u[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${p};

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `}},aoe=class{constructor(e,t,r,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let d=[a&&t>1?i-1:i,a&&r>1?o-1:o],u=[a&&t>1?t-1:t,a&&r>1?r-1:r],p;n?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${d[0]/u[0]},
          ${d[1]/u[1]},
          ${d[1]/u[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${p};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${l-1};
        bool hasNextRow = coords.z < ${r-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}};function noe(e){let{inputs:t,backend:r,attrs:a}=e,{images:n}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aoe(n.shape,l,d,s,i):new roe(n.shape,l,d,s,i);return r.runWebGLProgram(u,[n],"float32")}var soe={kernelName:Ii,backendName:"webgl",kernelFunc:noe},ioe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,n]=t,[,s,i]=e,o=[r&&s>1?a-1:a,r&&i>1?n-1:n],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,h=1/u,c=Math.ceil(p)*2+2,f=Math.ceil(h)*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(${d});
        const float widthScale = float(${u});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${h});

        const int winHeight = int(${c});
        const int winWidth = int(${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 >= ${s}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${a-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), ${n-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 ooe(e){let{inputs:t,backend:r,attrs:a}=e,{images:n,dy:s}=t,{alignCorners:i}=a,o=new ioe(s.shape,n.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var loe={kernelName:qf,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t,r,a,n){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let d=[a&&t>1?i-1:i,a&&r>1?o-1:o],u=[a&&t>1?t-1:t,a&&r>1?r-1:r],p=a?"0.5":"0.0",h;n?h="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${d[0]/u[0]},
          ${d[1]/u[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${h};

        // Compute the 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);
      }
    `}},doe=class{constructor(e,t,r,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let d=[a&&t>1?i-1:i,a&&r>1?o-1:o],u=[a&&t>1?t-1:t,a&&r>1?r-1:r],p=a?"0.5":"0.0",h;n?h="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${d[0]/u[0]},
          ${d[1]/u[1]},
          ${d[1]/u[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${h};

        // Compute the 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 < ${r-1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `}};function poe(e){let{inputs:t,backend:r,attrs:a}=e,{images:n}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new doe(n.shape,l,d,s,i):new uoe(n.shape,l,d,s,i);return r.runWebGLProgram(u,[n],n.dtype)}var hoe={kernelName:qu,backendName:"webgl",kernelFunc:poe},coe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,n]=t,[,s,i]=e,o=[r&&s>1?a-1:a,r&&i>1?n-1:n],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,h=1/u,c=Math.ceil(p)*2+2,f=Math.ceil(h)*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(${d});
        const float widthScale = float(${u});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${h});

        const int winHeight = int(${c});
        const int winWidth = int(${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 >= ${s}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float sourceFracRow =
              float(${o[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${o[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${a}) - 1),
                ${r} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${n}) - 1),
                ${r} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function foe(e){let{inputs:t,backend:r,attrs:a}=e,{images:n,dy:s}=t,{alignCorners:i}=a,o=new coe(s.shape,n.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var moe={kernelName:Hf,backendName:"webgl",kernelFunc:foe},goe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,n=e.map((i,o)=>a(o)).join(","),s=vt(r);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${n}));
      }
    `}},yoe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let a=$r("rc",r),n=`${a[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${a[r-2]} + 1 < ${this.outputShape[r-2]}`,i=vt(r);r===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${n}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(a.slice())};
          if(${n}){
            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${d(a.slice())};
            if(${n}) {
              result.a = ${u(a.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(c){return p(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",p(c)}function d(c){return c[r-2]="("+c[r-2]+" + 1)",p(c)}function u(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",p(c)}function p(c){let f=e.map((y,A)=>h(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function h(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function Aoe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{dims:s}=a,i=n.shape.length,o=w.parseAxisParam(s,n.shape);if(i===0)return ha({inputs:{x:n},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yoe(n.shape,o):new goe(n.shape,o);return r.runWebGLProgram(l,[n],n.dtype)}var xoe={kernelName:rl,backendName:"webgl",kernelFunc:Aoe},boe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],a=e[2];this.outputShape=e;let n="";typeof t=="number"?n=`float outputValue = ${t.toFixed(2)};`:n=`
        vec3 fill = vec3(${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]));
          ${n}
          if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${r}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},voe={kernelName:gl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:a}=e,{radians:n,fillValue:s,center:i}=t,o=r,l=new boe(a.shape,s),[d,u]=N.getImageCenter(i,a.shape[1],a.shape[2]),p=[[d,u,Math.sin(n),Math.cos(n)]];return o.runWebGLProgram(l,[a],a.dtype,p)}},woe=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,koe=it({opSnippet:woe}),Ioe={kernelName:al,backendName:"webgl",kernelFunc:koe},Soe="return inversesqrt(x);",Toe=it({opSnippet:Soe,cpuKernelImpl:YQ}),Coe={kernelName:Ti,backendName:"webgl",kernelFunc:Toe},R8=class{constructor(e,t,r,a,n,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=vt(n.length),l=vt(s.length),d="";r===1?d="i":r===2&&(d="i, j");let u=`getIndices(${d})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let h=`getUpdates(${p})`,c=t>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${n});

        void main() {
          ${l} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${u});
              flattenedIndex += index * ${c};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${h};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function Noe(e){let{inputs:t,backend:r,attrs:a}=e,{indices:n,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:d,strides:u,outputSize:p}=N.calculateShapes(s,n,i),h=[p/d,d];if(p===0)return r.makeTensorInfo(i,n.dtype);let c=ve({inputs:{x:n},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,d]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new R8(l,o,c.shape.length,f.shape.length,u,h),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var Eoe={kernelName:nl,backendName:"webgl",kernelFunc:Noe},Roe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let a,n;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)n="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let d=0;d<t.length;d++)l.push(`${i[d]}`),d<e&&o.push(`${i[d]}`);a=o.join(),n=l.join()}let s=vt(r);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
        if (cVal >= 1.0) {
          setOutput(getA(${n}));
        } else {
          setOutput(getB(${n}));
        }
      }
    `}};function Foe(e){let{inputs:t,backend:r}=e,{condition:a,t:n,e:s}=t,i=new Roe(a.shape.length,n.shape,n.shape.length);return r.runWebGLProgram(i,[a,n,s],Or(n.dtype,s.dtype))}var Moe={kernelName:sl,backendName:"webgl",kernelFunc:Foe},$oe=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${N.SELU_SCALEALPHA};
  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Poe=it({opSnippet:$oe}),Ooe={kernelName:Ku,backendName:"webgl",kernelFunc:Poe},zoe=yd+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,Doe=`
  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;
`,_oe=it({opSnippet:zoe,packedOpSnippet:Doe,cpuKernelImpl:JQ}),Loe={kernelName:Ni,backendName:"webgl",kernelFunc:_oe},Boe=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,Woe=it({opSnippet:Boe}),Voe={kernelName:Xu,backendName:"webgl",kernelFunc:Woe},Uoe=yd+`
  return sin(x);
`,Goe=it({opSnippet:Uoe}),joe={kernelName:Ci,backendName:"webgl",kernelFunc:Goe},Hoe=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,qoe=it({opSnippet:Hoe}),Koe={kernelName:ol,backendName:"webgl",kernelFunc:qoe},Xoe=`
  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;
`,Zoe=it({opSnippet:Xoe}),Yoe={kernelName:Zu,backendName:"webgl",kernelFunc:Zoe},Joe=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,paddings:i}=a;w.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<n.shape.length;++y)l.push([0,0]);let d=[],u=N8({inputs:{x:n},backend:r,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(p.length,s.length,!1),c=N.getReshapedPermuted(u.shape,s,o,!1),f=ve({inputs:{x:u},backend:r,attrs:{shape:p}}),m=Dr({inputs:{x:f},backend:r,attrs:{perm:h}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return d.push(u),d.push(f),d.push(m),d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Qoe={kernelName:ll,backendName:"webgl",kernelFunc:Joe};function ele(e){let{inputs:t,backend:r}=e,{indices:a,values:n,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${a.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${n.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let o=r.readSync(a.dataId),l=r.readSync(n.dataId),d=r.readSync(s.dataId),u=r.readSync(i.dataId)[0],[p,h,c,f,m]=eee(o,a.shape,a.dtype,l,n.dtype,d,u);return[r.makeTensorInfo(h,a.dtype,p),r.makeTensorInfo([h[0]],n.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],a.dtype,new Int32Array(m))]}var tle={kernelName:Xp,backendName:"webgl",kernelFunc:ele};function rle(e){let{inputs:t,backend:r}=e,{inputIndices:a,inputShape:n,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(n.dataId)),o=r.readSync(a.dataId),l=Array.from(r.readSync(s.dataId)),[d,u,p]=tee(o,a.shape,a.dtype,i,l);return[r.makeTensorInfo(u,a.dtype,d),r.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var ale={kernelName:Yu,backendName:"webgl",kernelFunc:rle};function nle(e){let{inputs:t,backend:r}=e,{data:a,indices:n,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=r.readSync(a.dataId),o=r.readSync(n.dataId),l=r.readSync(s.dataId),[d,u]=JI(i,a.shape,a.dtype,o,l,!0);return r.makeTensorInfo(u,a.dtype,d)}var sle={kernelName:Zp,backendName:"webgl",kernelFunc:nle};function ile(e){let{inputs:t,backend:r}=e,{data:a,indices:n,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=r.readSync(a.dataId),o=r.readSync(n.dataId),l=r.readSync(s.dataId),[d,u]=JI(i,a.shape,a.dtype,o,l);return r.makeTensorInfo(u,a.dtype,d)}var ole={kernelName:Yp,backendName:"webgl",kernelFunc:ile};function lle(e){let{inputs:t,backend:r,attrs:a}=e,{sparseIndices:n,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:d,strides:u,outputSize:p}=N.calculateShapes(s,n,o),h=!1,c=new R8(d,l,n.shape.length,s.shape.length,u,[p,1],h),f=r.runWebGLProgram(c,[s,n,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var ule={kernelName:Jp,backendName:"webgl",kernelFunc:lle};function dle(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,n.shape)[0],l=N.prepareSplitSize(n,s,o),d=n.shape.length,u=new Array(d).fill(0),p=n.shape.slice();return l.map(h=>{let c=[...p];c[o]=h;let f=Ad({inputs:{x:n},backend:r,attrs:{begin:u,size:c}});return u[o]+=h,f})}var ple={kernelName:ul,backendName:"webgl",kernelFunc:dle},lv="return sqrt(x);",hle=it({opSnippet:lv,packedOpSnippet:lv,cpuKernelImpl:ree}),cle={kernelName:Ei,backendName:"webgl",kernelFunc:hle},fle="return x * x;",mle=it({opSnippet:fle}),gle={kernelName:Ju,backendName:"webgl",kernelFunc:mle},uv="return (a - b) * (a - b);",yle=Ar({opSnippet:uv,packedOpSnippet:uv}),Ale={kernelName:Mi,backendName:"webgl",kernelFunc:yle};function xle({inputs:e,attrs:t,backend:r}){let{x:a}=e,n=Ka+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Gn(a.shape,n);return r.runWebGLProgram(s,[a],a.dtype)}var ble={kernelName:zi,backendName:"webgl",kernelFunc:xle},vle=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let a=r.length,n=vt(r.length),s=vt(r.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=r.map((l,d)=>(o++,r.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${o-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
      ${n} begin = ${n}(${e});
      ${n} strides = ${n}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function wle(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:d,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:h}=a,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(n.shape,s,i,o,l,d,u,p,h),v;if(m)v=ve({inputs:{x:n},backend:r,attrs:{shape:f}});else if(g||y){w.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let T=Ot.computeOutShape(A,x,b),E=Ad({inputs:{x:n},backend:r,attrs:{begin:A,size:T}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([n])){let T=r.readSync(n.dataId),E=Le(n.shape,n.dtype,T),R=aee(c,E,b,A);v=r.makeTensorInfo(f,n.dtype,R.values)}else{let T=new vle(A,b,c);v=r.runWebGLProgram(T,[n],n.dtype)}let C=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),C}var kle={kernelName:dl,backendName:"webgl",kernelFunc:wle};function Ile(e){let{inputs:t,backend:r,attrs:a}=e,{separator:n,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:d}=a,{data:u,dataSplits:p}=t,h=r.readSync(u.dataId),c=r.readSync(p.dataId),[f,m]=nee(h,c,n,s,i,o,l,d);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(p.shape,"int32",m)]}var Sle={kernelName:Qp,backendName:"webgl",kernelFunc:Ile};function Tle(e){let{inputs:t,backend:r,attrs:a}=e,{skipEmpty:n}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[d,u,p]=see(o,l,n),h=u.length;return[r.makeTensorInfo([h,2],"int32",d),r.makeTensorInfo([h],"string",u),r.makeTensorInfo([2],"int32",new Int32Array(p))]}var Cle={kernelName:Kf,backendName:"webgl",kernelFunc:Tle};function Nle(e){let{inputs:t,backend:r,attrs:a}=e,{numBuckets:n}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=iee(i,n);return r.makeTensorInfo(s.shape,"int32",o)}var Ele={kernelName:Xf,backendName:"webgl",kernelFunc:Nle},Rle="return tan(x);",Fle=it({opSnippet:Rle}),Mle={kernelName:pl,backendName:"webgl",kernelFunc:Fle},$le=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Ple=it({opSnippet:$le}),Ole={kernelName:Pi,backendName:"webgl",kernelFunc:Ple},zle=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let a=vt(this.rank),n=Dle(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${n}));
      }
    `}};function Dle(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let n=0;n<e.length;n++)a.push(`imod(${r[n]}, ${e[n]})`);return a.join()}function F8(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{reps:s}=a;if(n.dtype==="string"||n.shape.length>5){let o=r.readSync(n.dataId),l=n.dtype==="string"?o.map(p=>w.decodeString(p)):o,d=Le(n.shape,n.dtype,l),u=lee(d,s);return r.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new zle(n.shape,s);return r.runWebGLProgram(i,[n],n.dtype)}var _le={kernelName:Xn,backendName:"webgl",kernelFunc:F8},Lle=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));
         }
       }
     `}},Ble=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 ao(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function dv(e){let t=1;for(;t<e;)t*=2;return t}function Wle(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{k:s,sorted:i}=a,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),d=n.shape,u=d[d.length-1];if(r.shouldExecuteOnCPU([n])||u<o||s>l){let R=r.readSync(n.dataId),[z,M]=uee(R,d,n.dtype,s,i);return[r.makeTensorInfo(z.shape,z.dtype,z.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return d[d.length-1]=0,[r.makeTensorInfo(d,n.dtype,[]),r.makeTensorInfo(d,"int32",[])];if(u===1)return[n,Fh({attrs:{shape:d,dtype:"int32",value:0},backend:r})];let p=r.texData.get(n.dataId),h=p!==null&&p.isPacked,c=h?r.unpackTensor(n):n,f=w.sizeFromShape(d)/u,m=ve({inputs:{x:c},attrs:{shape:[f,u]},backend:r});h&&ao(r,c);let g=dv(s),y=dv(u),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,z,M)=>{let I=x(),D=new Lle(M),O=[[u],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[z]],j=A;A=r.runWebGLProgram(D,I,"int32",O),ao(r,j)};for(let R=1;R<g;R*=2){let z=R*2;for(let M=R;M>=1;M/=2)b(z,M,[f,y])}for(let R=y;R>g;R/=2){let z=x(),M=new Ble([f,R/2]),I=[[u],[A===null?1:0],[g]],D=A;A=r.runWebGLProgram(M,z,"int32",I),ao(r,D);let O=g/2,j=O*2;for(let X=O;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=Ad({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),ao(r,v);let C=w8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});ao(r,m);let T=d.slice(0,-1);T.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:T},backend:r}),ao(r,v);let E=C;return C=ve({inputs:{x:C},attrs:{shape:T},backend:r}),ao(r,E),[C,A]}var Vle={kernelName:hl,backendName:"webgl",kernelFunc:Wle},Ule=class{constructor(e,t,r,a,n,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${n});
              }
              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(${n});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${i} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function Gle(e){let{inputs:t,backend:r,attrs:a}=e,{image:n,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:d}=a,[u,p,h,c]=n.shape,[f,m]=d!=null?d:[p,h],g=[u,f,m,c],y=new Ule(p,h,i,o,l,g);return r.runWebGLProgram(y,[n,s],"float32")}var jle={kernelName:cl,backendName:"webgl",kernelFunc:Gle};function Hle(e){let{inputs:t,attrs:r,backend:a}=e,{axis:n}=r,{x:s}=t;hd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:d}=dee(i,n,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([d.length],"int32",d)]}var qle={kernelName:Zf,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r,attrs:a}=e,{value:n}=t,{axis:s}=a;s<0&&(s+=n.shape.length);let i=n,o=i.shape.length,l=n.shape[s],d=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(d[u++]=i.shape[m]);let p=[],h=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){h[s]=m;let g=Ad({inputs:{x:i},backend:r,attrs:{begin:h,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:d}});f[m]=y,p.push(g)}return p.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var Xle={kernelName:fl,backendName:"webgl",kernelFunc:Kle},Zle=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,a=e.batchSize,n=e.inSize,s=e.numSegments,i=s*Math.ceil(n/r);this.outputShape=[a,i];let o="0.0",l="sumValue",d=Math.floor(r/4)*4,u=r%4,p=`
        sumValue += dot(values, segFilter);
    `,h="";n%r>0&&(h=`
        if (inIdx < 0 || inIdx >= ${n}) {
          return initializationValue;
        }
      `);let c="";n%r>0&&(c=`
        if (inIdx < 0 || inIdx >= ${n}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${o};

      float getValue(int batch, int inIdx) {
        ${h}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${c}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${s})) * float(${r}));
        int currentSeg = int(mod(float(outIdx), float(${s})));

        float sumValue = 0.0;

        for (int i = 0; i < ${d}; 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 + ${d};
        if (${u===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${p}
        } else if (${u===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${p}
        } else if (${u===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${p}
        }
        setOutput(${l});
      }
    `}};function Yle(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,segmentIds:s}=t,{numSegments:i}=a,o=n.shape.length,l=[],d=0,u=N.getAxesPermutation([d],o),p=n;u!=null&&(p=Dr({inputs:{x:n},backend:r,attrs:{perm:u}}),l.push(p),d=N.getInnerMostAxes(1,o)[0]);let h=N.segment_util.computeOutShape(p.shape,d,i),c=w.sizeFromShape([p.shape[d]]),f=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=ah(n.dtype),g=(b,v,C,T,E)=>{let R=b.shape[0],z=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(z,E),I={windowSize:M,inSize:z,batchSize:R,numSegments:E},D=new Zle(I,v),O=r.compileAndRun(D,[b,C],T);if(l.push(O),O.shape[1]===E)return O;let j=E8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=F8({inputs:{x:j},backend:r,attrs:{reps:[z/M]}});return l.push(j),l.push(X),g(O,v,X,T,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:h}}),x=A;if(u!=null){l.push(A);let b=N.getUndoAxesPermutation(u);x=Dr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Jle={kernelName:eh,backendName:"webgl",kernelFunc:Yle},Qle=[ste,ote,dte,cte,mte,Ate,bte,wte,Tte,Nte,Fte,Pte,Dte,Wte,Gte,Hte,Kte,Jte,ere,rre,ire,cre,mre,yre,kre,Sre,Ere,Bee,Mre,Dre,Wre,qre,Xre,Yre,Qre,tae,nae,oae,dae,hae,fae,yae,xae,kae,Sae,Nae,Fae,$ae,Dae,Wae,jae,Kae,Yae,Jae,ene,rne,nne,ine,lne,hne,mne,Ane,bne,kne,Tne,Rne,Pne,Lee,zne,Ore,Lne,Vne,jne,Vee,Xne,Qne,tse,sse,lse,hse,mse,xse,kse,Tse,Nse,Mse,Pse,zse,Bse,Vse,Gse,Hse,Kse,Jse,rie,iie,fie,qee,Aie,vie,Iie,Cie,xre,Rie,Mie,Pie,Die,Wie,Gee,Uie,Gie,bre,die,qie,Yie,toe,Xee,soe,loe,hoe,moe,xoe,voe,Ioe,Coe,Eoe,Moe,Ooe,Loe,Voe,joe,Koe,pre,hie,Yoe,Qoe,tle,ale,sle,ole,ule,ple,cle,gle,Ale,ble,kle,Sle,Cle,Ele,pie,rte,Mle,Ole,_le,Vle,jle,ate,qle,Xle,Jle,Fie];for(let e of Qle)Ga(e);var Mn=Y();Mn.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Mn.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Mn.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Mn.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Mn.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Mn.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Mn.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Mn.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Mn.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Mn.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function eue(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,a=e.map(s=>`${t}[${s}]`),n=new Array(r-1);n[r-2]=a[r-1];for(let s=r-3;s>=0;--s)n[s]=`(${n[s+1]} * ${a[s+1]})`;return n}function cr(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 jc(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Lx(){return`
  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Wi(){return`
  ${Lx()}
  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 Je(){return`
    ${Wi()}
      let index = getGlobalIndex();
`}function tue(e,t,r,a=!1){let n=[];if(n.push(`
    let workGroupSizeX = ${r.workGroupSize[0]}u;
    let workGroupSizeY = ${r.workGroupSize[1]}u;
    let workGroupSizeZ = ${r.workGroupSize[2]}u;

    var<private> localId: vec3<u32>;
    var<private> globalId: vec3<u32>;
    var<private> numWorkgroups: vec3<u32>;

    // Only used when the y/z dimension of workgroup size is 1.
    fn getGlobalIndex() -> i32 {
      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
        return i32(globalId.x);
      }

      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
          localId.y * workGroupSizeX + localId.x;
      let workGroupID = (globalId - localId)/vec3<u32>(
          workGroupSizeX, workGroupSizeY, workGroupSizeZ);

      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
        workGroupID.y * numWorkgroups.x + workGroupID.x) *
        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
        localInvocationIndex);
    }
  `),a===!0)return n.push(`
      struct Matrix0 {
        numbers: array<${jc(t.dtype,r.isVec4)}>;
      };
      struct Uniform {
        size            : i32;
        numChannels     : i32;
        outShapeStrides : vec2<i32>;
        dispatchSize    : vec3<u32>;
      };

      @group(0) @binding(0) var<storage, write> result : Matrix0;
      @group(0) @binding(2) var<uniform> uniforms: Uniform;
    `),[pv,n.join(`
`),hv(t.shape),r.getUserCode()].join(`
`);let s="struct Uniforms { NAN : f32; ";r.variableNames.forEach((u,p)=>{s+=`${u.charAt(0).toLowerCase()+u.slice(1)}Shape : ${cr(e[p].shape.length)}; `}),s+=`outShape : ${cr(t.shape.length)} ; `;let i=t.shape.length-1;s+=`
       outShapeStrides: ${cr(i)}; `,r.size&&(s+="size : i32; "),r.uniforms&&(s+=r.uniforms),s+="};",n.push(s),r.atomic?n.push(`
    struct Matrix0 {
        numbers: array<atomic<i32>>;
    };

    @group(0) @binding(0) var<storage, read_write> result : Matrix0;
  `):n.push(`
    struct Matrix0 {
        numbers: array<${jc(t.dtype,r.isVec4)}>;
    };

