From 55fe9aa58b2ecf131f64d88fa443db15a9694a78 Mon Sep 17 00:00:00 2001 From: Nick McIntyre Date: Mon, 15 Aug 2022 10:51:28 -0500 Subject: [PATCH] Upgrade dependencies --- dist/numero.js | 2 +- dist/numero.js.LICENSE.txt | 8 ++++ karma.conf.js | 1 - package-lock.json | 92 +++++++++++++++++++------------------- package.json | 8 ++-- 5 files changed, 59 insertions(+), 52 deletions(-) diff --git a/dist/numero.js b/dist/numero.js index c5e8b52..df536aa 100644 --- a/dist/numero.js +++ b/dist/numero.js @@ -1,2 +1,2 @@ /*! 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null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:Ru(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Fu,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class Yu extends Fu{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:ho((()=>Tu(r).variable(!1)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:ho((()=>Tu(r).variable(!1)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const i=this.accumulatedGrads[n].variable,s=this.accumulatedUpdates[n].variable;ho((()=>{const e=wu(Su(i,this.rho),Su(Du(a),1-this.rho)),t=Su(Nu(ku(wu(s,this.epsilon)),ku(wu(i,this.epsilon))),a),n=wu(Su(s,this.rho),Su(Du(t),1-this.rho));i.assign(e),s.assign(n);const o=wu(Su(t,-this.learningRate),r);r.assign(o)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(fo(this.accumulatedGrads.map((e=>e.variable))),fo(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}function zu(e,t,n){const r={shape:e,value:t,dtype:n};return _i.runKernel(an,{},r)}Yu.className="Adadelta",lu(Yu);class Bu extends Fu{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:ho((()=>zu(r.shape,this.initialAccumulatorValue).variable(e)))}}const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const i=this.accumulatedGrads[n].variable;ho((()=>{const e=wu(i,Du(a));i.assign(e);const t=wu(Su(Nu(a,ku(wu(e,_i.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&fo(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),this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Bu.className="Adagrad",lu(Bu);const Pu=$i({pow_:function(e,t){let n=Ci(e,"base","pow"),r=Ci(t,"exp","pow");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(ar,a)}}),Hu=$i({sub_:function(e,t){let n=Ci(e,"a","sub"),r=Ci(t,"b","sub");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(Wr,a)}});class ju extends Fu{constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ho((()=>{this.accBeta1=Ru(t).variable(),this.accBeta2=Ru(n).variable()})),null==r&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ho((()=>{const n=Hu(1,this.accBeta1),r=Hu(1,this.accBeta2);t.forEach(((t,a)=>{const i=_i.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:ho((()=>Tu(i).variable(!1)))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:ho((()=>Tu(i).variable(!1)))});const s=Array.isArray(e)?e[a].tensor:e[t];if(null==s)return;const o=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,l=wu(Su(o,this.beta1),Su(s,1-this.beta1)),c=wu(Su(u,this.beta2),Su(Du(s),1-this.beta2)),p=Nu(l,n),d=Nu(c,r);o.assign(l),u.assign(c);const h=wu(Su(Nu(p,wu(ku(d),this.epsilon)),-this.learningRate),i);i.assign(h)})),this.accBeta1.assign(Su(this.accBeta1,this.beta1)),this.accBeta2.assign(Su(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&fo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&fo(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),ho((()=>{this.accBeta1.assign(Pu(this.beta1,this.iterations_+1)),this.accBeta2.assign(Pu(this.beta2,this.iterations_+1))}));const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}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)}}ju.className="Adam",lu(ju);const Uu=$i({abs_:function(e){const t=Ci(e,"x","abs");if("complex64"===t.dtype){const e={x:t};return _i.runKernel(kt,e)}{const e={x:t};return _i.runKernel(Qe,e)}}}),qu=$i({maximum_:function(e,t){let n=Ci(e,"a","maximum"),r=Ci(t,"b","maximum");[n,r]=di(n,r),"bool"===n.dtype&&(n=Ls(n,"int32"),r=Ls(r,"int32")),Lo(n.shape,r.shape);const a={a:n,b:r};return _i.runKernel(On,a)}});class Wu extends Fu{constructor(e,t,n,r=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ho((()=>{this.iteration=Ru(0).variable(),this.accBeta1=Ru(t).variable()})),null==r&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ho((()=>{const n=Hu(1,this.accBeta1),r=Nu(-this.learningRate,wu(Su(this.iteration,this.decay),1));t.forEach(((t,a)=>{const i=_i.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Tu(i).variable(!1)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:Tu(i).variable(!1)});const s=Array.isArray(e)?e[a].tensor:e[t];if(null==s)return;const o=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,l=wu(Su(o,this.beta1),Su(s,1-this.beta1)),c=Su(u,this.beta2),p=Uu(s),d=qu(c,p);o.assign(l),u.assign(d);const h=wu(Su(Nu(r,n),Nu(l,wu(d,this.epsilon))),i);i.assign(h)})),this.iteration.assign(wu(this.iteration,1)),this.accBeta1.assign(Su(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&fo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&fo(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)}}Wu.className="Adamax",lu(Wu);class Vu extends Fu{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=_i.registeredVariables[t];ho((()=>{const e=wu(Su(this.c,r),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=mo(Ru(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Vu.className="SGD",lu(Vu);class Gu extends Vu{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ru(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:ho((()=>Tu(r).variable(e)))}}const a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[t];null!=i&&ho((()=>{let e;const t=wu(Su(this.m,a),i);e=this.useNesterov?wu(Su(this.c,wu(i,Su(t,this.m))),r):wu(Su(this.c,t),r),a.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&fo(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Gu.className="Momentum",lu(Gu);class Ku extends Fu{constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=_i.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:ho((()=>Tu(r).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:ho((()=>Tu(r).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:ho((()=>Tu(r).variable(a)))});const i=Array.isArray(e)?e[n].tensor:e[t];if(null==i)return;const s=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;ho((()=>{const e=wu(Su(s,this.decay),Su(Du(i),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=wu(Su(t,this.decay),Su(i,1-this.decay)),u=Nu(Su(i,this.learningRate),ku(Hu(e,wu(Du(a),this.epsilon)))),l=wu(Su(o,this.momentum),u);s.assign(e),t.assign(a),o.assign(l);const c=Hu(r,l);r.assign(c)}else{const e=wu(Su(s,this.decay),Su(Du(i),1-this.decay)),t=wu(Su(o,this.momentum),Nu(Su(i,this.learningRate),ku(wu(e,this.epsilon))));s.assign(e),o.assign(t);const n=Hu(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&fo(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&fo(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&fo(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.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)}}Ku.className="RMSProp",lu(Ku);class Ju{static sgd(e){return new Vu(e)}static momentum(e,t,n=!1){return new Gu(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Ku(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new ju(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Yu(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Wu(e,t,n,r,a)}static adagrad(e,t=.1){return new Bu(e,t)}}const Xu=$i({acos_:function(e){const t={x:Ci(e,"x","acos")};return _i.runKernel(et,t)}}),Zu=$i({acosh_:function(e){const t={x:Ci(e,"x","acosh")};return _i.runKernel(tt,t)}}),Qu=$i({addN_:function(e){se(Array.isArray(e),(()=>"The argument passed to tf.addN() must be a list of tensors")),se(e.length>=1,(()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`));const t=e.map(((e,t)=>Ci(e,`tensors${t}`,"addN"))),n=t[0];t.forEach((e=>{if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")})),t.forEach((e=>{if(!de(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")}));const r=t;return _i.runKernel(rt,r)}}),el=$i({all_:function(e,t=null,n=!1){const r={x:Ci(e,"x","all","bool")},a={axis:t,keepDims:n};return _i.runKernel(at,r,a)}}),tl=$i({any_:function(e,t=null,n=!1){const r={x:Ci(e,"x","any","bool")},a={axis:t,keepDims:n};return _i.runKernel(it,r,a)}}),nl=$i({argMax_:function(e,t=0){const n={x:Ci(e,"x","argMax")},r={axis:t};return _i.runKernel(st,n,r)}}),rl=$i({argMin_:function(e,t=0){const n={x:Ci(e,"x","argMin")},r={axis:t};return _i.runKernel(ot,n,r)}}),al=$i({asin_:function(e){const t={x:Ci(e,"x","asin")};return _i.runKernel(ut,t)}}),il=$i({asinh_:function(e){const t={x:Ci(e,"x","asinh")};return _i.runKernel(lt,t)}}),sl=$i({atan_:function(e){const t={x:Ci(e,"x","atan")};return _i.runKernel(ct,t)}}),ol=$i({atan2_:function(e,t){let n=Ci(e,"a","atan2"),r=Ci(t,"b","atan2");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(dt,a)}}),ul=$i({atanh_:function(e){const t={x:Ci(e,"x","atanh")};return _i.runKernel(pt,t)}});function ll(e,t,n,r,a="NHWC",i){return dl(e,[...t,e[3]],n,i,r,null,null,_l(a))}function cl(e,t,n,r,a,i,s="channelsLast"){const[o,u]=ml(t);let l;if("channelsLast"===s)l=[o,u,e[3],e[3]];else{if("channelsFirst"!==s)throw new Error(`Unknown dataFormat ${s}`);l=[o,u,e[1],e[1]]}return dl(e,l,n,r,a,i,!1,s)}function pl(e,t,n,r,a,i,s="NDHWC"){const[o,u,l]=gl(t);let c,p;if("NDHWC"===s)p="channelsLast",c=[o,u,l,e[4],e[4]];else{if("NCDHW"!