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n!=null&&(c=v(n,"offset","batchNorm")),A(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),A(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Oo(a,i,l,c,u,s)}var _w=T({batchNorm2d_:xj});function yj(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),A(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),A(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank 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in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:o,strides:n,inputShape:s},m=D.runKernel(Xl,c,p);return i?z(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var lg=T({conv3DBackpropInput_:Rj});function Fj(r,e,t,n,o){let s=v(r,"x","conv3dTranspose"),a=v(e,"filter","conv3dTranspose");return lg(t,s,a,n,o)}var Oj=T({conv3dTranspose_:Fj});function Pj(r){let t={x:v(r,"x","cos")};return D.runKernel(Yn,t)}var Ia=T({cos_:Pj});function Mj(r){let t={x:v(r,"x","cosh")};return D.runKernel(ri,t)}var Iu=T({cosh_:Mj});function Lj(r,e=0,t=!1,n=!1){let s={x:v(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:n};return D.runKernel(Zn,s,a)}var Nu=T({cumsum_:Lj});function zj(r,e,t,n=!1){let o=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");A(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),A(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank 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Kj(r){let t={x:v(r,"x","zerosLike")};return D.runKernel(ys,t)}var Ie=T({zerosLike_:Kj});function Xj(r,e){let t=v(r,"a","div"),n=v(e,"b","div");[t,n]=Ge(t,n);let o=de(t,n),s=Ie(o),a=wn(n,s);return $t(a,s,o)}var Bm=T({divNoNan_:Xj});function Yj(r,e){let t=v(r,"t1","dot"),n=v(e,"t2","dot");A((t.rank===1||t.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${n.rank}.`);let o=t.rank===1?t.size:t.shape[1],s=n.rank===1?n.size:n.shape[0];if(A(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),t.rank===1&&n.rank===1){let a=z(t,[1,-1]),i=z(n,[-1,1]),l=je(a,i);return z(l,[])}else if(t.rank===1&&n.rank===2){let a=z(t,[1,-1]),i=z(n,[n.shape[0],n.shape[1]]),l=je(a,i);return z(l,[l.size])}else if(t.rank===2&&n.rank===1){let a=z(n,[-1,1]),i=je(t,a);return z(i,[i.size])}else{let a=z(n,[n.shape[0],n.shape[1]]);return je(t,a)}}var Aw=T({dot_:Yj});function Zj(r){let t={x:v(r,"x","elu")};return D.runKernel(si,t)}var Is=T({elu_:Zj});function Jj(r){let e=v(r,"x","erf");A(e.dtype==="int32"||e.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),e.dtype==="int32"&&(e=ne(e,"float32"));let t={x:e};return D.runKernel(ii,t)}var Vm=T({erf_:Jj});function Qj(r){let t={x:v(r,"x","exp")};return D.runKernel(eo,t)}var Yt=T({exp_:Qj});function eW(r,e=0){let t=v(r,"x","expandDims","string_or_numeric");A(e<=t.rank,()=>"Axis must be <= rank of the tensor");let n={input:t},o={dim:e};return D.runKernel(as,n,o)}var ir=T({expandDims_:eW});function tW(r){let t={x:v(r,"x","expm1")};return D.runKernel(li,t)}var Gm=T({expm1_:tW});function rW(r,e){let t=v(r,"x","tile","string_or_numeric");A(t.rank===e.length,()=>`Error in transpose: rank of input ${t.rank} must match length of reps ${e}.`);let n={x:t},o={reps:e};return D.runKernel(yn,n,o)}var On=T({tile_:rW});function nW(r,e,t,n="float32"){e==null&&(e=r);let o=Ce([r,e],n),s=r<=e?r:e;for(let i=0;i`Error in localResponseNormalization: x must 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t=v(r,"labels","sigmoidCrossEntropyWithLogits"),n=v(e,"logits","sigmoidCrossEntropyWithLogits");St(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Nr(n),s=M(n,t),a=Eu(Yt(Ue(Et(n))));return Q(ue(o,s),a)}function yq(r,e,t,n=0,o=jt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),St(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(.5);s=Q(M(s,ue(c,u)),M(p,u))}let l=xq(s,a);return Sr(l,i,o)}var NS=T({sigmoidCrossEntropy_:yq});function bq(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return qr((o,s,a)=>{let l=Um(s,[t],!0),u=ue(ne(s,"float32"),l);a([o,u]);let c=Ue(M(u,o));return{value:ye(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Mo(f.shape,[t]);return[M(z(f,x),ue(ne(h,"float32"),Yt(g))),M(z(f,x),ue(Yt(g),ne(h,"float32")))]}}})(r,e)}function wq(r,e,t,n=0,o=jt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),St(s.shape,a.shape,"Error in softmaxCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(s.shape[1]);s=Q(M(s,ue(c,u)),de(u,p))}let l=bq(s,a);return Sr(l,i,o)}var SS=T({softmaxCrossEntropy_:wq});var _q={fft:Ra,ifft:Bi,rfft:Fa,irfft:zu},kq={hammingWindow:nS,hannWindow:yg,frame:bg,stft:oS},Ds={flipLeftRight:iS,resizeNearestNeighbor:_g,resizeBilinear:wg,rotateWithOffset:aS,cropAndResize:sS,nonMaxSuppression:lS,nonMaxSuppressionAsync:pS,nonMaxSuppressionWithScore:mS,nonMaxSuppressionWithScoreAsync:fS,nonMaxSuppressionPadded:dS,nonMaxSuppressionPaddedAsync:hS},l_={bandPart:gS,gramSchmidt:xS,qr:bS},vq={absoluteDifference:wS,computeWeightedLoss:Sr,cosineDistance:_S,hingeLoss:kS,huberLoss:vS,logLoss:CS,meanSquaredError:IS,sigmoidCrossEntropy:NS,softmaxCrossEntropy:SS};var Or=class extends sg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ee(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return cg(e,t)}dispose(){this.iterations_!=null&&Ee(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Or,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Or{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:V(()=>Ie(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:V(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;V(()=>{let c=Q(M(l,this.rho),M(Pe(i),1-this.rho)),p=M(de(yt(Q(u,this.epsilon)),yt(Q(l,this.epsilon))),i),m=Q(M(u,this.rho),M(Pe(p),1-this.rho));l.assign(c),u.assign(m);let f=Q(M(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";en(rp);var np=class extends Or{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:V(()=>Na(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;V(()=>{let l=Q(i,Pe(a));i.assign(l);let u=Q(M(de(a,yt(Q(l,D.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};np.className="Adagrad";en(np);var op=class extends Or{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(n).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ue(1,this.accBeta1),o=ue(1,this.accBeta2);t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ie(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ie(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(M(c,this.beta1),M(u,1-this.beta1)),f=Q(M(p,this.beta2),M(Pe(u),1-this.beta2)),d=de(m,n),h=de(f,o);c.assign(m),p.assign(f);let g=Q(M(de(d,Q(yt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(M(this.accBeta1,this.beta1)),this.accBeta2.assign(M(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Fr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};op.className="Adam";en(op);var sp=class extends Or{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ue(1,this.accBeta1),o=de(-this.learningRate,Q(M(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ie(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ie(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=Q(M(c,this.beta1),M(u,1-this.beta1)),f=M(p,this.beta2),d=Et(u),h=Hr(f,d);c.assign(m),p.assign(h);let g=Q(M(de(o,n),de(m,Q(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Q(this.iteration,1)),this.accBeta1.assign(M(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(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)}};sp.className="Adamax";en(sp);var cl=class extends Or{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=D.registeredVariables[n];V(()=>{let i=Q(M(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(le(-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 not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};cl.className="SGD";en(cl);var ip=class extends cl{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulations[o]==null){let l=!1;this.accumulations[o]={originalName:`${n}/momentum`,variable:V(()=>Ie(s).variable(l))}}let a=this.accumulations[o].variable,i=Array.isArray(e)?e[o].tensor:e[n];i!=null&&V(()=>{let l,u=Q(M(this.m,a),i);this.useNesterov?l=Q(M(this.c,Q(i,M(u,this.m))),s):l=Q(M(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ee(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ip.className="Momentum";en(ip);var ap=class extends Or{constructor(e,t=.9,n=0,o=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:V(()=>Ie(s).variable(a))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:V(()=>Ie(s).variable(a))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:V(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedMeanSquares[o].variable,u=this.accumulatedMoments[o].variable;V(()=>{let c=Q(M(l,this.decay),M(Pe(i),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Q(M(p,this.decay),M(i,1-this.decay)),f=de(M(i,this.learningRate),yt(ue(c,Q(Pe(m),this.epsilon)))),d=Q(M(u,this.momentum),f);l.assign(c),p.assign(m),u.assign(d);let h=ue(s,d);s.assign(h)}else{let p=Q(M(l,this.decay),M(Pe(i),1-this.decay)),m=Q(M(u,this.momentum),de(M(i,this.learningRate),yt(Q(p,this.epsilon))));l.assign(p),u.assign(m);let f=ue(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ee(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ee(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ee(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};ap.className="RMSProp";en(ap);var Oa=class{static sgd(e){return new cl(e)}static momentum(e,t,n=!1){return new ip(e,t,n)}static rmsprop(e,t=.9,n=0,o=null,s=!1){return new ap(e,t,n,o,s)}static adam(e=.001,t=.9,n=.999,o=null){return new op(e,t,n,o)}static adadelta(e=.001,t=.95,n=null){return new rp(e,t,n)}static adamax(e=.002,t=.9,n=.999,o=null,s=0){return new sp(e,t,n,o,s)}static adagrad(e,t=.1){return new np(e,t)}};var pl={sgd:Oa.sgd,momentum:Oa.momentum,adadelta:Oa.adadelta,adagrad:Oa.adagrad,rmsprop:Oa.rmsprop,adamax:Oa.adamax,adam:Oa.adam};var Cq=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function uf(){return new Promise(r=>Cq(()=>r()))}var 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LH(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;ee([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=x-1-c.padInfo.top,k=Ce(a.shape,"float32"),E=1/(f*d),S=t.data.get(o.dataId).values,R=Ce(o.shape,"float32",S);for(let F=0;F=c.outHeight||Math.floor(te)!==te))for(let Y=0;Y=c.outWidth||Math.floor(re)!==re)continue;K+=R.get(F,te,re,P)}}k.set(K*E,F,j,G,P)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var BT={kernelName:jl,backendName:"cpu",kernelFunc:LH};function zH(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,scale:s,offset:a,mean:i,variance:l}=e;y.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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tt({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=N.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=pf(c,a,e[0].dtype,p),f=N.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var HT={kernelName:is,backendName:"cpu",kernelFunc:ml};function S_(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n;ee([o,s],"conv2d");let p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",_=new ct(m.outShape,o.dtype),k=y.computeStrides(o.shape),E=y.computeStrides(s.shape),S=k[0],R=w?k[1]:k[2],F=w?k[2]:1,P=w?1:k[1],j=_.strides[0],G=w?_.strides[1]:_.strides[2],U=w?_.strides[2]:1,X=w?1:_.strides[1],K=t.data.get(o.dataId).values,Z=t.data.get(s.dataId).values,te=_.values;for(let Y=0;Y=m.inHeight)continue;let we=xe*E[0],ke=re+ge*R;for(let De=0;De=m.inWidth)continue;let kt=we+qe*E[1],vt=ke+ut*F,pt=kt;for(let Ct=0;Ct=u.inDepth)continue;let Y=Z*F[0],re=j+te*R[1];for(let ie=0;ie=u.inHeight)continue;let ge=Y+ae*F[1],we=re+xe*R[2];for(let ke=0;ke=u.inWidth)continue;let ut=ge+Re*F[2],kt=we+qe*u.inChannels,vt=ut;for(let pt=0;ptMath.cos(r)),e1={kernelName:Yn,backendName:"cpu",kernelFunc:XH};var YH=Ae(ri,r=>Math.cosh(r)),t1={kernelName:ri,backendName:"cpu",kernelFunc:YH};function ZH(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=i,x=Ce([d,h,g,f],"float32"),b=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,_=t.data.get(o.dataId).values,k=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let S=0;S=c)continue;let X=h>1?(j-F)*(p-1)/(h-1):0,K=g>1?(G-P)*(m-1)/(g-1):0;for(let Z=0;Z1?F*(p-1)+Z*X:.5*(F+j)*(p-1);if(te<0||te>p-1){for(let Y=0;Y1?P*(m-1)+se*K:.5*(P+G)*(m-1);if(pe<0||pe>m-1){for(let we=0;we1?P*(m-1)+Y*K:.5*(P+G)*(m-1);if(re<0||re>m-1){for(let pe=0;pex+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Fs(o,Sf):d=new rn(o,Sf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...dl(o))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>y.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;tl.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return 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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; `;var Os=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` ${Le(s)} coords = getOutputCoords(); `,s===1)a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let l=Wt("coords",s);a+=` bool nextRowOutOfBounds = (${l[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = (${l[s-1]} + 1) >= ${this.outputShape[s-1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `}this.userCode=` vec4 binaryOperation(vec4 a, vec4 b) { ${e} } void main() { vec4 a = getAAtOutCoords(); vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); ${a} setOutput(result); } `}};function Ut(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t2={kernelName:us,backendName:"webgl",kernelFunc:Ut};function nn(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Ut({inputs:{x:n},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=Ut({inputs:{x:o},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var r2={kernelName:ql,backendName:"webgl",kernelFunc:nn};var Lk="return (a < 0.) ? b * a : a;",zk=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function s8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(zk,o.shape,a.shape):new qo(Lk,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var n2={kernelName:so,backendName:"webgl",kernelFunc:s8};var Bk="return (a < 0.) ? b * a : a;",Vk=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function i8(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(Vk,n.shape,o.shape):new qo(Bk,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)}var o2={kernelName:yo,backendName:"webgl",kernelFunc:i8};var Yg="if (isnan(x)) return x;",s2=` if (isnan(a)) return a; if (isnan(b)) return b; `,i2=` 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 ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Fs(a.shape,e):c=new rn(a.shape,r),i.runWebGLProgram(c,[a],l)}}function it({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,k]=w,E={dataId:_.dataId,dtype:_.dtype,shape:l.shape},S={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new qo(r,l.shape,u.shape);return c.runWebGLProgram(R,[E,S],fr(_.dtype,k.dtype))}),b=nn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||fr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&o!