    @group(0) @binding(0) var<storage, write> result : Matrix0;
  `),r.variableNames.forEach((u,p)=>{n.push(`
    struct Matrix${1+p} {
      numbers: array<${jc(e[p].dtype,r.isVec4)}>;
    };
    @group(0) @binding(${1+p}) var<storage, read> ${u} : Matrix${1+p};
    `)}),s!==""&&n.push(`
    @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms : Uniforms;
    `);let[o,l]=oue(t.shape,r.dispatchLayout),d=[pv,n.join(`
`),hv(t.shape),o,rue(t.shape.length)];if(r.atomic||d.push(aue(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let u=e.map(p=>nue(p,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(u)}return d.push(r.getUserCode()),d.join(`
`)}var pv=`
  // 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;
  }

  fn isNanCustom(val : f32) -> bool {
    if (val > 0.0) {
      return false;
    }
    if (val < 0.0) {
      return false;
    }
    if (val == 0.0) {
      return false;
    }
    return true;
  }
  fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
    return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
  }
`;function rue(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 aue(e,t,r){let a=e.length,n=jc(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
      result.numbers[flatIndex] = ${n}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
      result.numbers[flatIndex] = ${n}(value);
    }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
      result.numbers[flatIndex] = ${n}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
      result.numbers[flatIndex] = ${n}(value);
    }`,a>=2){let i=["d0","d1","d2","d3"].slice(0,a),o=cr(a);r?s+=`
      fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex / 4, value);
      }
      fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex / 4, value);
      }
    `:s+=`
      fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex, value);
      }
      fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : i32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `}return s}function nue(e,t,r,a){let n=sue(e,r);return e.shape.length<=t.length&&(n+=iue(e,t,r,a)),n}function sue(e,t){let r=e.name,a=e.shape.length,n=cr(a),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,a),o=i.map(u=>`${u} : i32`).join(", ");if(a<1)return t?`
        fn ${s}() -> vec4<f32> {
          return vec4<f32>(${r}.numbers[0]);
        }
      `:`
      fn ${s}() ->f32 {
        return f32(${r}.numbers[0]);
      }
    `;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,d=`${a}D`;return a===0&&(d="1D"),t?`
      fn ${s}(${o}) -> vec4<f32> {
        return vec4<f32>(${r}.numbers[getIndexFromCoords${d}(${n}(${i.join(",")}),
          ${l}) / 4]);
      }
      `:`
    fn ${s}(${o}) -> f32 {
      return f32(${r}.numbers[getIndexFromCoords${d}(${n}(${i.join(",")}),
        ${l})]);
    }
   `}function iue(e,t,r,a){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,d=cr(l);if(w.arraysEqual(e.shape,t)&&a)return r?`
        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
          return vec4<f32>(${n}.numbers[globalIndex]);
        }

        fn ${i}Coords(coords : ${d}) -> vec4<f32> {
          return vec4<f32>(${n}.numbers[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
        }
        `:`
      fn ${i}Index(globalIndex : i32) -> f32 {
        return f32(${n}.numbers[globalIndex]);
      }

      fn ${i}Coords(coords : ${d}) -> f32 {
        return f32(${n}.numbers[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
      }
      `;let u=N.getBroadcastDims(e.shape,t),p=l-o,h="";if(o===0)return r?`
      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
        return get${s}();
      }

      fn ${i}Coords(coords : ${d}) -> vec4<f32> {
        return get${s}();
      }
    `:`
      fn ${i}Index(globalIndex : i32) -> f32{
        return get${s}();
      }

      fn ${i}Coords(coords : ${d}) -> f32{
        return get${s}();
      }
    `;l<2&&u.length>=1?h="coords = 0;":h=u.map(g=>`coords[${g+p}] = 0;`).join(`
`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=cr(o),y=e.shape.map((A,x)=>`coords[${x+p}]`).join(", ");c=`${g}(${y})`}else c="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,m=`${o}D`;return r?`
      fn ${i}Index(globalIndex : i32) -> vec4<f32> {
        var coords = getCoordsFromIndex(globalIndex);
        ${h}
        return ${n}.numbers[getIndexFromCoords${m}(${c}, ${f}) / 4];
      }

      fn ${i}Coords(coordsIn : ${d}) -> vec4<f32> {
        var coords = coordsIn;
        ${h}
        return ${n}.numbers[getIndexFromCoords${m}(${c}, ${f}) / 4];
      }
    `:`
    fn ${i}Index(globalIndex : i32) -> f32 {
      var coords = getCoordsFromIndex(globalIndex);
      ${h}
      return f32(${n}.numbers[getIndexFromCoords${m}(${c}, ${f})]);
    }

    fn ${i}Coords(coordsIn : ${d}) -> f32 {
      var coords = coordsIn;
      ${h}
      return f32(${n}.numbers[getIndexFromCoords${m}(${c}, ${f})]);
    }
  `}function oue(e,t){let{x:r,y:a=[],z:n=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${cr(s)}{
      let globalIndex = getGlobalIndex();
      return getCoordsFromIndex(globalIndex);
    }
    `,s];let i="",o=[r,a,n],l=0;for(let h=0;h<o.length;h++){let c=o[h];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${h}]);`;else{let f=eue(c,"uniforms.outShape");i+=`var index${h} = i32(globalId[${h}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${h} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${h} - d${c[m]} * ${f[m]};`:i+=`index${h} = index${h} - d${c[m]} * ${f[m]};`}}let d=[];for(let h=0;h<l;h++)d.push(`d${h}`);let u=cr(l),p=`fn getOutputCoords() -> ${u} {
    ${i}
  `;return d.length===0?p+=`return ${u}(0); }`:p+=`return ${u}(${d.join(",")}); }`,[p,l]}function hv(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),a=cr(t),n=[];for(let i=0;i<t;i++)n.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
      return vec2<i32>(d0, d1);
    }`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${n[o]} = index2 / uniforms.outShapeStrides[${o}]`,d=o===r.length-1?`let ${n[o+1]} = index2 - ${n[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${n[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${d};`}).join("");return`
    fn getCoordsFromIndex(index : i32) -> ${a} {
      ${s}
      return ${a}(${n.join(",")});
    }
  `}var M8={};De(M8,{ArrayBufferToTypedArray:()=>P8,GPUBytesPerElement:()=>Iy,computeDispatch:()=>ze,computeWorkGroupSizeForConv2d:()=>Bx,computeWorkGroupSizeForMatMul:()=>$8,computeWorkPerThreadForConv2d:()=>Wx,flatDispatchLayout:()=>He,isWebGPUSupported:()=>Vx,tilesFitEvenlyIntoShape:()=>Hn});var Jl=65535,yo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Hn(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,a)=>r%e[a]===0)}function ze(e,t,r=[1,1,1],a=[1,1,1]){let[n,s,i]=[Math.ceil(yo(e.x.map(l=>t[l]))/(r[0]*a[0])),e.y?Math.ceil(yo(e.y.map(l=>t[l]))/(r[1]*a[1])):1,e.z?Math.ceil(yo(e.z.map(l=>t[l]))/(r[2]*a[2])):1];if(n<=Jl&&s<=Jl&&i<=Jl)return[n,s,i];w.assert(n>Jl&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let o=Math.ceil(Math.sqrt(n));return o>Jl?(o=Math.ceil(Math.cbrt(n)),w.assert(o<=Jl,()=>"Total dispatch size exceeds WebGPU maximum."),[o,o,o]):[o,o,1]}function Bx(e,t){let r=yo(e.x.map(n=>t[n])),a=yo(e.y.map(n=>t[n]));return r<=4?[4,16,1]:a<=4?[16,4,1]:[16,16,1]}function $8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function Wx(e,t){let r=yo(e.x.map(n=>t[n])),a=yo(e.y.map(n=>t[n]));return r<=4?[1,2,1]:a<=4?[2,1,1]:[2,2,1]}function He(e){return{x:e.map((t,r)=>r)}}function Iy(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 Vx(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var lue="return a + b;",uue="return areal * breal - aimag * bimag;",due="return areal * bimag + aimag * breal;",pue="return a / b;",hue="return a * b;",cue="return (a - b) * (a - b);",fue="return a - b;",mue="return f32(a == b);",gue="return vec4<f32>(a == b);",yue="return f32(a > b);",Aue="return vec4<f32>(a > b);",xue="return f32(a >= b);",bue="return vec4<f32>(a >= b);",vue="return f32(a < b);",wue="return vec4<f32>(a < b);",kue="return f32(a <= b);",Iue="return vec4<f32>(a <= b);",Sue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Tue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`,Cue=`
  if (isNanCustom(a)) { return a; }
  if (isNanCustom(b)) { return b; }
  `,O8=`
  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;
  }
  `,Nue=`
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `,Eue=`
  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);
  `,Rue="return f32(a != b);",Fue="return vec4<f32>(a != b);",Mue=`
  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);
  `,$ue=`
  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;
  ${O8}
  return resultTemp;
  `,Pue="if (a < 0.0) { return b * a; }  return a;",Oue=`
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;function cv(e,t){let r=t?O8:Cue;return t?`
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
    `+r+`
    return resultTemp;
  `:r+`
    return ${e}(a, b);
  `}function Mh(e,t){switch(e){case 0:return hue;case 1:return lue;case 2:return fue;case 3:return pue;case 4:return t?gue:mue;case 5:return t?Aue:yue;case 6:return t?bue:xue;case 7:return t?wue:vue;case 8:return t?Iue:kue;case 9:return t?Tue:Sue;case 10:return t?Fue:Rue;case 11:return cue;case 12:return t?Eue:Nue;case 14:return t?Oue:Pue;case 15:return cv("max",t);case 16:return cv("min",t);case 13:return t?$ue:Mue;case 17:return uue;case 18:return due;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var zue="return abs(a);",Due="return ceil(a);",_ue="return cos(a);",Lue=`
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`,Bue="return exp(a) - 1.0;",Wue="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Vue=`
  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;
`,Uue="return exp(a);",Gue="return floor(a);",jue="return a;",Hue=`if (a < 0.0) { return 1.0/0.0; }
  return log(a);`,que="return f32(!(a >= 1.0));",Kue="return -a;",Xue="return (a < 0.0) ? b * a : a;",Zue="if (a < 0.0) { return uniforms.alpha * a; } return a;",Yue="if(a < 0.0) { return 0.0; } return a;",Jue="return clamp(a, 0.0, 6.0);",Que="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ede=`
  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
  let isNaN = isNanCustomVec4(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;
`,tde="return 1.0/sqrt(a);",rde="return 1.0 / (1.0 + exp(-1.0 * a));",ade="return sin(a);",nde=`
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`,sde="return sqrt(a);",ide="return a * a;",ode=`
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,lde="return f32(i32((a)));";function nu(e,t){switch(e){case 0:return zue;case 2:return _ue;case 3:return Lue;case 1:return Due;case 4:return t?Vue:Wue;case 5:return Uue;case 6:return Bue;case 7:return Gue;case 8:return jue;case 9:return Hue;case 10:return que;case 11:return Kue;case 12:return Xue;case 15:return Zue;case 13:return t?ede:Yue;case 14:return t?Que:Jue;case 16:return tde;case 19:return rde;case 17:return ade;case 18:return nde;case 20:return sde;case 21:return ide;case 22:return ode;case 23:return lde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ts(e,t=!1){if(e===null)return null;if(e==="linear")return nu(8);if(e==="relu")return nu(13,t);if(e==="elu")return nu(4,t);if(e==="relu6")return nu(14,t);if(e==="prelu")return Mh(14,t);if(e==="sigmoid")return nu(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function z8(e,t,r,a){return w.assert(a%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
  var<workgroup> mm_Asub : array<array<vec4<f32>, ${a/e[0]}>, ${t}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${a}>;

  let RowPerThread = ${e[1]};
  let ColPerThread = ${e[0]};
  let TileInner = ${a};

  ${Wi()}

    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 ude=class{constructor(e,t,r,a=null,n=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=a!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=n,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],a=[this.tileAOuter,this.tileInner],n=[this.tileInner,this.tileBOuter];return[Hn(a,this.aShape.slice(1)),Hn(n,r.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
        }
        return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
        }
        return vec4<f32>(0.0)`,r="",a="";if(this.activation){let s=ts(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${s}
                }`:r=`
            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
              ${s}
            }`,a="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}
      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);
          ${n}
          ${a}
          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
        }
      }
      ${z8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
    `}};function Ux(e,t){let r=t[1]*e[1],a=t[0]*e[0],n=r>a?r:a;return`
    var<workgroup> mm_Asub : array<array<f32, ${n}>, ${r}>;
    var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${n}>;
    ${Wi()}
      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) / ${n} + 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 = ${n} / ${t[0]};
      let tileColA = i32(localId.x) * ColPerThreadA;
      let RowPerThreadB = ${n} / ${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 * ${n} + 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 * ${n} + inputRow,
              globalCol + innerCol, globalId);
          }
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${n}; 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 dde(e){return`
    let TileSize = ${e[0]*4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

    ${Wi()}
      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 D8=class{constructor(e,t,r,a=!1,n=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];this.workGroupSize=$8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let d=s!=null,u=o!=null;d&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=a,this.transposeB=n,this.addBias=d,this.activation=i,this.hasPreluActivationWeights=u;let p=this.outputShape[2],h=this.transposeB?[this.outputShape[0],p,l]:[this.outputShape[0],l,p];[this.fitA,this.fitB]=this.getShapeFit(h),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,a=t>r?t:r;this.outputShape[1]===1&&(a*=4),w.assert(a%this.workGroupSize[0]===0&&a%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[t,a],s=[a,r];return[Hn(n,this.aShape.slice(1)),Hn(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
           }
           return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
           }
           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
           }
           return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
           }
           return 0.0;`;let r="",a="";if(this.activation){let s=ts(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${s}
            }`:r=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${s}
              }
            `,a="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      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);
        ${n}
        ${a}
        setOutputAtCoords(batch, row, col, value);
      }
      ${this.outputShape[1]>1?Ux([this.workPerThread,this.workPerThread,1],this.workGroupSize):dde(this.workGroupSize)}
    `}};function pde(){return`
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Wi()}
      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 hde=class{constructor(e,t=!1,r=!1,a=null,n=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=a!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=n,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let r="",a="";if(this.activation){let s=ts(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${s}
            }`:r=`
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${s}
              }
            `,a="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      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);
        ${n}
        ${a}
        setOutputAtCoords(batch, row, col, value);
      }
      ${pde()}
    `}};function cde(e){let t=e[1]/2,r=e[0],a=t>r?t:r;return`
  var<workgroup> mm_Asub1 : array<array<f32, ${a}>, ${t}>;
  var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${a}>;
  var<workgroup> mm_Asub2 : array<array<f32, ${a}>, ${t}>;
  var<workgroup> mm_Bsub2 : array<array<f32, ${r}>, ${a}>;

  // 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.
  ${Wi()}
    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) / ${a} + 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 + ${a};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${a};
        }
      } 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 + ${a};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${a};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${a}; 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 + ${a};
          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${a};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${a}; 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 >= ${t} && writeCol >= 0) {
      mm_write(writeCol, globalCol, acc, globalId);
    }
  }
  `}var fde=class{constructor(e,t,r,a=null,n=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=a!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=n,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
        }
        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
         }
         return 0.0;`,r="",a="";if(this.activation){let s=ts(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            let b = getPreluActivationWeightsByOutputCoords(outCoord);
            ${s}
            }`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            ${s}
        }`,a="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${r}

      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;
          ${n}
          ${a}
          setOutputAtCoords(batch, row, col, value);
        }
      }
      ${cde(this.workGroupSize)}
    `}};function Ge(e){let{inputs:t,attrs:r}=e,{x:a}=t,{shape:n}=r,s=w.sizeFromShape(a.shape),i=w.inferFromImplicitShape(n,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${a.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var mde={kernelName:tl,backendName:"webgpu",kernelFunc:Ge};function Gx({a:e,b:t,transposeA:r,transposeB:a,backend:n,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let d=e.shape.length,u=t.shape.length,p=r?e.shape[d-2]:e.shape[d-1],h=a?t.shape[u-1]:t.shape[u-2],c=r?e.shape[d-1]:e.shape[d-2],f=a?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(p===h,()=>`Error in matMul: inner shapes (${p}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${a} must match.`);let b=r?[y,p,c]:[y,c,p],v=a?[A,f,h]:[A,h,f],C=Ge({inputs:{x:e},backend:n,attrs:{shape:b}}),T=Ge({inputs:{x:t},backend:n,attrs:{shape:v}}),E=[C,T],R=Math.max(y,A),z=p%4===0&&f%4===0&&!r&&!a&&f>=32,M;c*f<=32?M=new hde([R,c,f],r,a,s,l,i):!r&&!a&&(c<=16&&(f<=512||h>=2*f)||f<=16&&(c<=512||p>=2*c))?M=new fde(b,v,[R,c,f],s,l,i):z?M=new ude(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new D8(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,a,s,l,i);let I=[C,T];s&&I.push(s),i&&I.push(i);let D=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[p]}],O=n.runWebGPUProgram(M,I,e.dtype,D),j=Ge({inputs:{x:O},backend:n,attrs:{shape:x}});E.push(O);for(let X of E)n.disposeData(X.dataId);return j}function gde(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a;return Gx({a:n,b:s,transposeA:l,transposeB:d,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var yde={kernelName:Rs,backendName:"webgpu",kernelFunc:gde},fv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${Mh(this.op,!1)}
      }

      ${Je()}
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `}},Ade=class{constructor(e,t,r,a){this.variableNames=["A","B"],this.size=!0;let n=256;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=He(this.outputShape),this.lastDimensionSize=a?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=a,this.op=e,this.shaderKey=`binaryShared_${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 {
          ${Mh(this.op,!1)}
        }
        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
        ${Je()}

          // Fill in the shared memory buffer. Here we need a loop to make sure
          // that all data in A|B are uploaded when |sharedMemorySize| is larger
          // than work group size.
          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
          }
          workgroupBarrier();

          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
            let flatIndex = index * ${this.workPerThread} + i;
            if(flatIndex < uniforms.size) {
              let coords = getCoordsFromIndex(flatIndex);

              ${t}
              setOutputAtIndex(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `}},xde=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${Mh(this.op,this.isVec4)}
      }
      ${Je()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
    `}},_8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${Mh(this.op,!1)}
      }
      ${Je()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
      `}};function mv(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new xde(e,t,r);let a=t.length===1&&r.length>1&&t[0]<1024,n=r.length===1&&t.length>1&&r[0]<1024;return a||n?new Ade(e,t,r,n):new _8(e,t,r)}function Va(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var bde={kernelName:ui,backendName:"webgpu",kernelFunc:Va};function xd(e){let{inputs:t,backend:r}=e,{real:a,imag:n}=t,s=r.makeTensorInfo(a.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Va({inputs:{x:a},backend:r}),l=Va({inputs:{x:n},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var vde={kernelName:Lp,backendName:"webgpu",kernelFunc:xd},$h=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
      fn unaryOperation(a : f32) -> f32 {
        ${nu(this.op,!1)}
      }
      ${Je()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `}};function xr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:a,backend:n})=>{let{x:s}=a,i=n,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let d=i.tensorMap.get(s.dataId),u=t(d.values,o);return i.makeTensorInfo(s.shape,o,u)}let l=new $h(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Wr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:a}){return({inputs:n,backend:s})=>{let{a:i,b:o}=n,l=s;if(r&&i.dtype==="complex64"){let p=l.tensorMap.get(i.dataId),h=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[p.complexTensorInfos.real,h.complexTensorInfos.real],[p.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=mv(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Or(y.dtype,A.dtype))});else{let g=new fv(17,i.shape,o.shape),y=new fv(18,i.shape,o.shape),A=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:o.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=xd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let d=a||Or(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let p=l.tensorMap.get(i.dataId).values,h=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(p):p,f=i.dtype==="string"?N.fromUint8ToStringArray(h):h,[m,g]=t(i.shape,o.shape,c,f,d);return l.makeTensorInfo(g,d,m)}let u=mv(e,i.shape,o.shape);return l.runWebGPUProgram(u,[i,o],d)}}var{addImpl:wde,ceilImpl:kde,concatImpl:Ide,equalImpl:Sde,expImpl:Tde,expm1Impl:Cde,floorImpl:Nde,gatherNdImpl:Ede,gatherV2Impl:Rde,greaterEqualImpl:Fde,greaterImpl:Mde,lessEqualImpl:$de,lessImpl:Pde,logImpl:Ode,maxImpl:zde,maximumImpl:Dde,minimumImpl:_de,multiplyImpl:Lde,negImpl:Bde,notEqualImpl:Wde,prodImpl:Vde,rangeImpl:Ude,rsqrtImpl:Gde,simpleAbsImpl:jde,sliceImpl:Hde,stridedSliceImpl:qde,stringNGramsImpl:Kde,subImpl:Xde,tileImpl:Zde,topKImpl:Yde,transposeImpl:Jde,uniqueImpl:eAe}=Km,Qde=xr({opType:0,cpuKernelImpl:jde}),epe={kernelName:Fo,backendName:"webgpu",kernelFunc:Qde},tpe=Wr({opSnippet:1,cpuKernelImpl:wde,supportsComplex:!0}),rpe={kernelName:qn,backendName:"webgpu",kernelFunc:tpe},ape=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
      ${Je()}
        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, ${t});
          }
        }
      }
    `}};function npe(e){let{inputs:t,backend:r}=e,a=t;if(a.length===1)return Va({inputs:{x:a[0]},backend:r});let n=a.map(o=>o.dtype).reduce((o,l)=>Or(o,l)),s=a.map(o=>o.shape),i=new ape(s);return r.runWebGPUProgram(i,a,n)}var spe={kernelName:js,backendName:"webgpu",kernelFunc:npe},L8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let a=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,e.length),this.op=r==="min"?"<":">";let[n]=N.computeOutAndReduceShapes(e,a);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `,t=(a,n)=>this.outputShape.length===1?a:`${a}[${n}]`,r=a=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${a}]`;return`
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${e}

      // In order to get a flattened index into the input tensor, we need to
      // add back the index along the reduced dimension to |outputCoords|.
      // This function outputs the offset to the first value along
      // |axis| and the stride to get the next value of the input along |axis|.
      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
        let outputCoords = getCoordsFromIndex(outputIndex);
        var i = ${this.outputShape.length-1};

        var stride = 1;
        var inputStride = 1;
        var offset = 0;

        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
          let length = ${r(`${this.inputShape.length} - r`)};
          if (${this.inputShape.length} - r == uniforms.axis) {
            inputStride = stride;
          } else {
            offset = offset + ${t("outputCoords","i")} * stride;
            i = i - 1;
          }
          stride = stride * length;
        }

        return vec2<i32>(offset, inputStride);
      }

      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
        return coordInfo[0] + coordInfo[1] * index;
      }

      ${Je()}
        let outputIndex = index / i32(workGroupSizeX);
        let coordInfo = getInputCoordInfo(outputIndex);
        let Length = ${r("uniforms.axis")};

        var bestIndex = i32(localId.x);
        var bestValue = uniforms.infinityValue;

        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
            k = k + i32(workGroupSizeX)) {
          let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
          if (!isNanCustom(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]);
        }
      }
    `}},ipe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let a=0;a<r.length;a++)r[a]=e[t[a]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
      let TILE_DIM = ${this.workGroupSize[0]};
      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
      ${Lx()}
      fn main(@builtin(local_invocation_id) localId : vec3<u32>,
              @builtin(workgroup_id) workgroupId : vec3<u32>) {
        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
        let width = uniforms.outShape[0];
        let height = uniforms.outShape[1];
        if (x < width && y < height) {
          tile[localId.y][localId.x] =
              A.numbers[y * width + x];
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
        if (x < height && y < width) {
          setOutputAtIndex((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `}},ope=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let a=0;a<r.length;a++)r[a]=e[t[a]];this.outputShape=r,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=cr(this.outputShape.length),t=lpe(this.newDim);return`
      ${Je()}

        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let resRC = getCoordsFromIndex(flatIndex);
            setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(
              ${e}(${t}), uniforms.aShape)]);
          }
        }
      }
    `}};function lpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=`resRC[${a}]`;return r.join()}function Nl(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{perm:s}=a,i=r,o=n.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=n.shape[s[u]];if(r.shouldExecuteOnCPU([n])){let u=i.tensorMap.get(n.dataId).values,p=Jde(u,n.shape,n.dtype,s,l);return r.makeTensorInfo(l,n.dtype,p)}if(n.shape.length===2&&w.arraysEqual(s,[1,0])){let u=new ipe(n.shape,s);return i.runWebGPUProgram(u,[n],n.dtype)}let d=new ope(n.shape,s);return i.runWebGPUProgram(d,[n],n.dtype)}var upe={kernelName:Oi,backendName:"webgpu",kernelFunc:Nl};function dpe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s}=a,i=w.parseAxisParam(s,n.shape),o=N.getAxesPermutation(i,n.shape.length),l=n,d=[];o!=null&&(l=Nl({inputs:{x:n},backend:r,attrs:{perm:o}}),d.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=new L8(l.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],h=r.runWebGPUProgram(u,[l],"int32",p);return d.forEach(c=>r.disposeData(c.dataId)),h}var ppe={kernelName:Hs,backendName:"webgpu",kernelFunc:dpe};function hpe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s}=a,i=w.parseAxisParam(s,n.shape),o=N.getAxesPermutation(i,n.shape.length),l=n,d=[];o!=null&&(l=Nl({inputs:{x:n},backend:r,attrs:{perm:o}}),d.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=new L8(l.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],h=r.runWebGPUProgram(u,[l],"int32",p);return d.forEach(c=>r.disposeData(c.dataId)),h}var cpe={kernelName:Ru,backendName:"webgpu",kernelFunc:hpe},B8=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
      ${Je()}
      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});
        }
      }
    `}},W8=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
      ${Je()}
        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 fpe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1,u=N.computePool2DInfo(n.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return Va({inputs:{x:n},backend:r});let p,h=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?p=new W8(u):(p=new B8(u,"avg"),h.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),r.runWebGPUProgram(p,[n],n.dtype,h)}var mpe={kernelName:qs,backendName:"webgpu",kernelFunc:fpe};function gpe(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s}=t,{transposeA:i,transposeB:o}=a;return Gx({a:n,b:s,transposeA:i,transposeB:o,backend:r})}var ype={kernelName:Ks,backendName:"webgpu",kernelFunc:gpe},Ape=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${cr(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=cr(this.rank),t=xpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((a,n)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((a,n)=>`sourceLoc.${Sy[n]} = uniforms.start[${n}] + coords.${Sy[n]};`),`
      ${Je()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${r.join(`
`)}
          setOutputAtIndex(index, getSource(${t}));
        }
      }
    `}},Sy=["x","y","z","w","u","v"];function xpe(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 bd(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{begin:s,size:i}=a,[o,l]=Ot.parseSliceParams(n,s,i);if(Ot.assertParamsValid(n,o,l),r.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=r.tensorMap.get(n.dataId),h=Hde(p.values,o,l,n.shape,n.dtype);return r.makeTensorInfo(l,n.dtype,h)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,n.dtype,[]);let d=new Ape(o,l),u=[{type:"int32",data:o}];return r.runWebGPUProgram(d,[n],n.dtype,u)}var bpe={kernelName:il,backendName:"webgpu",kernelFunc:bd},vpe=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,crops:i}=a;w.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(n.shape,s,o),d=N.getPermuted(l.length,s.length),u=N.getReshapedPermuted(n.shape,s,o),p=N.getSliceBeginCoords(i,s.length),h=N.getSliceSize(u,i,s.length),c=[],f=Ge({inputs:{x:n},backend:r,attrs:{shape:l}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:d}}),g=Ge({inputs:{x:m},backend:r,attrs:{shape:u}}),y=bd({inputs:{x:g},backend:r,attrs:{begin:p,size:h}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},wpe={kernelName:Mo,backendName:"webgpu",kernelFunc:vpe},V8=Wr({opSnippet:10,dtype:"bool",cpuKernelImpl:Wde}),kpe={kernelName:Ko,backendName:"webgpu",kernelFunc:V8};function Ph(e){let{inputs:t,backend:r}=e,{input:a}=t,n=r.tensorMap.get(a.dataId);return Va({inputs:{x:n.complexTensorInfos.real},backend:r})}var Ipe={kernelName:Kp,backendName:"webgpu",kernelFunc:Ph};function Spe(e,t){let r=new $h(e.shape,23),a=t.runWebGPUProgram(r,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Ty(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{dtype:s}=a;if(s==="complex64"){if(n.dtype==="complex64")return Va({inputs:{x:n},backend:r});let i=Vt(n.shape),o=Ty({inputs:{x:n},backend:r,attrs:{dtype:"float32"}}),l=xd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(n.dtype==="complex64"){let i=Ph({inputs:{input:n},backend:r}),o=Ty({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(n.dtype,s)){let i=Va({inputs:{x:n},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Spe(n,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=V8({inputs:{a:n,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var Tpe={kernelName:Xs,backendName:"webgpu",kernelFunc:Ty},Cpe=xr({opType:1,cpuKernelImpl:kde}),Npe={kernelName:Zs,backendName:"webgpu",kernelFunc:Cpe},Epe=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
      ${Je()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          var clampedValue : vec4<f32>;
          for (var i = 0; i < 4; i = i + 1) {
            if (isNanCustom(value[i])) {
              clampedValue[i] = value[i];
            } else {
              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
            }
          }