==s)throw new Error(`Unknown dataFormat ${s}`);p="channelsFirst",c=[o,u,l,e[1],e[1]]}return hl(e,c,n,r,a,!1,p,i)}function dl(e,t,n,r,a,i,s=!1,o="channelsLast"){let[u,l,c,p]=[-1,-1,-1,-1];if("channelsLast"===o)[u,l,c,p]=e;else{if("channelsFirst"!==o)throw new Error(`Unknown dataFormat ${o}`);[u,p,l,c]=e}const[d,h,,f]=t,[m,g]=ml(n),[y,b]=ml(r),x=yl(d,y),v=yl(h,b),{padInfo:_,outHeight:w,outWidth:M}=function(e,t,n,r,a,i,s,o,u){let l,c,p;if("number"==typeof e){l={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};const a=function(e,t,n,r,a){null==r&&(r=fl(e,t,n));const i=e[1];return[bl((e[0]-t+2*r)/n+1,a),bl((i-t+2*r)/n+1,a)]}([t,n],i,r,e,o);c=a[0],p=a[1]}else if("same"===e){c=Math.ceil(t/r),p=Math.ceil(n/a);const e=Math.max(0,(c-1)*r+i-t),o=Math.max(0,(p-1)*a+s-n),u=Math.floor(e/2),d=e-u,h=Math.floor(o/2);l={top:u,bottom:d,left:h,right:o-h,type:"SAME"}}else if("valid"===e)l={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-i+1)/r),p=Math.ceil((n-s+1)/a);else{if("object"!=typeof e)throw Error(`Unknown padding parameter: ${e}`);{const d="channelsLast"===u?e[1][0]:e[2][0],h="channelsLast"===u?e[1][1]:e[2][1],f="channelsLast"===u?e[2][0]:e[3][0],m="channelsLast"===u?e[2][1]:e[3][1];l={top:d,bottom:h,left:f,right:m,type:0===d&&0===h&&0===f&&0===m?"VALID":"EXPLICIT"},c=bl((t-i+d+h)/r+1,o),p=bl((n-s+f+m)/a+1,o)}}return{padInfo:l,outHeight:c,outWidth:p}}(a,l,c,m,g,x,v,i,o),N=s?f*p:f;let S;return"channelsFirst"===o?S=[u,N,w,M]:"channelsLast"===o&&(S=[u,w,M,N]),{batchSize:u,dataFormat:o,inHeight:l,inWidth:c,inChannels:p,outHeight:w,outWidth:M,outChannels:N,padInfo:_,strideHeight:m,strideWidth:g,filterHeight:d,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:b,inShape:e,outShape:S,filterShape:t}}function hl(e,t,n,r,a,i=!1,s="channelsLast",o){let[u,l,c,p,d]=[-1,-1,-1,-1,-1];if("channelsLast"===s)[u,l,c,p,d]=e;else{if("channelsFirst"!==s)throw new Error(`Unknown dataFormat ${s}`);[u,d,l,c,p]=e}const[h,f,m,,g]=t,[y,b,x]=gl(n),[v,_,w]=gl(r),M=yl(h,v),N=yl(f,_),S=yl(m,w),{padInfo:k,outDepth:D,outHeight:T,outWidth:I}=function(e,t,n,r,a,i,s,o,u,l,c){let p,d,h,f;if("number"==typeof e){p={top:e,bottom:e,left:e,right:e,front:e,back:e,type:0===e?"VALID":"NUMBER"};const i=function(e,t,n,r,a,i){null==a&&(a=fl(e,t,r));const s=e[1],o=e[2];return[bl((e[0]-t+2*a)/r+1,i),bl((s-t+2*a)/r+1,i),bl((o-t+2*a)/r+1,i),1]}([t,n,r,1],o,0,a,e,c);d=i[0],h=i[1],f=i[2]}else if("same"===e){d=Math.ceil(t/a),h=Math.ceil(n/i),f=Math.ceil(r/s);const e=(d-1)*a+o-t,c=(h-1)*i+u-n,m=(f-1)*s+l-r,g=Math.floor(e/2),y=e-g,b=Math.floor(c/2),x=c-b,v=Math.floor(m/2);p={top:b,bottom:x,left:v,right:m-v,front:g,back:y,type:"SAME"}}else{if("valid"!==e)throw Error(`Unknown padding parameter: ${e}`);p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),h=Math.ceil((n-u+1)/i),f=Math.ceil((r-l+1)/s)}return{padInfo:p,outDepth:d,outHeight:h,outWidth:f}}(a,l,c,p,y,b,x,M,N,S,o),E=i?g*d:g;let 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u={indices:a,values:i,denseShape:s,defaultValue:o},l=_i.runKernel(Or,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}}),Rh=$i({sparseReshape_:function(e,t,n){const r=Ci(e,"inputIndices","sparseReshape","int32"),a=Ci(t,"inputShape","sparseReshape","int32"),i=Ci(n,"newShape","sparseReshape","int32");if(2!==r.rank)throw new Error(`Input indices should be Tensor2D but received shape\n ${r.shape}`);if(1!==a.rank)throw new Error(`Input shape should be Tensor1D but received shape ${a.shape}`);if(1!==i.rank)throw new Error(`New shape should be Tensor1D but received shape ${i.shape}`);const s={inputIndices:r,inputShape:a,newShape:i},o=_i.runKernel(Rr,s);return{outputIndices:o[0],outputShape:o[1]}}}),Fh=$i({sparseSegmentMean_:function(e,t,n){const r=Ci(e,"data","sparseSegmentMean"),a=Ci(t,"indices","sparseSegmentMean","int32"),i=Ci(n,"segmentIds","sparseSegmentMean","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but 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got ${e.length}`);t=e[0]}else t=e;return t}function rb(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return e[0];throw new wg(`Expected exactly 1 Shape; got ${e.length}`)}return e}function ab(e){let t=0;for(const n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}class ib{constructor(e,t="float32",n="Variable",r=!0,a=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=Wg(),n=null==n?"Variable":n,this.originalName=sy(n),this.name=oy(this.originalName),this.trainable_=r,this.constraint=a,this.val=Td(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function sb(e){return e.map((e=>e.read()))}function ob(e){e.forEach((e=>{e[0].write(e[1])}))}class ub{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class lb{constructor(e,t,n,r,a,i,s){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=s,this.id=Wg(),null!=i&&(this.originalName=sy(i),this.name=oy(this.originalName)),this.rank=t.length}}let cb=0;class pb{constructor(e,t){this.callArgs=t,this.id=cb++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let db=0;class hb extends ou{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=db++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const e=this.getClassName();t=Cg(e)+"_"+Gg(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new _g(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new wg(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Ig(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Ig(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new vg(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new vg(`Layer ${this.name} is not connected, no input to return.`);return Ig(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new vg(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new vg(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Ig(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((e=>e()))}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach((t=>t.trainable=e)),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((e=>e.trainable)):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((e=>!e.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Eg(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=Eg(this.inputSpec);if(e.length!==t.length)throw new wg(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;na.maxNDim)throw new wg(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${i}`);if(null!=a.minNDim&&i=0?e[r]:e[e.length+r];if(null!=i&&-1===[i,null].indexOf(s))throw new wg(`Input ${n} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${i} but got shape ${e}.`)}}if(null!=a.shape)for(let e=0;e{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of Eg(e))t.push(n.shape);this.build(Ig(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let r=this.call(e,t);const a=Eg(r),i=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),i.push(e);if(r=Ig(i),null!=this.activityRegularizer)throw new Mg("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=Eg(e);const t=[];for(const n of e)t.push(n.shape);return Ig(t)}(e),r=this.computeOutputShape(n);let a;const i="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),a=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new lb(i,n,this,Eg(e),t,this.name,r))):new lb(i,r,this,Eg(e),t,this.name),this.addInboundNode(e,a,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new Mg("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new vg(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new vg(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new _g(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return ab(this.weights)}build(e){this.built=!0}getWeights(e=!1){return sb(e?this.trainableWeights:this.weights)}setWeights(e){ho((()=>{const t=this.weights;if(t.length!==e.length)throw new wg(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(0===t.length)return;const n=[],r=sb(t);for(let a=0;aa.apply(l.read()))),null==i&&(i=!0),i?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=Eg(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);t.forEach((e=>{if(null!=e)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,r,a,i,s=null){const o=Eg(e);t=Eg(t),n=Eg(n),r=Eg(r),a=tb(a),i=tb(i);const u=[],l=[],c=[];for(const e of o)u.push(e.sourceLayer),l.push(e.nodeIndex),c.push(e.tensorIndex);new pb({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:i},s);for(let e=0;ee.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function fb(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{const e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{const t=[];for(let n=0;ne.name)),u=[],l=t.names();for(const e of o)-1!==l.indexOf(e)?u.push(t.getValue(e)):u.push(null);null!