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=o(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Os(e,l.shape,u.shape,t):f=new qo(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function xl(r,e=!1){if(r==="linear")return e?ZA:HA;if(r==="relu")return e?QA:XA;if(r==="elu")return e?JA:KA;if(r==="relu6")return e?e2:YA;if(r==="prelu")return e?Vk:Bk;if(r==="leakyrelu")return e?zk:Lk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Tf=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:g=`vec4 activation(vec4 x) { ${i} }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!hl(o.shape,l)&&!(c.texture!==null&&hl(c.shape,l))?u2(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var c2={kernelName:fs,backendName:"webgl",kernelFunc:ce};var Jg=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } `),this.userCode=` const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { ${c} return getX(batch, inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; float sumValue = 0.0; for (int i = 0; i < ${i}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${u} } int inIdx = inOffset + ${i}; if (${l===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); ${u} } else if (${l===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); ${u} } else if (${l===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); ${u} } setOutput(sumValue); } `}};var Wk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` 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 = ${l}(values, minMaxValue); } `,f="vec4";t==="all"?(i="1.0",m=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,f="bvec4"):t==="any"&&(i="0.0",m=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,f="bvec4");let d="";s%n>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { 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) { ${d} return getX(batch, inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; vec4 minMaxValue = vec4(${i}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${m} } int inIdx = inOffset + ${c}; if (${p===1}) { ${f} values = ${f}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${p===2}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${p===3}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${u}); } `}};function a8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function vn(r,e,t,n){let o=a8(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Le(this.rank),s=Dk("rc",this.rank),a=new Array(this.rank);for(let c=0;c=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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} return acos(x); `,m8=ve({opSnippet:p8}),x2={kernelName:Ks,backendName:"webgl",kernelFunc:m8};var f8=hr+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,d8=ve({opSnippet:f8}),y2={kernelName:Xs,backendName:"webgl",kernelFunc:d8};var b2="return a + b;",h8=it({opSnippet:b2,packedOpSnippet:b2,supportsComplex:!0,cpuKernelImpl:xA}),w2={kernelName:xn,backendName:"webgl",kernelFunc:h8};var Kk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} float result = ${o}; setOutput(result); } `}};var Xk=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} vec4 result = ${o}; setOutput(result); } `}};function Qg(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Ut({inputs:{x:n[0]},backend:t});if(n.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=Qg({inputs:n.slice(0,l),backend:t}),c=Qg({inputs:n.slice(l),backend:t});return Qg({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>fr(l,u)),s=n.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new Xk(n[0].shape,s):new Kk(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var _2={kernelName:Wn,backendName:"webgl",kernelFunc:Qg};function g8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=o;c!=null&&(p=zt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=vn(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var k2={kernelName:Vl,backendName:"webgl",kernelFunc:g8};function x8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=o;c!=null&&(p=zt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=vn(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var v2={kernelName:Gl,backendName:"webgl",kernelFunc:x8};var Yk=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${o}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${o}; i++) { int inIdx = ${l}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Zk=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Wt("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=` ${R} sourceLocR = ${R}(${c.join()}, 0); ++${c[l-1]}; ${R} sourceLocG = ${R}(${c.join()}, 0); ++${c[l-2]}; ${R} sourceLocA = ${R}(${c.join()}, 0); --${c[l-1]}; ${R} sourceLocB = ${R}(${c.join()}, 0); --${c[l-2]};`}else m=l,p=` ${u} sourceLocR = coords; ++${c[l-1]}; ${u} sourceLocG = coords; ++${c[l-2]}; ${u} sourceLocA = coords; --${c[l-1]}; ${u} sourceLocB = coords; --${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Wt("sourceLocR",m-1).concat("inIdx.r"),x=Wt("sourceLocG",m-1).concat("inIdx.g"),b=Wt("sourceLocB",m-1).concat("inIdx.b"),w=Wt("sourceLocA",m-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",k=o?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${w.join()})));`,E=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,S=o?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${f.join()}), vec2(${f.slice(-2).join()})); }`;this.userCode=` float getAChannel(${h.join()}) { return getChannel(getA(${f.join()}), vec2(${f.slice(-2).join()})); } ${S} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[l-1]} < ${i[l-1]-1}; bool hasNextRow = ${c[l-2]} < ${i[l-2]-1}; ${p} ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d}, sourceLocB${d}, sourceLocA${d}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${E}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${k} vec4 candidate = ${E}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${_}(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 C2(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new Yk(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=C2(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function I2(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=N.computeOptimalWindowSize(s),i=new Zk(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=I2(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ex(r,e,t,n){let o=[t];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,o),l=y.sizeFromShape(i),u=ce({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=C2(r,u,n);s.push(c);let p=ce({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return I2(r,e,n)}function y8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=zt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=ex(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var N2={kernelName:Un,backendName:"webgl",kernelFunc:y8};function b8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=zt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=ex(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var S2={kernelName:na,backendName:"webgl",kernelFunc:b8};var w8=hr+` if (abs(x) > 1.) { return NAN; } return asin(x); `,_8=ve({opSnippet:w8}),T2={kernelName:Ys,backendName:"webgl",kernelFunc:_8};var k8=hr+"return log(x + sqrt(x * x + 1.0));",v8=ve({opSnippet:k8}),E2={kernelName:Zs,backendName:"webgl",kernelFunc:v8};var C8=hr+` return atan(x); `,I8=ve({opSnippet:C8}),A2={kernelName:Js,backendName:"webgl",kernelFunc:I8};var N8=s2+` return atan(a, b); `,S8=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+i2+` return result; `,T8=it({opSnippet:N8,packedOpSnippet:S8}),D2={kernelName:ei,backendName:"webgl",kernelFunc:T8};var E8=hr+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,A8=ve({opSnippet:E8}),$2={kernelName:Qs,backendName:"webgl",kernelFunc:A8};var Wi=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let R=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${l}); const ivec2 pads = ivec2(${f}, ${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 float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${p}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${c}) { 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 ${R} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,E=a%4,S=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${w}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${l}); const ivec2 pads = ivec2(${f}, ${d}); const float initializationValue = ${b}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${p}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${S} } int xC = xCCorner + ${k}; if (${E===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${S} } else if (${E===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${S} } else if (${E===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${S} } } setOutput(${_}); } `}},Qu=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),n){let P=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${l}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${m}) { 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 ${P} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let k="max",E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");let S=Math.floor(a/4)*4,R=a%4,F=` if (${w}) { avgValue += dot(values, ones); } else { minMaxValue = ${k}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${l}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); const float initializationValue = ${_}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${_}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${S}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), getValue(batch, xD, xR, xC + 3 * ${m}, ch) ); ${F} } int xC = xCCorner + ${S}; if (${R===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${F} } else if (${R===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), initializationValue, initializationValue ); ${F} } else if (${R===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), initializationValue ); ${F} } } setOutput(${E}); } } `}};function D8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;$s(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ut({inputs:{x:o},backend:t});let p=new Wi(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var R2={kernelName:qn,backendName:"webgl",kernelFunc:D8};function $8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=N.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new Qu(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var F2={kernelName:oa,backendName:"webgl",kernelFunc:$8};var Jk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); 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 < ${l}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${u}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},Qk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*n*o);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); 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 < ${p}; wD += ${l}) { float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${m}; wR += ${u}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${f}; wC += ${c}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function R8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Qk(m);return t.runWebGLProgram(f,[o],a.dtype)}var O2={kernelName:Wl,backendName:"webgl",kernelFunc:R8};function F8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;$s([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new Jk(c);return t.runWebGLProgram(p,[o],a.dtype)}var P2={kernelName:jl,backendName:"webgl",kernelFunc:F8};function O8(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return Ju({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var M2={kernelName:Hn,backendName:"webgl",kernelFunc:O8};var ev=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(N.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${l}; float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}};var tv=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(N.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${l}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } `}};var P8=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new tv(n.shape,o.shape,s.shape,c,p,l):new ev(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},L2={kernelName:no,backendName:"webgl",kernelFunc:P8};var rv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),n=`uniform int start[${this.rank}];`,o=M8(this.rank),s,a=e.map((i,l)=>`sourceLoc.${nv[l]} = start[${l}] + coords.