          setOutputAtIndex(index, clampedValue);
        }
      }
    `}},Rpe=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
      ${Je()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isNanCustom(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `}};function Fpe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{clipValueMin:s,clipValueMax:i}=a,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(n.shape)%4===0?o=new Epe(n.shape):o=new Rpe(n.shape),r.runWebGPUProgram(o,[n],n.dtype,l)}var Mpe={kernelName:Kn,backendName:"webgpu",kernelFunc:Fpe},$pe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let a=1;a<this.offsetLength;a++)e.push(`else if (yC < uniforms.offset${[a]}){ setOutputAtCoords(coords.x, coords.y, getT${a}(yR, yC - uniforms.offset${a-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
      ${Je()}
        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 a0(e){let{inputs:t,backend:r}=e,{input:a}=t,n=r.tensorMap.get(a.dataId);return Va({inputs:{x:n.complexTensorInfos.imag},backend:r})}var Ppe={kernelName:Gp,backendName:"webgpu",kernelFunc:a0};function Cy(e,t,r){let a=e[0].dtype;if(a==="complex64"){let c=e.map(A=>Ph({inputs:{input:A},backend:r})),f=e.map(A=>a0({inputs:{input:A},backend:r})),m=Cy(c,t,r),g=Cy(f,t,r),y=xd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let n=r.shouldExecuteOnCPU(e);if(a==="string"&&(n=!0),n){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return Ge({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Ide(f,m,a,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,a,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=Ope(e,t,r),o=s.map(c=>c.shape),l=new $pe(o),d=[],u=new Array(o.length-1);if(u.length>0){u[0]=o[0][1],d.push({type:"int32",data:[u[0]]});for(let c=1;c<u.length;c++)u[c]=u[c-1]+o[c][1],d.push({type:"int32",data:[u[c]]})}let p=r.runWebGPUProgram(l,s,s[0].dtype,d);s.forEach(c=>r.disposeData(c.dataId));let h=Ge({inputs:{x:p},backend:r,attrs:{shape:i}});return r.disposeData(p.dataId),h}function Ope(e,t,r){let a=N.computeOutShape(e.map(n=>n.shape),t);return{tensors2D:e.map(n=>Ge({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape.slice(0,t)),w.sizeFromShape(n.shape.slice(t))]}})),outShape:a}}function U8(e){let{inputs:t,backend:r,attrs:a}=e,{axis:n}=a,s=w.parseAxisParam(n,t[0].shape)[0],i=N.computeOutShape(t.map(d=>d.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(d=>w.sizeFromShape(d.shape)>0);if(o.length===1)return Va({inputs:{x:o[0]},backend:r});let l=o.map(d=>d.shape);return N.assertParamsConsistent(l,s),Cy(o,s,r)}var zpe={kernelName:$o,backendName:"webgpu",kernelFunc:U8},Dpe=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
    ${Je()}

      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);
        }
      }
    }
  `}},_pe=class{constructor(e,t=!1,r=null,a=!1,n=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=a,this.hasLeakyreluAlpha=n,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],a=this.outputShape[3],n=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Hn(e,[r,n]),Hn(t,[n,a])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
    let divBy4Remainder${e} = flatIndex${e} % 4;
    let divBy4Index${e} = flatIndex${e} / 4;
    let curData${e} = x.numbers[divBy4Index${e}];
    if (divBy4Remainder${e} == 0) {
      temp = curData${e};
    } else {
      // TODO: This could end up being a redundant load with another one in
      // the same shader invocation. Perhaps there's an opportunity for
      // optimization
      let nextData${e} = x.numbers[divBy4Index${e} + 1];
      if (divBy4Remainder${e} == 1) {
        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
      } else if (divBy4Remainder${e} == 2) {
        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
      } else if (divBy4Remainder${e} == 3) {
        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
      }
    }
    `}getUserCode(){let e=z8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
        let outCol = r % uniforms.outShape[2];
        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
        let inChCoord = c % uniforms.xShape[3];
        var coord = vec4<i32>(
            batch,
            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
            inChCoord);
        var resData = vec4<f32>(0.0);
        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
          // the 'same' padding type.
          if (coordsInBounds4D(coord, uniforms.xShape)) {
            resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];
          } else {
            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
          ${this.getSampleAWithRemainder(1)}
          resData = temp;
          if (WCol == (uniforms.filterDims[1] - 1)) {
            coord = vec4<i32>(
              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
              ${this.getSampleAWithRemainder(2)}
            if (inChCoord == 0) {
              resData = vec4<f32>(resData.xyz, temp.x);
            } else if (inChCoord == 1) {
              resData = vec4<f32>(resData.xy, temp.xy);
            } else {
              resData = vec4<f32>(resData.x, temp.xyz);
            }
          }
          `}
        return resData;`,r=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${t}
         }
         return vec4<f32>(0.0);
        `,a=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter / 4 + col];
         }
         return vec4<f32>(0.0);
        `,n="",s="";if(this.activation){let o=ts(this.activation,this.isVec4);if(this.hasPreluActivationWeights)n=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${o}
        }`;else{if(this.hasLeakyreluAlpha)throw n=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
          let b = getLeakyreluAlphaByOutputCoords(outCoord);
          ${o}
        }`,new Error("Leakyrelu is not supported.");n=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          ${o}
        }`}s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
        ${n}
        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          let r = row;
          let c = col * 4;
          var batch = i32(globalId.z);
          ${r}
        }

        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          ${a}
        }

        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
          var batch = i32(globalId.z);
          var value = valueInput;
          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
          {
            let outCoord = vec4<i32>(
              batch,
              row / uniforms.outShape[2],
              row % uniforms.outShape[2],
              col * 4);
            ${i}
            ${s}
            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${e}
      `}},Lpe=class{constructor(e,t=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Bx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Wx(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=a,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[e,r],n=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Hn(a,[s,o]),Hn(n,[o,i])]}getUserCode(){let e=Ux(this.elementsPerThread,this.workGroupSize),t=`
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
    let coord = vec4<i32>(
        batch,
        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
        col % uniforms.xShape[3]);
    // The bounds checking is always needed since we use it to pad zero for the
    // 'same' padding type.
    if(coordsInBounds4D(coord, uniforms.xShape)) {
      return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;`,r=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${t}
    }
    return 0.0;
    `,a=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter + col];
	 }
	 return 0.0;
	 `,n="",s="";if(this.activation){let o=ts(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${o}
                }`:n=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
                    ${o}
                  }
                `,s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
    ${n}
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      ${r}
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      ${a}
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      ${i}
      ${s}
      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }
    ${e}
  `}},Bpe=class{constructor(e,t=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=a,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let a=ts(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${a}
             }`:e=`
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                    ${a}
                  }
                `,t="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
      ${e}
      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
        let coord = vec4<i32>(batch, row, col, chan);
        if(coordsInBounds4D(coord, uniforms.xShape)) {
          return getX(batch, row, col, chan);
        }
        return 0.0;
      }

      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
        let coord = vec4<i32>(row, col, xChannel, outChannel);
        if(coordsInBounds4D(coord, uniforms.wShape)) {
          return getW(row, col, xChannel, outChannel);
        }
        return 0.0;
      }

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          ${r}
          ${t}
          setOutputAtCoords(batch, row, col, chan, value);
        }
      }

      ${Wi()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let outChannel = coords[3];

        var acc = 0.0;

        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
              let v = readInp(batch, coordRow, coordCol, xChannel);
              let f = readFilt(row, col, xChannel, outChannel);
              acc = acc + v * f;
            }
          }
        }

        writeResult(batch, coords[1], coords[2], outChannel, acc);
      }
    `}};function Wpe({x:e,filter:t,convInfo:r,backend:a,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,d=r.dataFormat==="channelsLast",u=!1,p=!1,h=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(h){let y=r.inHeight*r.inWidth*r.inChannels;c=Ge({inputs:{x:e},backend:a,attrs:{shape:[1,r.batchSize,y]}}),f=Ge({inputs:{x:t},backend:a,attrs:{shape:[1,y,r.outChannels]}})}else{let y=d?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=Ge({inputs:{x:e},backend:a,attrs:{shape:[1,y,r.inChannels]}}),f=Ge({inputs:{x:t},backend:a,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=Gx({a:c,b:f,transposeA:u,transposeB:p,backend:a,bias:n,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ge({inputs:{x:m},backend:a,attrs:{shape:r.outShape}});return a.disposeData(c.dataId),a.disposeData(f.dataId),a.disposeData(m.dataId),g}function Vpe({x:e,filter:t,convInfo:r,backend:a,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:d,inChannels:u,strideWidth:p,strideHeight:h,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*d*u,v=m*f,C=[v,b],T=!1,E=!1,R=[],z=Ge({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),M=Ge({inputs:{x:t},backend:a,attrs:{shape:[1,b,-1]}});R.push(z),R.push(M);let I=new Dpe(C,x),D=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[p,h]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],O=a.runWebGPUProgram(I,[z],z.dtype,D),j=Ge({inputs:{x:O},backend:a,attrs:{shape:[1,C[0],C[1]]}});R.push(O),R.push(j);let X=[1,C[0],C[1]],_=new D8(X,[1,v,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,n,o,s),K=X[1],W=X[2],ee=r.outChannels,Q=[{type:"int32",data:[K]},{type:"int32",data:[ee]},{type:"int32",data:[W]}],ne=[j,M];n&&ne.push(n),s&&ne.push(s);let Z=a.runWebGPUProgram(_,ne,j.dtype,Q),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],ie=Ge({inputs:{x:Z},backend:a,attrs:{shape:ae}});R.push(Z);for(let xe of R)a.disposeData(xe.dataId);return ie}function G8({x:e,filter:t,convInfo:r,backend:a,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=n!=null,d=s!=null,u;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return Wpe({x:e,filter:t,convInfo:r,backend:a,bias:n,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return Vpe({x:e,filter:t,convInfo:r,backend:a,bias:n,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let p=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&r.outChannels>=32,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(p)u=new Bpe(r,l,o,d);else{h?u=new _pe(r,l,o,d):u=new Lpe(r,l,o,d);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(n),d&&m.push(s),a.runWebGPUProgram(u,m,e.dtype,f)}function Upe(e){let{inputs:t,attrs:r,backend:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:d,dimRoundingMode:u}=r,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(n.shape,s.shape,i,d,o,u,!1,p);return G8({x:n,filter:s,convInfo:h,backend:a})}var Gpe={kernelName:Ys,backendName:"webgpu",kernelFunc:Upe},jpe=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=Bx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Wx(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
      return 0.0;
    }
    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
      return 0.0;
    }
    let coord = vec4<i32>(
        batch,
        i32(xR),
        i32(xC),
        col % uniforms.outBackprop[3]);
    return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      let coordX = uniforms.filterDims.x - 1 -
          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
      let coordY = uniforms.filterDims.y - 1 -
          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
          coordX >= 0 && coordY >= 0) {
        let coord = vec4<i32>(coordX, coordY, col,
            row % uniforms.outBackprop[3]);
        return W.numbers[getIndexFromCoords4D(coord, uniforms.wShape)];
      }
      return 0.0;
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }

    ${Ux(this.elementsPerThread,this.workGroupSize)}
  `}},Hpe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
    ${Je()} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d1 = coords[${r}];

        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
        let dyRCorner = dyCorner.x;
        let dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;
        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
          let wRPerm = uniforms.filterDims.x - 1 - wR;
          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
              wRPerm < 0) {
            continue;
          }
          let idyR = dyR;

          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
            let wCPerm = uniforms.filterDims.y - 1 - wC;
            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
                fract(dyC) > 0.0 || wCPerm < 0) {
              continue;
            }
            let idyC = dyC;

            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
              if (${this.isChannelsLast}) {
                let xValue = getDy(batch, idyR, idyC, d2);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              } else {
                let xValue = getDy(batch, d2, idyR, idyC);
                let wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd = dotProd + xValue * wValue;
              }

            }
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
  `}};function qpe(e){let{inputs:t,backend:r,attrs:a}=e,{dy:n,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a,p=N.convertConv2DDataFormat(d),h=N.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),c=[{type:"int32",data:[h.filterHeight,h.filterWidth]},{type:"int32",data:[h.filterHeight-1-h.padInfo.top,h.filterWidth-1-h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.batchSize,h.outHeight,h.outWidth,h.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Hpe(h);else{f=new jpe(h);let m=h.inShape[1]*h.inShape[2],g=h.inShape[3],y=h.filterHeight*h.filterWidth*h.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[n,s],"float32",c)}var Kpe={kernelName:Js,backendName:"webgpu",kernelFunc:qpe},Xpe=xr({opType:2}),Zpe={kernelName:Qs,backendName:"webgpu",kernelFunc:Xpe},Ype=xr({opType:3}),Jpe={kernelName:ei,backendName:"webgpu",kernelFunc:Ype},Qpe=class{constructor(e,t,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[n]=t;this.outputShape=[n,r[0],r[1],e],this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=a==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,a,n]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
      ${Je()}
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let height_ratio = f32(${r});
        let width_ratio = f32(${s});
        let b = coords[0];
        let y = coords[1];
        let x = coords[2];
        let d = coords[3];
        // get box vals
        let y1 = getBoxes(b, 0);
        let x1 = getBoxes(b, 1);
        let y2 = getBoxes(b, 2);
        let x2 = getBoxes(b, 3);
        // get image in batch index
        let bInd = i32(round(getBoxInd(b)));
        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
          return;
        }
        let height_scale = ${a};
        let width_scale = ${i};
        let in_y = ${n};
        if( in_y < 0.0 || in_y > ${e} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let in_x = ${o};
        if( in_x < 0.0 || in_x > ${t} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
        if(${this.methodId} == 1) {
          // Compute the four integer indices.
          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
          let top = topLeft + (topRight - topLeft) * fracCR.x;
          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          let newValue = top + (bottom - top) * fracCR.y;
          setOutputAtIndex(index, newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          let sourceNearestCR = vec2<i32>(floor(
            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
          let newValue = getImage(
            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutputAtIndex(index, newValue);
        }
      }
    }
    `}},ehe=e=>{let{inputs:t,backend:r,attrs:a}=e,{image:n,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:d}=a,u=new Qpe(n.shape[3],s.shape,o,l),p=[{type:"float32",data:[d]}];return r.runWebGPUProgram(u,[n,s,i],"float32",p)},the={kernelName:Oo,backendName:"webgpu",kernelFunc:ehe},rhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
      ${Je()}
        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 = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ahe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockSize:s,dataFormat:i}=a,o=n.shape[0],l=i==="NHWC"?n.shape[1]:n.shape[2],d=i==="NHWC"?n.shape[2]:n.shape[3],u=i==="NHWC"?n.shape[3]:n.shape[1],p=l*s,h=d*s,c=u/(s*s),f=i==="NHWC"?[o,p,h,c]:[o,c,p,h],m=[{type:"int32",data:[s]}],g=new rhe(f,i);return r.runWebGPUProgram(g,[n],n.dtype,m)}var nhe={kernelName:zo,backendName:"webgpu",kernelFunc:ahe},j8=class{constructor(e,t=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=a,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let a=ts(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${a}
        }`:e=`
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
            ${a}
          }
        `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
      ${e}

      ${Lx()}
      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
        let batch = 0;
        let r = i32(globalId.x);
        let c = i32(globalId.y) * 4;
        let d2 = i32(globalId.z) * 4;
        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
        let d1 = d2;
        let q = 0;

        let xRCorner = xRCCorner.x;
        let xCCorner = xRCCorner.y;

        var wVals : array<vec4<f32>, 9>;
        wVals[0] = getW(0, 0, d1, q);
        wVals[1] = getW(0, 1, d1, q);
        wVals[2] = getW(0, 2, d1, q);
        wVals[3] = getW(1, 0, d1, q);
        wVals[4] = getW(1, 1, d1, q);
        wVals[5] = getW(1, 2, d1, q);
        wVals[6] = getW(2, 0, d1, q);
        wVals[7] = getW(2, 1, d1, q);
        wVals[8] = getW(2, 2, d1, q);

        var xVals : array<array<vec4<f32>, 6>, 3>;
        for (var wR = 0; wR < 3; wR = wR + 1) {
          let xR = xRCorner + wR * uniforms.dilation[0];
          for (var wC = 0; wC < 6; wC = wC + 1) {
            let xC = xCCorner + wC * uniforms.dilation[1];
            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
              xVals[wR][wC] = vec4<f32>(0.0);
            } else {
              xVals[wR][wC] = getX(batch, xR, xC, d1);
            }
          }
        }

        var dotProd : array<vec4<f32>, 4>;
        dotProd[0] = vec4<f32>(0.0);
        dotProd[1] = vec4<f32>(0.0);
        dotProd[2] = vec4<f32>(0.0);
        dotProd[3] = vec4<f32>(0.0);

        for (var wR = 0; wR < 3; wR = wR + 1) {
          for (var wC = 0; wC < 3; wC = wC + 1) {
            let indexW = wR * 3 + wC;
            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
          }
        }

        for (var i = 0; i < 4; i = i + 1) {
          let coords = vec4<i32>(batch, r, c + i, d2);
          if (coordsInBounds4D(coords, uniforms.outShape)) {
            ${r}
            ${t}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `}},H8=class{constructor(e,t=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
      channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=a,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let a=ts(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${a}
        }`:e=`
          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
            ${a}
          }
        `,t="dotProd = activation(dotProd, coords);"}let r=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
      ${e}

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
          value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          setOutputAtCoords(batch, row, col, chan, value);
        }
      }

      ${Wi()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
        let d2 = coords[3];
        let d1 = d2 / uniforms.channelMul;
        let q = d2 - d1 * uniforms.channelMul;

        let inputRowStart = xRCCorner.x;
        let inputColStart = xRCCorner.y;
        let inputRowEnd = inputRowStart + uniforms.filterHeight *
            uniforms.dilation[0];
        let inputColEnd = inputColStart + uniforms.filterWidth *
            uniforms.dilation[1];

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;

        // Extract if checking out of for loop for performance.
        if (inputRowStart >= 0 && inputColStart >= 0 &&
          inputRowEnd < uniforms.inDims[0] &&
              inputColEnd < uniforms.inDims[1]) {
            // Here using a constant value |this.convInfo.filterHeight| instead
            // of uniform value is in order to loop unrolling.
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

                let xVal = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          } else {
            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
              let xR = inputRowStart + wR * uniforms.dilation[0];

              if (xR < 0 || xR >= uniforms.inDims[0]) {
                continue;
              }

              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
                let xC = inputColStart + wC * uniforms.dilation[1];

                if (xC < 0 || xC >= uniforms.inDims[1]) {
                  continue;
                }

                let xVal = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          }

        ${r}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `}};function she(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:d}=a,u=l;u==null&&(u=[1,1]);let p=N.computeConv2DInfo(n.shape,s.shape,i,u,o,d,!0),h=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],c;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.filterHeight===3&&p.inChannels%4===0?c=new j8(p):(c=new H8(p),h.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),r.runWebGPUProgram(c,[n,s],n.dtype,h)}var ihe={kernelName:ti,backendName:"webgpu",kernelFunc:she},q8=Wr({opSnippet:0,cpuKernelImpl:Lde,supportsComplex:!0}),ohe={kernelName:xi,backendName:"webgpu",kernelFunc:q8},lhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
         if (isNanCustom(candidate)) {
          bestValue = uniforms.NAN;
         } else if (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
           {  bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let r=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
       }

       ${`
         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
       `}
       fn getOffset(outputIndex : i32) -> i32 {
         let outputCoords = getCoordsFromIndex(outputIndex);
         let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
          return offset;
       }
       ${Je()}
         let outputIndex = index / i32(workGroupSizeX);
         let offset = getOffset(outputIndex);
         var bestValue = ${t};
         let Length = uniforms.reduceSize;
         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[offset + k]);
           ${e}
         }
         xBestValues[localId.x] = bestValue;
         workgroupBarrier();

         var reduceSize = min(u32(Length), workGroupSizeX);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (localId.x < currentSize) {
            let candidate = xBestValues[localId.x + interval];
            ${e}
            xBestValues[localId.x] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (localId.x == 0u && outputIndex < uniforms.size) {
          ${r}
        }
       }
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      ${Je()}
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      }
  `}},Mhe={kernelName:oi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:a,scale:n,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,d=r,u=[a,i,o],p=null;s!=null&&(p=s.shape,u.push(s));let h=null;n!=null&&(h=n.shape,u.push(n));let c=new Fhe(a.shape,i.shape,o.shape,p,h),f=[{type:"float32",data:[l]}];return d.runWebGPUProgram(c,u,a.dtype,f)}};function $he(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dataFormat:u,dilations:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=a,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(n.shape,s.shape,l,p,d,h,!1,m);return G8({x:n,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var Phe={kernelName:Fs,backendName:"webgpu",kernelFunc:$he};function Ohe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dimRoundingMode:p,activation:h}=a,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${c}'`);let f=N.computeConv2DInfo(n.shape,s.shape,l,c,d,p,!0),m=[n,s],g=i!=null,y=o!=null;g&&m.push(i),y&&m.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],x;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4===0?x=new j8(f,g,h,y):(x=new H8(f,g,h,y),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),r.runWebGPUProgram(x,m,"float32",A)}var zhe={kernelName:Ms,backendName:"webgpu",kernelFunc:Ohe},Dhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${cr(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
        ${Je()}
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          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)));
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          }