=r&&(r.maxNumTensors=-1/0,r.minNumTensors=1/0);const c=o.join(",")+"|"+t.names().sort().join(",");let p,d=bb.get(c);if(null==d){const e=function(e,t){se(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],r={};if(1===e.length){const a=wb(e[0],t);n=a.sorted,r=a.recipientMap}else{const a=new Set;for(const i of e){const{sorted:e,recipientMap:s}=wb(i,t);for(const t of e)a.has(t.name)||(n.push(t),a.add(t.name));for(const e in s)null==r[e]&&(r[e]=new Set),s[e].forEach((t=>r[e].add(t)))}}return{sorted:n,recipientCounts:_b(r)}}(s,t);d=e.sorted,p=e.recipientCounts,bb.put(c,d),xb.put(c,p)}p={},a||Object.assign(p,xb.get(c));const h=new yb(t);for(let e=0;er.maxNumTensors&&(r.maxNumTensors=e),e0;){const e=i[i.length-1];if(n.has(e.name)){i.pop();continue}const t=s[s.length-1]===i.length-1;if(0===e.inputs.length||t)i.pop(),r.push(e),n.add(e.name),t&&s.pop();else{s.push(i.length-1);for(const t of e.inputs)null==a[t.name]&&(a[t.name]=new Set),a[t.name].add(e.name),n.has(t.name)||i.push(t)}}return{sorted:r,recipientMap:a}}function Mb(e){let t;if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;tku(Nc(Su(e,e),t,!0))))}Ge().registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",(()=>100),(function(e){null!=bb&&bb.setMaxEntries(e),null!=xb&&xb.setMaxEntries(e)}));class Sb extends ou{getConfig(){return{}}}class kb extends Sb{constructor(e){super(),this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>{const t=Nb(e,this.axis),n=Bl(t,0,this.maxValue);return Su(e,Nu(n,wu(my(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}kb.className="MaxNorm",lu(kb);class Db extends Sb{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>Nu(e,wu(my(),Nb(e,this.axis)))))}getConfig(){return{axis:this.axis}}}Db.className="UnitNorm",lu(Db);class Tb extends Sb{apply(e){return Bp(e)}}Tb.className="NonNeg",lu(Tb);class Ib extends Sb{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=e.minValue?e.minValue:this.defaultMinValue,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.rate=null!=e.rate?e.rate:this.defaultRate,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>{const t=Nb(e,this.axis),n=wu(Su(this.rate,Bl(t,this.minValue,this.maxValue)),Su(1-this.rate,t));return Su(e,Nu(n,wu(my(),t)))}))}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}}Ib.className="MinMaxNorm",lu(Ib);const Eb={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Cb(e){return $g(e)}function Ab(e,t={}){return Rg(e,uu.getMap().classNameMap,t,"constraint")}function Lb(e){return null==e?null:"string"==typeof e?Ab({className:e in Eb?Eb[e]:e,config:{}}):e instanceof Sb?e:Ab(e)}function $b(e){return new kb(e)}function Ob(e){return new Db(e)}function Rb(){return new Tb}function Fb(e){return new Ib(e)}function Yb(){return new Oy}function zb(){return new Ry}function Bb(e){return new Fy(e)}function Pb(e){return new Yy(e)}function Hb(e){return new zy(e)}function jb(e){return new By(e)}function Ub(e){return new Py(e)}function qb(e){return new Hy(e)}function Wb(e){return new jy(e)}function Vb(e){return new Uy(e)}function Gb(e){return new qy(e)}function Kb(e){return new Wy(e)}function Jb(e){return new Vy(e)}function Xb(e){return new Gy(e)}function Zb(e){return new Ky(e)}async function Qb(e){if(null==e)return;const t=[],n=[],r=[];for(const a in e){const i=e[a];if("number"!=typeof i){const e=i;t.push(e.data()),n.push(a),r.push(e)}}if(t.length>0){const a=await Promise.all(t);for(let 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ho((()=>{const n=Bl(t,my(),Number.MAX_VALUE),r=qc(wu(1,n)),a=Bl(e,my(),Number.MAX_VALUE),i=qc(wu(1,a));return op(Dy(Hu(r,i)),-1)}))},squaredHinge:function(e,t){return ho((()=>{const n=qu(0,Hu(1,Su(e,t)));return op(Dy(n),-1)}))},hinge:function(e,t){return ho((()=>{const n=qu(0,Hu(1,Su(e,t)));return op(n,-1)}))},categoricalHinge:function(e,t){return ho((()=>{const n=Nc(Su(e,t),-1),r=wc(Su(Hu(1,e),t),-1);return qu(0,wu(1,Hu(r,n)))}))},logcosh:function(e,t){return ho((()=>{const n=Math.log(2),r=Hu(t,e),a=Hu(wu(r,Vc(Su(-2,r))),n);return op(a,-1)}))},categoricalCrossentropy:mx,sparseCategoricalCrossentropy:gx,binaryCrossentropy:yx,kullbackLeiblerDivergence:function(e,t){return ho((()=>{const n=Bl(e,my(),1),r=Bl(t,my(),1);return Nc(Su(e,qc(Nu(n,r))),-1)}))},poisson:function(e,t){return ho((()=>{const n=qc(wu(my(),t));return op(Hu(t,Su(e,n)),-1)}))},cosineProximity:bx};function vx(e){if("string"==typeof e){if(e in xx)return xx[e];let t=`Unknown loss ${e}`;throw 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Please make sure its serialized length is <= 1048576.`)}}function Lx(e){if(null===e)return!0;if("object"==typeof e){if(Object.getPrototypeOf(e)===Object.prototype){const t=Object.keys(e);for(const n of t){if("string"!=typeof n)return!1;if(!Lx(e[n]))return!1}return!0}if(Array.isArray(e)){for(const t of e)if(!Lx(t))return!1;return!0}return!1}{const t=typeof e;return"string"===t||"number"===t||"boolean"===t}}function $x(e,t,n=console.log){let r="";for(let n=0;n0&&(r=r.slice(0,r.length-1)+" "),r+=e[n],r=r.slice(0,t[n]),r+=" ".repeat(t[n]-r.length);n(r)}function Ox(e,t,n){let r,a;try{a=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){a="multiple"}try{r=JSON.stringify(e.outputShape)}catch(e){r="multiple"}$x([`${e.name} (${e.getClassName()})`,a,r,e.countParams().toString()],t,n)}function Rx(e,t,n,r){let a,i;try{i=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){i="multiple"}try{a=JSON.stringify(e.outputShape)}catch(e){a="multiple"}const s=[];for(const t of e.inboundNodes)if(!(null!=n&&n.length>0&&-1===n.indexOf(t)))for(let e=0;ee.name))}`);Yg(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. 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Found: ${this.outputs.map((e=>e.name))}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const e of this.outputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;this.outputLayers.push(t),this.outputLayersNodeIndices.push(n),this.outputLayersTensorIndices.push(r)}for(const e of this.inputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;Dg(0===n,"input layer has >1 nodes"),Dg(0===r,"input layer has >1 tensors"),this.inputLayers.push(t),this.inputLayersNodeIndices.push(n),this.inputLayersTensorIndices.push(r)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let t=0;te.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));const t={},n={},r={},a={},i={},s=[],o=(e,t,n,r,a,u)=>{null!=r&&null!=a&&null!=u||(r=e.sourceLayer,a=e.nodeIndex,u=e.tensorIndex);const l=r.inboundNodes[a];if(-1!==n.indexOf(l))throw new _g(`The tensor ${e.name} at layer "${r.name}" is part of a cycle.`);if(-1!==t.indexOf(l))return;this.containerNodes.add(Px.nodeKey(r,a)),r.id in i||(i[r.id]=Object.keys(i).length),-1===n.indexOf(l)&&n.push(l);const c=l.inboundLayers.length;for(let e=0;e=0;)n.splice(n.indexOf(l),1);s.push(l)},u=[],l=[];for(const e of this.outputs)o(e,u,l);const c=s.slice().reverse();for(const e of c){n[e.id]=e,e.id in t||(t[e.id]=0);let i=t[e.id];const s=null==r[e.outboundLayer.id]?0:r[e.outboundLayer.id];i=Math.max(i,s),r[e.outboundLayer.id]=i,a[e.outboundLayer.id]=e.outboundLayer,t[e.id]=i;for(let r=0;rparseInt(e,10))).sort(Fg);this.layers=[];for(const e of h){const t=d[e];t.sort(((e,t)=>{const n=i[e.id],r=i[t.id];return nr?1:0}));for(const e of t)e instanceof Px&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=d,h=Object.keys(p).map((e=>parseInt(e,10))).sort(Fg);const f=this.inputs.slice(),m=[];for(const e of h)for(const t of p[e]){const e=t.outboundLayer;if(null!=e){for(const n of t.inputTensors)if(-1===f.indexOf(n))throw new _g(`Graph disconnected: cannot obtain value for tensor ${n} at layer "${e.name}". The following previous layers were accessed without issue: ${m}`);for(const e of t.outputTensors)f.push(e);m.push(e.name)}}this.nodesByDepth=p;const g=this.layers.map((e=>e.name));for(const e of g){const t=g.filter((t=>t===e)).length;if(1!==t)throw new _g(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new pb({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach((t=>{t._trainableWeights.forEach((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new wg("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let r=0;for(const e of this.layers)for(const t of e.weights){if(null!=n[t.originalName])throw new wg(`Duplicate weight name: ${t.originalName}`);n[t.originalName]=t,r++}const a=[];for(const r in e){let i=r;if(null==n[r]){const e=r.split("/");i=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[i])a.push([n[i],e[r]]);else if(t)throw new wg(`Provided weight data has no target variable: ${r}`);delete n[i]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new wg(`${e.length} of ${r} weights are not set: ${e}`)}ob(a)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Bx}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=zx(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ho((()=>{e=Eg(e);const n=new yb;for(let t=0;t{let n;return e=Eg(e),n=null==t?kg(null,e.length):Eg(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=tb(e);if(t.length!==this.inputLayers.length)throw new wg(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let e=0;eparseInt(e,10))).sort(Fg);if(r.length>1)for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const r=[];for(let t=0;tparseInt(e,10))).sort(Fg);for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,r=e.inputTensors,a=e.outputTensors,i=new Array;for(const e of r)e.id in n&&i.push(n[e.id]);if(i.length===r.length){let r,s,o,u,l={};if(null!=e.callArgs&&(l=e.callArgs),1===i.length){const[e,n]=i[0];null==l.mask&&(l.mask=n),o=Eg(t.call(e,l)),u=Eg(t.computeMask(e,n)),r=[e],s=[n]}else r=i.map((e=>e[0])),s=i.map((e=>e[1])),null==l.mask&&(l.mask=s),o=Eg(t.call(r,l)),u=Eg(t.computeMask(r,s));if(t.activityRegularizer)throw new Mg("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e{const e=[];for(const t of this.layers)for(let n=0;n0){const e=[];for(let n=0;n0&&e.apply(Ig(n),r)}function u(e){const n=e.name,i=cx(e,null!