${nv[l]};`);s=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${a.join(` `)} `,this.userCode=` ${n} void main() { ${s} setOutput(getSource(${o})); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},nv=["x","y","z","w","u","v"];function M8(r){if(r===1)return"sourceLoc";if(r<=6)return nv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var ov=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),n=Wt("coords",this.rank),o=Wt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=` result.x = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${o[this.rank-1]}; 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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). 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${n} }`:s?_=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:_=` float activation(float x) { ${n} } `,k="result = activation(result);");let E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${_} const ivec2 strides = ivec2(${l}, ${u}); const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${w}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${p}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${g}) { 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 (${h===1}) { if (${g}) { dotProd += getX(batch, xR, xC, ${d}) * getW(wR, wC, ${d}, d2); } else { dotProd += getX(batch, ${d}, xR, xC) * getW(wR, wC, ${d}, d2); } } else if (${h===2}) { vec2 wValues = vec2( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2) ); if (${g}) { vec2 xValues = vec2( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${h===3}) { vec3 wValues = vec3( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2), getW(wR, wC, ${d} + 2, d2) ); if (${g}) { vec3 xValues = vec3( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1), getX(batch, xR, xC, ${d} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC), getX(batch, ${d} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${E} ${k} setOutput(result); } `}},fv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${o}); 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 < ${p}; wF++) { int xF = xFCorner + wF * ${l}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${h===1}) { dotProd += getX(batch, xF, xR, xC, ${d}) * getW(wF, wR, wC, ${d}, d2); } else if (${h===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${h===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1), getX(batch, xF, xR, xC, ${d} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2), getW(wF, wR, wC, ${d} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}};var dv=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:o,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=n,{left:f,top:d}=l,h=s*o,g=Pt(),x=m==="channelsLast",b=x?0:1,w=x?1:2,_="";for(let k=0;k<=1;k++)for(let E=0;E<=1;E++)_+=` blockIndex = rc.y + ${E}; pos = rc.x + ${k}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${u})) * ${i} - ${d}; d0 = offsetY + ${p} * (pos / ${h}); if(d0 < ${t[b]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.); d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.)); if(d1 < ${t[w]} && d1 >= 0) { ch = int(mod(float(pos), ${s}.)); if (${x}) { innerDims = vec2(d1, ch); result[${k*2+E}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${k*2+E}] = 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; ${_} ${g.output} = result; } `}};function rx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Hk,w=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=ce({inputs:{x:r},backend:n,attrs:{shape:[1,_,t.inChannels]}}),E=ce({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),S=Ju({a:k,b:E,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ce({inputs:{x:S},backend:n,attrs:{shape:t.outShape}}),x.push(k),x.push(E),x.push(S)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},E=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(hl(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let S=ce({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(S);let R=Ju({a:k,b:S,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),F=n.texData.get(R.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=E,F.shape=t.outShape,g=Ut({inputs:{x:R},backend:n}),g.shape=t.outShape,x.push(R)}for(let _ of x)n.disposeIntermediateTensorInfo(_);return g}function nx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,w=!1,_=[],k=ce({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=ce({inputs:{x:e},backend:n,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});_.push(k),_.push(E);let S=new dv(x,k.shape,t),R=n.runWebGLProgram(S,[k],"float32"),F=ce({inputs:{x:R},backend:n,attrs:{shape:[1,x[0],x[1]]}});_.push(R),_.push(F);let P=o!=null,j=s!=null,G=i==="leakyrelu",U=i?xl(i,!0):null,X=new Tf(F.shape,E.shape,[1,g,t.outChannels],b,w,P,U,j,G),K=[F,E];if(o&&K.push(o),j&&K.push(s),G){let re=n.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));K.push(re),_.push(re)}let Z=n.runWebGLProgram(X,K,"float32"),te=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=ce({inputs:{x:Z},backend:n,attrs:{shape:te}});_.push(Z);for(let re of _)n.disposeIntermediateTensorInfo(re);return Y}function q8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=rx({x:o,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=nx({x:o,filter:s,convInfo:m,backend:t});else{let h=new Af(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=ce({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var eD={kernelName:Kn,backendName:"webgl",kernelFunc:q8};var hv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${o}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${a}) { float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } else { float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},gv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${p}]; ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - 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) / ${o}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${a}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},xv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { int xF = wF + yF * ${t} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${n} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${o} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},yv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${l}, ${u}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${o}; 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 = ${o} - 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 H8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new hv(m);return t.runWebGLProgram(f,[o,s],"float32")}var tD={kernelName:Hl,backendName:"webgl",kernelFunc:H8};function K8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new gv(m);return t.runWebGLProgram(f,[o,s],"float32")}var rD={kernelName:Xn,backendName:"webgl",kernelFunc:K8};function X8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=N.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new fv(u);return t.runWebGLProgram(c,[o,s],"float32")}var nD={kernelName:aa,backendName:"webgl",kernelFunc:X8};function Y8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=N.computeConv3DInfo(o.shape,l,a,1,i),c=new xv(u);return t.runWebGLProgram(c,[o,s],"float32")}var oD={kernelName:Kl,backendName:"webgl",kernelFunc:Y8};function Z8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new yv(u);return t.runWebGLProgram(c,[o,s],"float32")}var sD={kernelName:Xl,backendName:"webgl",kernelFunc:Z8};var J8=Yg+` return cos(x); `,Q8=ve({opSnippet:J8}),iD={kernelName:Yn,backendName:"webgl",kernelFunc:Q8};var eX=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,tX=ve({opSnippet:eX}),aD={kernelName:ri,backendName:"webgl",kernelFunc:tX};var bv=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,k]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${w}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${a}) { return; } float height_scale = ${x}; float width_scale = ${_}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${d} ) { setOutput(float(${s})); return; } float in_x = ${k}; if( in_x < 0.0 || in_x > ${h} ) { setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${f} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var rX=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new bv(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},lD={kernelName:ni,backendName:"webgl",kernelFunc:rX};var ox=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${uD(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${Le(o)} coords = getOutputCoords(); int end = ${cD(o,"coords")}; float val = ${s}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${l}; ${cD(o,"coords")} = idx; val += getX(${uD(o,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function uD(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function cD(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function nX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=N.getAxesPermutation([s],l),c=o;u!=null&&(c=zt({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=o.shape[p],f=Ut({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new ox(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new ox(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=zt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var pD={kernelName:Zn,backendName:"webgl",kernelFunc:nX};function oX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=Ug(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=yA(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var mD={kernelName:Yl,backendName:"webgl",kernelFunc:oX};var wv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int h = ${this.getHeightCoordString()}; int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; int in_h = h / ${t}; int offset_h = imod(h, ${t}); int in_w = w / ${t}; int offset_w = imod(w, ${t}); int offset_d = (offset_h * ${t} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function sX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new wv(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var fD={kernelName:oi,backendName:"webgl",kernelFunc:sX};var Df=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";n&&(o?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:x=` float activation(float x) { ${n} } `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${c}, ${p}); const ivec2 pads = ivec2(${l}, ${u}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${g}; int q = d2 - d1 * ${g}; 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 < ${d}; wR++) { int xR = xRCorner + wR * ${m}; if (xR < 0 || xR >= ${a}) { continue; } for (int wC = 0; wC < ${h}; wC++) { int xC = xCCorner + wC * ${f}; if (xC < 0 || xC >= ${i}) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${w} ${b} setOutput(result); } `}};var $f=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let k=0;k= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { xTexelR${k}C${S}.zw = vec2(0.); } } else { xTexelR${k}C${S} = vec4(0.); } xCOffset = xC + 1 - 2; if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { vec4 previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { previous.zw = vec2(0.); } xR${k}C${S} = vec4(previous.zw, xTexelR${k}C${S}.xy); } else { xR${k}C${S} = vec4(0, 0, xTexelR${k}C${S}.xy); } `:x+=` if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) { xTexelR${k}C${S} = getX(batch, xR, xC, d1); } else { xTexelR${k}C${S} = vec4(0.); } xR${k}C${S} = xTexelR${k}C${S}; `,S+1= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S+2} = getX(batch, xR, xCOffset, d1); } `,f>1&&(x+=` xCOffset -= 2; if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S} = getX(batch, xR, xCOffset, d1); } else { xTexelR${k}C${S} = vec4(0.); } `),x+=` xR${k}C${S+1} = vec4( xTexelR${k}C${S}.zw, xTexelR${k}C${S+2}.xy); `):x+=` xCOffset = xC + ${R}; if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S+2} = getX(batch, xR, xCOffset, d1); } xR${k}C${S+1} = xTexelR${k}C${S+2}; `}}else S= 0 && xR < ${a}) { `,u%2==1?(x+=` xCOffset = xC + 1 - ${p}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S} = getX(batch, xR, xCOffset, d1); } else { xTexelR${k}C${S} = vec4(0.); } if(xC + 1 >= 0 && xC + 1 < ${i}) { xTexelR${k}C${S+2} = getX(batch, xR, xC + 1, d1); } else { xTexelR${k}C${S+2} = vec4(0.); } xR${k}C${S} = vec4( xTexelR${k}C${S}.zw, xTexelR${k}C${S+2}.zw); `,S+1= 0 && xCOffset < ${i}) { final = getX(batch, xR, xCOffset, d1); } xR${k}C${S+1} = vec4(xTexelR${k}C${S+2}.xy, final.xy); `)):(x+=` if(xC >= 0 && xC < ${i}) { xTexelR${k}C${S} = getX(batch, xR, xC, d1); } else { xTexelR${k}C${S} = vec4(0.); } xCOffset = xC + ${p}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${k}C${S+2} = getX(batch, xR, xCOffset, d1); } else { xTexelR${k}C${S+2} = vec4(0.); } xR${k}C${S} = vec4( xTexelR${k}C${S}.xy, xTexelR${k}C${S+2}.xy); `,S+1`Error in depthwiseConv2d: Either strides or dilations must be 1. 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} float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},kv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${l}; dm++) { int d2 = d1 * ${l} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function aX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=N.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new _v(p);return t.runWebGLProgram(m,[o,s],"float32")}var hD={kernelName:Zl,backendName:"webgl",kernelFunc:aX};function lX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new kv(p);return t.runWebGLProgram(m,[o,s],"float32")}var gD={kernelName:Jl,backendName:"webgl",kernelFunc:lX};var vv=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function uX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),a=ce({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new vv(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ce({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var xD={kernelName:Ql,backendName:"webgl",kernelFunc:uX};var Cv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${p}, ${m}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${u}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${l}; w++) { int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function cX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=N.