          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
        }
      }
      `}};function _he(e){let{inputs:t,backend:r}=e,{params:a,indices:n}=t,s=n.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,d,u,p]=N.prepareAndValidate(a,n),h=Ge({inputs:{x:n},backend:r,attrs:{shape:[d,i]}}),c=Ge({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape)/u,u]}});if(r.shouldExecuteOnCPU([a,n])||a.dtype==="string"){let A=r.readSync(n.dataId),x=r.bufferSync(a),b=Ede(A,x,a.dtype,d,i,u,p,a.shape,o);return r.makeTensorInfo(l,a.dtype,b.values)}let f=new Dhe(i,[d,u]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=r.runWebGPUProgram(f,[c,h],c.dtype,m),y=Ge({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(h.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var Lhe={kernelName:Vo,backendName:"webgpu",kernelFunc:_he},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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Whe(this.aShape,"i32");return`
      ${Je()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `}};function Whe(e,t="int"){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e.length;n++)n===2?a.push(`${t}(getIndices(resRC.x, resRC.z))`):a.push(`${r[n]}`);return a.join()}function Y8(e){let{inputs:t,backend:r,attrs:a}=e,{x:n,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,n.shape)[0],d=N.segment_util.collectGatherOpShapeInfo(n,s,l,o),u=w.sizeFromShape(s.shape),p=[],h=Ge({inputs:{x:n},backend:r,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),c=Ge({inputs:{x:s},backend:r,attrs:{shape:[d.batchSize,u/d.batchSize]}});p.push(h),p.push(c);let f=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize];if(r.shouldExecuteOnCPU([n,s])){let A=r.tensorMap.get(c.dataId).values,x=Le(c.shape,c.dtype,A),b=r.tensorMap.get(h.dataId).values,v=Le(h.shape,h.dtype,b),C=Rde(v,x,f);return p.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(d.outputShape,C.dtype,C.values)}let m=new Bhe(h.shape,f),g=r.runWebGPUProgram(m,[h,c],h.dtype);p.push(g);let y=Ge({inputs:{x:g},backend:r,attrs:{shape:d.outputShape}});return p.forEach(A=>r.disposeData(A.dataId)),y}var Vhe={kernelName:Wo,backendName:"webgpu",kernelFunc:Y8},Uhe=Wr({opSnippet:5,cpuKernelImpl:Mde,dtype:"bool"}),Ghe={kernelName:Uo,backendName:"webgpu",kernelFunc:Uhe},jhe=Wr({opSnippet:6,dtype:"bool",cpuKernelImpl:Fde}),Hhe={kernelName:li,backendName:"webgpu",kernelFunc:jhe};function qhe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{alpha:s}=a,i=[{type:"float32",data:[s]}],o=new $h(n.shape,15);return o.uniforms="alpha : f32;",r.runWebGPUProgram(o,[n],"float32",i)}var Khe={kernelName:di,backendName:"webgpu",kernelFunc:qhe},Xhe=Wr({opSnippet:7,dtype:"bool",cpuKernelImpl:Pde}),Zhe={kernelName:Go,backendName:"webgpu",kernelFunc:Xhe},Yhe=Wr({opSnippet:8,dtype:"bool",cpuKernelImpl:$de}),Jhe={kernelName:jo,backendName:"webgpu",kernelFunc:Yhe},Qhe=xr({opType:9,cpuKernelImpl:Ode}),ece={kernelName:pi,backendName:"webgpu",kernelFunc:Qhe},tce=Wr({opSnippet:9,dtype:"bool"}),rce={kernelName:Ho,backendName:"webgpu",kernelFunc:tce},ace=xr({opType:10}),nce={kernelName:Vu,backendName:"webgpu",kernelFunc:ace};function J8(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{reductionIndices:s,keepDims:i}=a;return Oh(n,s,i,"max",r)}var sce={kernelName:hi,backendName:"webgpu",kernelFunc:J8},ice=Wr({opSnippet:15,cpuKernelImpl:Dde}),oce={kernelName:ci,backendName:"webgpu",kernelFunc:ice};function lce(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1,u=N.computePool2DInfo(n.shape,s,i,d,o,l),p,h=[];if(u.filterHeight===1&&u.filterWidth===1){if(w.arraysEqual(u.inShape,u.outShape))return Va({inputs:{x:n},backend:r});p=new W8(u),h.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else p=new B8(u,"max"),h.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return r.runWebGPUProgram(p,[n],n.dtype,h)}var uce={kernelName:fi,backendName:"webgpu",kernelFunc:lce};function dce(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{keepDims:s,axis:i}=a;return Oh(n,i,s,"mean",r)}var pce={kernelName:mi,backendName:"webgpu",kernelFunc:dce};function hce(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a;return Oh(n,s,i,"min",r)}var cce={kernelName:gi,backendName:"webgpu",kernelFunc:hce},fce=Wr({opSnippet:16,cpuKernelImpl:_de}),mce={kernelName:yi,backendName:"webgpu",kernelFunc:fce},gce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((a,n)=>{this.uniforms+=` pad${n} : vec2<i32>;`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,d)=>`uniforms.pad${d}[0]`).join(","),r=this.xShape.map((l,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e===1?"start":"start[i]",n=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=cr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${Je()}
        if (index < uniforms.size) {
          let start = ${i}(${t});
          let end = ${i}(${r});
          var outC = getCoordsFromIndex(index);
          for (var i = 0; i < ${e}; i = i + 1) {
            if (${s} < ${a}) {
              ${s} = ${a} * 2 - ${s} - ${this.offset};
            } else if(${s} >= ${n}) {
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            }
          }
          let coords = outC - start;
          setOutputAtIndex(index, getX(${o}));
        }
      }
    `}},yce={kernelName:Ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:a}=e,{paddings:n,mode:s}=t,i=r,o=n.map(d=>({type:"int32",data:[d[0],d[1]]})),l=new gce(a.shape,n,s);return i.runWebGPUProgram(l,[a],a.dtype,o)}};function Ace(e){let{inputs:t,backend:r}=e,{x:a}=t;if(r.shouldExecuteOnCPU([a])){let s=r.tensorMap.get(a.dataId),[i,o]=Bde(s.values,a.shape,a.dtype);return r.makeTensorInfo(o,a.dtype,i)}let n=new $h(a.shape,11);return r.runWebGPUProgram(n,[a],a.dtype)}var xce={kernelName:qo,backendName:"webgpu",kernelFunc:Ace};function bce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:a}=e,{boxes:n,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,d=r.readSync(n.dataId),u=r.readSync(s.dataId),{selectedIndices:p}=Ha.nonMaxSuppressionV3Impl(d,u,i,o,l);return r.makeTensorInfo([p.length],"int32",new Int32Array(p))}var vce={kernelName:Xo,backendName:"webgpu",kernelFunc:bce};function wce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:a}=e,{boxes:n,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:d}=a,u=r.readSync(n.dataId),p=r.readSync(s.dataId),h=i,c=o,f=l,m=d,{selectedIndices:g,selectedScores:y}=Ha.nonMaxSuppressionV5Impl(u,p,h,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var kce={kernelName:Zo,backendName:"webgpu",kernelFunc:wce};function bf(e){let{inputs:t,backend:r}=e,{x:a}=t;if(a.dtype==="complex64"){let n=Ph({inputs:{input:a},backend:r}),s=bf({inputs:{x:n},backend:r}),i=a0({inputs:{input:a},backend:r}),o=bf({inputs:{x:i},backend:r}),l=xd({inputs:{real:s,imag:o},backend:r});return r.disposeData(n.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return vd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:r})}var Ice={kernelName:ml,backendName:"webgpu",kernelFunc:bf};function Q8(e){let{inputs:t,backend:r}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let n=Ph({inputs:{input:a},backend:r}),s=Q8({inputs:{x:n},backend:r}),i=a0({inputs:{input:a},backend:r}),o=bf({inputs:{x:i},backend:r}),l=xd({inputs:{real:s,imag:o},backend:r});return r.disposeData(n.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return vd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}var Sce={kernelName:Yo,backendName:"webgpu",kernelFunc:Q8};function Tce(e){let{inputs:t,backend:r,attrs:a}=e,{axis:n}=a;if(t.length===1)return Ny({inputs:{input:t[0]},backend:r,attrs:{dim:n}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=Ny({inputs:{input:u},backend:r,attrs:{dim:n}});return o.push(p),p}),d=U8({inputs:l,backend:r,attrs:{axis:n}});return o.forEach(u=>r.disposeData(u.dataId)),d}var Cce={kernelName:Qo,backendName:"webgpu",kernelFunc:Tce},Nce=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,a)=>r[0]+e[a]+r[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,a)=>{this.uniforms+=` pad${a} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=cr(e),r=this.xShape.map((d,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((d,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${a})`:`${a}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
      ${Je()}
        if (index < uniforms.size) {
          let start = ${n};
          let end = ${s};
          let outC = getCoordsFromIndex(index);

          if (${i} || ${o}) {
            setOutputAtIndex(index, uniforms.constantValue);
          } else {
            let coords = outC - start;
            setOutputAtIndex(index, getX(${l}));
          }
        }
      }
    `}},eS=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{paddings:s,constantValue:i}=a;if(s.every(d=>w.arraysEqual(d,[0,0])))return Va({inputs:{x:n},backend:r});if(w.sizeFromShape(n.shape)===0){let d=s.map((u,p)=>u[0]+n.shape[p]+u[1]);return vd({backend:r,attrs:{shape:d,value:i,dtype:n.dtype}})}let o=[{type:"float32",data:[i]}];s.map(d=>o.push({type:"int32",data:[d[0],d[1]]}));let l=new Nce(n.shape,s);return r.runWebGPUProgram(l,[n],n.dtype,o)},Ece={kernelName:bi,backendName:"webgpu",kernelFunc:eS},Rce=Wr({opSnippet:13}),Fce={kernelName:vi,backendName:"webgpu",kernelFunc:Rce};function Mce(e){let{inputs:t,backend:r}=e,{x:a,alpha:n}=t,s=new _8(14,a.shape,n.shape);return r.runWebGPUProgram(s,[a,n],"float32")}var $ce={kernelName:wi,backendName:"webgpu",kernelFunc:Mce};function Pce(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,keepDims:i}=a;return Oh(n,s,i,"prod",r)}var Oce={kernelName:el,backendName:"webgpu",kernelFunc:Pce},zce=e=>{let{backend:t,attrs:r}=e,{start:a,stop:n,step:s,dtype:i}=r,o=Ude(a,n,s,i);return t.makeTensorInfo([o.length],i,o)},Dce={kernelName:ju,backendName:"webgpu",kernelFunc:zce},tS=Wr({opSnippet:3}),_ce={kernelName:ri,backendName:"webgpu",kernelFunc:tS},Lce=xr({opType:13}),Bce={kernelName:ki,backendName:"webgpu",kernelFunc:Lce},Wce=xr({opType:14}),Vce={kernelName:Si,backendName:"webgpu",kernelFunc:Wce},Uce=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
      ${Je()}
        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 Gce(e){let{inputs:t,backend:r,attrs:a}=e,{images:n}=t,{alignCorners:s,size:i,halfPixelCenters:o}=a,[l,d]=i,u=s&&l>1?1:0,p=s&&d>1?1:0,h=[{type:"float32",data:[u,p]},{type:"float32",data:[o?.5:0]}],c=new Uce(n.shape,l,d);return r.runWebGPUProgram(c,[n],"float32",h)}var jce={kernelName:Ii,backendName:"webgpu",kernelFunc:Gce},Hce=class{constructor(e,t,r,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=a,this.shaderKey=`resizeNearest_${a}`}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",`
      ${Je()}
        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 qce(e){let{inputs:t,backend:r,attrs:a}=e,{images:n}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=s&&l>1?1:0,p=s&&d>1?1:0,h=[{type:"float32",data:[u,p]},{type:"float32",data:[s?.5:0]}],c=new Hce(n.shape,l,d,i);return r.runWebGPUProgram(c,[n],n.dtype,h)}var Kce={kernelName:qu,backendName:"webgpu",kernelFunc:qce},Xce=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
          cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
        ${Je()}

          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));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `}},Zce={kernelName:gl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:a}=e,{radians:n,fillValue:s,center:i}=t,o=r,l=new Xce(a.shape,s),[d,u]=N.getImageCenter(i,a.shape[1],a.shape[2]),p=[{type:"float32",data:[d]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?p.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):p.push({type:"float32",data:s}),o.runWebGPUProgram(l,[a],a.dtype,p)}},Yce=xr({opType:16,cpuKernelImpl:Gde}),Jce={kernelName:Ti,backendName:"webgpu",kernelFunc:Yce},Qce=class{constructor(e,t,r,a,n,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=He(e),this.dispatch=ze(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${a}_${this.sliceDimGreaterThanOne}_${i}`;let o=cr(n.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=a,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",a="",n="",s="";this.updatesRank===1?(a="coords[0]",n="flattenedIndex",s=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
        return index;
      }
      `):this.updatesRank===2&&(a="coords[0], coords[1]",n="vec2<i32>(flattenedIndex, coords[1])",s=`
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
        let d0 = index / uniforms.updatesShape[1];
        let d1 = index - d0 * uniforms.updatesShape[1];
        return vec2<i32>(d0, d1);
      }
      `);let i=`getUpdates(${a})`,o=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
     var assumed = atomicLoad(&(result.numbers[flatIndex]));
     var success = 0;
     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
       assumed = resValue[0];
       success = resValue[1];
     }
     `;return`
    ${s}

      ${Je()}

        if (index < uniforms.size) {
          let coords = getUpdatesCoordsFromFlatIndex(index);
          var flattenedIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexInside = i32(round(${t}));
            flattenedIndex = flattenedIndex + indexInside * ${r};
          }
          let updateValue = ${i};
          let flatIndex = getOutputIndexFromCoords(${n});

         ${o}
        }
      }`}};function efe(e){let{inputs:t,backend:r,attrs:a}=e,{indices:n,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:d,strides:u,outputSize:p}=N.calculateShapes(s,n,i),h=[p/d,d];if(p===0)return r.makeTensorInfo(i,n.dtype);let c=Ge({inputs:{x:n},backend:r,attrs:{shape:[l,o]}}),f=Ge({inputs:{x:s},backend:r,attrs:{shape:[l,d]}}),m=f.dtype,g=vd({backend:r,attrs:{shape:h,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:u},{type:"int32",data:[y]}],x=new Qce(f.shape,o,c.shape.length,f.shape.length,u,h,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=Ge({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var tfe={kernelName:nl,backendName:"webgpu",kernelFunc:efe},rfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],n=[];for(let s=0;s<this.outputShape.length;s++)n.push(`${r[s]}`),s<this.cRank&&a.push(`${r[s]}`);e=a.join(),t=n.join()}return`
      ${Je()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputAtIndex(index, getA(${t}));
          } else {
            setOutputAtIndex(index, getB(${t}));
          }
        }
      }
    `}};function afe(e){let{inputs:t,backend:r}=e,{condition:a,t:n,e:s}=t,i=new rfe(a.shape.length,n.shape,n.shape.length);return r.runWebGPUProgram(i,[a,n,s],Or(n.dtype,s.dtype))}var nfe={kernelName:sl,backendName:"webgpu",kernelFunc:afe},sfe=xr({opType:19}),ife={kernelName:Ni,backendName:"webgpu",kernelFunc:sfe},ofe=xr({opType:17}),lfe={kernelName:Ci,backendName:"webgpu",kernelFunc:ofe},ufe=xr({opType:18}),dfe={kernelName:ol,backendName:"webgpu",kernelFunc:ufe},rS=Wr({opSnippet:2,cpuKernelImpl:Xde,supportsComplex:!0}),pfe={kernelName:$i,backendName:"webgpu",kernelFunc:rS};function hfe(e){let{inputs:t,backend:r,attrs:a}=e,{logits:n}=t,{dim:s}=a,i=w.parseAxisParam([s],n.shape),o=J8({inputs:{x:n},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),d=Ge({inputs:{x:o},backend:r,attrs:{shape:l}}),u=rS({inputs:{a:n,b:d},backend:r}),p=K8({inputs:{x:u},backend:r}),h=jx({inputs:{x:p},backend:r,attrs:{axis:i,keepDims:!1}}),c=Ge({inputs:{x:h},backend:r,attrs:{shape:l}}),f=tS({inputs:{a:p,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(d.dataId),r.disposeData(u.dataId),r.disposeData(p.dataId),r.disposeData(h.dataId),r.disposeData(c.dataId),f}var cfe={kernelName:Fi,backendName:"webgpu",kernelFunc:hfe},ffe=e=>{let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,paddings:i}=a;w.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<n.shape.length;++y)l.push([0,0]);let d=[],u=eS({inputs:{x:n},backend:r,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(p.length,s.length,!1),c=N.getReshapedPermuted(u.shape,s,o,!1),f=Ge({inputs:{x:u},backend:r,attrs:{shape:p}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:h}}),g=Ge({inputs:{x:m},backend:r,attrs:{shape:c}});return d.push(u),d.push(f),d.push(m),d.forEach(y=>r.disposeData(y.dataId)),g},mfe={kernelName:ll,backendName:"webgpu",kernelFunc:ffe},gfe=class{constructor(e,t,r,a,n,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${a}_${o}`;let l=cr(n.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let d="";r===1?d="i":r===2&&(d="i, j"),this.indicesSnippet=`getIndices(${d})`;let u="";a===1?u="i":a===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
      ${Je()}

        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 yfe(e){let{inputs:t,backend:r,attrs:a}=e,{sparseIndices:n,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:d,strides:u,outputSize:p}=N.calculateShapes(s,n,o),h=!1,c=[{type:"int32",data:[d]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new gfe(d,l,n.shape.length,s.shape.length,u,[p,1],h),m=r.runWebGPUProgram(f,[s,n,i],s.dtype,c),g=Ge({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Afe={kernelName:Jp,backendName:"webgpu",kernelFunc:yfe};function xfe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,n.shape)[0],l=N.prepareSplitSize(n,s,o),d=n.shape.length,u=new Array(d).fill(0),p=n.shape.slice();return l.map(h=>{let c=[...p];c[o]=h;let f=bd({inputs:{x:n},backend:r,attrs:{begin:u,size:c}});return u[o]+=h,f})}var bfe={kernelName:ul,backendName:"webgpu",kernelFunc:xfe},vfe=xr({opType:20}),wfe={kernelName:Ei,backendName:"webgpu",kernelFunc:vfe},kfe={kernelName:Ju,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,a=t,n=new $h(r.shape,21);return a.runWebGPUProgram(n,[r],r.dtype)}},Ife=Wr({opSnippet:11}),Sfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Ife},Tfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=cr(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((a,n)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${n}] + uniforms.begin[${n}]`:`coords[${r-1}] * uniforms.strides[${n}] + uniforms.begin[${n}]`)).join(",")}return`
       ${Je()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           setOutputAtIndex(index, getX(${t}));
         }
       }
     `}};function Cfe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:d,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:h}=a,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(n.shape,s,i,o,l,d,u,p,h),v;if(m)v=Ge({inputs:{x:n},backend:r,attrs:{shape:f}});else if(g||y){w.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let C=Ot.computeOutShape(A,x,b),T=bd({inputs:{x:n},backend:r,attrs:{begin:A,size:C}});v=Ge({inputs:{x:T},backend:r,attrs:{shape:f}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([n])){let C=r.readSync(n.dataId),T=Le(n.shape,n.dtype,C),E=qde(c,T,b,A);v=r.makeTensorInfo(f,n.dtype,E.values)}else{let C=new Tfe(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(C,[n],n.dtype,T);v=Ge({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Nfe={kernelName:dl,backendName:"webgpu",kernelFunc:Cfe};function Efe(e){let{inputs:t,backend:r,attrs:a}=e,{separator:n,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:d}=a,{data:u,dataSplits:p}=t,h=r.readSync(u.dataId),c=r.readSync(p.dataId),[f,m]=Kde(h,c,n,s,i,o,l,d);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(p.shape,"int32",m)]}var Rfe={kernelName:Qp,backendName:"webgpu",kernelFunc:Efe},Ffe=xr({opType:22}),Mfe={kernelName:Pi,backendName:"webgpu",kernelFunc:Ffe},$fe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let a=0;a<r.length;a++)r[a]=e[a]*t[a];this.outputShape=r,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Pfe(this.rank,"uniforms.");return`
      ${Je()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `}};function Pfe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e;n++)a.push(`(${r[n]} % ${t}aShape[${n}])`);return a.join()}function Ofe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{reps:s}=a;if(r.shouldExecuteOnCPU([n])||n.dtype==="string"||n.shape.length>=5){let o=r.readSync(n.dataId),l=n.dtype==="string"?o.map(p=>w.decodeString(p)):o,d=Le(n.shape,n.dtype,l),u=Zde(d,s);return r.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new $fe(n.shape,s);return r.runWebGPUProgram(i,[n],n.dtype)}var zfe={kernelName:Xn,backendName:"webgpu",kernelFunc:Ofe},Dfe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
        dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
        ${Je()}
          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));
            }
          }
        }
      `}},_fe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
        ${Je()}
          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 eu(e,t){t!==null&&e.disposeData(t.dataId)}function yv(e){let t=1;for(;t<e;)t*=2;return t}function Lfe(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{k:s,sorted:i}=a,o=n.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([n])){let b=r.readSync(n.dataId),[v,C]=Yde(b,o,n.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(C.shape,C.dtype,C.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,n.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[n,vd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let d=w.sizeFromShape(o)/l,u=Ge({inputs:{x:n},attrs:{shape:[d,l]},backend:r}),p=yv(s),h=yv(l),c=null,f=()=>c===null?[u,u]:[u,c],m=(b,v,C)=>{let T=f(),E=new Dfe(C),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],z=c;c=r.runWebGPUProgram(E,T,"int32",R),eu(r,z)};for(let b=1;b<p;b*=2){let v=b*2;for(let C=b;C>=1;C/=2)m(v,C,[d,h])}for(let b=h;b>p;b/=2){let v=f(),C=new _fe([d,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[p]}],E=c;c=r.runWebGPUProgram(C,v,"int32",T),eu(r,E);let R=p/2,z=R*2;for(let M=R;M>=1;M/=2)m(z,M,c.shape)}let g=c;c=bd({inputs:{x:c},backend:r,attrs:{begin:0,size:[d,s]}}),eu(r,g);let y=Y8({inputs:{x:u,indices:c},backend:r,attrs:{axis:1,batchDims:1}});eu(r,u);let A=o.slice(0,-1);A.push(s),g=c,c=Ge({inputs:{x:c},attrs:{shape:A},backend:r}),eu(r,g);let x=y;return y=Ge({inputs:{x:y},attrs:{shape:A},backend:r}),eu(r,x),[y,c]}var Bfe={kernelName:hl,backendName:"webgpu",kernelFunc:Lfe},Wfe=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=He(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
          fn mapCoord(outCoord : f32, len : f32) -> f32{
            var inCoord = outCoord;
            if(uniforms.fillModeId == 2) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  if (inCoord < sz2) {
                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
                    inCoord;
                  }
                  if (inCoord < -len) {
                    inCoord = inCoord + sz2;
                  } else {
                    inCoord = -inCoord - 1.0;
                  }
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
                  if (inCoord >= len) {
                    inCoord = sz2 - inCoord - 1.0;
                  }
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 3) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 4) {
              return clamp(outCoord, 0.0, len - 1.0);
            }
            return outCoord;
          }
          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
            channel : i32) -> f32 {
            var outputValue : f32;
            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
                outputValue = getImage(batch, coordY, coordX, channel);
            } else {
              outputValue = uniforms.fillValue;
            }
            return outputValue;
          }

          ${Je()}
            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 Vfe(e){let{inputs:t,backend:r,attrs:a}=e,{image:n,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:d}=a,[u,p,h,c]=n.shape,[f,m]=d!=null?d:[p,h],g=[u,f,m,c],y=new Wfe(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[n,s],"float32",b)}var Ufe={kernelName:cl,backendName:"webgpu",kernelFunc:Vfe};function Gfe(e){let{inputs:t,backend:r,attrs:a}=e,{value:n}=t,{axis:s}=a;s<0&&(s+=n.shape.length);let i=n,o=i.shape.length,l=n.shape[s],d=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(d[u++]=i.shape[m]);let p=[],h=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){h[s]=m;let g=bd({inputs:{x:i},backend:r,attrs:{begin:h,size:c}}),y=Ge({inputs:{x:g},backend:r,attrs:{shape:d}});f[m]=y,p.push(g)}return p.forEach(m=>r.disposeData(m.dataId)),f}var jfe={kernelName:fl,backendName:"webgpu",kernelFunc:Gfe},Hfe=[yde,epe,rpe,spe,ppe,cpe,mpe,ype,wpe,Tpe,Npe,Mpe,vde,zpe,Gpe,Kpe,Zpe,Jpe,the,nhe,ihe,phe,che,mhe,ghe,yhe,xhe,vhe,khe,Ehe,She,Che,Mhe,Phe,zhe,Lhe,Vhe,Ghe,Hhe,bde,Ppe,Khe,Zhe,Jhe,ece,rce,nce,sce,oce,uce,pce,cce,mce,yce,ohe,xce,vce,kce,kpe,Sce,Cce,Ece,Fce,$ce,Oce,Dce,Ipe,_ce,Bce,Vce,mde,jce,Kce,Zce,Jce,tfe,nfe,ife,lfe,dfe,bpe,Nfe,Rfe,cfe,mfe,Afe,bfe,wfe,kfe,Sfe,pfe,uhe,Mfe,zfe,Bfe,Ufe,upe,jfe,Ice];for(let e of Hfe)Ga(e);var qfe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let a=Av(e,t);if(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(a).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(a).shift();return this.usedBuffers.get(a).push(s),s}this.numBytesAllocated+=e;let n=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(a).push(n),n}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let a=Av(t,r);this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let n=this.usedBuffers.get(a),s=n.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");n.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},a=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Av(e,t){return`${e}_${t}`}var aS=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=He(this.outputShape),this.dispatch=ze(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>"};