=t.customObjects?t.customObjects:{});i.setFastWeightInitDuringBuild(r),a[n]=i,e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new wg(`Corrupted configuration, expected array for nodeData: ${e}`);s(i,e)}))}const l=t.name,c=t.layers;for(const e of c)u(e);for(;!zg(i);)for(const e of c){const t=a[e.name];if(t.name in i){const e=i[t.name];delete i[t.name];for(const n of e)o(t,n)}}const p=[],d=[],h=t.inputLayers;for(const e of h){const t=e[0],n=e[1],r=e[2];Dg(t in a);const i=a[t].inboundNodes[n].outputTensors;p.push(i[r])}const f=t.outputLayers;for(const e of f){const t=e[0],n=e[1],r=e[2];Dg(t in a);const i=a[t].inboundNodes[n].outputTensors;d.push(i[r])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new wg("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ho((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}function Hx(e,t){return function(e,t,n){const r=t.length;if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>null));if(1===r)return Array.isArray(e)&&1===e.length?e:"object"==typeof e&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}if("object"==typeof e&&Object.keys(e).length>0&&"object"==typeof e[Object.keys(e)[0]]){const n=[];return t.forEach((t=>{t in e?n.push(e[t]):n.push(null)})),n}throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. 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For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new wg("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===e.inboundNodes.length){if(null==e.batchInputShape)throw new wg("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=gb({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(t)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==e.inboundNodes.length)throw new wg(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(1!==e.inboundNodes[0].outputTensors.length)throw new wg("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=fb(this.outputs[0])}this.inboundNodes=[],new pb({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:kg(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs[0].shape})}else{const t=e.apply(this.outputs[0]);if(Array.isArray(t))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[t],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return null==this.model&&this.build(),this.model.call(e,t)}build(e){if(rb(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new av({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){null==this.model&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new _g("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new _g("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return null==this.model&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return null==this.model&&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 null==this.model?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new _g("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 _g("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={},r=!1){let a,i={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new wg("Legacy serialization format not supported yet.");a=t}else se(null!=t.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),a=t.layers,delete t.layers,i=t;const s=new e(i);if(!(s instanceof ov))throw new Mg(`Sequential.fromConfig called on non-Sequential input: ${s}`);for(const e of a){const t=cx(e,void 0,r);r&&t.setFastWeightInitDuringBuild(!0),s.add(t)}return s}set stopTraining(e){if(null==this.model)throw new wg("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new wg("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}function uv(e){return new av(e)}function lv(e){return new ov(e)}function cv(e,t){return null==t&&(t={}),async function(e,t){if(null==t&&(t={}),"string"==typeof e){const n=es(e,t);if(0===n.length)n.push(Gs(e,t));else if(n.length>1)throw new wg(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return async function(e,t,n){if(null==n&&(n={}),null==e.load)throw new wg("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const r=await e.load();let a=r.modelTopology;null!=a.model_config&&(a=a.model_config);const i=null==n.strict||n.strict,s=null!=r.weightData&&null!=r.weightSpecs&&i,o=cx(Yx(a),void 0,s),u=r.trainingConfig;if(null!=u&&o.loadTrainingConfig(u),null!=r.userDefinedMetadata&&o.setUserDefinedMetadata(r.userDefinedMetadata),null!=r.weightData){if(null==r.weightSpecs)throw new wg("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:e,optimizerWeights:t}=function(e,t){const n=Bi(e,t),r={},a=[];return t.forEach((e=>{"optimizer"===e.group?a.push({name:e.name,tensor:n[e.name]}):r[e.name]=n[e.name]})),{modelWeights:r,optimizerWeights:a}}(r.weightData,r.weightSpecs);o.loadWeights(e,i),null!=o.optimizer&&t.length>0&&await o.optimizer.setWeights(t),fo(e),fo(t.map((e=>e.tensor)))}return o}(e,0,t)}(e,t)}function pv(e){return gb(e)}function dv(e,t){ux.registerCallbackConstructor(e,t)}ov.className="Sequential",lu(ov);class hv extends ou{getConfig(){return{}}}class fv extends hv{apply(e,t=1){return function(e,t=1){if(1!==t)throw new Mg(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return dc(e)}(e,t)}}fv.className="elu",lu(fv);class mv extends hv{apply(e){return Kp(e)}}mv.className="selu",lu(mv);class gv extends hv{apply(e){return Bp(e)}}gv.className="relu",lu(gv);class yv extends hv{apply(e){return ho((()=>pp(6,Bp(e))))}}yv.className="relu6",lu(yv);class bv extends hv{apply(e){return e}}bv.className="linear",lu(bv);class xv extends hv{apply(e){return Dl(e)}}xv.className="sigmoid",lu(xv);class vv extends hv{apply(e){return function(e){return ho((()=>{const t=wu(.5,Su(.2,e));return Bl(t,0,1)}))}(e)}}vv.className="hardSigmoid",lu(vv);class _v extends hv{apply(e){return Vc(e)}}_v.className="softplus",lu(_v);class wv extends hv{apply(e){return function(e){return ho((()=>Nu(e,wu(Uu(e),1))))}(e)}}wv.className="softsign",lu(wv);class Mv extends hv{apply(e){return Il(e)}}Mv.className="tanh",lu(Mv);class Nv extends hv{apply(e,t=-1){return id(e,t)}}Nv.className="softmax",lu(Nv);class Sv extends hv{apply(e,t=-1){return Kc(e,t)}}Sv.className="logSoftmax",lu(Sv);class kv extends hv{apply(e,t=1){return ho((()=>Su(Dl(Su(e,t)),e)))}}kv.className="swish",lu(kv);class Dv extends hv{apply(e){return ho((()=>Su(e,Il(Vc(e)))))}}function Tv(e){return e.getClassName()}function Iv(e,t={}){return Rg(e,uu.getMap().classNameMap,t,"activation")}function Ev(e){if(null==e){return Iv({className:"linear",config:{}})}if("string"==typeof e){const t={};return t.className=e,t.config={},Iv(t)}return e instanceof hv?e:Iv(e)}function Cv(e){if(null!=e&&"object"!=typeof e)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}Dv.className="mish",lu(Dv);class Av extends ou{}class Lv extends Av{constructor(e){super(),Cv(e),this.l1=null==e||null==e.l1?.01:e.l1,this.l2=null==e||null==e.l2?.01:e.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(e){return ho((()=>{let t=up([1]);return this.hasL1&&(t=wu(t,Nc(Su(this.l1,Uu(e))))),this.hasL2&&(t=wu(t,Nc(Su(this.l2,Dy(e))))),Ml(t,[])}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Lv.className="L1L2",lu(Lv);const $v={l1l2:"L1L2"};function Ov(e){return $g(e)}function Rv(e,t={}){return Rg(e,uu.getMap().classNameMap,t,"regularizer")}function Fv(e){return null==e?null:"string"==typeof e?Rv({className:e in $v?$v[e]:e,config:{}}):e instanceof Av?e:Rv(e)}class Yv extends hb{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=nb(e);let n=Bp(e);return null!=this.maxValue&&(n=Bl(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Yv.className="ReLU",lu(Yv);class zv extends hb{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=nb(e);return Bc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}zv.className="LeakyReLU",lu(zv);class Bv extends hb{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Qy(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Fv(e.alphaRegularizer),this.alphaConstraint=Lb(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new wg(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=rb(e)).slice(1);if(null!=this.sharedAxes)for(const e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let t=1;t(ty(t),"channelsFirst"===t?Io(e,[0,2,3,1]):e)))}function Gv(e,t){return ho((()=>(ty(t),"channelsFirst"===t?Io(e,[0,2,3,4,1]):e)))}function Kv(e,t,n,r=[1,1],a="valid",i,s,o=null){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),3!==e.rank&&4!