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new Cv(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=ce({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var yD={kernelName:la,backendName:"webgl",kernelFunc:cX};var pX="return (x >= 0.0) ? x : (exp(x) - 1.0);",mX=` 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; `,fX=ve({opSnippet:pX,packedOpSnippet:mX}),bD={kernelName:si,backendName:"webgl",kernelFunc:fX};var dX="return (b >= 1.0) ? a : a * (b + 1.0);",hX=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,gX=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(hX,n.shape,o.shape):new qo(dX,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},wD={kernelName:eu,backendName:"webgl",kernelFunc:gX};var xX=` return vec4(equal(a, b)); `,yX="return float(a == b);",bX=it({opSnippet:yX,packedOpSnippet:xX,dtype:"bool"}),_D={kernelName:ai,backendName:"webgl",kernelFunc:bX};var wX=` // 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 = ${N.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,_X=ve({opSnippet:wX}),kD={kernelName:ii,backendName:"webgl",kernelFunc:_X};var vD="return exp(x);",Iv=ve({opSnippet:vD,packedOpSnippet:vD,cpuKernelImpl:_A}),CD={kernelName:eo,backendName:"webgl",kernelFunc:Iv};function sx(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(y.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),ce({inputs:{x:s},backend:n,attrs:{shape:i}})}var ID={kernelName:as,backendName:"webgl",kernelFunc:sx};var ND="return exp(x) - 1.0;",kX=ve({opSnippet:ND,packedOpSnippet:ND,cpuKernelImpl:kA}),SD={kernelName:li,backendName:"webgl",kernelFunc:kX};var ix=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.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 = ${s}; 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int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var Ev=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Pt(),[n,o]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${t.output} = result; } `}};var FD={kernelName:Oc,backendName:"webgl",kernelFunc:TX},_p;function TX(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&o instanceof ImageBitmap,[u,c]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],p=[c,u],m=[c,u,s];(i||a||l)&&(_p==null&&(_p=document.createElement("canvas").getContext("2d")),_p.canvas.width=u,_p.canvas.height=c,_p.drawImage(o,0,0,u,c),o=_p.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Ar.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),o);let d=W().getBool("WEBGL_PACK")?new Ev(m):new Tv(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function EX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),x,b=[];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"))x=rx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)x=nx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,E=f==="leakyrelu",S=f?xl(f,!1):null,R=new Af(g,_,S,k,E),F=[o,s];if(a&&F.push(a),i&&F.push(i),E){let P=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));F.push(P),b.push(P)}x=t.runWebGLProgram(R,F,"float32")}let w=ce({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var OD={kernelName:ws,backendName:"webgl",kernelFunc:EX};function AX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${a}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${l}; setOutput(result); } `}};var f7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new Rv(o.shape,s,a,i,l):new $v(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},QD={kernelName:ca,backendName:"webgl",kernelFunc:f7};var Fv=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${o}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${o}) * float(${s}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}};var d7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new Fv(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},e$={kernelName:su,backendName:"webgl",kernelFunc:d7};function t$(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=ce({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=vn(i,r.dtype,"max",n),u=ce({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function Ov(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let S=0;S<_.length;S++)_[S]=o.shape[c[S]];let k=wp(w,o.shape,o.dtype,c,_);f=t.makeTensorInfo(_,o.dtype);let E=t.texData.get(f.dataId);E.values=k}else f=yl(o,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let w=t.texData.get(f.dataId).values,_=EA(w,y.sizeFromShape(h),g,o.dtype);x=t.makeTensorInfo(g,o.dtype);let k=t.texData.get(x.dataId);k.values=_}else x=t$(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var r$={kernelName:ao,backendName:"webgl",kernelFunc:Ov};var h7=Xg+` return max(a, b); `,g7=` vec4 result = vec4(max(a, b)); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+gl+` return result; `,x7=it({opSnippet:h7,packedOpSnippet:g7,cpuKernelImpl:AA}),n$={kernelName:lo,backendName:"webgl",kernelFunc:x7};function y7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;$s(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ut({inputs:{x:o},backend:t});let p=new Wi(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var o$={kernelName:uo,backendName:"webgl",kernelFunc:y7};function b7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=N.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new Qu(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var s$={kernelName:pa,backendName:"webgl",kernelFunc:b7};var Pv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${l}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${s}; wR += ${o}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},Mv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); 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 < ${l}; wD += ${s}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${u}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${d} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${u} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function w7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Qu(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new Mv(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var i$={kernelName:au,backendName:"webgl",kernelFunc:w7};function _7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;$s([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Wi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Pv(m),x=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var a$={kernelName:iu,backendName:"webgl",kernelFunc:_7};function l$(r,e,t,n){let o=new Wi(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Wi(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var u$={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,o,s,u,a),[p,m]=l$(n,i,c,l);return[p,m]}};function c$(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=ce({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=vn(i,"float32","mean",n),u=ce({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var p$={kernelName:co,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let R=0;Rc[0]+e[p]+c[1]);let o=e.length,s=Le(o),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),u=n==="reflect"?0:1;if(o===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${u}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${u}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); for (int i = 0; i < ${o}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${u}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${u}; } } ${s} coords = outC - start; setOutput(getX(${l})); } `}};var zv=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=Le(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Wt("rc",o),u=Wt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; } else if (source >= end) { source = (end - 1) * 2 - source + ${m}; } source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${u.join()}), ${p}); ${l[o-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${u.join()}), ${p}); } `}else{let d=` ${s} source = rc; ${s} lt = ${s}(lessThan(source, start)); ${s} gte = ${s}(greaterThanEqual(source, end)); ${s} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${m}) + gte * ((end - 1) * 2 - source + ${m}); source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${u.join()}), ${p}); ${l[o-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${u.join()}), ${p}); } rc = outputLoc; ${l[o-2]} += 1; if(${l[o-2]} < ${this.outputShape[o-2]}) { ${d} result[2] = getChannel(getX(${u.join()}), ${p}); ${l[o-1]} += 1; if(${c}) { ${d} result[3] = getChannel(getX(${u.join()}), ${p}); } } `}this.userCode=` const ${s} start = ${s}(${a}); const ${s} end = ${s}(${i}); void main() { ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${f} setOutput(result); } `}};var N7=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zv(n.shape,o,s):new Lv(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},d$={kernelName:ma,backendName:"webgl",kernelFunc:N7};var S7=`if (b == 0.0) return NAN; return mod(a, b);`,T7=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+gl+` return result; `,E7=it({opSnippet:S7,packedOpSnippet:T7}),h$={kernelName:bi,backendName:"webgl",kernelFunc:E7};var Bv=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; 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})); } `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var A7=` if (a == b) { return 1.0; }; return a / b;`,D7=` // 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; `,Vv=it({opSnippet:A7,packedOpSnippet:D7,checkOutOfBounds:!0}),g$={kernelName:Qn,backendName:"webgl",kernelFunc:Vv};var x$="return a - b;",Gv=it({opSnippet:x$,packedOpSnippet:x$,supportsComplex:!0,cpuKernelImpl:zA}),y$={kernelName:Do,backendName:"webgl",kernelFunc:Gv};function jv(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=y.parseAxisParam([s],o.shape),i=Ov({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=ce({inputs:{x:i},backend:t,attrs:{shape:l}}),c=Gv({inputs:{a:o,b:u},backend:t}),p=Iv({inputs:{x:c},backend:t}),m=Ef({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ce({inputs:{x:m},backend:t,attrs:{shape:l}}),d=Vv({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var b$={kernelName:Eo,backendName:"webgl",kernelFunc:jv};function $7(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:jv({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new Bv(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var w$={kernelName:uu,backendName:"webgl",kernelFunc:$7};var _$="return -x;";function R7(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=RA(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fs(n.shape,_$):o=new rn(n.shape,_$),t.runWebGLProgram(o,[n],n.dtype)}var k$={kernelName:cs,backendName:"webgl",kernelFunc:R7};var F7=Tr.nonMaxSuppressionV3Impl;function O7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=F7(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var v$={kernelName:_i,backendName:"webgl",kernelFunc:O7};var P7=Tr.nonMaxSuppressionV4Impl;function M7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=P7(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var C$={kernelName:ki,backendName:"webgl",kernelFunc:M7};var L7=Tr.nonMaxSuppressionV5Impl;function z7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=L7(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var I$={kernelName:vi,backendName:"webgl",kernelFunc:z7};var Wv=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${o}), float(${n}), float(index == coords.y))); } `}};var B7=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=y.sizeFromShape(o.shape),u=new Wv(l,s,a,i),c=ce({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ce({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},N$={kernelName:ho,backendName:"webgl",kernelFunc:B7};function Ff(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=Va({inputs:{input:n},backend:t}),s=Ff({inputs:{x:o},backend:t}),a=ec({inputs:{input:n},backend:t}),i=Ff({inputs:{x:a},backend:t}),l=nn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Rf({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var S$={kernelName:ys,backendName:"webgl",kernelFunc:Ff};function T$(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Va({inputs:{input:n},backend:t}),s=T$({inputs:{x:o},backend:t}),a=ec({inputs:{input:n},backend:t}),i=Ff({inputs:{x:a},backend:t}),l=nn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Rf({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var E$={kernelName:ps,backendName:"webgl",kernelFunc:T$};function V7(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return sx({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=sx({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=mv({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var A$={kernelName:ms,backendName:"webgl",kernelFunc:V7};var Uv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=Le(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(float(${n})); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(float(${n})); } else { ${s} coords = outC - start; setOutput(getX(${l})); } } `}};var qv=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=Le(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Wt("rc",o),u=Wt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1; if(${c}) { `,o===1?"":`} rc = outputLoc; ${l[o-2]} += 1; if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${l[o-1]} += 1; if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qv(o.shape,s,a):new Uv(o.shape,s,a);return t.runWebGLProgram(i,[o],o.dtype)},D$={kernelName:go,backendName:"webgl",kernelFunc:Hv};var G7=` 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); `,j7=` // 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)); `+gl+` return result; `,W7=it({opSnippet:G7,packedOpSnippet:j7}),$$={kernelName:xo,backendName:"webgl",kernelFunc:W7};function U7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=y.parseAxisParam(s,o.shape),c=u,p=N.getAxesPermutation(c,i),m=o;p!