      ${Je()}
        let flatIndexBase = index * uniforms.numChannels;
        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
          let flatIndex = flatIndexBase + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndexBase);
            let values = ${e};
            result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,a)=>r===this.lastUniformData[a])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),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 r=e.createBindGroupLayout({entries:t}),a=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:a}}},Kfe=class extends aS{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 r=e.createBindGroupLayout({entries:t}),a=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:a}}},Xfe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),nS=class extends Iu{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Vx())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 qfe(this.device),this.tensorMap=new Dp(this,kr()),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 nS.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 r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:a}=this.tensorMap.get(e);a!=null&&(this.disposeData(a.real.dataId,!0),this.disposeData(a.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,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()},n=w.sizeFromShape(t)*Iy(r);return this.tensorMap.set(a,{dtype:r,values:e,bufferInfo:{byteSize:n,usage:this.defaultGpuBufferUsage()},refCount:1}),a}move(e,t,r,a,n){if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*Iy(a);this.tensorMap.set(e,{dtype:a,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:n})}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 aS),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Kfe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=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()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let a;if(t.dtype==="complex64"){let n=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=n[0],i=n[1];a=N.mergeRealAndImagArrays(s,i)}else{let n=await this.getBufferData(t);a=P8(n,t.dtype)}return this.convertAndCacheOnCPU(e,a),a}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(a=>w.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],a=!1;this.programTimersStack==null?(this.programTimersStack=r,a=!0):this.activeTimers.push(r),this.activeTimers=r,e();let n=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(n);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,d)=>({name:s[d],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let a;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let n=r.map(s=>w.encodeString(s));a=this.write(n,e,t)}else a=this.write(r,e,t);return{dataId:a,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let n={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(n)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let a=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(a,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(a,o,s.data.length).set(s.data):new Float32Array(a,o,s.data.length).set(s.data)});let n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(n,0,a,0,t),{offset:0,size:t,buffer:n}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let n=0;n<e;n++)t.push({binding:n+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),a=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:a}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,a,n){if(!n){if(n=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(n.shape)===0){let T=this.tensorMap.get(n.dataId);return T.values=w.getTypedArrayFromDType(n.dtype,0),n}this.uploadToGPU(n.dataId)}let s=[{type:"float32",data:[NaN]}],i=t.concat(n).map(T=>T.shape),o="int32";i.map(T=>{s.push({type:o,data:T})});let l=w.computeStrides(n.shape);if(s.push({type:o,data:l}),e.size){let T=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?T/4:T]})}a&&(s=[...s,...a]);let d=this.makeUniforms(s),u=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=u.map(T=>T.dtype).concat(n.dtype),h=u.map(T=>N.getBroadcastDims(T.shape,n.shape)),c=u.map(T=>w.arraysEqual(T.shape,n.shape)).join("_"),f=h.map(T=>T.join("_")).join(";"),m=Z8(e,i,p,f,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(m,()=>X8(this.device,e,y,u,n)),x=this.activeTimers!=null,b=Nhe(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(n),d);this.ensureCommandEncoderReady();let v=this.getComputePass();x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(n.dataId);let C={byteSize:d.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:d.buffer};return this.uniformDisposalQueue.push(C),Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),n}runFromPixelsProgram(e,t,r,a,n){let s=this.device.createBindGroup({layout:r.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:a},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,s),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(n),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let a=new BigUint64Array(r.getMappedRange()),n=Number(a[1]-a[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n/1e6}shouldExecuteOnCPU(e,t=Xfe){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Hx=nS;Hx.nextDataId=0;var sS={};De(sS,{WebGPUBackend:()=>Hx,webgpu_util:()=>M8});Vx()&&Al("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),r={},a=t.features.has("timestamp-query");a?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 n=await t.requestDevice(r);return new Hx(n,a)},3);var Gt=(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))(Gt||{}),n0=(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))(n0||{}),iS;function Zfe(e){iS=e.wasm.cwrap(Rs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Yfe(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s,bias:i,preluActivationWeights:o}=t;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a,h=r.dataIdMap.get(n.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=n0[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?n.shape[2]:n.shape[1],A=d?s.shape[1]:s.shape[2],x=yl.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],n.dtype),v=r.dataIdMap.get(b.dataId).id,C=new Uint8Array(new Int32Array(n.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return iS(h,C,n.shape.length,c,T,s.shape.length,l,d,g,f,m,p||0,v),b}var Jfe={kernelName:Rs,backendName:"wasm",setupFunc:Zfe,kernelFunc:Yfe};function br(e,t){let r;function a(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function n(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,d=i.makeOutput(o.shape,t||o.dtype),u=i.dataIdMap.get(d.dataId).id;return w.sizeFromShape(d.shape)===0||r(l,Gt[o.dtype],u),d}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:n}}var Qfe=br(Fo);function Vr(e,t,r){let a;function n(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:d,b:u}=l,p=o.dataIdMap.get(d.dataId).id,h=o.dataIdMap.get(u.dataId).id,c=r!=null?r:d.dtype,f=N.assertAndGetBroadcastShape(d.shape,u.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(d.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return a(p,g,d.shape.length,h,y,u.shape.length,Gt[d.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var eme=!0,tme=Vr(qn,eme),oS;function rme(e){oS=e.wasm.cwrap(js,null,["array","number","number","number"])}function ame(e){let{inputs:t,backend:r}=e,a=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let n=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),i=r.dataIdMap.get(a.dataId).id;return oS(s,n.length,Gt[a.dtype],i),a}var nme={kernelName:js,backendName:"wasm",setupFunc:rme,kernelFunc:ame};function s0(e){let{inputs:{x:t},backend:r}=e,a=r.makeOutput(t.shape,t.dtype),n=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(n),a}var sme={kernelName:ui,backendName:"wasm",kernelFunc:s0},lS;function ime(e){lS=e.wasm.cwrap(Oi,null,["number","array","number","number","number","array","number"])}function ku(e){let{inputs:t,backend:r,attrs:a}=e,[n,s]=lme(t.x.shape,a.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=ome(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:n,dtype:t.x.dtype};if(i){let f=s0({inputs:t,backend:r});return f.shape=o,f}let d=r.makeOutput(o,l.dtype),u=r.dataIdMap.get(l.dataId).id,p=r.dataIdMap.get(d.dataId).id,h=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return lS(u,c,l.shape.length,Gt[l.dtype],p,h,s.length),d}function ome(e,t){let r=new Array(e.length);for(let a=0;a<r.length;a++)r[a]=e[t[a]];return r}function lme(e,t){let r=[],a=[];for(let n=0;n<e.length;++n)e[n]!==1&&r.push(e[n]),e[t[n]]!==1&&a.push(t[n]);for(let n=0;n<a.length;++n){let s=-1;for(let i=0;i<a.length;++i)a[i]>=n&&(s===-1||a[s]>a[i])&&(s=i);a[s]=n}return[r,a]}var ume={kernelName:Oi,backendName:"wasm",kernelFunc:ku,setupFunc:ime};function Vi(e,t,r){let a=e.shape,n=e.shape.length,s=w.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,n),l=null,d=!1;if(o!=null){let u=new Array(n);for(let h=0;h<u.length;h++)u[h]=a[o[h]];i=N.getInnerMostAxes(i.length,n),l=ku({inputs:{x:e},attrs:{perm:o},backend:r});let p=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==p&&(d=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:d}}var uS;function dme(e){uS=e.wasm.cwrap(Nu,null,["number, number, number"])}function pme(e){let{backend:t,inputs:r,attrs:a}=e,{axis:n,keepDims:s}=a,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:h}=Vi(i,n,t);if(h){let A=t.dataIdMap.get(d.dataId).id;l=d,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",u,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;uS(o,g,A)}if(h&&t.disposeData(d.dataId),s){let A=N.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var hme={kernelName:Nu,backendName:"wasm",setupFunc:dme,kernelFunc:pme},dS;function cme(e){dS=e.wasm.cwrap(Eu,null,["number, number, number"])}function fme(e){let{backend:t,inputs:r,attrs:a}=e,{axis:n,keepDims:s}=a,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:h}=Vi(i,n,t);if(h){let A=t.dataIdMap.get(d.dataId).id;l=d,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",u,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;dS(o,g,A)}if(h&&t.disposeData(d.dataId),s){let A=N.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var mme={kernelName:Eu,backendName:"wasm",setupFunc:cme,kernelFunc:fme},pS;function gme(e){pS=e.wasm.cwrap(Hs,null,["number","number","number","number","number"])}function yme(e){let{backend:t,inputs:r,attrs:a}=e,{axis:n}=a,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:d,axes:u,inputWasTransposed:p}=Vi(s,n,t);if(p){let y=t.dataIdMap.get(d.dataId).id;y!==i&&(l=d,o=y)}let h=l.shape.slice(0,-1),c=t.makeOutput(h,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[u[0]];return pS(o,Gt[l.dtype],m,g,f),p&&t.disposeData(d.dataId),c}var Ame={kernelName:Hs,backendName:"wasm",kernelFunc:yme,setupFunc:gme},hS;function xme(e){hS=e.wasm.cwrap(qs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bme(e){let{inputs:t,attrs:r,backend:a}=e,n=t.x,s=a.dataIdMap.get(n.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:d}=r,u=N.computePool2DInfo(n.shape,i,o,1,l,d),p=u.filterHeight,h=u.filterWidth,c=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,A=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=a.makeOutput(u.outShape,"float32"),v=a.dataIdMap.get(b.dataId).id;return hS(s,n.shape[0],n.shape[1],n.shape[2],p,h,c,f,m,g,y,A,x,v),b}var vme={kernelName:qs,backendName:"wasm",setupFunc:xme,kernelFunc:bme};function ta(e){let{inputs:t,attrs:r}=e,{x:a}=t,{shape:n}=r,s=w.sizeFromShape(a.shape),i=w.inferFromImplicitShape(n,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var wme={kernelName:tl,backendName:"wasm",kernelFunc:ta},cS;function kme(e){cS=e.wasm.cwrap(Ks,null,["number","array","number","number","array","number","number","number","number"])}function Ime(e){let{inputs:t,backend:r,attrs:a}=e,{a:n,b:s}=t,{transposeA:i,transposeB:o}=a;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=n.shape.length,d=s.shape.length,u=i?n.shape[l-2]:n.shape[l-1],p=o?s.shape[d-1]:s.shape[d-2],h=i?n.shape[l-1]:n.shape[l-2],c=o?s.shape[d-2]:s.shape[d-1],f=n.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=yl.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)).concat([h,c]);w.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,u,h]:[g,h,u],b=o?[y,c,p]:[y,p,c],v=ta({inputs:{x:n},backend:r,attrs:{shape:x}}),C=ta({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(C.dataId).id,R=i?v.shape[2]:v.shape[1],z=o?C.shape[1]:C.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,z],v.dtype),D=r.dataIdMap.get(I.dataId).id,O=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return cS(T,O,v.shape.length,E,j,C.shape.length,i,o,D),r.disposeData(v.dataId),r.disposeData(C.dataId),I.shape=A,I}var Sme={kernelName:Ks,backendName:"wasm",setupFunc:kme,kernelFunc:Ime};function No(e){let{inputs:{x:t},attrs:{begin:r,size:a},backend:n}=e,[s,i]=Ot.parseSliceParams(t,r,a),o=Ot.isSliceContinous(t.shape,s,i),l=n.readSync(t.dataId),d=n.makeOutput(i,t.dtype),u=w.computeStrides(t.shape),p=n.dataIdMap.get(d.dataId);if(o){let f=Ot.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(f,f+w.sizeFromShape(i)):n.typedArrayFromHeap(d).set(l.subarray(f,f+w.sizeFromShape(i))),d}if(t.dtype==="string"){let f=mf(l,s,i,t.shape,t.dtype);return p.stringBytes=f,d}let h=n.typedArrayFromHeap(d),c=t.shape.length;if(c===2)Tme(l,u[0],h,s,i);else if(c===3)Cme(l,u[0],u[1],h,s,i);else if(c===4)Nme(l,u[0],u[1],u[2],h,s,i);else{let f=mf(l,s,i,t.shape,t.dtype);h.set(f)}return d}function Tme(e,t,r,a,n){let s=0,i=a[0],o=a[1],l=i+n[0];for(let d=i;d<l;d++){let u=d*t+o;r.set(e.subarray(u,u+n[1]),s),s+=n[1]}}function Cme(e,t,r,a,n,s){let i=0,o=n[0],l=n[1],d=n[2],u=o+s[0],p=l+s[1];for(let h=o;h<u;h++)for(let c=l;c<p;c++){let f=h*t+c*r+d;a.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Nme(e,t,r,a,n,s,i){let o=0,l=s[0],d=s[1],u=s[2],p=l+i[0],h=d+i[1],c=u+i[2],f=s[3];for(let m=l;m<p;m++)for(let g=d;g<h;g++)for(let y=u;y<c;y++){let A=m*t+g*r+y*a+f;n.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Eme={kernelName:il,backendName:"wasm",kernelFunc:No};function Rme(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{blockShape:s,crops:i}=a,o=s.reduce((y,A)=>y*A),l=N.getReshaped(n.shape,s,o),d=N.getPermuted(l.length,s.length),u=N.getReshapedPermuted(n.shape,s,o),p=N.getSliceBeginCoords(i,s.length),h=N.getSliceSize(u,i,s.length),c=ta({inputs:{x:n},backend:r,attrs:{shape:l}}),f=ku({inputs:{x:c},backend:r,attrs:{perm:d}}),m=ta({inputs:{x:f},backend:r,attrs:{shape:u}}),g=No({inputs:{x:m},backend:r,attrs:{begin:p,size:h}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var Fme={kernelName:Mo,backendName:"wasm",kernelFunc:Rme};function zh(e){let{inputs:{x:t},attrs:{dtype:r},backend:a}=e,n=a.makeOutput(t.shape,r),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(n).set(s),n}var Mme={kernelName:Xs,backendName:"wasm",kernelFunc:zh},$me=br(Zs),fS;function Pme(e){fS=e.wasm.cwrap(Kn,null,["number","number","number","number"])}function Ome(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{clipValueMin:s,clipValueMax:i}=a,o=r.dataIdMap.get(n.dataId).id,l=r.makeOutput(n.shape,n.dtype),d=r.dataIdMap.get(l.dataId).id;return fS(o,s,i,d),l}var zme={kernelName:Kn,backendName:"wasm",setupFunc:Pme,kernelFunc:Ome};function mS(e){let{inputs:t,backend:r}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],n=N.computeOutShape(t.map(c=>c.shape),a),s=t.filter(c=>w.sizeFromShape(c.shape)>0);if(s.length===1)return s0({inputs:{x:s[0]},backend:r});let i=r.makeOutput(n,t[0].dtype);if(w.sizeFromShape(n)===0)return i;let o=s.map(c=>c.shape);if(N.assertParamsConsistent(o,a),s[0].dtype==="string"){let c=s.map(x=>{let b=w.sizeFromShape(x.shape.slice(a));return ta({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),f=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));n=N.computeOutShape(c.map(x=>x.shape),1);let m=c[0].shape[0]===1,g=yx(f,n,t[0].dtype,m),y=N.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=N.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,a)),d=0,u=s.map(c=>{let f=w.sizeFromShape(c.shape.slice(a));return d+=f,f}),p=s.map(c=>r.typedArrayFromHeap(c)),h=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let f=c*d;for(let m=0;m<p.length;m++){let g=u[m],y=c*g,A=p[m].subarray(y,y+g);h.set(A,f),f+=g}}return i}var Dme={kernelName:$o,backendName:"wasm",kernelFunc:mS},gS;function _me(e){gS=e.wasm.cwrap(Ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lme(e){let{inputs:t,attrs:r,backend:a}=e,{x:n,filter:s}=t,i=a.dataIdMap.get(n.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:d,pad:u,dimRoundingMode:p,dataFormat:h}=r,c=N.convertConv2DDataFormat(h),f=N.computeConv2DInfo(n.shape,s.shape,l,d,u,p,!1,c),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,R=f.inChannels,z=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let I=a.makeOutput(f.outShape,"float32"),D=a.dataIdMap.get(I.dataId).id;return gS(i,n.shape[0],n.shape[1],n.shape[2],o,m,g,y,A,x,b,M,v,C,T,E,R,z,D),I}var Bme={kernelName:Ys,backendName:"wasm",setupFunc:_me,kernelFunc:Lme},yS;function Wme(e){yS=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vme(e){let{backend:t,inputs:r,attrs:a}=e,{dy:n,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,inputShape:u}=a,p=1,h=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(u,s.shape,i,p,o,d,!1,h),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:v,outWidth:C,strideHeight:T,strideWidth:E}=c,R=m-1-c.padInfo.top,z=g-1-c.padInfo.left,M=c.dataFormat==="channelsLast",I=w.computeStrides(c.inShape),D=w.computeStrides(n.shape),[O,j,X]=w.computeStrides(s.shape),_=I[0],K=M?I[1]:I[2],W=M?I[2]:1,ee=M?1:I[1],Q=D[0],ne=M?D[1]:D[2],Z=M?D[2]:1,ae=M?1:D[1],ie=t.makeOutput(c.inShape,"float32"),xe=t.dataIdMap.get(ie.dataId).id,be=t.dataIdMap.get(n.dataId).id,Te=t.dataIdMap.get(s.dataId).id;return yS(be,Te,f,m,g,A,x,y,v,C,b,T,E,R,z,O,j,X,_,K,W,ee,Q,ne,Z,ae,xe),ie}var Ume={kernelName:Js,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme},Gme=br(Qs),jme=br(ei),AS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(AS||{}),xS;function Hme(e){xS=e.wasm.cwrap(Oo,null,["number","number","number","number","array","number","number","number","number","number"])}function qme(e){let{backend:t,inputs:r,attrs:a}=e,{method:n,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:d}=r,u=l.shape[0],[p,h]=i,c=[u,p,h,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=zh({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(d.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(o.shape).buffer);return xS(g,y,A,u,v,p,h,AS[n],s,b),m!=null&&t.disposeData(m.dataId),x}var Kme={kernelName:Oo,backendName:"wasm",setupFunc:Hme,kernelFunc:qme},bS;function Xme(e){bS=e.wasm.cwrap(Po,null,["number","number","number","number","number","number"])}function Zme(e){let{inputs:t,backend:r,attrs:a}=e,{x:n}=t,{axis:s,exclusive:i,reverse:o}=a,l=n.shape.length;w.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let d=N.getAxesPermutation([s],l),u=n;d!==null&&(u=ku({inputs:{x:n},attrs:{perm:d},backend:r}));let p=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[p],l);let h=r.makeOutput(u.shape,u.dtype),c=u.shape[p],f=r.dataIdMap.get(u.dataId).id,m=r.dataIdMap.get(h.dataId).id;bS(f,i?1:0,o?1:0,c,m,Gt[n.dtype]);let g=h;if(d!==null){let y=N.getUndoAxesPermutation(d);g=ku({inputs:{x:h},attrs:{perm:y},backend:r}),r.disposeData(u.dataId),r.disposeData(h.dataId)}return g}var Yme={kernelName:Po,backendName:"wasm",setupFunc:Xme,kernelFunc:Zme},vS;function Jme(e){vS=e.wasm.cwrap(zo,null,["number","number","number","array","number","array","array","number","number"])}function Qme(e){let{backend:t,inputs:r,attrs:a}=e,{x:n}=r,{blockSize:s,dataFormat:i}=a,o=n.shape[0],l=i==="NHWC"?n.shape[1]:n.shape[2],d=i==="NHWC"?n.shape[2]:n.shape[3],u=i==="NHWC"?n.shape[3]:n.shape[1],p=l*s,h=d*s,c=u/(s*s),f=i==="NHWC"?[o,p,h,c]:[o,c,p,h],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(n.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(n.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return vS(g,s,i==="NHWC"?1:0,y,n.shape.length-1,A,x,f.length,b),m}var e0e={kernelName:zo,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme},wS;function t0e(e){wS=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function r0e(e){let{inputs:t,attrs:r,backend:a}=e,{x:n,filter:s}=t,i=a.dataIdMap.get(n.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:d,pad:u,dimRoundingMode:p}=r,h=d==null?[1,1]:d,c=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,C=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,z=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=a.makeOutput(c.outShape,"float32"),I=a.dataIdMap.get(M.dataId).id;return wS(i,n.shape[0],n.shape[1],n.shape[2],o,f,m,g,y,A,x,z,b,v,C,T,E,R,I),M}var a0e={kernelName:ti,backendName:"wasm",setupFunc:t0e,kernelFunc:r0e},n0e=br(ai),s0e=!1,i0e=Vr(Do,s0e,"bool"),o0e=br(ni,"float32");function Ey(e){let{inputs:t,attrs:r,backend:a}=e,{input:n}=t,{dim:s}=r,i=n.shape.length,o=n.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ta({inputs:{x:n},backend:a,attrs:{shape:o}})}var l0e={kernelName:_o,backendName:"wasm",kernelFunc:Ey};function kS(e){let{attrs:{shape:t,value:r,dtype:a},backend:n}=e,s=n.makeOutput(t,a);return n.typedArrayFromHeap(s).fill(r),s}var u0e={kernelName:Du,backendName:"wasm",kernelFunc:kS},IS;function d0e(e){IS=e.wasm.cwrap(Bo,null,["number","number","number","number","number","number"])}function p0e(e){let{inputs:t,backend:r}=e,{image:a}=t,n=r.makeOutput(a.shape,a.dtype),s=r.dataIdMap.get(a.dataId).id,i=r.dataIdMap.get(n.dataId).id,[o,l,d,u]=a.shape;return IS(s,o,l,d,u,i),n}var h0e={kernelName:Bo,backendName:"wasm",kernelFunc:p0e,setupFunc:d0e},c0e=br(si),f0e=!1,m0e=Vr(ii,f0e),SS;function g0e(e){SS=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number"])}function y0e(e){let{backend:t,inputs:r,attrs:a}=e,{varianceEpsilon:n}=a,{x:s,mean:i,variance:o,offset:l,scale:d}=r,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=d!=null?t.dataIdMap.get(d.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return SS(u,p,h,c,f,n,g),m}var A0e={kernelName:oi,backendName:"wasm",setupFunc:g0e,kernelFunc:y0e},TS;function x0e(e){TS=e.wasm.cwrap(Fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function b0e(e){let{inputs:t,attrs:r,backend:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dataFormat:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(n.shape,s.shape,l,u,d,h),g=n0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(n.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,z=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,D=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,_=m.batchSize,K=m.inHeight,W=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ee=a.makeOutput(m.outShape,"float32"),Q=a.dataIdMap.get(ee.dataId).id,ne=o==null?0:a.dataIdMap.get(o.dataId).id;return TS(y,_,K,W,A,v,C,b,T,E,R,z,X,M,I,D,O,j,x,g,ne,f||0,Q),ee}var v0e={kernelName:Fs,backendName:"wasm",setupFunc:x0e,kernelFunc:b0e},CS;function w0e(e){CS=e.wasm.cwrap(Ms,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 k0e(e){let{inputs:t,attrs:r,backend:a}=e,{x:n,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dataFormat:p,dimRoundingMode:h,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(n.shape,s.shape,l,u,d,h,!0),g=n0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(n.dataId).id,A=a.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,z=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,D=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,_=m.batchSize,K=m.inHeight,W=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ee=a.makeOutput(m.outShape,"float32"),Q=a.dataIdMap.get(ee.dataId).id,ne=o==null?0:a.dataIdMap.get(o.dataId).id;return CS(y,_,K,W,A,v,C,b,T,E,R,z,X,M,I,D,O,j,x,g,ne,f||0,Q),ee}var I0e={kernelName:Ms,backendName:"wasm",setupFunc:w0e,kernelFunc:k0e},NS;function S0e(e){NS=e.wasm.cwrap(Vo,null,["number","number","number","number","number","number","array","number"])}function T0e(e){let{backend:t,inputs:r}=e,{params:a,indices:n}=r,[s,i,o,l]=Gy.prepareAndValidate(a,n),d=t.makeOutput(s,a.dtype);if(i===0)return d;let u=n.shape,p=u[u.length-1],h=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(n.