==e.rank)throw new wg(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new wg(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Vv(e,i);if("causal"===a)throw new Mg("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=qd({x:u,filter:t,strides:r,pad:"same"===a?"same":"valid",dilations:s,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===i&&(u=Io(u,[0,3,1,2])),u}))}jv.className="Softmax",lu(jv);class Jv extends hb{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jv.verifyArgs(t),this.rank=e,Hg(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Mg(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Uv(t.kernelSize,e,"kernelSize"),this.strides=Uv(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,ny(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,ty(this.dataFormat),this.activation=Ev(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=Qy(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lb(t.biasConstraint),this.biasRegularizer=Fv(t.biasRegularizer),this.activityRegularizer=Fv(t.activityRegularizer),this.dilationRate=Uv(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new wg(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new wg(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new wg(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(Dg("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,3))throw new wg(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Tv(this.activation),useBias:this.useBias,biasInitializer:Zy(this.biasInitializer),biasRegularizer:Ov(this.biasRegularizer),activityRegularizer:Ov(this.activityRegularizer),biasConstraint:Cb(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Xv extends Jv{constructor(e,t){super(e,t),this.kernel=null,Xv.verifyArgs(t),this.filters=t.filters,Hg(this.filters,"filters"),this.kernelInitializer=Qy(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lb(t.kernelConstraint),this.kernelRegularizer=Fv(t.kernelRegularizer)}build(e){e=rb(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ho((()=>{let t;e=nb(e);const n=null==this.bias?null:this.bias.read(),r=Ug(this.activation.getClassName());if(null!=r&&2===this.rank)t=Kv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=function(e,t,n,r=1,a="valid",i,s=1){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),3!==e.shape.length)throw new wg(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new wg(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new wg(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if("channelsFirst"===i&&(e=Io(e,[0,2,1])),"causal"===a)throw new Mg("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Wl(e,t,r,"same"===a?"same":"valid","NWC",s);return null!=n&&(o=Iy(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=Kv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Mg("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,r=[1,1,1],a="valid",i,s){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),4!==e.rank&&5!==e.rank)throw new wg(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new wg(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=Gv(e,i);if("causal"===a)throw new Mg("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Kl(o,t,r,"same"===a?"same":"valid","NDHWC",s),null!=n&&(o=Iy(o,n)),"channelsFirst"===i&&(o=Io(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=rb(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e 0 but got ${JSON.stringify(e.filters)}`)}}class Zv extends Xv{constructor(e){super(2,e),Zv.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,2))throw new wg(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Zv.className="Conv2D",lu(Zv);class Qv extends Xv{constructor(e){super(3,e),Qv.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new wg(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Qv.className="Conv3D",lu(Qv);class e_ extends Zv{constructor(e){if(super(e),this.inputSpec=[new ub({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new wg(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=rb(e)).length)throw new wg("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ub({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ho((()=>{let t=nb(e);if(4!==t.shape.length)throw new wg(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,i;"channelsFirst"===this.dataFormat?(a=2,i=3):(a=1,i=2);const s=n[a],o=n[i],u=this.kernelSize[0],l=this.kernelSize[1],c=this.strides[0],p=this.strides[1],d=[r,Wv(s,c,u,this.padding),Wv(o,p,l,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Io(t,[0,2,3,1]));let h=Gl(t,this.kernel.read(),d,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(h=Io(h,[0,3,1,2])),null!=this.bias&&(h=Iy(h,this.bias.read(),this.dataFormat)),null!=this.activation&&(h=this.activation.apply(h)),h}))}computeOutputShape(e){const t=(e=rb(e)).slice();let n,r,a;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3):(n=3,r=1,a=2);const i=this.kernelSize[0],s=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=Wv(t[r],o,i,this.padding),t[a]=Wv(t[a],u,s,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}e_.className="Conv2DTranspose",lu(e_);class t_ extends Qv{constructor(e){if(super(e),this.inputSpec=[new ub({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new wg(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(5!==(e=rb(e)).length)throw new wg("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ub({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return ho((()=>{let t=nb(e);if(5!==t.shape.length)throw new wg(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,i,s;"channelsFirst"===this.dataFormat?(s=2,a=3,i=4):(s=1,a=2,i=3);const o=n[s],u=n[a],l=n[i],c=this.kernelSize[0],p=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],f=this.strides[1],m=this.strides[2],g=[r,Wv(o,h,c,this.padding),Wv(u,f,p,this.padding),Wv(l,m,d,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Io(t,[0,2,3,4,1]));let y=Xl(t,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(y=Io(y,[0,4,1,2,3])),null!==this.bias&&(y=Iy(y,this.bias.read(),this.dataFormat)),null!==this.activation&&(y=this.activation.apply(y)),y}))}computeOutputShape(e){const t=(e=rb(e)).slice();let n,r,a,i;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3,i=4):(n=4,r=1,a=2,i=3);const s=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=Wv(t[r],l,s,this.padding),t[a]=Wv(t[a],c,o,this.padding),t[i]=Wv(t[i],p,u,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}t_.className="Conv3DTranspose",lu(t_);class n_ extends Xv{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new wg("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new wg("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new wg(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=Qy(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Fv(t.depthwiseRegularizer),this.depthwiseConstraint=Lb(t.depthwiseConstraint),this.pointwiseInitializer=Qy(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Fv(t.pointwiseRegularizer),this.pointwiseConstraint=Lb(t.pointwiseConstraint)}build(e){if((e=rb(e)).length{let t;if(e=nb(e),1===this.rank)throw new Mg("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=Io(e,[0,2,3,1])),t=Jp(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=Iy(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=Io(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Zy(this.depthwiseInitializer),e.pointwiseInitializer=Zy(this.pointwiseInitializer),e.depthwiseRegularizer=Ov(this.depthwiseRegularizer),e.pointwiseRegularizer=Ov(this.pointwiseRegularizer),e.depthwiseConstraint=Cb(this.depthwiseConstraint),e.pointwiseConstraint=Cb(this.pointwiseConstraint),e}}n_.className="SeparableConv";class r_ extends n_{constructor(e){super(2,e)}}r_.className="SeparableConv2D",lu(r_);class a_ extends Xv{constructor(e){super(1,e),a_.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,1))throw new wg(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}a_.className="Conv1D",lu(a_);class i_ extends hb{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[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 ho((()=>{if(e=nb(e),"channelsLast"===this.dataFormat){const t=vy(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return vy(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=vy(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return vy(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}i_.className="Cropping2D",lu(i_);class s_ extends hb{constructor(e){var t;super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,Bg(Jg,"InterpolationFormat",t)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ho((()=>{let t=nb(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=Io(t,[0,2,3,1]);const e=this.size[0]*n[2],r=this.size[1]*n[3],a="nearest"===this.interpolation?Uh.resizeNearestNeighbor(t,[e,r]):Uh.resizeBilinear(t,[e,r]);return Io(a,[0,3,1,2])}{const e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?Uh.resizeNearestNeighbor(t,[e,r]):Uh.resizeBilinear(t,[e,r])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}}s_.className="UpSampling2D",lu(s_);class o_ extends Jv{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=Qy(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lb(e.depthwiseConstraint),this.depthwiseRegularizer=Fv(e.depthwiseRegularizer)}build(e){if((e=rb(e)).length<4)throw new wg(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new wg(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ho((()=>{let t=function(e,t,n=[1,1],r="valid",a,i){return ho((()=>{null==a&&(a="channelsLast"),ty(a);let s=Vv(e,a);if(4!==e.rank)throw new wg(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new wg(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return s=ac(s,t,n,"same"===r?"same":"valid","NHWC",i),"channelsFirst"===a&&(s=Io(s,[0,3,1,2])),s}))}(e=nb(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=Iy(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=rb(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=qv(t,this.kernelSize[0],this.padding,this.strides[0]),i=qv(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,a,i]:[e[0],a,i,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Zy(this.depthwiseInitializer),e.depthwiseRegularizer=Ov(this.depthwiseRegularizer),e.depthwiseConstraint=Cb(this.depthwiseRegularizer),e}}function u_(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new wg("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function l_(e,t,n,r=!1,a,i,s=!1,o=!1){return ho((()=>{const u=t.shape.length;if(u<3)throw new wg(`Input should be at least 3D, but is ${u}D.