=null&&(m=zt({inputs:{x:o},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=FA(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=ce({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=hu(o.dtype),w=vn(x,b,"prod",t);f=ce({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(x),l.push(w)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=ce({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var R$={kernelName:Ci,backendName:"webgl",kernelFunc:U7};var Kv=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=OA(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},F$={kernelName:fa,backendName:"webgl",kernelFunc:Kv};var q7="return 1.0 / x;",H7=ve({opSnippet:q7}),O$={kernelName:Ii,backendName:"webgl",kernelFunc:H7};var K7=hr+` return (x < 0.0) ? 0.0 : x; `,X7=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Y7=ve({opSnippet:K7,packedOpSnippet:X7}),P$={kernelName:bo,backendName:"webgl",kernelFunc:Y7};var Z7=hr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,J7=` 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; `,Q7=ve({opSnippet:Z7,packedOpSnippet:J7}),M$={kernelName:_o,backendName:"webgl",kernelFunc:Q7};var Xv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${l}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${m}; // 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); } `}};var Yv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, ${c[1]/p[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${l}.0, ${l}.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 = ${m}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function eY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Yv(o.shape,l,u,s,a):new Xv(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var L$={kernelName:wo,backendName:"webgl",kernelFunc:eY};var Zv=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${c}); const float widthScale = float(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function tY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new Zv(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var z$={kernelName:mu,backendName:"webgl",kernelFunc:tY};var Jv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${l}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${f}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}};function rY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=new Jv(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var B$={kernelName:da,backendName:"webgl",kernelFunc:rY};var Qv=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${c}); const float widthScale = float(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float sourceFracRow = float(${l[0]}) * (float(dyR) / float(${u[0]})); float sourceFracCol = float(${l[1]}) * (float(dyC) / float(${u[1]})); int sourceNearestRow = int(min( float(int(${o}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${s}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function nY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new Qv(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var V$={kernelName:pu,backendName:"webgl",kernelFunc:nY};var e0=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Mr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Mr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Mr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},o=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");s=t}else y.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Yi))throw new Se(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Yr(l,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new B("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 B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Yi.className="Sequential";J.registerClass(Yi);function OL(r){return new In(r)}function PL(r){return new Yi(r)}function ML(r,e){return e==null&&(e={}),FL(r,e)}function jx(r){return Ix(r)}function LL(r,e){an.registerCallbackConstructor(r,e)}var ln=class extends J.Serializable{getConfig(){return{}}},G0=class extends ln{apply(e,t=1){return oL(e,t)}};G0.className="elu";J.registerClass(G0);var j0=class extends ln{apply(e){return Pu(e)}};j0.className="selu";J.registerClass(j0);var W0=class extends ln{apply(e){return Nr(e)}};W0.className="relu";J.registerClass(W0);var U0=class extends ln{apply(e){return V(()=>Ts(6,Nr(e)))}};U0.className="relu6";J.registerClass(U0);var q0=class extends ln{apply(e){return e}};q0.className="linear";J.registerClass(q0);var H0=class extends ln{apply(e){return Wr(e)}};H0.className="sigmoid";J.registerClass(H0);var K0=class extends ln{apply(e){return iL(e)}};K0.className="hardSigmoid";J.registerClass(K0);var X0=class extends ln{apply(e){return Ss(e)}};X0.className="softplus";J.registerClass(X0);var Y0=class extends ln{apply(e){return sL(e)}};Y0.className="softsign";J.registerClass(Y0);var Z0=class extends ln{apply(e){return Li(e)}};Z0.className="tanh";J.registerClass(Z0);var ud=class extends ln{apply(e,t=-1){return $a(e,t)}};ud.className="softmax";J.registerClass(ud);var J0=class extends ln{apply(e,t=-1){return Au(e,t)}};J0.className="logSoftmax";J.registerClass(J0);var Q0=class extends ln{apply(e,t=1){return V(()=>Wr(e.mul(t)).mul(e))}};Q0.className="swish";J.registerClass(Q0);function zs(r){return r.getClassName()}function eC(r,e={}){return Ui(r,J.SerializationMap.getMap().classNameMap,e,"activation")}function Bs(r){if(r==null){let e={};return e.className="linear",e.config={},eC(e)}if(typeof r=="string"){let e={};return e.className=r,e.config={},eC(e)}else return r instanceof ln?r:eC(r)}function tC(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var rC=class extends J.Serializable{},pc=class extends rC{constructor(e){super();tC(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 V(()=>{let t=xt([1]);return this.hasL1&&(t=Q(t,ye(M(this.l1,Et(e))))),this.hasL2&&(t=Q(t,ye(M(this.l2,ic(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};pc.className="L1L2";J.registerClass(pc);function zL(r){return tC(r),new pc({l1:r!=null?r.l1:null,l2:0})}function BL(r){return tC(r),new pc({l2:r!=null?r.l2:null,l1:0})}var VL={l1l2:"L1L2"};function lt(r){return Ip(r)}function GL(r,e={}){return Ui(r,J.SerializationMap.getMap().classNameMap,e,"regularizer")}function _t(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in VL?VL[r]:r,config:{}};return GL(t)}else return r instanceof rC?r:GL(r)}var cd=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Nr(e);return this.maxValue!=null&&(n=sr(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};cd.className="ReLU";J.registerClass(cd);var pd=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Sa(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};pd.className="LeakyReLU";J.registerClass(pd);var md=class extends Me{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=dt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=Lt(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 B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=et(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)t[o-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Ft(e),e==="channelsFirst"?We(r,[0,2,3,1]):r))}function nC(r,e){return V(()=>(Ft(e),e==="channelsFirst"?We(r,[0,2,3,4,1]):r))}function IQ(r,e,t,n=1,o="valid",s,a=1){return V(()=>{if(s==null&&(s=Kr()),Ft(s),r.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=We(r,[0,2,1])),o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=vu(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=on(i,t)),i})}function jL(r,e,t,n=[1,1],o="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Kr()),Ft(s),r.rank!==3&&r.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=xd(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zo.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=We(l,[0,3,1,2])),l})}function NQ(r,e,t,n=[1,1,1],o="valid",s,a){return V(()=>{if(s==null&&(s=Kr()),Ft(s),r.rank!==4&&r.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=nC(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Mm(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=on(i,t)),s==="channelsFirst"&&(i=We(i,[0,4,1,2,3])),i})}var Wp=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Wp.verifyArgs(t),this.rank=e,qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Se(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Nl(t.kernelSize,e,"kernelSize"),this.strides=Nl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=Bs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lt(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Nl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ln("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hx(e.kernelSize,"number",1,3))throw new B(`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:zs(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},mc=class extends Wp{constructor(e,t){super(e,t);this.kernel=null,mc.verifyArgs(t),this.filters=t.filters,qt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lt(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=et(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let n,o=this.bias==null?null:this.bias.read(),s=gx(this.activation.getClassName());if(s!=null&&this.rank===2)n=jL(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=IQ(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=jL(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=NQ(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=et(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s 0 but got ${JSON.stringify(e.filters)}`)}},Sl=class extends mc{constructor(e){super(2,e);Sl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hx(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sl.className="Conv2D";J.registerClass(Sl);var fc=class extends mc{constructor(e){super(3,e);fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};fc.className="Conv3D";J.registerClass(fc);var yd=class extends Sl{constructor(e){super(e);if(this.inputSpec=[new At({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=et(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new At({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Oe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=gd(l,m,c,this.padding),h=gd(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=We(n,[0,2,3,1]));let x=Cu(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=We(x,[0,3,1,2])),this.bias!=null&&(x=on(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=et(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=gd(t[o],l,a,this.padding),t[s]=gd(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};yd.className="Conv2DTranspose";J.registerClass(yd);var oC=class extends mc{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 B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("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 B(`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=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=Lt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=Lt(t.pointwiseConstraint)}build(e){if(e=et(e),e.length{e=Oe(e);let n;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=We(e,[0,2,3,1])),n=Zm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=on(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=We(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseConstraint),e.pointwiseConstraint=Mt(this.pointwiseConstraint),e}};oC.className="SeparableConv";var bd=class extends oC{constructor(e){super(2,e)}};bd.className="SeparableConv2D";J.registerClass(bd);var dc=class extends mc{constructor(e){super(1,e);dc.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"&&!hx(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};dc.className="Conv1D";J.registerClass(dc);var wd=class extends Me{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 V(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=qf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return qf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=qf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return qf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};wd.className="Cropping2D";J.registerClass(wd);var _d=class extends Me{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,YM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Oe(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=We(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return We(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_d.className="UpSampling2D";J.registerClass(_d);function SQ(r,e,t=[1,1],n="valid",o,s){return V(()=>{o==null&&(o=Kr()),Ft(o);let a=xd(r,o);if(r.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Cs(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=We(a,[0,3,1,2])),a})}var kd=class extends Wp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lt(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=et(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Oe(e);let n=SQ(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=on(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=un(t,this.kernelSize[0],this.padding,this.strides[0]),a=un(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseRegularizer),e}};kd.className="DepthwiseConv2D";J.registerClass(kd);function sC(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function iC(r,e,t,n=!1,o,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Lr(2,l));if(e=We(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===l-1&&(o=ir(o,-1)),o=We(o,u)),n&&(e=Kt(e,0),o!