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(d.dataId).id;return NS(h,Gt[a.dtype],c,i,p,o,f,m),d}var C0e={kernelName:Vo,backendName:"wasm",setupFunc:S0e,kernelFunc:T0e},ES;function N0e(e){ES=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function E0e(e){let{backend:t,inputs:r,attrs:a}=e,{x:n,indices:s}=r,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,n.shape)[0],d=t.readSync(s.dataId),u=n.shape[l];for(let T=0;T<d.length;++T){let E=d[T];w.assert(E<=u-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${u-1}]`)}let p=N.segment_util.collectGatherOpShapeInfo(n,s,l,o),h=ta({inputs:{x:n},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),c=w.sizeFromShape(s.shape),f=ta({inputs:{x:s},attrs:{shape:[p.batchSize,c/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,c/p.batchSize,p.sliceSize],g=t.makeOutput(m,n.dtype);if(w.sizeFromShape(n.shape)===0)return g;let y=h.shape.length-1,A=t.dataIdMap.get(h.dataId).id,x=t.dataIdMap.get(f.dataId).id,b=t.dataIdMap.get(g.dataId).id,v=new Uint8Array(new Int32Array(w.computeStrides(h.shape)).buffer),C=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return ES(A,Gt[n.dtype],v,y,x,p.batchSize,C,b),t.disposeData(h.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var R0e={kernelName:Wo,backendName:"wasm",setupFunc:N0e,kernelFunc:E0e},F0e=!1,M0e=Vr(Uo,F0e,"bool"),$0e=!1,P0e=Vr(li,$0e,"bool"),RS;function O0e(e){RS=e.wasm.cwrap(di,null,["number","number","number","number"])}function z0e(e){let{inputs:{x:t},attrs:{alpha:r},backend:a}=e,n=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;RS(n,Gt[t.dtype],r,i)}return s}var D0e={kernelName:di,backendName:"wasm",setupFunc:O0e,kernelFunc:z0e},_0e=!1,L0e=Vr(Go,_0e,"bool"),B0e=!1,W0e=Vr(jo,B0e,"bool"),V0e=br(pi),U0e=!1,G0e=Vr(Ho,U0e,"bool"),FS;function j0e(e){FS=e.wasm.cwrap(hi,null,["number","number","number","number"])}function H0e(e){let{backend:t,inputs:r,attrs:a}=e,{reductionIndices:n,keepDims:s}=a,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:h}=Vi(i,n,t);if(h){let A=t.dataIdMap.get(d.dataId).id;l=d,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("max",u,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;FS(o,Gt[i.dtype],g,A)}if(h&&t.disposeData(d.dataId),s){let A=N.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var q0e={kernelName:hi,backendName:"wasm",setupFunc:j0e,kernelFunc:H0e},K0e=!1,X0e=Vr(ci,K0e),MS;function Z0e(e){MS=e.wasm.cwrap(fi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Y0e(e){let{inputs:t,attrs:r,backend:a}=e,n=t.x,s=a.dataIdMap.get(n.dataId).id;w.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:d}=r,u=N.computePool2DInfo(n.shape,i,o,1,l,d),p=u.filterHeight,h=u.filterWidth,c=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,A=u.dilationWidth,x=u.strideHeight,b=u.strideWidth,v=u.inChannels,C=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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nge={kernelName:gi,backendName:"wasm",setupFunc:rge,kernelFunc:age},sge=!1,ige=Vr(yi,sge),OS=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(OS||{}),zS;function oge(e){zS=e.wasm.cwrap(Ai,null,["number","array","number","number","array","array","number","number"])}function lge(e){let{inputs:{x:t},backend:r,attrs:{paddings:a,mode:n}}=e,s=a.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(f=>f[0]),p=a.map(f=>f[1]),h=new Uint8Array(new Int32Array(u).buffer),c=new Uint8Array(new Int32Array(p).buffer);return zS(i,d,t.shape.length,Gt[t.dtype],h,c,OS[n],l),o}var uge={kernelName:Ai,backendName:"wasm",kernelFunc:lge,setupFunc:oge},dge=!0,pge=Vr(xi,dge),hge=br(qo);function qx(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=r[0],n=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:n,pSelectedScores:s,pValidOutputs:i}}var 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Promise.all([Y().getAsync("WASM_HAS_SIMD_SUPPORT"),Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((r,a)=>{let n={};n.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let d=lye,u=new Blob([d],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?bv(e,t,gp!=null?gp:l):l+o},Kx&&(n.instantiateWasm=dye(bv(e,t,gp!=null?gp:"")));let s=!1;n.onAbort=()=>{s||wp||(wp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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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 hT=`
  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];
  }
`,cT=`
  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;
  }
`,fT=`
  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);
  }
`,mT=`
  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;
  }
`,gT=`
  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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x=Math.cos(A),b=Math.sin(A),v=.213,C=.715,T=.072;y.colorMatrix([v+x*(1-v)+b*-v,C+x*-C+b*-C,T+x*-T+b*(1-T),0,0,v+x*-v+b*.143,C+x*(1-C)+b*.14,T+x*-T+b*-.283,0,0,v+x*-v+b*-(1-v),C+x*-C+b*C,T+x*(1-T)+b*T,0,0,0,0,0,1,0])},desaturateLuminance:()=>{y.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{y.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{y.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{y.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{y.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{y.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{y.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{y.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:A=>{let 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AT:null),ce.filter=!!Ct,!Ct||!Ct.add)return t.debug&&se("input process error: cannot initialize filters"),{tensor:null,canvas:lt};Ct.reset(),t.filter.brightness!==0&&Ct.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Ct.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ct.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ct.add("blur",t.filter.blur),t.filter.saturation!==0&&Ct.add("saturation",t.filter.saturation),t.filter.hue!==0&&Ct.add("hue",t.filter.hue),t.filter.negative&&Ct.add("negative"),t.filter.sepia&&Ct.add("sepia"),t.filter.vintage&&Ct.add("brownie"),t.filter.sepia&&Ct.add("sepia"),t.filter.kodachrome&&Ct.add("kodachrome"),t.filter.technicolor&&Ct.add("technicolor"),t.filter.polaroid&&Ct.add("polaroid"),t.filter.pixelate!==0&&Ct.add("pixelate",t.filter.pixelate),Ct.get()>0?ir=Ct.apply(lt):ir=Ct.draw(lt)}else Yx(lt,ir),Ct&&(Ct=null),ce.filter=!!Ct;if(!r)return{tensor:null,canvas:ir};if(!ir)throw new Error("canvas error: cannot create output");let 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Id=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],p0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],fb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],mb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],VT=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:a,landmarks:e.landmarks,confidence:e.confidence}},hb=(e,t,r)=>{let a=t.shape[1],n=t.shape[2],s=[e.startPoint[1]/a,e.startPoint[0]/n,e.endPoint[1]/a,e.endPoint[0]/n],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Xe.tf255);return re(i),o},h0=(e,t)=>{let r=p0(e),a=Id(e),n=[t*a[0]/2,t*a[1]/2];return{startPoint:[r[0]-n[0],r[1]-n[1]],endPoint:[r[0]+n[0],r[1]+n[1]],landmarks:e.landmarks,confidence:e.confidence}},c0=e=>{let t=p0(e),r=Id(e),a=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-a),Math.round(t[1]-a)],endPoint:[Math.round(t[0]+a),Math.round(t[1]+a)],landmarks:e.landmarks,confidence:e.confidence}},UT=e=>{let t=e.map(a=>a[0]),r=e.map(a=>a[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},cb=[[1,0,0],[0,1,0],[0,0,1]],Fye=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Mye=(e,t)=>Fye(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var BT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Rl=(e,t)=>{let r=0;for(let a=0;a<e.length;a++)r+=e[a]*t[a];return r},$ye=(e,t)=>{let r=[];for(let a=0;a<e.length;a++)r.push(e[a][t]);return r},WT=(e,t)=>{let r=[],a=e.length;for(let n=0;n<a;n++){r.push([]);for(let s=0;s<a;s++)r[n].push(Rl(e[n],$ye(t,s)))}return r},GT=(e,t)=>{let r=Math.cos(e),a=Math.sin(e),n=[[r,-a,0],[a,r,0],[0,0,1]],s=BT(t[0],t[1]),i=WT(s,n),o=BT(-t[0],-t[1]);return WT(i,o)},Pye=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],a=[-Rl(t[0],r),-Rl(t[1],r)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]},Oye=(e,t)=>[Rl(e,t[0]),Rl(e,t[1])];function jT(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let a=0;a<t.strides.length;a++){let n=t.strides[a],s=Math.floor((e+n-1)/n),i=Math.floor((e+n-1)/n),o=t.anchors[a];for(let l=0;l<s;l++){let d=n*(l+.5);for(let u=0;u<i;u++){let p=n*(u+.5);for(let h=0;h<o;h++)r.push([p,d])}}}return r}function HT(e,t,r,a,n){let s=Id(t),i=e.map(c=>[s[0]/n*(c[0]-n/2),s[1]/n*(c[1]-n/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?GT(r,[0,0]):cb,d=o?i.map(c=>[...Oye(c,l),c[2]]):i,u=o?Pye(a):cb,p=p0(t),h=[Rl(p,u[0]),Rl(p,u[1])];return d.map(c=>[Math.trunc(c[0]+h[0]),Math.trunc(c[1]+h[1]),Math.trunc(c[2]||0)])}function qT(e,t,r,a){let n=t.landmarks.length>=ub.count?ub.symmetryLine:Lh.symmetryLine,s=0,i=cb,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=Mye(t.landmarks[n[0]],t.landmarks[n[1]]),s&&s!==0&&Math.abs(s)>.2){let d=p0(t),u=[d[0]/r.shape[2],d[1]/r.shape[1]],p=Ie.rotateWithOffset(r,s,0,u);i=GT(-s,d),o=hb(t,p,[a,a]),re(p)}else o=hb(t,r,[a,a]);else o=hb(t,r,[a,a]);return[s,i,o]}var zye=e=>{let t=e.map(a=>a[0]),r=e.map(a=>a[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},KT=(e,t)=>{let r=zye(e),a=Id(t);return{startPoint:[r[0]-a[0]/2,r[1]-a[1]/2],endPoint:[r[0]+a[0]/2,r[1]+a[1]/2]}};var XT=6,Dye=1.2,$n,ZT=null,Ui=0,Wh=null,f0=()=>Ui;async function YT(e){var t;return ce.initial&&($n=null),$n?e.debug&&se("cached model:",$n.modelUrl):$n=await Ue((t=e.face.detector)==null?void 0:t.modelPath),Ui=$n.inputs[0].shape?$n.inputs[0].shape[2]:0,Wh=Se(Ui,"int32"),ZT=an(jT(Ui)),$n}function _ye(e){let t={};t.boxStarts=Oe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,ZT),t.boxSizes=Oe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Wh),t.centersNormalized=pe(t.centers,Wh),t.halfBoxSize=pe(t.boxSizesNormalized,Xe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=ue(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Wh),t.endNormalized=L(t.ends,Wh);let r=ed([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(a=>re(t[a])),r}async function JT(e,t){var o,l,d,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Ui,Ui]),r.div=pe(r.resized,Xe.tf127),r.normalized=he(r.div,Xe.tf05);let a=$n==null?void 0:$n.execute(r.normalized);if(Array.isArray(a)){let p=a.sort((h,c)=>h.size-c.size);r.concat384=kt([p[0],p[2]],2),r.concat512=kt([p[1],p[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=Ye(r.concat,0)}else r.batch=Ye(a);re(a),r.boxes=_ye(r.batch),r.logits=Oe(r.batch,[0,0],[-1,1]),r.sigmoid=Sr(r.logits),r.scores=Ye(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0);let n=await r.nms.array(),s=[],i=await r.scores.data();for(let p=0;p<n.length;p++){let h=i[n[p]];if(h>(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let c={};c.bbox=Oe(r.boxes,[n[p],0],[1,-1]),c.slice=Oe(r.batch,[n[p],XT-1],[1,-1]),c.squeeze=Ye(c.slice),c.landmarks=U(c.squeeze,[XT,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:h},g=VT(m,[(e.shape[2]||0)/Ui,(e.shape[1]||0)/Ui]),y=h0(g,t.face.scale||Dye),A=c0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(p=>re(r[p])),s}var m0={};Qd(m0,{connected:()=>Ab,kpt:()=>yb});var yb=["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"],Ab={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 eC=224,Lye,Bye=5,g0=[8,16,32,32,32];async function tC(){let e=[],t=0;for(;t<Bye;){let r=0,a=t;for(;a<g0.length&&g0[a]===g0[t];)r+=2,a++;let n=g0[t],s=Math.ceil(eC/n),i=Math.ceil(eC/n);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let d=0;d<r;++d)e.push({x:(l+.5)/i,y:(o+.5)/s});t=a}Lye={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function ns(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],a=[Math.min(...r[0]),Math.min(...r[1])],n=[Math.max(...r[0]),Math.max(...r[1])],s=[a[0],a[1],n[0]-a[0],n[1]-a[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function rC(e,t=[1,1]){let r=[e.map(d=>d[0]),e.map(d=>d[1])],a=[Math.min(...r[0]),Math.min(...r[1])],n=[Math.max(...r[0]),Math.max(...r[1])],s=[(a[0]+n[0])/2,(a[1]+n[1])/2],i=Math.max(s[0]-a[0],s[1]-a[1],-s[0]+n[0],-s[1]+n[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function y0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var sC={initial:!0},ga={detector:null,landmarks:null},Sd={detector:[224,224],landmarks:[256,256]},xb=Number.MAX_SAFE_INTEGER,Vye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},x0=null,Vh,Gi=[[0,0],[0,0],[0,0],[0,0]],aC=0,nC=e=>1-1/(1+Math.exp(e));async function iC(e){if(sC.initial&&(ga.detector=null),!ga.detector&&e.body.detector&&e.body.detector.modelPath){ga.detector=await Ue(e.body.detector.modelPath);let t=Object.values(ga.detector.modelSignature.inputs);Sd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Sd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ga.detector&&se("cached model:",ga.detector.modelUrl);return await tC(),ga.detector}async function oC(e){if(sC.initial&&(ga.landmarks=null),ga.landmarks)e.debug&&se("cached model:",ga.landmarks.modelUrl);else{ga.landmarks=await Ue(e.body.modelPath);let t=Object.values(ga.landmarks.modelSignature.inputs);Sd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Sd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ga.landmarks}async function Uye(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let a;if(Vh&&(r.cropped=Ie.cropAndResize(e,[Vh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Gi=[[0,0],n,s,[0,0]],r.pad=ja(r.cropped||e,Gi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),a=pe(r.resize,Xe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),a=pe(r.resize,Xe.tf255)):a=pe(r.cropped||e,Xe.tf255);return Object.keys(r).forEach(n=>re(r[n])),a}function Gye(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Gi[2][0]+Gi[2][1])/t[0]-Gi[2][0]),Math.trunc(r.position[1]*(t[1]+Gi[1][0]+Gi[1][1])/t[1]-Gi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Vh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Vh[1],r.positionRaw[1]+Vh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function jye(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),a=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(a.position[2]||0))/2;let n=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");n.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function Hye(e,t,r){var f;let a={};[a.ld,a.segmentation,a.heatmap,a.world,a.poseflag]=(f=ga.landmarks)==null?void 0:f.execute(e,Vye.landmarks);let n=(await a.poseflag.data())[0],s=await a.ld.data(),i=await a.world.data();Object.keys(a).forEach(m=>re(a[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=nC(s[l*m+3]),y=nC(s[l*m+4]),A=Math.trunc(100*g*y*n)/100,x=[s[l*m+0]/Sd.landmarks[0],s[l*m+1]/Sd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:yb[m],positionRaw:x,position:b,distance:v,score:A})}if(n<(t.body.minConfidence||0))return null;jye(o);let d=Gye(o,r),u=d.map(m=>m.position),p=ns(u,[r[0],r[1]]),h={};for(let[m,g]of Object.entries(Ab)){let y=[];for(let A=0;A<g.length-1;A++){let x=d.find(v=>v.part===g[A]),b=d.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}h[m]=y}return{id:0,score:Math.trunc(100*n)/100,box:p.box,boxRaw:p.boxRaw,keypoints:d,annotations:h}}async function bb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],a=(t.body.skipTime||0)>oe()-aC,n=xb<(t.body.skipFrames||0);if(t.skipAllowed&&a&&n&&x0!==null)xb++;else{let s={};s.landmarks=await Uye(e,256),x0=await 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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 ss,Fl=0,vb=[],uC=0,wb=Number.MAX_SAFE_INTEGER;async function dC(e){if(ce.initial&&(ss=null),ss)e.debug&&se("cached model:",ss.modelUrl);else{ss=await Ue(e.object.modelPath);let t=Object.values(ss.modelSignature.inputs);Fl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ss}async function qye(e,t,r){if(!e)return[];let a={},n=[],s=await e.array();a.squeeze=Ye(e);let i=Kt(a.squeeze,6,1);a.stack=nr([i[1],i[0],i[3],i[2]],1),a.boxes=Ye(a.stack),a.scores=Ye(i[4]),a.classes=Ye(i[5]),re([e,...i]),a.nms=await Ie.nonMaxSuppressionAsync(a.boxes,a.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await a.nms.data(),l=0;for(let d of Array.from(o)){let u=Math.trunc(100*s[0][d][4])/100,p=s[0][d][5],h=Td[p].label,[c,f]=[s[0][d][0]/Fl,s[0][d][1]/Fl],m=[c,f,s[0][d][2]/Fl-c,s[0][d][3]/Fl-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])];n.push({id:l++,score:u,class:p,label:h,box:g,boxRaw:m})}return Object.keys(a).forEach(d=>re(a[d])),n}async function kb(e,t){let r=(t.object.skipTime||0)>oe()-uC,a=wb<(t.object.skipFrames||0);return t.skipAllowed&&r&&a&&vb.length>0?(wb++,vb):(wb=0,new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Fl,Fl]),o=t.object.enabled?ss==null?void 0:ss.execute(i,["tower_0/detections"]):null;uC=oe(),re(i);let l=await qye(o,s,t);vb=l,n(l)}))}var b0={};Qd(b0,{connected:()=>Sb,kpt:()=>Ib});var Ib=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Sb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Nr,hC=0,Gr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Tb=Number.MAX_SAFE_INTEGER;async function cC(e){return ce.initial&&(Nr=null),Nr?e.debug&&se("cached model:",Nr.modelUrl):Nr=await Ue(e.body.modelPath),Nr}async function Kye(e,t){let[r,a]=e.shape,n=U(e,[a*r]),s=hr(n,0),i=(await s.data())[0];if(re([n,s]),i>t){let o=Ta(n,0),l=ad(o,r),d=(await l.data())[0],u=pe(o,Se(r,"int32")),p=(await u.data())[0];return re([l,u]),[d,p,i]}return[0,0,i]}async function Cb(e,t){let r=(t.body.skipTime||0)>oe()-hC,a=Tb<(t.body.skipFrames||0);return t.skipAllowed&&r&&a&&Object.keys(Gr.keypoints).length>0?(Tb++,[Gr]):(Tb=0,new Promise(async n=>{var p;let s=q(()=>{if(!(Nr==null?void 0:Nr.inputs[0].shape))return null;let h=Ie.resizeBilinear(e,[Nr.inputs[0].shape[2],Nr.inputs[0].shape[1]],!1),c=L(h,Xe.tf2);return he(c,Xe.tf1)}),i;if(t.body.enabled&&(i=Nr==null?void 0:Nr.execute(s)),hC=oe(),re(s),i){Gr.keypoints.length=0;let h=i.squeeze();re(i);let c=h.unstack(2);re(h);for(let f=0;f<c.length;f++){let[m,g,y]=await Kye(c[f],t.body.minConfidence);y>(((p=t.body)==null?void 0:p.minConfidence)||0)&&Gr.keypoints.push({score:Math.round(100*y)/100,part:Ib[f],positionRaw:[m/Nr.inputs[0].shape[2],g/Nr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Nr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Nr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}Gr.score=Gr.keypoints.reduce((h,c)=>c.score>h?c.score:h,0);let o=Gr.keypoints.map(h=>h.position[0]),l=Gr.keypoints.map(h=>h.position[1]);Gr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let d=Gr.keypoints.map(h=>h.positionRaw[0]),u=Gr.keypoints.map(h=>h.positionRaw[1]);Gr.boxRaw=[Math.min(...d),Math.min(...u),Math.max(...d)-Math.min(...d),Math.max(...u)-Math.min(...u)];for(let[h,c]of Object.entries(Sb)){let f=[];for(let m=0;m<c.length-1;m++){let g=Gr.keypoints.find(A=>A.part===c[m]),y=Gr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Gr.annotations[h]=f}n([Gr])}))}var Xye=["angry","disgust","fear","happy","sad","surprise","neutral"],Oa,v0=[],mC=0,gC=0,Nb=Number.MAX_SAFE_INTEGER;async function yC(e){var t;return ce.initial&&(Oa=null),Oa?e.debug&&se("cached model:",Oa.modelUrl):Oa=await Ue((t=e.face.emotion)==null?void 0:t.modelPath),Oa}async function Eb(e,t,r,a){var i,o;if(!Oa)return[];let n=Nb<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-gC;return t.skipAllowed&&s&&n&&mC===a&&v0[r]&&v0[r].length>0?(Nb++,v0[r]):(Nb=0,new Promise(async l=>{var u,p;let d=[];if((u=t.face.emotion)==null?void 0:u.enabled){let h={},c=(Oa==null?void 0:Oa.inputs[0].shape)?Oa.inputs[0].shape[2]:0;h.resize=Ie.resizeBilinear(e,[c,c],!1),h.channels=L(h.resize,Xe.rgb),h.grayscale=ke(h.channels,3,!0),h.grayscaleSub=he(h.grayscale,Xe.tf05),h.grayscaleMul=L(h.grayscaleSub,Xe.tf2),h.emotion=Oa==null?void 0:Oa.execute(h.grayscaleMul),gC=oe();let f=await h.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((p=t.face.emotion)==null?void 0:p.minConfidence)||0)&&d.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:Xye[m]});d.sort((m,g)=>g.score-m.score),Object.keys(h).forEach(m=>re(h[m]))}v0[r]=d,mC=a,l(d)}))}var ya,Rb=[],xC=0,bC=0,vC=Number.MAX_SAFE_INTEGER;async function wC(e){return ce.initial&&(ya=null),ya?e.debug&&se("cached model:",ya.modelUrl):ya=await Ue(e.face.mobilefacenet.modelPath),ya}async function Fb(e,t,r,a){var i,o;if(!ya)return[];let n=vC<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-bC;return t.skipAllowed&&s&&n&&xC===a&&Rb[r]?(vC++,Rb[r]):new Promise(async l=>{var u;let d=[];if(((u=t.face.embedding)==null?void 0:u.enabled)&&(ya==null?void 0:ya.inputs[0].shape)){let p={};p.crop=Ie.resizeBilinear(e,[ya.inputs[0].shape[2],ya.inputs[0].shape[1]],!1),p.data=ya==null?void 0:ya.execute(p.crop);let h=await p.data.data();d=Array.from(h)}Rb[r]=d,xC=a,bC=oe(),l(d)})}var is,ji=0,Zye=2.3,Mb=Ya.leftEyeLower0,$b=Ya.rightEyeLower0,Cd={leftBounds:[Mb[0],Mb[Mb.length-1]],rightBounds:[$b[0],$b[$b.length-1]]},Nd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function CC(e){var t;return ce.initial&&(is=null),is?e.debug&&se("cached model:",is.modelUrl):is=await Ue((t=e.face.iris)==null?void 0:t.modelPath),ji=is.inputs[0].shape?is.inputs[0].shape[2]:0,ji===-1&&(ji=64),is}function w0(e,t,r,a){for(let n=0;n<db.length;n++){let{key:s,indices:i}=db[n],o=Ya[`${r}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let d=i[l];e[o[l]]=[t[d][0],t[d][1],(t[d][2]+e[o[l]][2])/2]}}}var Yye=e=>{let t=e[Cd.leftBounds[0]][2],r=e[Cd.rightBounds[0]][2];return t-r},IC=(e,t,r,a,n,s=!1)=>{let i=c0(h0(UT([e[r],e[a]]),Zye)),o=Id(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/n,i.startPoint[0]/n,i.endPoint[1]/n,i.endPoint[0]/n]],[0],[ji,ji]);if(s&&ce.kernels.includes("flipleftright")){let d=Ie.flipLeftRight(l);re(l),l=d}return{box:i,boxSize:o,crop:l}},SC=(e,t,r,a=!1)=>{let n=[];for(let s=0;s<Nd.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];n.push([(a?1-i/ji:i/ji)*r[0]+t.startPoint[0],o/ji*r[1]+t.startPoint[1],l])}return{rawCoords:n,iris:n.slice(Nd.index)}},TC=(e,t,r)=>{let a=e[Ya[`${r}EyeUpper0`][Nd.upperCenter]][2],n=e[Ya[`${r}EyeLower0`][Nd.lowerCenter]][2],s=(a+n)/2;return t.map((i,o)=>{let l=s;return o===2?l=a:o===4&&(l=n),[i[0],i[1],l]})};async function NC(e,t,r,a){if(!is)return r.debug&&se("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:s,crop:i}=IC(e,t,Cd.leftBounds[0],Cd.leftBounds[1],a,!0),{box:o,boxSize:l,crop:d}=IC(e,t,Cd.rightBounds[0],Cd.rightBounds[1],a,!0),u=kt([i,d]);re(i),re(d);let p=is.execute(u);re(u);let h=await p.data();re(p);let c=h.slice(0,Nd.numCoordinates*3),{rawCoords:f,iris:m}=SC(c,n,s,!0),g=h.slice(Nd.numCoordinates*3),{rawCoords:y,iris:A}=SC(g,o,l),x=Yye(e);Math.abs(x)<30?(w0(e,f,"left",null),w0(e,y,"right",null)):x<1?w0(e,f,"left",["EyeUpper0","EyeLower0"]):w0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=TC(e,m,"left"),v=TC(e,A,"right");return e.concat(b).concat(v)}var Pn={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},os=null,Ed=0;async function RC(e,t){var o,l,d,u,p,h,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Pn.timestamp,a=Pn.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!a||Pn.boxes.length===0?(Pn.boxes=await JT(e,t),Pn.timestamp=oe(),Pn.skipped=0):Pn.skipped++;let n=[],s=[],i=0;for(let g=0;g<Pn.boxes.length;g++){let y=Pn.