`);const l=[1,0].concat(hy(2,u));if(t=Io(t,l),null!=i)throw new Mg("The rnn() functoin of the deeplearn.js backend does not support constants yet.");s&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=a&&((a=Ls(Ls(a,"bool"),"float32")).rank===u-1&&(a=Ic(a,-1)),a=Io(a,l)),r&&(t=Hp(t,0),null!=a&&(a=Hp(a,0)));const c=[];let p,d=n;const h=t.shape[0],f=kd(t);let m,g;null!=a&&(m=kd(a));for(let t=0;te(n,d)));if(null==a)p=r[0],d=r[1];else{const e=ho((()=>{const e=m[t],n=Hu(bp(e),e),a=wu(Su(r[0],e),Su(d[0],n)),i=d.map(((t,a)=>wu(Su(r[1][a],e),Su(t,n))));return{output:a,newStates:i}}));p=e.output,d=e.newStates}o&&c.push(p)}return o&&(g=hd(c,1)),[p,g,d]}))}o_.className="DepthwiseConv2D",lu(o_);class c_ extends hb{constructor(e){let t;if(super(e),null==e.cell)throw new wg("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new b_({cells:e.cell}):e.cell,null==t.stateSize)throw new wg("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new ub({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){return null==this.states_?hy(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null)):this.states_}setStates(e){this.states_=e}computeOutputShape(e){eb(e)&&(e=e[0]);let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let r;if(r=this.returnSequences?[e[0],e[1],n]:[e[0],n],this.returnState){const n=[];for(const r of t)n.push([e[0],r]);return[r].concat(n)}return r}computeMask(e,t){return ho((()=>{Array.isArray(t)&&(t=t[0]);const e=this.returnSequences?t:null;if(this.returnState){const t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ne.shape[e.shape.length-1])),a))throw new wg(`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((e=>new ub({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){ho((()=>{if(!this.stateful)throw new vg("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new wg("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(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>up([n,e]))):this.states_=[up([n,this.cell.stateSize])];else if(null==e)fo(this.states_),null!=this.keptStates&&(fo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>up([n,e]))):this.states_[0]=up([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new wg(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);!0===t?this.keptStates.push(this.states_.slice()):fo(this.states_);for(let t=0;tmo(e.clone())))}))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=u_(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let i=[],s=[];if(null!=n){t.initialState=n,i=i.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new ub({shape:e.shape}));s=s.concat(this.stateSpec)}if(null!=r&&(t.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof lb){const n=[e].concat(i),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return ho((()=>{const n=null==t?null:t.mask,r=null==t?null:t.training;let a=null==t?null:t.initialState;e=nb(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));const i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==i)throw new wg(`RNN Layer has ${i} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const s={training:r},o=l_(((e,t)=>{const n=this.cell.call([e].concat(t),s);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],c=o[2];this.stateful&&this.resetStates(c,r);const p=this.returnSequences?l:u;return this.returnState?[p].concat(c):p}))}getInitialState(e){return ho((()=>{let t=up(e.shape);return t=Nc(t,[1,2]),t=yy(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?My(t,[1,e]):t)):this.cell.stateSize>1?[My(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),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===c_.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const r=cx(t.cell,n);return new e(Object.assign(t,{cell:r}))}}c_.className="RNN",lu(c_);class p_ extends hb{}class d_ extends p_{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,Hg(this.units,"units"),this.activation=Ev(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Fv(e.kernelRegularizer),this.recurrentRegularizer=Fv(e.recurrentRegularizer),this.biasRegularizer=Fv(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rb(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 ho((()=>{if(2!==e.length)throw new wg(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const r=null!=t.training&&t.training;let a;0bp(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0bp(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,s=this.recurrentDropoutMask;a=Sy(null!=i?Su(e,i):e,this.kernel.read()),null!=this.bias&&(a=Iy(a,this.bias.read())),null!=s&&(n=Su(n,s));let o=wu(a,Sy(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Tv(this.activation),useBias:this.useBias,kernelInitializer:Zy(this.kernelInitializer),recurrentInitializer:Zy(this.recurrentInitializer),biasInitializer:Zy(this.biasInitializer),kernelRegularizer:Ov(this.kernelRegularizer),recurrentRegularizer:Ov(this.recurrentRegularizer),biasRegularizer:Ov(this.biasRegularizer),activityRegularizer:Ov(this.activityRegularizer),kernelConstraint:Cb(this.kernelConstraint),recurrentConstraint:Cb(this.recurrentConstraint),biasConstraint:Cb(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}d_.className="SimpleRNNCell",lu(d_);class h_ extends c_{constructor(e){e.cell=new d_(e),super(e)}call(e,t){return ho((()=>{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return new e(t)}}h_.className="SimpleRNN",lu(h_);class f_ extends p_{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new wg("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Hg(this.units,"units"),this.activation=Ev(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ev(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Fv(e.kernelRegularizer),this.recurrentRegularizer=Fv(e.recurrentRegularizer),this.biasRegularizer=Fv(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=rb(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ho((()=>{if(2!==e.length)throw new wg(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=null!=t.training&&t.training;let r=e[1];e=e[0],0bp(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0bp(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));const a=this.dropoutMask,i=this.recurrentDropoutMask;let s,o,u;0{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}m_.className="GRU",lu(m_);class g_ extends p_{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,Hg(this.units,"units"),this.activation=Ev(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ev(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Fv(e.kernelRegularizer),this.recurrentRegularizer=Fv(e.recurrentRegularizer),this.biasRegularizer=Fv(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=rb(e))[e.length-1];let r;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;r=new((t=class extends $y{apply(t,r){const a=e.apply([n]),i=(new Ry).apply([n]),s=e.apply([2*n]);return wy(wy(a,i),s)}}).className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ho((()=>{const n=null!=t.training&&t.training;if(3!==e.length)throw new wg(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1];const a=e[2];e=e[0],0bp(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0bp(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,s=this.recurrentDropoutMask;let o,u,l,c;0{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}y_.className="LSTM",lu(y_);class b_ extends p_{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ho((()=>{let n=e.slice(1);const r=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();const a=[];let i;for(let s=0;s{iy(`RNNCell_${r}`,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign({},e,t)}static fromConfig(e,t,n={}){const r=[];for(const e of t.cells)r.push(cx(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return sb(e)}setWeights(e){const t=[];for(const n of this.cells){const r=n.weights.length,a=e.splice(r);for(let e=0;enull!=i?i(t(),n):Ey(t(),n),o=()=>Cy(s,t,r);return!a||a<=1?mo(o().clone()):Array(a).fill(void 0).map(o).map((e=>mo(e.clone())))}b_.className="StackedRNNCells",lu(b_);class v_ extends c_{constructor(e){if(e.unroll)throw new Mg("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Mg("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new ub({ndim:5})]}call(e,t){return ho((()=>{if(null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new wg("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ho((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=up([r[0],...r.slice(2)]);return Array.isArray(t)?Array(t.length).fill(a):[a]}))}resetStates(e,t=!1){ho((()=>{if(!this.stateful)throw new vg("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(null==n[0])throw new wg("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(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>up(a))):this.states_=[up(a)];else if(null==e)fo(this.states_),null!