=null&&(o=Kt(o,0)));let c=[],p,m=t,f=e.shape[0],d=cr(e),h;o!=null&&(h=cr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let _=V(()=>{let k=h[x],E=tr(k).sub(k),S=w[0].mul(k).add(m[0].mul(E)),R=m.map((F,P)=>w[1][P].mul(k).add(F.mul(E)));return{output:S,newStates:R}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Vt(c,1)),[p,g,m]})}var cn=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("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 At({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 Lr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Cx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ni.shape[i.shape.length-1]),a))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new At({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Cn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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(o=>xt([n,o])):this.states_=[xt([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>xt([n,o])):this.states_[0]=xt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Ee(this.states_);for(let o=0;oDt(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=sC(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new At({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof Br){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new B(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:o},u=iC((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=xt(e.shape);return t=ye(t,[1,2]),t=Wa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?bx(t,[1,n]):t):this.cell.stateSize>1?[bx(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===cn.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=Yr(o,n);return new e(Object.assign(t,{cell:s}))}};cn.className="RNN";J.registerClass(cn);var Tl=class extends Me{},qp=class extends Tl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,qt(this.units,"units"),this.activation=Bs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=sc([1,Ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=sc([1,Ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=et(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 V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0tr(e),rate:this.dropout,training:o})),0tr(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Qo(M(e,a),this.kernel.read()):s=Qo(e,this.kernel.read()),this.bias!=null&&(s=on(s,this.bias.read())),i!=null&&(n=M(n,i));let l=Q(s,Qo(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:zs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};qp.className="SimpleRNNCell";J.registerClass(qp);var vd=class extends cn{constructor(e){e.cell=new qp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};vd.className="SimpleRNN";J.registerClass(vd);var Hp=class extends Tl{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 B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,qt(this.units,"units"),this.activation=Bs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Bs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=sc([1,Ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=sc([1,Ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=et(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 V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0tr(e),rate:this.dropout,training:n,count:3})),0tr(o),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cd.className="GRU";J.registerClass(Cd);var El=class extends Tl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,qt(this.units,"units"),this.activation=Bs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Bs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=sc([1,Ms([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=sc([1,Ms([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=et(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends sn{apply(l,u){let c=s.apply([a]),p=new ac().apply([a]),m=s.apply([a*2]);return A0(A0(c,p),m)}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0tr(e),rate:this.dropout,training:n,count:4})),0tr(o),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Id.className="LSTM";J.registerClass(Id);var Up=class extends Tl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i{Ps(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(Yr(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return td(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a_x(e(),t),a=()=>wl(s,e,n);return!o||o<=1?Dt(a().clone()):Array(o).fill(void 0).map(a).map(l=>Dt(l.clone()))}var TQ=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=xt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Cn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new B("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(()=>xt(s)):this.states_=[xt(s)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xt(s)):this.states_[0]=xt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Ee(this.states_);for(let i=0;iDt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=un(u,o[0],s,a[0],i[0]),m=un(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};aC.className="ConvRNN2D";var Kp=class extends El{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,qt(this.filters,"filters"),this.kernelSize=Nl(n,2,"kernelSize"),this.kernelSize.forEach(l=>qt(l,"kernelSize")),this.strides=Nl(o||1,2,"strides"),this.strides.forEach(l=>qt(l,"strides")),this.padding=s||"valid",Xr(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=Nl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>qt(l,"dilationRate"))}build(e){var t;e=et(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends sn{apply(m,f){let d=u.apply([c]),h=Ir([c]),g=u.apply([c*2]);return Tp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0tr(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(ie,se,pe)=>!se||!se[pe]?ie:M(se[pe],ie),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0tr(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),w=3,[_,k,E,S]=ur(this.kernel.read(),i,w),[R,F,P,j]=this.useBias?ur(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,R,this.padding),p=this.inputConv(p,k,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,S,j,this.padding);let[G,U,X,K]=ur(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,U),x=this.recurrentConv(x,X),b=this.recurrentConv(b,K);let Z=this.recurrentActivation.apply(Q(c,h)),te=this.recurrentActivation.apply(Q(p,g)),Y=Q(M(te,a),M(Z,this.activation.apply(Q(m,x)))),re=M(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=TQ(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=Ur(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?on(s,n,this.dataFormat):s}recurrentConv(e,t){return Ur(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Kp.className="ConvLSTM2DCell";J.registerClass(Kp);var Nd=class extends aC{constructor(e){let t=new Kp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Nd.className="ConvLSTM2D";J.registerClass(Nd);var Xp=class extends Me{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o{this.invokeCallHook(e,t);let n=Oe(e);if(0_x(n,this.rate,s,this.seed),()=>n,o)}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()}};Xp.className="Dropout";J.registerClass(Xp);var Sd=class extends Xp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Sd.className="SpatialDropout1D";J.registerClass(Sd);var Td=class extends Me{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,qt(this.units,"units"),this.activation=Bs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lt(e.kernelConstraint),this.biasConstraint=Lt(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=et(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=et(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e),o=gx(this.activation.getClassName()),s;return o!=null?s=Qo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Qo(n,this.kernel.read()),this.bias!=null&&(s=on(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:zs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Td.className="Dense";J.registerClass(Td);var Ed=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=et(e);for(let t of e.slice(1))if(t==null)throw new B(`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],Jo(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:zs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ad.className="Activation";J.registerClass(Ad);var Dd=class extends Me{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 V(()=>(e=Oe(e),tL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Dd.className="RepeatVector";J.registerClass(Dd);var $d=class extends Me{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Oe(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};$d.className="Reshape";J.registerClass($d);var Rd=class extends Me{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=Lr(1,e.dims.length+1);if(!y.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 At({ndim:this.dims.length+1})]}computeOutputShape(e){e=et(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return We(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Rd.className="Permute";J.registerClass(Rd);var Fd=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),o=-1;return il(Lo(n,this.maskValue),o)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e),o=-1,s=!0,a=il(Lo(n,this.maskValue),o,s);return n.mul(a.asType(n.dtype))})}};Fd.className="Masking";J.registerClass(Fd);var Od=class extends Me{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(wt(e.inputLength))}this.inputDim=e.inputDim,qt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,qt(this.outputDim,"outputDim"),this.embeddingsInitializer=dt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=Lt(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 V(()=>this.maskZero?(e=Oe(e),Lo(e,Ie(e))):null)}computeOutputShape(e){if(e=et(e),this.inputLength==null)return[...e,this.outputDim];let t=wt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o{this.invokeCallHook(e,t);let n=Oe(e);return n.dtype!=="int32"&&(n=ja(n,"int32")),wx(this.embeddings.read(),n.as1D()).reshape(et(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Mt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Od.className="Embedding";J.registerClass(Od);var Al=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Se}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;ss.length);e.indexOf(null)===-1&&Zo(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],o=e.map(s=>s.rank);if(o.indexOf(null)===-1){let s=Ms(o);for(let a of e){let i=a.rank;for(let l=0;l1){let c=Lr(1,u).concat([0]);n.push(We(l,c)),s=!0}else n.push(l)}let a=this.mergeFunction(n),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=We(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Lr(0,i-1));a=We(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let o=1;o{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(o=>o==null))return null;t=t.map(o=>o==null?o:ir(o,0));let n=t[0];for(let o=1;o{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>Tp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of t.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let o=[];for(let a=0;a3||e.shape.length>3)throw new Se("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Se("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return V(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;un){a=o-n;let l=[];for(let u=0;u0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new B(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,a)=>Gd(s,e[a].shape.length)):o=[Gd(this.axes,t.shape.length),Gd(this.axes,n.shape.length)],this.normalize&&(t=rd(t,o[0]),n=rd(n,o[1])),EQ(t,n,o)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Gd(this.axes,e.length),Gd(this.axes,t.length)],n}computeOutputShape(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);t.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};jd.className="Dot";J.registerClass(jd);var Wd=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return wl(()=>Ep(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Wd.className="GaussianNoise";J.registerClass(Wd);var Ud=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?wl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ep(n.shape,1,s))},()=>n,t.training||!1):n})}};Ud.className="GaussianDropout";J.registerClass(Ud);var qd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return wl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=tn(Es(n),this.rate);u=ja(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};qd.className="AlphaDropout";J.registerClass(qd);function Hd(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=_w(r,e,t,n,o,s);else if(r.rank===3)a=kw(r,e,t,n,o,s);else if(r.rank===4)a=vw(r,e,t,n,o,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function AQ(r,e,t,n,o=.001){return V(()=>{let s=Yc(r,n),a=s.mean,i=s.variance;return[Hd(r,a,i,t,e,o),a,i]})}function DQ(r,e,t,n,o=.001){return V(()=>{let s=Yc(r,n),a=s.mean,i=s.variance,l=[];for(let d of Lr(0,r.rank))n.