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=qT((d=t.face.detector)==null?void 0:d.rotation,y,e,((u=t.face.mesh)==null?void 0:u.enabled)?Ed:f0()),(p=t==null?void 0:t.filter)==null?void 0:p.equalization){let v=await i0(b.tensor);re(b.tensor),b.tensor=v}if(b.boxScore=Math.round(100*y.confidence)/100,(h=t.face.mesh)==null?void 0:h.enabled)if(!os)t.debug&&se("face mesh detection requested, but model is not loaded");else{let[v,C,T]=os.execute(b.tensor),E=await C.data();b.faceScore=Math.round(100*E[0])/100;let R=U(T,[-1,3]),z=await R.array();if(re([T,R,C,v]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{((f=t.face.iris)==null?void 0:f.enabled)&&(z=await NC(z,b.tensor,t,Ed)),b.mesh=HT(z,y,A,x,Ed),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Ed]);for(let I of Object.keys(Ya))b.annotations[I]=Ya[I].map(D=>b.mesh[D]);b.score=b.faceScore;let M={...KT(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=fb(M,e),b.boxRaw=mb(M,e),s.push(M)}}else{b.box=fb(y,e),b.boxRaw=mb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(v=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*v[0]/f0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*v[1]/f0()]),b.meshRaw=b.mesh.map(v=>[v[0]/(e.shape[2]||0),v[1]/(e.shape[1]||0),(v[2]||0)/Ed]);for(let v of Object.keys(Lh))b.annotations[v]=[b.mesh[Lh[v]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?n.push(b):re(b.tensor)}return Pn.boxes=s,n}async function FC(e){var t;return ce.initial&&(os=null),os?e.debug&&se("cached 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p={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((h=t.face.description)==null?void 0:h.enabled){let f=zb(e),m=Aa==null?void 0:Aa.execute(f);PC=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(p.gender=y[0]<=.5?"female":"male",p.genderScore=Math.min(.99,A));let x=Ta(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let C=await m.find(R=>R.shape[1]===100).data();p.age=Math.round(C[b-1]>C[b+1]?10*b-100*C[b-1]:10*b+100*C[b+1])/10;let T=m.find(R=>R.shape[1]===1024),E=T?await T.data():[];p.descriptor=Array.from(E),m.forEach(R=>re(R))}k0[r]=p,OC=a,u(p)}))}function I0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Uh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function LC(e,t,r){let a=t.shape[1],n=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/n,e.endPoint[1]/a,e.endPoint[0]/n]];return Ie.cropAndResize(t,s,[0],r)}function BC(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:a,palmLandmarks:n,confidence:e.confidence}}function S0(e,t=1.5){let r=Uh(e),a=I0(e),n=[t*a[0]/2,t*a[1]/2],s=[r[0]-n[0],r[1]-n[1]],i=[r[0]+n[0],r[1]+n[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function T0(e){let t=Uh(e),r=I0(e),n=Math.max(...r)/2,s=[t[0]-n,t[1]-n],i=[t[0]+n,t[1]+n];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function Jye(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function WC(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Jye(r)}var DC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Hi(e,t){let r=0;for(let a=0;a<e.length;a++)r+=e[a]*t[a];return r}function Qye(e,t){let r=[];for(let a=0;a<e.length;a++)r.push(e[a][t]);return r}function _C(e,t){let r=[],a=e.length;for(let n=0;n<a;n++){r.push([]);for(let s=0;s<a;s++)r[n].push(Hi(e[n],Qye(t,s)))}return r}function Lb(e,t){let r=Math.cos(e),a=Math.sin(e),n=[[r,-a,0],[a,r,0],[0,0,1]],s=DC(t[0],t[1]),i=_C(s,n),o=DC(-t[0],-t[1]);return _C(i,o)}function VC(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],a=[-Hi(t[0],r),-Hi(t[1],r)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function Bb(e,t){return[Hi(e,t[0]),Hi(e,t[1])]}var 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a={};a.reshape=U(t,[-1,7,2]),a.div=pe(a.reshape,this.inputSizeTensor),a.landmarks=ue(a.div,this.anchors[r]);let n=L(a.landmarks,this.inputSizeTensor);return Object.keys(a).forEach(s=>re(a[s])),n}async predict(t,r){let a={};a.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),a.div=pe(a.resize,Xe.tf127),a.image=he(a.div,Xe.tf1),a.batched=this.model.execute(a.image),a.predictions=Ye(a.batched),a.slice=Oe(a.predictions,[0,0],[-1,1]),a.sigmoid=Sr(a.slice),a.scores=Ye(a.sigmoid);let n=await a.scores.data();a.boxes=Oe(a.predictions,[0,1],[-1,4]),a.norm=this.normalizeBoxes(a.boxes),a.nms=await Ie.nonMaxSuppressionAsync(a.norm,a.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await a.nms.array(),i=[];for(let o of s){let l={};l.box=Oe(a.norm,[o,0],[1,-1]),l.slice=Oe(a.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=U(l.norm,[-1,2]);let d=await l.box.data(),u=d.slice(0,2),p=d.slice(2,4),h=await l.palmLandmarks.array(),c={startPoint:u,endPoint:p,palmLandmarks:h,confidence:n[o]},f=BC(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(a).forEach(o=>re(a[o])),i}};var r2e=5,jC=1.65,HC=[0,5,9,13,17,1,2],a2e=0,n2e=2,qC=0,Vb=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),a=t.map(i=>i[1]),n=[Math.min(...r),Math.min(...a)],s=[Math.max(...r),Math.max(...a)];return{startPoint:n,endPoint:s}}getBoxForPalmLandmarks(t,r){let a=t.map(s=>Bb([...s,1],r)),n=this.calculateLandmarksBoundingBox(a);return S0(T0(n),r2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),a=S0(T0(r),jC);a.palmLandmarks=[];for(let n=0;n<HC.length;n++)a.palmLandmarks.push(t[HC[n]].slice(0,2));return a}transformRawCoords(t,r,a,n){let s=I0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Lb(a,[0,0]),d=o.map(c=>[...Bb(c,l),c[2]]),u=VC(n),p=[...Uh(r),1],h=[Hi(p,u[0]),Hi(p,u[1])];return d.map(c=>[Math.trunc(c[0]+h[0]),Math.trunc(c[1]+h[1]),Math.trunc(c[2])])}async estimateHands(t,r){let a=!1,n,s=(r.hand.skipTime||0)>oe()-qC,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(n=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,n&&n.length>0&&(n.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...n],this.storedBoxes.length>0&&(a=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let d=this.storedBoxes[l];if(!!d)if(r.hand.landmarks){let u=r.hand.rotation?WC(d.palmLandmarks[a2e],d.palmLandmarks[n2e]):0,p=Uh(d),h=[p[0]/t.shape[2],p[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,u,0,h):t.clone(),f=Lb(-u,p),m=a?this.getBoxForPalmLandmarks(d.palmLandmarks,f):d,g=LC(m,c,[this.inputSize,this.inputSize]),y=pe(g,Xe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);qC=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=U(x,[-1,3]),C=await v.array();re(x),re(v);let T=this.transformRawCoords(C,m,u,f),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:d.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let u=S0(T0(d),jC),p={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};o.push(p)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var jr={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=>jr.nameMapping[e],getPoints:e=>jr.pointsMapping[e]},qi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>qi.nameMapping[e]},_t={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=>_t.nameMapping[e]},Ml=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,a){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,a])}direction(t,r,a){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,a])}weight(t,r){this.weights[t]=r;let a=this.weights.reduce((n,s)=>n+s,0);this.weightsRelative=this.weights.map(n=>n*5/a)}matchAgainst(t,r){let a=0;for(let n in t){let s=t[n],i=this.curls[n];if(typeof i=="undefined"){a+=this.weightsRelative[n];continue}for(let[o,l]of i)if(s===o){a+=l*this.weightsRelative[n];break}}for(let n in r){let s=r[n],i=this.directions[n];if(typeof i=="undefined"){a+=this.weightsRelative[n];continue}for(let[o,l]of i)if(s===o){a+=l*this.weightsRelative[n];break}}return a/10}};var{thumb:mn,index:ls,middle:us,ring:$l,pinky:Pl}=jr,{none:gn,half:i2e,full:yn}=qi,{verticalUp:Rd,verticalDown:cxe,horizontalLeft:Ub,horizontalRight:o2e,diagonalUpRight:l2e,diagonalUpLeft:Fd,diagonalDownRight:fxe,diagonalDownLeft:mxe}=_t,Ki=new Ml("thumbs up");Ki.curl(mn,gn,1);Ki.direction(mn,Rd,1);Ki.direction(mn,Fd,.25);Ki.direction(mn,l2e,.25);for(let e of[jr.index,jr.middle,jr.ring,jr.pinky])Ki.curl(e,yn,1),Ki.direction(e,Ub,1),Ki.direction(e,o2e,1);var Yt=new Ml("victory");Yt.curl(mn,i2e,.5);Yt.curl(mn,gn,.5);Yt.direction(mn,Rd,1);Yt.direction(mn,Fd,1);Yt.curl(ls,gn,1);Yt.direction(ls,Rd,.75);Yt.direction(ls,Fd,1);Yt.curl(us,gn,1);Yt.direction(us,Rd,1);Yt.direction(us,Fd,.75);Yt.curl($l,yn,1);Yt.direction($l,Rd,.2);Yt.direction($l,Fd,1);Yt.direction($l,Ub,.2);Yt.curl(Pl,yn,1);Yt.direction(Pl,Rd,.2);Yt.direction(Pl,Fd,1);Yt.direction(Pl,Ub,.2);Yt.weight(ls,2);Yt.weight(us,2);var Xi=new Ml("point");Xi.curl(mn,yn,1);Xi.curl(ls,gn,.5);Xi.curl(us,yn,.5);Xi.curl($l,yn,.5);Xi.curl(Pl,yn,.5);Xi.weight(ls,2);Xi.weight(us,2);var Zi=new Ml("middle finger");Zi.curl(mn,gn,1);Zi.curl(ls,yn,.5);Zi.curl(us,yn,.5);Zi.curl($l,yn,.5);Zi.curl(Pl,yn,.5);Zi.weight(ls,2);Zi.weight(us,2);var Md=new Ml("open palm");Md.curl(mn,gn,.75);Md.curl(ls,gn,.75);Md.curl(us,gn,.75);Md.curl($l,gn,.75);Md.curl(Pl,gn,.75);var KC=[Ki,Yt,Xi,Zi,Md];var u2e=.7,Ol={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 XC(e,t,r,a){let n=(t-a)/(e-r),s=Math.atan(n)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function YC(e,t){if(!e||!t)return[0,0];let r=XC(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let a=XC(e[1],e[2],t[1],t[2]);return[r,a]}function ZC(e,t=1){let r=0,a=0,n=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?a=1*t:n=1*t,[r,a,n]}function d2e(e,t,r){let a=e[0]-t[0],n=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],d=e[2]-t[2],u=e[2]-r[2],p=t[2]-r[2],h=Math.sqrt(a*a+i*i+d*d),c=Math.sqrt(n*n+o*o+u*u),f=Math.sqrt(s*s+l*l+p*p),m=(f*f+h*h-c*c)/(2*f*h);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Ol.NO_CURL_START_LIMIT?y=qi.none:g>Ol.HALF_CURL_START_LIMIT?y=qi.half:y=qi.full,y}function JC(e,t,r,a){let n;return a===Math.abs(e)?e>0?n=_t.horizontalLeft:n=_t.horizontalRight:a===Math.abs(t)?t>0?n=_t.horizontalLeft:n=_t.horizontalRight:r>0?n=_t.horizontalLeft:n=_t.horizontalRight,n}function QC(e,t,r,a){let n;return a===Math.abs(e)?e<0?n=_t.verticalDown:n=_t.verticalUp:a===Math.abs(t)?t<0?n=_t.verticalDown:n=_t.verticalUp:r<0?n=_t.verticalDown:n=_t.verticalUp,n}function p2e(e,t,r,a,n,s,i,o){let l,d=QC(e,t,r,a),u=JC(n,s,i,o);return d===_t.verticalUp?u===_t.horizontalLeft?l=_t.diagonalUpLeft:l=_t.diagonalUpRight:u===_t.horizontalLeft?l=_t.diagonalDownLeft:l=_t.diagonalDownRight,l}function h2e(e,t,r,a){let n=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],d=t[1]-r[1],u=Math.max(Math.abs(n),Math.abs(s),Math.abs(i)),p=Math.max(Math.abs(o),Math.abs(l),Math.abs(d)),h=0,c=0,f=0,m=p/(u+1e-5);m>1.5?h+=Ol.DISTANCE_VOTE_POWER:m>.66?c+=Ol.DISTANCE_VOTE_POWER:f+=Ol.DISTANCE_VOTE_POWER;let g=Math.sqrt(n*n+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+d*d),x=Math.max(g,y,A),b=e[0],v=e[1],C=r[0],T=r[1];x===g?(C=r[0],T=r[1]):x===A&&(b=t[0],v=t[1]);let z=YC([b,v],[C,T]),M=ZC(z,Ol.TOTAL_ANGLE_VOTE_POWER);h+=M[0],c+=M[1],f+=M[2];for(let D of a){let O=ZC(D,Ol.SINGLE_ANGLE_VOTE_POWER);h+=O[0],c+=O[1],f+=O[2]}let I;return h===Math.max(h,c,f)?I=QC(l,o,d,p):f===Math.max(c,f)?I=JC(s,n,i,u):I=p2e(l,o,d,p,s,n,i,u),I}function eN(e){let t=[],r=[],a=[],n=[];if(!e)return{curls:a,directions:n};for(let s of jr.all){let i=jr.getPoints(s),o=[],l=[];for(let d of i){let u=e[d[0]],p=e[d[1]],h=YC(u,p),c=h[0],f=h[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of jr.all){let i=s===jr.thumb?1:0,o=jr.getPoints(s),l=e[o[i][0]],d=e[o[i+1][1]],u=e[o[3][1]],p=d2e(l,d,u),h=h2e(l,d,u,t[s].slice(i));a[s]=p,n[s]=h}return{curls:a,directions:n}}function C0(e){if(!e||e.length===0)return null;let t=eN(e),r={};for(let a of jr.all)r[jr.getName(a)]={curl:qi.getName(t.curls[a]),direction:_t.getName(t.directions[a])};return r}function tN(e){let t=[];if(!e||e.length===0)return t;let r=eN(e);for(let a of KC){let n=a.matchAgainst(r.curls,r.directions);n>=u2e&&t.push({name:a.name,confidence:n})}return t}var rN={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]},$d,Pd,aN;async function jb(e,t){let r=await aN.estimateHands(e,t);if(!r)return[];let a=[];for(let n=0;n<r.length;n++){let s={};if(r[n].landmarks)for(let u of Object.keys(rN))s[u]=rN[u].map(p=>r[n].landmarks[p]);let i=r[n].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let u of i)u[0]<o[0]&&(o[0]=u[0]),u[1]<o[1]&&(o[1]=u[1]),u[0]>o[2]&&(o[2]=u[0]),u[1]>o[3]&&(o[3]=u[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[n].box?[Math.trunc(Math.max(0,r[n].box.topLeft[0])),Math.trunc(Math.max(0,r[n].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[n].box.bottomRight[0])-Math.max(0,r[n].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[n].box.bottomRight[1])-Math.max(0,r[n].box.topLeft[1]))]:[0,0,0,0],l=[r[n].box.topLeft[0]/(e.shape[2]||0),r[n].box.topLeft[1]/(e.shape[1]||0),(r[n].box.bottomRight[0]-r[n].box.topLeft[0])/(e.shape[2]||0),(r[n].box.bottomRight[1]-r[n].box.topLeft[1])/(e.shape[1]||0)];let d=C0(i);a.push({id:n,score:Math.round(100*r[n].confidence)/100,boxScore:Math.round(100*r[n].boxConfidence)/100,fingerScore:Math.round(100*r[n].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:d})}return a}async function Hb(e){var r,a;ce.initial&&($d=null,Pd=null),!$d||!Pd?[$d,Pd]=await Promise.all([e.hand.enabled?Ue((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ue((a=e.hand.skeleton)==null?void 0:a.modelPath):null]):(e.debug&&se("cached model:",$d.modelUrl),e.debug&&se("cached model:",Pd.modelUrl));let t=new Wb($d);return aN=new Vb(t,Pd),[$d,Pd]}var or=[null,null],c2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Yi=[[0,0],[0,0]],f2e=["hand","fist","pinch","point","face","tip","pinchtip"],sN=4,iN=1.6,m2e=512,g2e=1.4,N0=Number.MAX_SAFE_INTEGER,qb=0,ds=[0,0],jt={boxes:[],hands:[]},oN={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(ce.initial&&(or[0]=null),or[0])e.debug&&se("cached model:",or[0].modelUrl);else{E0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),or[0]=await Ue((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(or[0].modelSignature.inputs);Yi[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Yi[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return or[0]}async function uN(e){var t;if(ce.initial&&(or[1]=null),or[1])e.debug&&se("cached model:",or[1].modelUrl);else{or[1]=await Ue((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(or[1].modelSignature.inputs);Yi[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Yi[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return or[1]}async function y2e(e,t){let r=[];if(!e||!or[0])return r;let a={},n=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,m2e),i=Math.round(s*n/8)*8;a.resize=Ie.resizeBilinear(e,[s,i]),a.cast=me(a.resize,"int32"),[a.rawScores,a.rawBoxes]=await or[0].executeAsync(a.cast,c2e),a.boxes=Ye(a.rawBoxes,[0,2]),a.scores=Ye(a.rawScores,[0]);let o=ra(a.scores,1);re(o[sN]),o.splice(sN,1),a.filtered=nr(o,1),re(o),a.max=hr(a.filtered,1),a.argmax=Ta(a.filtered,1);let l=0;a.nms=await Ie.nonMaxSuppressionAsync(a.boxes,a.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await a.nms.data(),u=await a.max.data(),p=await a.argmax.data();for(let h of Array.from(d)){let c=Oe(a.boxes,h,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=y0(m,g2e),y=[Math.trunc(m[0]*ds[0]),Math.trunc(m[1]*ds[1]),Math.trunc(m[2]*ds[0]),Math.trunc(m[3]*ds[1])],A=u[h],x=f2e[p[h]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(a).forEach(h=>re(a[h])),r.sort((h,c)=>c.score-h.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function Kb(e,t,r){let a={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&&or[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let n={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Ie.cropAndResize(e,[s],[0],[Yi[1][0],Yi[1][1]],"bilinear"),n.div=pe(n.crop,Xe.tf255),[n.score,n.keypoints]=or[1].execute(n.div,["Identity_1","Identity"]);let i=(await n.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){a.fingerScore=o,n.reshaped=U(n.keypoints,[-1,3]);let u=(await n.reshaped.array()).map(p=>[p[0]/Yi[1][1],p[1]/Yi[1][0],p[2]||0]).map(p=>[p[0]*t.boxRaw[2],p[1]*t.boxRaw[3],p[2]||0]);a.keypoints=u.map(p=>[ds[0]*(p[0]+t.boxRaw[0]),ds[1]*(p[1]+t.boxRaw[1]),p[2]||0]),a.landmarks=C0(a.keypoints);for(let p of Object.keys(oN))a.annotations[p]=oN[p].map(h=>a.landmarks&&a.keypoints[h]?a.keypoints[h]:null)}Object.keys(n).forEach(l=>re(n[l]))}return a}async function Xb(e,t){var n,s;if(!or[0]||!or[1]||!((n=or[0])==null?void 0:n.inputs[0].shape)||!((s=or[1])==null?void 0:s.inputs[0].shape))return[];ds=[e.shape[2]||0,e.shape[1]||0],N0++;let r=(t.hand.skipTime||0)>oe()-qb,a=N0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&a?jt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-qb,l=N0<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(u=>Kb(e,u,t))):t.skipAllowed&&o&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(u=>Kb(e,u,t))):(jt.boxes=await y2e(e,t),qb=oe(),jt.hands=await Promise.all(jt.boxes.map(u=>Kb(e,u,t))),N0=0);let d=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<jt.hands.length;u++){let p=rC(jt.hands[u].keypoints,ds);if(p.box[2]/(e.shape[2]||1)>.05&&p.box[3]/(e.shape[1]||1)>.05&&jt.hands[u].fingerScore&&jt.hands[u].fingerScore>(t.hand.minConfidence||0)){let h=y0(p.box,iN),c=y0(p.boxRaw,iN);jt.boxes.push({...d[u],box:h,boxRaw:c})}}for(let u=0;u<jt.hands.length;u++){let p=ns(jt.hands[u].keypoints,ds);jt.hands[u].box=p.box,jt.hands[u].boxRaw=p.boxRaw}i(jt.hands)})}var Er,R0=[],Zb=Number.MAX_SAFE_INTEGER,pN=0,hN=0;async function cN(e){var t;return ce.initial&&(Er=null),Er?e.debug&&se("cached model:",Er.modelUrl):Er=await Ue((t=e.face.liveness)==null?void 0:t.modelPath),Er}async function Yb(e,t,r,a){var i,o;if(!Er)return 0;let n=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-hN,s=Zb<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&n&&s&&pN===a&&R0[r]?(Zb++,R0[r]):(Zb=0,new Promise(async l=>{let d=Ie.resizeBilinear(e,[(Er==null?void 0:Er.inputs[0].shape)?Er.inputs[0].shape[2]:0,(Er==null?void 0:Er.inputs[0].shape)?Er.inputs[0].shape[1]:0],!1),u=Er==null?void 0:Er.execute(d),p=(await u.data())[0];R0[r]=Math.round(100*p)/100,pN=a,hN=oe(),re([d,u]),l(R0[r])}))}var Gh={};Qd(Gh,{connected:()=>M0,horizontal:()=>Jb,kpt:()=>F0,relative:()=>e5,vertical:()=>Qb});var F0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Jb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Qb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],e5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],M0={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,xa={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function t5(e){for(let t of Jb){let r=e.keypoints.findIndex(n=>n.part===t[0]),a=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[r]&&e.keypoints[a]&&e.keypoints[r].position[0]<e.keypoints[a].position[0]){let n=e.keypoints[r];e.keypoints[r]=e.keypoints[a],e.keypoints[a]=n}}for(let t of Qb){let r=e.keypoints.findIndex(n=>n&&n.part===t[0]),a=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[r]&&e.keypoints[a]&&e.keypoints[r].position[1]<e.keypoints[a].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of e5){let a=e.keypoints.findIndex(d=>d&&d.part===t[0]),n=e.keypoints.findIndex(d=>d&&d.part===t[1]),s=e.keypoints.findIndex(d=>d&&d.part===r[0]),i=e.keypoints.findIndex(d=>d&&d.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[a]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0])]:[0,0],l=e.keypoints[n]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let d=e.keypoints[a];e.keypoints[a]=e.keypoints[n],e.keypoints[n]=d}}}function gN(e){for(let t=0;t<e.length;t++)if(e[t]&&xa.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-xa.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-xa.keypoints[t].positionRaw[1])];r[0]<mN&&r[1]<mN?e[t]=xa.keypoints[t]:xa.keypoints[t]=e[t]}else xa.keypoints[t]=e[t];return e}function yN(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;xa.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=ja(e,xa.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let a=me(r.resize,"int32");return Object.keys(r).forEach(n=>re(r[n])),a}function AN(e,t){e.keypoints=e.keypoints.filter(a=>a&&a.position);for(let a of e.keypoints)a.position=[a.position[0]*(t[0]+xa.padding[2][0]+xa.padding[2][1])/t[0]-xa.padding[2][0],a.position[1]*(t[1]+xa.padding[1][0]+xa.padding[1][1])/t[1]-xa.padding[1][0]],a.positionRaw=[a.position[0]/t[0],a.position[1]/t[1]];let r=ns(e.keypoints.map(a=>a.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var ba,$0=0,r5=Number.MAX_SAFE_INTEGER,zl={boxes:[],bodies:[],last:0};async function xN(e){return ce.initial&&(ba=null),ba?e.debug&&se("cached model:",ba.modelUrl):(E0(["size"],e),ba=await 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h=s[3*p+2];if(h>t.body.minConfidence){let c=[s[3*p+1],s[3*p+0]];o.push({part:F0[p],score:Math.round(100*h)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ns(o.map(p=>p.position),[r.shape[2],r.shape[1]]),d={};for(let[p,h]of Object.entries(M0)){let c=[];for(let f=0;f<h.length-1;f++){let m=o.find(y=>y.part===h[f]),g=o.find(y=>y.part===h[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}d[p]=c}let u={id:n,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:d};t5(u),a.push(u)}}return a.sort((n,s)=>s.score-n.score),a.length>t.body.maxDetected&&(a.length=t.body.maxDetected),a}async function a5(e,t){if(!ba||!(ba==null?void 0:ba.inputs[0].shape))return[];t.skipAllowed||(zl.boxes.length=0),r5++;let r=(t.body.skipTime||0)>oe()-zl.last,a=r5<(t.body.skipFrames||0);return t.skipAllowed&&r&&a?zl.bodies:new Promise(async n=>{let s={};r5=0,s.input=yN(e,$0),s.res=ba==null?void 0:ba.execute(s.input),zl.last=oe();let i=await s.res.array();zl.bodies=s.res.shape[2]===17?await x2e(i,t,e):await b2e(i,t,e);for(let o of zl.bodies)AN(o,[e.shape[2]||1,e.shape[1]||1]),gN(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),n(zl.bodies)})}var Od,P0=[],vN=0,n5=Number.MAX_SAFE_INTEGER,z0=0,O0=2.5;async function wN(e){if(!Od||ce.initial){Od=await Ue(e.object.modelPath);let t=Object.values(Od.modelSignature.inputs);z0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&se("cached model:",Od.modelUrl);return Od}async function v2e(e,t,r){let a=0,n=[];for(let l of[1,2,4])q(async()=>{let d=l*13,u=Ye(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)===Td.length)),p=Ye(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)<Td.length)),c=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),f=await u.array();for(let m=0;m<u.shape[0];m++)for(let g=0;g<u.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%d))/d,x=(.5+Math.trunc(m/d))/d,b=c[m].map(I=>I*(d/l/z0)),[v,C]=[A-O0/l*b[0],x-O0/l*b[1]],[T,E]=[A+O0/l*b[2]-v,x+O0/l*b[3]-C],R=[v,C,T,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let z=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:a++,score:Math.round(100*y)/100,class:g+1,label:Td[g].label,box:z.map(I=>Math.trunc(I)),boxRaw:R};n.push(M)}}});e.forEach(l=>re(l));let s=n.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=n.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return n=n.filter((l,d)=>o.includes(d)).sort((l,d)=>d.score-l.score),n}async function s5(e,t){let r=(t.object.skipTime||0)>oe()-vN,a=n5<(t.object.skipFrames||0);return t.skipAllowed&&r&&a&&P0.length>0?(n5++,P0):(n5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?P0:new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[z0,z0],!1),o=pe(i,Xe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let d;t.object.enabled&&(d=Od.execute(l)),vN=oe(),re(l);let u=await v2e(d,s,t);P0=u,n(u)}))}var Hh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],w2e=Hh.length,jh=Hh.reduce((e,t,r)=>(e[t]=r,e),{}),k2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lxe=k2e.map(([e,t])=>[jh[e],jh[t]]),IN=[["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 SN(e){let t=e.reduce(({maxX:r,maxY:a,minX:n,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(a,o),minX:Math.min(n,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function TN(e,[t,r],[a,n]){let s=t/a,i=r/n,o=(d,u)=>({id:u,score:d.score,boxRaw:[d.box[0]/n,d.box[1]/a,d.box[2]/n,d.box[3]/a],box:[Math.trunc(d.box[0]*i),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*i),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:p,part:h,position:c})=>({score:p,part:h,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/a,c.y/a]})),annotations:{}});return e.map((d,u)=>o(d,u))}var i5=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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o5(e,t,r,a){return{y:a.get(e,t,r),x:a.get(e,t,r+w2e)}}function l5(e,t,r){let{heatmapY:a,heatmapX:n,id:s}=e,{y:i,x:o}=o5(a,n,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function u5(e,t,r){return e<t?t:e>r?r:e}function CN(e,t,r,a){let n=r-e,s=a-t;return n*n+s*s}function d5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var An,S2e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],D0=1,zd=16,T2e=50**2;function NN(e,t,r,a,n,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:u5(Math.round(y.y/zd),0,A-1),x:u5(Math.round(y.x/zd),0,x-1)}),[d,u]=a.shape,p=l(t.position,d,u),h=o(p),f=d5(t.position,h);for(let y=0;y<i;y++){let A=l(f,d,u),x=o5(A.y,A.x,r,n);f=d5({x:A.x*zd,y:A.y*zd},{x:x.x,y:x.y})}let m=l(f,d,u),g=a.get(m.y,m.x,r);return{position:f,part:Hh[r],score:g}}function C2e(e,t,r,a,n){let 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t.reduce((a,{position:n,score:s},i)=>(EN(e,n,i)||(a+=s),a),0)/t.length}function F2e(e,t,r,a,n,s){let i=[],o=E2e(s,t);for(;i.length<n&&!o.empty();){let l=o.dequeue(),d=l5(l.part,zd,e);if(EN(i,d,l.part.id))continue;let u=C2e(l,t,e,r,a);u=u.filter(c=>c.score>s);let p=R2e(i,u),h=SN(u);p>s&&i.push({keypoints:u,box:h,score:Math.round(100*p)/100})}return i}async function p5(e,t){let r=q(()=>{if(!An.inputs[0].shape)return[];let i=Ie.resizeBilinear(e,[An.inputs[0].shape[2],An.inputs[0].shape[1]]),o=he(pe(me(i,"float32"),127.5),1),d=An.execute(o,S2e).map(u=>Ye(u,[0]));return d[1]=Sr(d[1]),d}),a=await Promise.all(r.map(i=>i.buffer()));for(let i of r)re(i);let n=await F2e(a[0],a[1],a[2],a[3],t.body.maxDetected,t.body.minConfidence);return An.inputs[0].shape?TN(n,[e.shape[1],e.shape[2]],[An.inputs[0].shape[2],An.inputs[0].shape[1]]):[]}async function RN(e){return!An||ce.initial?An=await Ue(e.body.modelPath):e.debug&&se("cached model:",An.modelUrl),An}var On,h5=!1;async function 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p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,h=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;n.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],p,h,0,0,2*Math.PI),n.stroke(),a.fillPolygons&&(n.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,n.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){n.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,n.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,h=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;n.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],p,h,0,0,2*Math.PI),n.stroke(),a.fillPolygons&&(n.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,n.fill())}if(a.drawGaze&&((s=u.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let p=u.box[0]+u.box[2]/2-u.box[3]*Dd(u.rotation.angle.yaw)/90,h=u.box[1]+u.box[3]/2+u.box[2]*Dd(u.rotation.angle.pitch)/90,c=new Path2D(`