=this.keptStates&&(fo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>up(a))):this.states_[0]=up(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new wg(`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()):fo(this.states_);for(let t=0;tmo(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:i,dilationRate:s}=this.cell,o="channelsFirst"===t,u=e[o?3:2],l=e[o?4:3],c=qv(u,r[0],a,i[0],s[0]),p=qv(l,r[1],a,i[1],s[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}}v_.className="ConvRNN2D";class __ extends g_{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:i,dilationRate:s}=e;super(Object.assign({},e,{units:t})),this.filters=t,Hg(this.filters,"filters"),this.kernelSize=Uv(n,2,"kernelSize"),this.kernelSize.forEach((e=>Hg(e,"kernelSize"))),this.strides=Uv(r||1,2,"strides"),this.strides.forEach((e=>Hg(e,"strides"))),this.padding=a||"valid",ny(this.padding),this.dataFormat=i||"channelsLast",ty(this.dataFormat),this.dilationRate=Uv(s||1,2,"dilationRate"),this.dilationRate.forEach((e=>Hg(e,"dilationRate")))}build(e){var t;e=rb(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new wg(`The channel dimension of the input should be defined. Found ${e[n]}`);const r=e[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const i=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,r=this.filters;e=new((t=class extends $y{apply(e,t){return _y([n.apply([r]),lp([r]),n.apply([2*r])])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ho((()=>{if(3!==e.length)throw new wg(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,r=e[0],a=e[1],i=e[2];0bp(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const s=this.dropoutMask,o=(e,t,n)=>t&&t[n]?Su(t[n],e):e;let u=o(r,s,0),l=o(r,s,1),c=o(r,s,2),p=o(r,s,3);0bp(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const d=this.recurrentDropoutMask;let h=o(a,d,0),f=o(a,d,1),m=o(a,d,2),g=o(a,d,3);const[y,b,x,v]=ld(this.kernel.read(),4,3),[_,w,M,N]=this.useBias?ld(this.bias.read(),4):[null,null,null,null];u=this.inputConv(u,y,_,this.padding),l=this.inputConv(l,b,w,this.padding),c=this.inputConv(c,x,M,this.padding),p=this.inputConv(p,v,N,this.padding);const[S,k,D,T]=ld(this.recurrentKernel.read(),4,3);h=this.recurrentConv(h,S),f=this.recurrentConv(f,k),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T);const I=this.recurrentActivation.apply(wu(u,h)),E=this.recurrentActivation.apply(wu(l,f)),C=wu(Su(E,i),Su(I,this.activation.apply(wu(c,m)))),A=Su(this.recurrentActivation.apply(wu(p,g)),this.activation.apply(C));return[A,A,C]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var a=0;for(r=Object.getOwnPropertySymbols(e);a{this.invokeCallHook(e,t);const n=nb(e);if(0Ey(n,this.rate,r,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}M_.className="Dropout",lu(M_);class N_ extends M_{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}N_.className="SpatialDropout1D",lu(N_);class S_ extends hb{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Hg(this.units,"units"),this.activation=Ev(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lb(e.kernelConstraint),this.biasConstraint=Lb(e.biasConstraint),this.kernelRegularizer=Fv(e.kernelRegularizer),this.biasRegularizer=Fv(e.biasRegularizer),this.activityRegularizer=Fv(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=rb(e))[e.length-1];null==this.kernel&&(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){const t=(e=rb(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e),r=Ug(this.activation.getClassName());let a;return null!=r?a=Sy(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Sy(n,this.kernel.read()),null!=this.bias&&(a=Iy(a,this.bias.read())),null!=this.activation&&(a=this.activation.apply(a))),a}))}getConfig(){const e={units:this.units,activation:Tv(this.activation),useBias:this.useBias,kernelInitializer:Zy(this.kernelInitializer),biasInitializer:Zy(this.biasInitializer),kernelRegularizer:Ov(this.kernelRegularizer),biasRegularizer:Ov(this.biasRegularizer),activityRegularizer:Ov(this.activityRegularizer),kernelConstraint:Cb(this.kernelConstraint),biasConstraint:Cb(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}S_.className="Dense",lu(S_);class k_ extends hb{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=rb(e);for(const t of e.slice(1))if(null==t)throw new wg(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],cy(e,1)]}call(e,t){return ho((()=>{this.invokeCallHook(e,t);let n=nb(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t{this.invokeCallHook(e,t);const n=nb(e);return this.activation.apply(n)}))}getConfig(){const e={activation:Tv(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}D_.className="Activation",lu(D_);class T_ extends hb{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ho((()=>function(e,t){return ho((()=>{if(2!==e.shape.length)throw new wg(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);return My(yy(e,1),[1,t,1])}))}(e=nb(e),this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}T_.className="RepeatVector",lu(T_);class I_ extends hb{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e{this.invokeCallHook(e,t);const n=nb(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return Ml(n,a)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}I_.className="Reshape",lu(I_);class E_ extends hb{constructor(e){if(super(e),null==e.dims)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.`);const t=hy(1,e.dims.length+1);if(!de(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 ub({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=rb(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return Io(nb(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}E_.className="Permute",lu(E_);class C_ extends hb{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=nb(e);return tl(yp(n,this.maskValue),-1)}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e),r=tl(yp(n,this.maskValue),-1,!0);return Su(n,Ls(r,n.dtype))}))}}C_.className="Masking",lu(C_);class A_ extends hb{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Eg(e.inputLength))}this.inputDim=e.inputDim,Hg(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Hg(this.outputDim,"outputDim"),this.embeddingsInitializer=Qy(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Fv(e.embeddingsRegularizer),this.activityRegularizer=Fv(e.activityRegularizer),this.embeddingsConstraint=Lb(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 ho((()=>this.maskZero?(e=nb(e),yp(e,Tu(e))):null))}computeOutputShape(e){if(e=rb(e),null==this.inputLength)return[...e,this.outputDim];const t=Eg(this.inputLength);if(t.length!==e.length-1)throw new wg(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r{this.invokeCallHook(e,t);let n=nb(e);"int32"!==n.dtype&&(n=gy(n,"int32"));const r=ky(this.embeddings.read(),Ml(n,[n.size]));return Ml(r,rb(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Zy(this.embeddingsInitializer),embeddingsRegularizer:Ov(this.embeddingsRegularizer),activityRegularizer:Ov(this.activityRegularizer),embeddingsConstraint:Cb(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}A_.className="Embedding",lu(A_);class L_ extends hb{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Mg}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length1)throw new wg(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;te.length));-1===e.indexOf(null)&&1===Yg(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ho((()=>{if(this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=dy(n);for(let n of e){const e=n.rank;for(let t=0;t1){const a=hy(1,e).concat([0]);t.push(Io(r,a)),n=!0}else t.push(r)}let r=this.mergeFunction(t);const a=r.rank;if(n)if(null==a){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=Ml(Io(Ml(r,[-1,t]),[1,0]),n)}else if(a>1){const e=[a-1].concat(hy(0,a-1));r=Io(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==e[0]?null:e[0].slice(1);for(let n=1;n{if(null==t)return null;if(!Array.isArray(t))throw new wg("`mask` should be an Array");if(!Array.isArray(e))throw new wg("`inputs` should be an Array");if(t.length!==e.length)throw new wg(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:Ic(e,0))))[0];for(let e=1;e{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new wg("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ho((()=>_y(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new wg("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(const e of t.slice(1)){if(null==n[r]||null==e[r]){n[r]=null;break}n[r]+=e[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new wg("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new wg("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new wg(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ho((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const r=[];for(let n=0;n"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Mg("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new wg(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new wg(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>B_(t,e[n].shape.length))):[B_(this.axes,n.shape.length),B_(this.axes,r.shape.length)],this.normalize&&(n=px(n,t[0]),r=px(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Mg("batchDot is not implemented for tensors of 4D or higher rank yet");if(se(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),se(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"==typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new Mg("batchDot is not implemented for complex64-type Tensors yet.");const r=e.shape.length,a=t.shape.length;null==n&&(n=[r-1,a-2]);const i=n;return ho((()=>{let n,s;if(r>a){n=r-a;const e=[];for(let t=0;tr){n=a-r;const t=[];for(let e=0;e0){let e;e=r>a?r+a-3:r-1;const t=[];for(let r=e;r"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Mg("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);const a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}P_.className="Dot",lu(P_);class H_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e);return Cy((()=>wu(Ny(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}H_.className="GaussianNoise",lu(H_);class j_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e);return this.rate>0&&this.rate<1?Cy((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return Su(n,Ny(n.shape,1,e))}),(()=>n),t.training||!1):n}))}}j_.className="GaussianDropout",lu(j_);class U_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||nb(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ho((()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),r=()=>{const t=nb(e),r=-1.7580993408473766;let a=Rc(Fp(n),this.rate);a=gy(a,"float32");const i=((1-this.rate)*(1+this.rate*r**2))**-.5,s=-i*r*this.rate,o=wu(Su(t,a),Su(wu(a,-1),r));return wu(Su(o,i),s)};return Cy(r,(()=>nb(e)),t.training||!1)}return e}))}}function q_(e,t,n,r,a,i=.001){let s;if(2===e.rank)s=Ll(e,t,n,r,a,i);else if(3===e.rank)s=$l(e,t,n,r,a,i);else{if(4!