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[Hd(r,u,c,m,p,o),a,i]})}function $Q(r,e,t,n,o=.001){return y.arraysEqual(n.slice().sort(),Lr(0,r.rank-1))?AQ(r,e,t,n,o):DQ(r,e,t,n,o)}var Kd=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Lt(e.betaConstraint),this.gammaConstraint=Lt(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=et(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new At({ndim:e.length,axes:{[t]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,o=Oe(e),s=o.shape,a=s.length,i=Lr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Ko(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Lr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return Hd(o,b,w,_,k,this.epsilon)}else return Hd(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=$Q(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let k=1-_,E=b.read(),S=E.sub(w).mul(k);b.write(E.sub(S))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Mt(this.betaConstraint),gammaConstraint:Mt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Kd.className="BatchNormalization";J.registerClass(Kd);var Xd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=et(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s=t)throw new Error(`Invalid axis: 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integers.");if(t==null&&(t=Kr()),t!=="channelsLast"&&t!=="channelsFirst")throw new B(`Unknown data format: ${t}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=et(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>RQ(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Yd.className="ZeroPadding2D";J.registerClass(Yd);function Wx(r,e,t,n,o,s){return V(()=>{Ft(o),T0(s),Xr(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=Kr()),s==null&&(s="max"),r=xd(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Ea(r,e,t,i):a=va(r,e,t,i),o==="channelsFirst"&&(a=We(a,[0,3,1,2])),a})}function WL(r,e,t,n,o,s){return V(()=>{Ft(o),T0(s),Xr(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=Kr()),s==null&&(s="max"),r=nC(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=qm(r,e,t,i):a=Om(r,e,t,i),o==="channelsFirst"&&(a=We(a,[0,4,1,2,3])),a})}var lC=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(qt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xr(this.padding),this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){e=et(e);let t=un(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=Wa(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return _n(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Zd=class extends lC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),Wx(e,t,n,o,s,"max")}};Zd.className="MaxPooling1D";J.registerClass(Zd);var Jd=class extends lC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),Wx(e,t,n,o,s,"avg")}};Jd.className="AveragePooling1D";J.registerClass(Jd);var uC=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`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];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),Xr(this.padding),this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=un(t,this.poolSize[0],this.padding,this.strides[0]),n=un(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Qd=class extends uC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),Wx(e,t,n,o,s,"max")}};Qd.className="MaxPooling2D";J.registerClass(Qd);var eh=class extends uC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),Wx(e,t,n,o,s,"avg")}};eh.className="AveragePooling2D";J.registerClass(eh);var cC=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),Xr(this.padding),this.inputSpec=[new At({ndim:5})]}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=un(t,this.poolSize[0],this.padding,this.strides[0]),n=un(n,this.poolSize[1],this.padding,this.strides[1]),o=un(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},th=class extends cC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),WL(e,t,n,o,s,"max")}};th.className="MaxPooling3D";J.registerClass(th);var rh=class extends cC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Xr(o),WL(e,t,n,o,s,"avg")}};rh.className="AveragePooling3D";J.registerClass(rh);var pC=class extends Me{constructor(e){super(e);this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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n=C("tensorListId",r,e,t),o=C("index",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(n.id).getItem(o,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let n=C("indices",r,e,t),o=C("tensor",r,e,t),s=C("elementShape",r,e,t),a=C("numElements",r,e,t),i=lz(o,n,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=C("elementShape",r,e,t),o=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=az(n,o,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let n=C("tensorListId",r,e,t),o=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(n.id).gather(o,a,s)]}case"TensorListStack":{let n=C("tensorListId",r,e,t),o=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(n.id).stack(o,s,a)]}case"TensorListFromTensor":{let n=C("tensor",r,e,t),o=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=iz(n,o,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let n=C("tensorListId",r,e,t),o=t.getTensorList(n.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[o.concat(s,a)]}case"TensorListPushBack":{let n=C("tensorListId",r,e,t),o=C("tensor",r,e,t),s=t.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=C("tensorListId",r,e,t),o=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=C("tensor",r,e,t),o=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=uz(n,s,o);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function pz(r,e,t){let[n,o]=C("fusedOps",r,e,t),s=n==="biasadd",a=o==="prelu",i=n==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=uh(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:o,leakyreluAlpha:h}}var mz=(r,e,t)=>{switch(r.op){case"Conv1D":{let n=C("stride",r,e,t),o=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[vu(C("x",r,e,t),C("filter",r,e,t),n,o,s,a)]}case"Conv2D":{let n=C("strides",r,e,t),o=uh(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[Ur(C("x",r,e,t),C("filter",r,e,t),[n[1],n[2]],o,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:n,pad:o,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=pz(r,e,t);return[zo.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[n[1],n[2]],pad:o,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:o,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=pz(r,e,t);return[zo.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[n[1],n[2]],pad:o,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=C("outputShape",r,e,t),o=C("strides",r,e,t),s=uh(r,e,t);return[Cu(C("x",r,e,t),C("filter",r,e,t),n,[o[1],o[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=C("strides",r,e,t),o=uh(r,e,t),s=C("dilations",r,e,t),a=C("dataFormat",r,e,t).toUpperCase();return[Cs(C("input",r,e,t),C("filter",r,e,t),[n[1],n[2]],o,a,[s[1],s[2]])]}case"Conv3D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[Mm(C("x",r,e,t),C("filter",r,e,t),[n[1],n[2],n[3]],o,s,[a[1],a[2],a[3]])]}case"AvgPool":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[va(C("x",r,e,t),[s[1],s[2]],[n[1],n[2]],o)]}case"MaxPool":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Ea(C("x",r,e,t),[s[1],s[2]],[n[1],n[2]],o)]}case"MaxPoolWithArgmax":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=Bw(C("x",r,e,t),[s[1],s[2]],[n[1],n[2]],o,a);return[i,l]}case"AvgPool3D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Om(C("x",r,e,t),[s[1],s[2],s[3]],[n[1],n[2],n[3]],o)]}case"MaxPool3D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[qm(C("x",r,e,t),[s[1],s[2],s[3]],[n[1],n[2],n[3]],o)]}case"Dilation2D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("dilations",r,e,t),a=n[1],i=n[2],l=s[1],u=s[2];return[zm(C("x",r,e,t),C("filter",r,e,t),[a,i],o,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var fz=(r,e,t)=>{switch(r.op){case"Fill":{let n=C("shape",r,e,t),o=C("dtype",r,e,t),s=C("value",r,e,t);return[Na(n,s,o)]}case"LinSpace":{let n=C("start",r,e,t),o=C("stop",r,e,t),s=C("num",r,e,t);return[Fw(n,o,s)]}case"Multinomial":{let n=C("logits",r,e,t),o=C("numSamples",r,e,t),s=C("seed",r,e,t);return[Vw(n,o,s)]}case"OneHot":{let n=C("indices",r,e,t),o=C("depth",r,e,t),s=C("onValue",r,e,t),a=C("offValue",r,e,t);return[ks(n,o,s,a)]}case"Ones":return[Ir(C("shape",r,e,t),C("dtype",r,e,t))];case"OnesLike":return[tr(C("x",r,e,t))];case"RandomUniform":return[Es(C("shape",r,e,t),C("minval",r,e,t),C("maxval",r,e,t),C("dtype",r,e,t))];case"Range":{let n=C("start",r,e,t),o=C("stop",r,e,t),s=C("step",r,e,t);return[Jc(n,o,s,C("dtype",r,e,t))]}case"TruncatedNormal":{let n=C("shape",r,e,t),o=C("mean",r,e,t),s=C("stdDev",r,e,t),a=C("seed",r,e,t);return[Vu(n,o,s,C("dtype",r,e,t),a)]}case"Zeros":return[xt(C("shape",r,e,t),C("dtype",r,e,t))];case"ZerosLike":return[Ie(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function GC(r,e,t){let n=C("boxes",r,e,t),o=C("scores",r,e,t),s=C("maxOutputSize",r,e,t),a=C("iouThreshold",r,e,t),i=C("scoreThreshold",r,e,t),l=C("softNmsSigma",r,e,t);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}}var dz=async(r,e,t)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:n,scores:o,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}=GC(r,e,t),u=await Ds.nonMaxSuppressionWithScoreAsync(n,o,s,a,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:n,scores:o,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=GC(r,e,t),l=C("padToMaxOutputSize",r,e,t),u=await Ds.nonMaxSuppressionPaddedAsync(n,o,s,a,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:n,scores:o,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=GC(r,e,t);return[await Ds.nonMaxSuppressionAsync(n,o,s,a,i)]}case"Where":{let n=ne(C("condition",r,e,t),"bool"),o=[await sf(n)];return n.dispose(),o}case"ListDiff":return Zw(C("x",r,e,t),C("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var hz=(r,e,t)=>{switch(r.op){case"TopKV2":{let n=C("x",r,e,t),o=C("k",r,e,t),s=C("sorted",r,e,t),a=nf(n,o,s);return[a.values,a.indices]}case"Unique":{let n=C("x",r,e,t),o=ep(n);return[o.values,o.indices]}case"UniqueV2":{let n=C("x",r,e,t),o=C("axis",r,e,t),s=ep(n,o);return[s.values,s.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gz=(r,e,t)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let n=C("default",r,e,t);return[xr(r.name,e,t)||n];case"Placeholder":return[xr(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=C("x",r,e,t);return[Gs(u)]}case"IdentityN":return C("x",r,e,t).map(u=>Gs(u));case"Snapshot":let o=C("x",r,e,t);return[Gs(o)];case"Shape":return[Gt(C("x",r,e,t).shape,"int32")];case"ShapeN":return C("x",r,e,t).map(u=>Gt(u.shape));case"Size":return[le(C("x",r,e,t).size,"int32")];case"Rank":return[le(C("x",r,e,t).rank,"int32")];case"NoOp":return[le(1)];case"Print":let s=C("x",r,e,t),a=C("data",r,e,t),i=C("message",r,e,t),l=C("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;ue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),V(()=>{let o=cr(t),s=n.length,a=o.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let o=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let o=C("keyDType",r,e,t),s=C("valueDType",r,e,t),a=new jC(o,s);return 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wz=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[je(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[We(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[n,o]=C("fusedOps",r,e,t),s=n==="biasadd",a=o==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[zo.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:o,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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Nz(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>Zr(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var Dte=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$te=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Rte=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function UC(r){return Dte.indexOf(r.op)>=0}function Ete(r){return $te.indexOf(r.op)>=0}function Ate(r){return Rte.indexOf(r.op)>=0}var Yp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),o=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(e,t){let n=qC(e,t,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=Vs(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!xr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!xr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[o]=Zr(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];t[o.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[o]=Zr(n);return this.graph.nodes[o]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Zr(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}};var HC=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=Cr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Cr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let o=Cr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Yp(Yx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Yx.Instance.transformGraph(e.modelInitializer);this.initializer=new Yp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Cr.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ve)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,o)=>(t[n]=e[o],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Sz(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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t.set(r,o.value),o.value}function qz(r,e=tI){return Uz(r,e)}function Uz(r,e,t=new Set){let n=r[0];if(t.has(n))throw new Error("Circular references are not supported.");let o=e(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(Dl(n)){let s=Array.isArray(n)?[]:{};t.add(n);for(let a in n){let i=r.map(u=>u[a]),l=Uz(i,e,t);s[a]=l}return t.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function tI(r){return r===null?null:Dl(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function py(r,e){let t=new Map;cy(r,e,t);for(let o of Array.from(t.keys())){let s=t.get(o);if(y.isPromise(s)){let a=await s;t.set(o,a)}}return cy(r,e,t)}function Dl(r){return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Ve))}function Hz(r){return r==null||Gte(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Ve||y.isTypedArray(r)}function Gte(r){return r===null||typeof r!="object"&&typeof r!="function"}function Kz(r){return Wz(r,jte)}function jte(r){return r instanceof Ve?