            M ${u.box[0]+u.box[2]/2} ${u.box[1]}
            C
              ${p} ${u.box[1]},
              ${p} ${u.box[1]+u.box[3]},
              ${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
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            M ${u.box[0]} ${u.box[1]+u.box[3]/2}
            C 
              ${u.box[0]} ${h},
              ${u.box[0]+u.box[2]} ${h},
              ${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
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a=vr(ps,r);if(!t||!e)return;let n=Dl(e);if(!!n){n.lineJoin="round";for(let i=0;i<t.length;i++){if(n.strokeStyle=a.color,n.fillStyle=a.color,n.lineWidth=a.lineWidth,n.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(qh(n,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(n.fillStyle=a.shadowColor,n.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),n.fillStyle=a.labelColor,n.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(n.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 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t){if(a.drawBoxes&&(n.strokeStyle=a.color,n.fillStyle=a.color,qh(n,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(n.fillStyle=a.shadowColor,n.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),n.fillStyle=a.labelColor,n.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),n.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)n.fillStyle=a.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:a.color,y5(n,i[0],i[1],0,a);if(a.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let d=o[o.length-1][2]||0;n.fillStyle=a.useDepth?`rgba(${127.5+2*d}, ${127.5-2*d}, 255, 0.5)`:a.color,n.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};n.font=a.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(a.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){n.beginPath();let d=o[l][2]||0;n.strokeStyle=a.useDepth?`rgba(${127.5+l*d}, ${127.5-l*d}, 255, 0.5)`:a.color,n.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),n.lineTo(o[l][0],o[l][1]),n.stroke()}};n.lineWidth=a.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function w5(e,t,r){let a=vr(ps,r);if(!t||!e)return;let n=Dl(e);if(!!n){n.lineJoin="round",n.font=a.font;for(let s of t)if(a.drawBoxes){if(n.strokeStyle=a.color,n.fillStyle=a.color,qh(n,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;a.shadowColor&&a.shadowColor!==""&&(n.fillStyle=a.shadowColor,n.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),n.fillStyle=a.labelColor,n.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}n.stroke()}}}async function WN(e,t,r){let a=vr(ps,r);if(!t||!e)return;let n=Dl(e);if(!!n){n.lineJoin="round",n.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(n.strokeStyle=a.color,n.fillStyle=a.color,qh(n,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${s}`;a.shadowColor&&a.shadowColor!==""&&(n.fillStyle=a.shadowColor,n.fillText(i,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),n.fillStyle=a.labelColor,n.fillText(i,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2])}n.stroke()}}}async function VN(e,t){if(!e||!t)return;let r=Dl(t);!r||r.drawImage(e,0,0)}async function UN(e,t,r){if(!t||!t.performance||!t||!e)return null;let a=oe(),n=vr(ps,r),s=Promise.all([x5(e,t.face,n),b5(e,t.body,n),v5(e,t.hand,n),w5(e,t.object,n),A5(e,t.gesture,n)]);return g5=ce.perfadd?g5+Math.round(oe()-a):Math.round(oe()-a),t.performance.draw=g5,s}var _d=.1,k5=.5;function D2e(e,t,r){let a=!1,n=r.length-1;for(let s=0;s<r.length;n=s++)r[s].y>t!=r[n].y>t&&e<(r[n].x-r[s].x)*(t-r[s].y)/(r[n].y-r[s].y)+r[s].x&&(a=!a);return a}async function GN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,a=await e.tensor.buffer(),n=[];for(let i of Ya.silhouette)n.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});_d&&_d>0&&(n=n.map(i=>({x:i.x>.5?i.x+_d:i.x-_d,y:i.y>.5?i.y+_d:i.y-_d})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)D2e(i/t,o/t,n)||(a.set(k5*a.get(0,o,i,0),0,o,i,0),a.set(k5*a.get(0,o,i,1),0,o,i,1),a.set(k5*a.get(0,o,i,2),0,o,i,2));let s=a.toTensor();return re(a),s}var L2e=e=>{let t=(p,h)=>Math.atan2(p[1]-h[1],p[0]-h[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],a=1,n=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=n?e.mesh[473]:e.mesh[468],i=n?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=n?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],a*(s[1]-i[1])/o[1]-r[1]],d=Math.sqrt(l[0]**2+l[1]**2);return d=Math.min(d,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:d}},jN=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},a=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},n=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,C,T,E]=m,R,z,M;return x<1?x>-1?(M=Math.asin(x),z=Math.atan2(-C,g),R=Math.atan2(-v,b)):(M=-Math.PI/2,z=-Math.atan2(T,E),R=0):(M=Math.PI/2,z=Math.atan2(T,E),R=0),isNaN(R)&&(R=0),isNaN(z)&&(z=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-z,roll:2*-M}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(m=>[m[0]*t[0]/o,m[1]*t[1]/o,m[2]]),d=r(a(l[1],l[0])),u=r(a(l[3],l[2])),p=r(n(u,d));u=n(d,p);let h=[u[0],u[1],u[2],d[0],d[1],d[2],p[0],p[1],p[2]],c=s(h),f=i.length===478?L2e(e):{bearing:0,strength:0};return{angle:c,matrix:h,gaze:f}};var I5=async(e,t)=>{var c,f,m,g,y,A,x,b,v,C,T,E,R,z,M,I,D,O,j,X,_,K;let r=oe(),a,n,s,i,o,l,d,u,p=[];e.state="run:face";let h=await RC(t,e.config);if(e.performance.face=ce.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!h)return[];for(let W=0;W<h.length;W++){if(e.analyze("Get Face"),!h[W].tensor||h[W].tensor.isDisposedInternal){se("Face object is disposed:",h[W].tensor);continue}if((c=e.config.face.detector)==null?void 0:c.mask){let ae=await GN(h[W]);re(h[W].tensor),h[W].tensor=ae}let ee=h[W].mesh&&h[W].mesh.length>200?jN(h[W],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=((f=e.config.face.emotion)==null?void 0:f.enabled)?Eb(h[W].tensor||pt([]),e.config,W,h.length):[]:(e.state="run:emotion",r=oe(),i=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Eb(h[W].tensor||pt([]),e.config,W,h.length):[],e.performance.emotion=ce.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=((g=e.config.face.antispoof)==null?void 0:g.enabled)?lb(h[W].tensor||pt([]),e.config,W,h.length):0:(e.state="run:antispoof",r=oe(),l=((y=e.config.face.antispoof)==null?void 0:y.enabled)?await lb(h[W].tensor||pt([]),e.config,W,h.length):0,e.performance.antispoof=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?d=((A=e.config.face.liveness)==null?void 0:A.enabled)?Yb(h[W].tensor||pt([]),e.config,W,h.length):0:(e.state="run:liveness",r=oe(),d=((x=e.config.face.liveness)==null?void 0:x.enabled)?await Yb(h[W].tensor||pt([]),e.config,W,h.length):0,e.performance.liveness=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?n=((b=e.config.face.gear)==null?void 0:b.enabled)?tb(h[W].tensor||pt([]),e.config,W,h.length):null:(e.state="run:gear",r=oe(),n=((v=e.config.face.gear)==null?void 0:v.enabled)?await tb(h[W].tensor||pt([]),e.config,W,h.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(a=((C=e.config.face.ssrnet)==null?void 0:C.enabled)?ab(h[W].tensor||pt([]),e.config,W,h.length):null,s=((T=e.config.face.ssrnet)==null?void 0:T.enabled)?ib(h[W].tensor||pt([]),e.config,W,h.length):null):(e.state="run:ssrnet",r=oe(),a=((E=e.config.face.ssrnet)==null?void 0:E.enabled)?await ab(h[W].tensor||pt([]),e.config,W,h.length):null,s=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await ib(h[W].tensor||pt([]),e.config,W,h.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=((z=e.config.face.mobilefacenet)==null?void 0:z.enabled)?Fb(h[W].tensor||pt([]),e.config,W,h.length):null:(e.state="run:mobilefacenet",r=oe(),o=((M=e.config.face.mobilefacenet)==null?void 0:M.enabled)?await Fb(h[W].tensor||pt([]),e.config,W,h.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?u=((I=e.config.face.description)==null?void 0:I.enabled)?Db(h[W].tensor||pt([]),e.config,W,h.length):null:(e.state="run:description",r=oe(),u=((D=e.config.face.description)==null?void 0:D.enabled)?await Db(h[W].tensor||pt([]),e.config,W,h.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([a,s,i,o,u,n,l,d]=await Promise.all([a,s,i,o,u,n,l,d])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&a&&s&&(u={...u,age:a.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&n&&(u={...u,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(u.descriptor=o),!((_=e.config.face.iris)==null?void 0:_.enabled);let Q=h[W].annotations&&h[W].annotations.leftEyeIris&&h[W].annotations.leftEyeIris[0]&&h[W].annotations.rightEyeIris&&h[W].annotations.rightEyeIris[0]&&h[W].annotations.leftEyeIris.length>0&&h[W].annotations.rightEyeIris.length>0&&h[W].annotations.leftEyeIris[0]!==null&&h[W].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(h[W].annotations.leftEyeIris[3][0]-h[W].annotations.leftEyeIris[1][0]),Math.abs(h[W].annotations.rightEyeIris[4][1]-h[W].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ne=((K=e.config.face.detector)==null?void 0:K.return)?Ye(h[W].tensor):null;re(h[W].tensor),h[W].tensor&&delete h[W].tensor;let Z={...h[W],id:W};(u==null?void 0:u.age)&&(Z.age=u.age),(u==null?void 0:u.gender)&&(Z.gender=u.gender),(u==null?void 0:u.genderScore)&&(Z.genderScore=u==null?void 0:u.genderScore),(u==null?void 0:u.descriptor)&&(Z.embedding=u==null?void 0:u.descriptor),(u==null?void 0:u.race)&&(Z.race=u==null?void 0:u.race),i&&(Z.emotion=i),l&&(Z.real=l),d&&(Z.live=d),Q&&Q!==0&&(Z.iris=Math.trunc(500/Q/11.7)/100),ee&&(Z.rotation=ee),ne&&(Z.tensor=ne),p.push(Z),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 HN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let a=e[r].keypoints.find(l=>l.part==="leftWrist"),n=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&a&&n&&a.position[1]<s.position[1]&&n.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&a&&a.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&n&&n.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},qN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let a=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),n=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(a/n)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${a<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},KN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let a=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],n=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(a*n),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),d=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(d=!0,t.push({iris:r,gesture:"facing center"}));let p=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],h=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(p>.06||h>.06)&&(d=!1),p>h?p>.05&&t.push({iris:r,gesture:"looking right"}):h>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(d=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),d&&t.push({iris:r,gesture:"looking center"})}return t},XN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let a=[];if(e[r].annotations)for(let[n,s]of Object.entries(e[r].annotations))n!=="palmBase"&&Array.isArray(s)&&s[0]&&a.push({name:n.toLowerCase(),position:s[0]});if(a&&a.length>0){let n=a.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${n.name} forward`});let s=a.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let n=tN(e[r].keypoints);for(let s of n)t.push({hand:r,gesture:s.name})}}return t};var Ee={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},S5=0;function ZN(e,t){var i,o,l,d,u,p,h,c,f,m,g,y,A,x,b,v,C,T,E,R,z,M,I,D,O,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let a=Date.now()-e.timestamp,n=a<1e3?8-Math.log(a+1):1;if(e.canvas&&(Ee.canvas=e.canvas),e.error&&(Ee.error=e.error),!Ee.body||e.body.length!==Ee.body.length)Ee.body=JSON.parse(JSON.stringify(e.body));else for(let _=0;_<e.body.length;_++){let K=e.body[_].box.map((Z,ae)=>((n-1)*Ee.body[_].box[ae]+Z)/n),W=e.body[_].boxRaw.map((Z,ae)=>((n-1)*Ee.body[_].boxRaw[ae]+Z)/n),ee=e.body[_].keypoints.map((Z,ae)=>{var ie,xe,be,Te,Re,$e,_e,qe,Ze;return{score:Z.score,part:Z.part,position:[Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].position[0]||0)+(Z.position[0]||0))/n:Z.position[0],Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].position[1]||0)+(Z.position[1]||0))/n:Z.position[1],Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].position[2]||0)+(Z.position[2]||0))/n:Z.position[2]],positionRaw:[Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/n:Z.positionRaw[0],Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/n:Z.positionRaw[1],Ee.body[_].keypoints[ae]?((n-1)*(Ee.body[_].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/n:Z.positionRaw[2]],distance:[Ee.body[_].keypoints[ae]?((n-1)*(((ie=Ee.body[_].keypoints[ae].distance)==null?void 0:ie[0])||0)+(((xe=Z.distance)==null?void 0:xe[0])||0))/n:(be=Z.distance)==null?void 0:be[0],Ee.body[_].keypoints[ae]?((n-1)*(((Te=Ee.body[_].keypoints[ae].distance)==null?void 0:Te[1])||0)+(((Re=Z.distance)==null?void 0:Re[1])||0))/n:($e=Z.distance)==null?void 0:$e[1],Ee.body[_].keypoints[ae]?((n-1)*(((_e=Ee.body[_].keypoints[ae].distance)==null?void 0:_e[2])||0)+(((qe=Z.distance)==null?void 0:qe[2])||0))/n:(Ze=Z.distance)==null?void 0:Ze[2]]}}),Q={},ne={connected:{}};((o=(i=t.body)==null?void 0:i.modelPath)==null?void 0:o.includes("efficientpose"))?ne=b0:((d=(l=t.body)==null?void 0:l.modelPath)==null?void 0:d.includes("blazepose"))?ne=m0:((p=(u=t.body)==null?void 0:u.modelPath)==null?void 0:p.includes("movenet"))&&(ne=Gh);for(let[Z,ae]of Object.entries(ne.connected)){let ie=[];for(let xe=0;xe<ae.length-1;xe++){let be=ee.find(Re=>Re.part===ae[xe]),Te=ee.find(Re=>Re.part===ae[xe+1]);be&&Te&&ie.push([be.position,Te.position])}Q[Z]=ie}Ee.body[_]={...e.body[_],box:K,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ee.hand||e.hand.length!==Ee.hand.length)Ee.hand=JSON.parse(JSON.stringify(e.hand));else for(let _=0;_<e.hand.length;_++){let K=e.hand[_].box.map((ne,Z)=>((n-1)*Ee.hand[_].box[Z]+ne)/n),W=e.hand[_].boxRaw.map((ne,Z)=>((n-1)*Ee.hand[_].boxRaw[Z]+ne)/n);Ee.hand[_].keypoints.length!==e.hand[_].keypoints.length&&(Ee.hand[_].keypoints=e.hand[_].keypoints);let ee=e.hand[_].keypoints&&e.hand[_].keypoints.length>0?e.hand[_].keypoints.map((ne,Z)=>ne.map((ae,ie)=>((n-1)*(Ee.hand[_].keypoints[Z][ie]||1)+(ae||0))/n)):[],Q={};if(Object.keys(Ee.hand[_].annotations).length!==Object.keys(e.hand[_].annotations).length)Ee.hand[_].annotations=e.hand[_].annotations,Q=Ee.hand[_].annotations;else if(e.hand[_].annotations)for(let ne of Object.keys(e.hand[_].annotations))Q[ne]=e.hand[_].annotations[ne]&&e.hand[_].annotations[ne][0]?e.hand[_].annotations[ne].map((Z,ae)=>Z.map((ie,xe)=>((n-1)*Ee.hand[_].annotations[ne][ae][xe]+ie)/n)):null;Ee.hand[_]={...e.hand[_],box:K,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ee.face||e.face.length!==Ee.face.length)Ee.face=JSON.parse(JSON.stringify(e.face));else for(let _=0;_<e.face.length;_++){let K=e.face[_].box.map((ee,Q)=>((n-1)*Ee.face[_].box[Q]+ee)/n),W=e.face[_].boxRaw.map((ee,Q)=>((n-1)*Ee.face[_].boxRaw[Q]+ee)/n);if(e.face[_].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=(h=e.face[_].rotation)==null?void 0:h.matrix,ee.angle={roll:((n-1)*(((f=(c=Ee.face[_].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[_].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/n,yaw:((n-1)*(((A=(y=Ee.face[_].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[_].rotation)==null?void 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2Q==`;async function H2e(e){let t=(n,s="application/octet-stream")=>fetch(`data:${s};base64,${n}`).then(i=>i.blob()),r,a;switch(e.config.warmup){case"face":r=await t(B0);break;case"body":case"full":r=await t(W0);break;default:r=null}if(r){let n=await createImageBitmap(r);a=await e.detect(n,e.config),n.close()}return a}async function q2e(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+B0;break;case"full":case"body":r="data:image/jpeg;base64,"+W0;break;default:r=null}let a;if(typeof Image!="undefined")a=new Image;else if(ce.Image)a=new ce.Image;else return;a.onload=async()=>{let n=Ur(a.naturalWidth,a.naturalHeight);if(!n)se("Warmup: Canvas not found"),t(void 0);else{let s=n.getContext("2d");s&&s.drawImage(a,0,0);let i=await e.image(n),o=await e.detect(i.tensor,e.config);t(o)}},r?a.src=r:t(void 0)})}async function K2e(e){let t=n=>Buffer.from(n,"base64"),r;e.config.warmup==="face"?r=t(B0):r=t(W0);let a;if("node"in We){let n=(void 0).decodeJpeg(r),s=n.expandDims(0);e.tf.dispose(n),a=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&se("Warmup tfjs-node not loaded");return a}async function t9(e,t){let r=oe();if(e.state="warmup",t&&(e.config=vr(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null};let a;return new Promise(async n=>{typeof createImageBitmap=="function"?a=await H2e(e):typeof Image!="undefined"||ce.Canvas!==void 0?a=await q2e(e):a=await K2e(e);let s=oe();e.config.debug&&se("Warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),n(a)})}var Ld,Kh,Xh,V0,r9=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");tp(this,Ld,void 0);tp(this,Kh,void 0);tp(this,Xh,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!ep(this,Kh))return;let r=this.tf.engine().state.numTensors,a=ep(this,Ld);rp(this,Ld,r);let n=r-a;n!==0&&se(...t,n)});tp(this,V0,t=>{if(!ep(this,Xh))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof et))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",JN);fe(this,"distance",L0);fe(this,"match",QN);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=ce,xs.wasmPath=Dh["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Zy}/dist/`,xs.modelBasePath=ce.browser?"../models/":"file://models/",xs.backend=ce.browser?"humangl":"tensorflow",this.version=Jx,Object.defineProperty(this,"version",{value:Jx}),this.config=JSON.parse(JSON.stringify(xs)),Object.seal(this.config),t&&(this.config=vr(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",wT(this.config),this.tf=We,this.state="idle",rp(this,Ld,0),rp(this,Kh,!1),rp(this,Xh,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new f5,this.draw={options:ps,canvas:(r,a)=>VN(r,a),face:(r,a,n)=>x5(r,a,n),body:(r,a,n)=>b5(r,a,n),hand:(r,a,n)=>v5(r,a,n),gesture:(r,a,n)=>A5(r,a,n),object:(r,a,n)=>w5(r,a,n),person:(r,a,n)=>WN(r,a,n),all:(r,a,n)=>UN(r,a,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=MC,this.faceUVMap=$C,this.gl=Nt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(xs)),this.config.backend=t}validate(t){return m1(xs,t||this.config)}now(){return oe()}image(t,r=!0){return kd(t,this.config,r)}async segmentation(t,r){return MN(t,r,this.config)}enhance(t){return zb(t)}compare(t,r){return bT(this.config,t,r)}async init(){await _0(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),a=Object.values(this.models).filter(i=>i).length;t&&(this.config=vr(this.config,t)),this.env.initial&&(this.config.debug&&se(`version: ${this.version}`),this.config.debug&&se(`tfjs version: ${this.tf.version["tfjs-core"]}`),await _0(this)||se("error: backend check failed"),await Qu(),this.env.browser&&(this.config.debug&&se("configuration:",this.config),this.config.debug&&se("environment:",this.env),this.config.debug&&se("tf flags:",this.tf.ENV.flags))),await PN(this),this.env.initial&&this.config.debug&&se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==a&&(await ON(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ZN(t,this.config)}async warmup(t){let r=oe(),a=await t9(this,t),n=oe();return this.performance.warmup=Math.trunc(n-r),a}async profile(t,r){let a=await this.tf.profile(()=>this.detect(t,r)),n={};for(let o of 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tensor"),this.emit("error"),a({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=oe(),this.config.skipAllowed=await xT(this.config,o.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(oe()-n):Math.trunc(oe()-n),this.analyze("Check Changed:");let l=[],d=[],u=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?I5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(n=oe(),l=this.config.face.enabled?await I5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-n):Math.trunc(oe()-n)),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 h=this.config.body.maxDetected===-1?vr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?d=this.config.body.enabled?p5(o.tensor,h):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?d=this.config.body.enabled?bb(o.tensor,h):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("efficientpose"))?d=this.config.body.enabled?Cb(o.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("movenet"))&&(d=this.config.body.enabled?a5(o.tensor,h):[]),this.performance.body&&delete this.performance.body):(n=oe(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?d=this.config.body.enabled?await p5(o.tensor,h):[]:((v=this.config.body.modelPath)==null?void 0:v.includes("blazepose"))?d=this.config.body.enabled?await bb(o.tensor,h):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?d=this.config.body.enabled?await Cb(o.tensor,h):[]:((T=this.config.body.modelPath)==null?void 0:T.includes("movenet"))&&(d=this.config.body.enabled?await a5(o.tensor,h):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-n):Math.trunc(oe()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?vr(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?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?jb(o.tensor,c):[]:((M=(z=this.config.hand.detector)==null?void 0:z.modelPath)==null?void 0:M.includes("handtrack"))&&(u=this.config.hand.enabled?Xb(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(n=oe(),((D=(I=this.config.hand.detector)==null?void 0:I.modelPath)==null?void 0:D.includes("handdetect"))?u=this.config.hand.enabled?await jb(o.tensor,c):[]:((j=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:j.includes("handtrack"))&&(u=this.config.hand.enabled?await Xb(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-n):Math.trunc(oe()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?p=this.config.object.enabled?s5(o.tensor,this.config):[]:((_=this.config.object.modelPath)==null?void 0:_.includes("centernet"))&&(p=this.config.object.enabled?kb(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=oe(),((K=this.config.object.modelPath)==null?void 0:K.includes("nanodet"))?p=this.config.object.enabled?await s5(o.tensor,this.config):[]:((W=this.config.object.modelPath)==null?void 0:W.includes("centernet"))&&(p=this.config.object.enabled?await kb(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-n):Math.trunc(oe()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,d,u,p]=await Promise.all([l,d,u,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(n=oe(),f=[...qN(l),...HN(d),...XN(u),...KN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-n):Math.trunc(oe()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((Q=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:d,hand:u,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return e9(l,d,u,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",a(this.result)})}};Ld=new WeakMap,Kh=new WeakMap,Xh=new WeakMap,V0=new WeakMap;return nE(Z2e);})();
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use backend file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * Human main module
 * @default Human Library
 * @summary <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. */