{value:r.clone(),recurse:!1}:Dl(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var ch=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new 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Xt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},r3=class extends Xt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},o3=class extends Xt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},s3=class extends Xt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},i3=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Fo.getTensorsInContainer(e.value),n=this.transform(e.value),o=Fo.getTensorsInContainer(n);for(let s of t)Fo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},a3=class extends Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},oI=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Fo.getTensorsInContainer(e.value),n=await this.transform(e.value),o=Fo.getTensorsInContainer(n);for(let s of t)Fo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Jp=class extends Xt{constructor(){super();this.outputQueue=new Zp,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}}},l3=class extends Jp{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Fo.getTensorsInContainer(e.value),n=this.transform(e.value),o=Fo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Fo.isTensorInList(s,o)||s.dispose();return!0}},nI=class extends Xt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Ha;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Ha||(Ha={}));var Qz=class extends Xt{constructor(e,t=Ha.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function o(a){return a instanceof Xt?{value:a.next().then(l=>(t++,l.done&&n++,l.value)),recurse:!1}:{value:null,recurse:!0}}let s=await py(this.iterators,o);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ha.FAIL:throw new Error(`Zipped streams should have the same length. 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If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=c3.alea(t||y.now().toString());return pn(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await o.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,pn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Zi.MAX_BUFFER_SIZE=1e4;function pn(r,e=null){return new class extends Zi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function p3(r){return pn(async()=>rI(r),r.length)}function m3(r){if(!Dl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t{let t=await py(r,n=>{if(n instanceof Zi)return{value:n.iterator(),recurse:!1};if(Dl(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return e3(t,Ha.SHORTEST)},e)}function Wte(r){if(r===null)return null;let e=r[0];return Hz(e)?{value:Ute(r),recurse:!1}:{value:null,recurse:!0}}function Ute(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Vt(r):$r(r)}var mh=class extends Zi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var my='"',fh=Symbol("out"),f3=Symbol("field"),fy=Symbol("quote"),iI=Symbol("quoteafterquote"),d3=Symbol("quoteinquote"),dh=class extends Zi{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 mh(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.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&&y.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((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};for(let s=0;s14||!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(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new hh(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(y.sizeFromShape(t));return n.set(e,n.length-e.length),$r(n,t)}};var gh=class extends Xt{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=Gt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=Vi([a,s,l,i],[1,4])}else this.cropBox=Vi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new gh(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.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=eg.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=ir(ne(e,"float32"),0),n;n=Ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return z(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var xh=class{};var dy=class extends Xt{split(e){return new h3(this,e)}},h3=class extends dy{constructor(e,t){super();this.upstream=e,this.impl=new g3(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},g3=class extends Jp{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}};var aI=class extends Xt{decodeUTF8(){return new y3(this)}},y3=class extends dy{constructor(e){super();this.upstream=e,this.impl=new b3(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},b3=class extends Jp{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=x3();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return W().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}};var yh=class extends aI{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(W().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>n(new Error("Aborted")),s.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,o);s.readAsArrayBuffer(a)}this.offset=o}),done:!1}}};async function w3(r,e={}){let t,n;typeof r=="string"?t=r:(t=r.url,n=qte(r));let o=await y.fetch(t,n);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new yh(s,e)}else throw new Error(o.statusText)}var qte=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function hy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var bh=class extends xh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(hy(this.input)&&W().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new yh(this.input,this.options)}};var wh=class extends xh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return hy(this.url)?new bh(this.url,this.fileOptions).iterator():w3(this.url,this.fileOptions)}};function _3(r,e={}){return new dh(new wh(r),e)}function k3(r){let e=ph(r);return pn(async()=>e)}function v3(r){return pn(async()=>{let e=await r();return ph(()=>e.next())})}async function C3(r,e){return gh.create(r,e)}async function I3(r){return hh.create(r)}var N3="3.0.0";var Hte={tfjs:yI,"tfjs-core":bI,"tfjs-data":wI,"tfjs-layers":_I,"tfjs-converter":kI,"tfjs-backend-cpu":w_,"tfjs-backend-webgl":Pk,"tfjs-backend-wasm":x0};export{ss as Abs,Ks as Acos,Xs as Acosh,rp as AdadeltaOptimizer,np as AdagradOptimizer,op as AdamOptimizer,sp as AdamaxOptimizer,xn as Add,Wn as AddN,Vl as All,Gl as Any,Un as ArgMax,na as ArgMin,Ys as Asin,Zs as Asinh,Js as Atan,ei as Atan2,Qs as Atanh,qn as AvgPool,oa as AvgPool3D,Wl as AvgPool3DGrad,jl as AvgPoolGrad,px as BackendWasm,Hn as BatchMatMul,sa as BatchToSpaceND,Ul as Bincount,Pb as BroadcastTo,Ux as Callback,Sx as CallbackList,An as Cast,ti as Ceil,Dn as ClipByValue,ql as Complex,ia as ComplexAbs,is as Concat,Kn as Conv2D,Hl as Conv2DBackpropFilter,Xn as Conv2DBackpropInput,aa as Conv3D,Kl as Conv3DBackpropFilterV2,Xl as Conv3DBackpropInputV2,Yn as Cos,ri as Cosh,ni as CropAndResize,Zn as Cumsum,Ex as CustomCallback,Ja as DataStorage,Yl as DenseBincount,oi as DepthToSpace,Jn as DepthwiseConv2dNative,Zl as DepthwiseConv2dNativeBackpropFilter,Jl as DepthwiseConv2dNativeBackpropInput,Ql as Diag,la as Dilation2D,Fc as Dilation2DBackpropFilter,Rc as Dilation2DBackpropInput,Rb as ENV,Hx as EarlyStopping,si as Elu,eu as EluGrad,jh as Environment,ai as Equal,ii as Erf,eo as Exp,as as ExpandDims,li as Expm1,tu as FFT,ua as Fill,ui as FlipLeftRight,to as Floor,ro as FloorDiv,Oc as FromPixels,no as FusedBatchNorm,ws as FusedConv2D,_s as FusedDepthwiseConv2D,Wg as GPGPUContext,ci as GatherNd,ls as GatherV2,ly as GraphModel,pi as Greater,oo as GreaterEqual,Tx as History,ru as IFFT,us as Identity,nu as Imag,At as InputSpec,mi as IsFinite,fi as IsInf,di as IsNan,Ws as KernelBackend,ca as LRN,su as LRNGrad,ed as LayerVariable,In as LayersModel,so as LeakyRelu,hi as Less,gi as LessEqual,ou as LinSpace,io as Log,xi as Log1p,Mb as LogSoftmax,yi as LogicalAnd,Qa as LogicalNot,el as LogicalOr,vg as MathBackendCPU,Kg as MathBackendWebGL,ao as Max,uo as MaxPool,pa as MaxPool3D,au as MaxPool3DGrad,iu as MaxPoolGrad,lu as MaxPoolWithArgmax,lo as Maximum,co as Mean,po as Min,mo as Minimum,ma as MirrorPad,bi as Mod,ip as MomentumOptimizer,uu as Multinomial,fo as Multiply,cs as Neg,_i as NonMaxSuppressionV3,ki as NonMaxSuppressionV4,vi as NonMaxSuppressionV5,wi as NotEqual,VI as OP_SCOPE_SUFFIX,ho as OneHot,ps as OnesLike,Or as Optimizer,ms as Pack,go as PadV2,hV as Pool,xo as Pow,yo as Prelu,Ci as Prod,ap as RMSPropOptimizer,cn as RNN,fa as Range,Gb as Rank,cu as Real,Qn as RealDiv,Ii as Reciprocal,jt as Reduction,bo as Relu,_o as Relu6,fs as Reshape,wo as ResizeBilinear,mu as ResizeBilinearGrad,da as ResizeNearestNeighbor,pu as ResizeNearestNeighborGrad,ko as Reverse,Fi as RotateWithOffset,vo as Round,Co as Rsqrt,cl as SGDOptimizer,Ni as ScatterNd,ds as Select,Si as Selu,Yi as Sequential,No as Sigmoid,Ei as Sign,Io as Sin,Ti as Sinh,hs as Slice,Eo as Softmax,Ai as Softplus,ha as SpaceToBatchND,fu as SparseToDense,gs as SplitV,So as Sqrt,ga as Square,Ao as SquaredDifference,$n as Step,Di as StridedSlice,Do as Sub,To as Sum,Br as SymbolicTensor,$i as Tan,$o as Tanh,Ve as Tensor,ct as TensorBuffer,yn as Tile,Ri as TopK,Ro as Transpose,du as Unique,xs as Unpack,xa as UnsortedSegmentSum,nl as Variable,ys as ZerosLike,bs as _FusedMatMul,Et as abs,Sm as acos,Tm as acosh,Q as add,xw as addN,wu as all,il as any,al as argMax,Em as argMin,Am as asin,Dm as asinh,$m as atan,Rm as atan2,Fm as atanh,va as avgPool,Om as avgPool3d,gw as backend,N as backend_util,dj as basicLSTMCell,Oo as batchNorm,_w as batchNorm2d,kw as batchNorm3d,vw as batchNorm4d,Ca as batchToSpaceND,Cw as bincount,kU as booleanMaskAsync,ll as broadcastTo,eg as browser,Ce as buffer,YL as callbacks,ne as cast,Pm as ceil,sr as clipByValue,Rn as clone,bn as complex,Qe as concat,Iw as concat1d,Nw as concat2d,Sw as concat3d,Tw as concat4d,S0 as constraints,vu as conv1d,Ur as conv2d,Cu as conv2dTranspose,Mm as conv3d,Oj as conv3dTranspose,yV as copyRegisteredKernels,Ia as cos,Iu as cosh,lf as cosineWindow,Nu as cumsum,qr as customGrad,gy as data,Ew as denseBincount,ig as deprecationWarn,Lm as depthToSpace,Cs as depthwiseConv2d,QL as deregisterOp,Vc as device_util,jj as diag,zm as dilation2d,SG as disableDeprecationWarnings,Ee as dispose,TG as disposeVariables,de as div,Bm as divNoNan,Aw as dot,r_ as dropout,Is as elu,NG as enableDebugMode,IG as enableProdMode,n_ as enclosingPowerOfTwo,vs as engine,W as env,wn as equal,Vm as erf,Yt as exp,ir as expandDims,Gm as expm1,Xc as eye,Ra as fft,Na as fill,FG as findBackend,OG as findBackendFactory,Ns as floor,bu as floorDiv,Mk as forceHalfFloat,zo as fused,Po as gather,t_ as gatherND,tg as gather_util,$G as getBackend,Wh as getGradient,Mc as getKernel,_m as getKernelsForBackend,lA as gpgpu_util,yW as grad,bW as grads,er as greater,tn as greaterEqual,Bi as ifft,Su as imag,Ds as image,DU as inTopKAsync,$0 as initializers,jx as input,Cr as io,zu as irfft,Dw as isFinite,$w as isInf,Rw as isNaN,Dt as keep,Tr as kernel_impls,dC as layers,Sa as leakyRelu,Tu as less,Pn as lessEqual,l_ as linalg,Fw as linspace,Sz as loadGraphModel,ML as loadLayersModel,jm as localResponseNormalization,ar as log,Eu as log1p,Ow as logSigmoid,Au as logSoftmax,Um as logSumExp,dr as logicalAnd,Ta as logicalNot,Du as logicalOr,zw as logicalXor,vq as losses,je as matMul,yN as math,lr as max,Ea as maxPool,qm as maxPool3d,Bw as maxPoolWithArgmax,Hr as maximum,gt as mean,qc as memory,yC as metrics,zi as min,Ts as minimum,Hm as mirrorPad,Km as mod,OL as model,bC as models,Yc as moments,IU as movingAverage,M as mul,KW as multiRNNCell,Vw as multinomial,Ue as neg,uf as nextFrame,Gu as norm,Lo as notEqual,ks as oneHot,Ir as ones,tr as onesLike,T as op,QW as outerProduct,Rr as pad,r4 as pad1d,o4 as pad2d,i4 as pad3d,l4 as pad4d,Gw as pool,Fr as pow,Da as prelu,iw as print,$u as prod,EG as profile,x4 as rand,I4 as randomGamma,dg as randomNormal,Es as randomUniform,Jc as range,DG as ready,ul as real,Xm as reciprocal,yu as registerBackend,LL as registerCallbackConstructor,zb as registerGradient,tl as registerKernel,JL as registerOp,wC as regularizers,Nr as relu,Fu as relu6,RG as removeBackend,z as reshape,Kt as reverse,F4 as reverse1d,P4 as reverse2d,L4 as reverse3d,B4 as reverse4d,Fa as rfft,Ym as round,Ou as rsqrt,le as scalar,e_ as scatterND,rg as scatter_util,Pu as selu,Zm as separableConv2d,PL as sequential,J as serialization,FN as setBackend,PG as setPlatform,JZ as setWasmPath,QZ as setWasmPaths,V_ as setWebGLContext,Zw as setdiff1dAsync,Ig as shared,Wr as sigmoid,Jm as sign,kq as signal,Mu as sin,Lu as sinh,Fe as slice,Qm as slice1d,hg as slice2d,ef as slice3d,Qc as slice4d,or as slice_util,$a as softmax,Ss as softplus,Aa as spaceToBatchND,af as sparseToDense,_q as spectral,ur as split,yt as sqrt,Pe as square,Bu as squaredDifference,_n as squeeze,Vt as stack,As as step,tf as stridedSlice,ue as sub,ye as sum,hu as sumOutType,rf as tan,Li as tanh,$r as tensor,Gt as tensor1d,Vi as tensor2d,cw as tensor3d,mU as tensor4d,fU as tensor5d,dU as tensor6d,Fo as tensor_util,DN as test_util,V as tidy,On as tile,AG as time,nf as topk,pl as train,We as transpose,Vu as truncatedNormal,ep as unique,xV as unregisterGradient,gV as unregisterKernel,of as unsortedSegmentSum,cr as unstack,fr as upcastType,y as util,wW as valueAndGrad,_W as valueAndGrads,Jw as variable,cg as variableGrads,Hte as version,Tz as version_converter,CG as version_core,w_ as version_cpu,Gp as version_layers,x0 as version_wasm,Pk as version_webgl,o8 as webgl,nA as webgl_util,$t as where,sf as whereAsync,xt as zeros,Ie as zerosLike}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use 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 See the LICENSE file. */ //# sourceMappingURL=tfjs.esm.js.map