==e.rank)throw new Mg(`batchNormalization is not implemented for array of rank ${e.rank} yet`);s=Ol(e,t,n,r,a,i)}return s}U_.className="AlphaDropout",lu(U_);class W_ extends hb{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Qy(e.betaInitializer||"zeros"),this.gammaInitializer=Qy(e.gammaInitializer||"ones"),this.movingMeanInitializer=Qy(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Qy(e.movingVarianceInitializer||"ones"),this.betaConstraint=Lb(e.betaConstraint),this.gammaConstraint=Lb(e.gammaConstraint),this.betaRegularizer=Fv(e.betaRegularizer),this.gammaRegularizer=Fv(e.gammaRegularizer)}build(e){e=rb(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new wg(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new ub({ndim:e.length,axes:{[t]:n}})];const r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ho((()=>{const n=null!=t.training&&t.training,r=nb(e),a=r.shape,i=a.length,s=hy(0,i),o=this.axis>=0?this.axis:this.axis+i;s.splice(o,1);const u=kg(1,i);u[o]=a[o];const l=s.slice();l.sort();const c=!de(l,hy(0,i).slice(0,i-1));if(!n)return(()=>{if(c){const e=Ml(this.movingMean.read(),u),t=Ml(this.movingVariance.read(),u),n=this.center?Ml(this.beta.read(),u):null,a=this.scale?Ml(this.gamma.read(),u):null;return q_(r,e,t,n,a,this.epsilon)}return q_(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[p,d,h]=function(e,t,n,r,a=.001){return de(r.slice().sort(),hy(0,e.rank-1))?function(e,t,n,r,a=.001){return ho((()=>{const i=fp(e,r),s=i.mean,o=i.variance;return[q_(e,s,o,n,t,a),s,o]}))}(e,t,n,r,a):function(e,t,n,r,a=.001){return ho((()=>{const i=fp(e,r),s=i.mean,o=i.variance,u=[];for(const t of hy(0,e.rank))-1!==r.indexOf(t)?u.push(1):u.push(e.shape[t]);const l=Ml(s,u),c=Ml(o,u),p=null==t?null:Ml(t,u),d=null==n?null:Ml(n,u);return[q_(e,l,c,d,p,a),s,o]}))}(e,t,n,r,a)}(r,this.gamma.read(),this.beta.read(),s,this.epsilon),f=(e,t,n)=>{ho((()=>{const r=1-n,a=e.read(),i=Su(Hu(a,t),r);e.write(Hu(a,i))}))};return(()=>{f(this.movingMean,d,this.momentum),f(this.movingVariance,h,this.momentum)})(),p}))}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Zy(this.betaInitializer),gammaInitializer:Zy(this.gammaInitializer),movingMeanInitializer:Zy(this.movingMeanInitializer),movingVarianceInitializer:Zy(this.movingVarianceInitializer),betaRegularizer:Ov(this.betaRegularizer),gammaRegularizer:Ov(this.gammaRegularizer),betaConstraint:Cb(this.betaConstraint),gammaConstraint:Cb(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}W_.className="BatchNormalization",lu(W_);class V_ extends hb{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Qy(e.betaInitializer||"zeros"),this.gammaInitializer=Qy(e.gammaInitializer||"ones"),this.betaRegularizer=Fv(e.betaRegularizer),this.gammaRegularizer=Fv(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=rb(e)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let e=0;e=t)throw new Error(`Invalid axis: ${e}`);if(this.axis.length!==Yg(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,!0):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,!0):this.beta=null,this.built=!0}call(e,t){const n=nb(e),r=n.shape,a=r.length;return ho((()=>{let{mean:e,variance:t}=fp(n,this.axis,!0);const i=kg(1,a);for(const e of this.axis)i[e]=r[e];const s=e=>null!=e&&e.shape.length!==a?Ml(e,i):e;let o=this.scale?s(this.gamma.read()):null,u=this.center?s(this.beta.read()):null;const l=[],c=[];for(let e=0;e=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return ho((()=>{return t=nb(e),n=this.padding,r=this.dataFormat,ho((()=>{if(4!==t.rank)throw new wg(`temporalPadding expects input tensor to be 4-D, but received a ${t.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new wg("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r="channelsLast"),"channelsLast"!==r&&"channelsFirst"!==r)throw new wg(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let e;return e="channelsFirst"===r?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],vp(t,e)}));var t,n,r}))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}function K_(e,t,n,r,a,i){return ho((()=>{let s;ty(a),ry(i),ny(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==i&&(i="max"),e=Vv(e,a);const o="same"===r?"same":"valid";return s="max"===i?ap(e,t,n,o):Nl(e,t,n,o),"channelsFirst"===a&&(s=Io(s,[0,3,1,2])),s}))}function J_(e,t,n,r,a,i){return ho((()=>{let s;ty(a),ry(i),ny(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==i&&(i="max"),e=Gv(e,a);const o="same"===r?"same":"valid";return s="max"===i?ip(e,t,n,o):Sl(e,t,n,o),"channelsFirst"===a&&(s=Io(s,[0,4,1,2,3])),s}))}G_.className="ZeroPadding2D",lu(G_);class X_ extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new wg(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if(Hg(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new wg(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,ny(this.padding),this.inputSpec=[new ub({ndim:3})]}computeOutputShape(e){const t=qv((e=rb(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ho((()=>{this.invokeCallHook(e,t),e=yy(nb(e),2);const n=this.poolingFunction(nb(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dd(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Z_ extends X_{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"max")}}Z_.className="MaxPooling1D",lu(Z_);class Q_ extends X_{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"avg")}}Q_.className="AveragePooling1D",lu(Q_);class ew extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new wg(`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];Hg(this.poolSize,"poolSize"),Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),ny(this.padding),this.inputSpec=[new ub({ndim:4})]}computeOutputShape(e){e=rb(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=qv(t,this.poolSize[0],this.padding,this.strides[0]),n=qv(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ho((()=>(this.invokeCallHook(e,t),this.poolingFunction(nb(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class tw extends ew{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"max")}}tw.className="MaxPooling2D",lu(tw);class nw extends ew{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"avg")}}nw.className="AveragePooling2D",lu(nw);class rw extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new wg(`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];Hg(this.poolSize,"poolSize"),Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),ny(this.padding),this.inputSpec=[new ub({ndim:5})]}computeOutputShape(e){e=rb(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=qv(t,this.poolSize[0],this.padding,this.strides[0]),n=qv(n,this.poolSize[1],this.padding,this.strides[1]),r=qv(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return ho((()=>(this.invokeCallHook(e,t),this.poolingFunction(nb(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class aw extends rw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),J_(e,t,n,r,a,"max")}}aw.className="MaxPooling3D",lu(aw);class iw extends rw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),J_(e,t,n,r,a,"avg")}}iw.className="AveragePooling3D",lu(iw);class sw extends hb{constructor(e){super(e),this.inputSpec=[new ub({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Mg}}class ow extends sw{constructor(e){super(e||{})}call(e,t){return ho((()=>{const t=nb(e);return op(t,1)}))}}ow.className="GlobalAveragePooling1D",lu(ow);class uw extends sw{constructor(e){super(e||{})}call(e,t){return ho((()=>{const t=nb(e);return wc(t,1)}))}}uw.className="GlobalMaxPooling1D",lu(uw);class lw extends hb{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),this.inputSpec=[new ub({ndim:4})]}computeOutputShape(e){return"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Mg}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class cw extends lw{call(e,t){return ho((()=>{const t=nb(e);return"channelsLast"===this.dataFormat?op(t,[1,2]):op(t,[2,3])}))}}cw.className="GlobalAveragePooling2D",lu(cw);class pw extends lw{call(e,t){return ho((()=>{const t=nb(e);return"channelsLast"===this.dataFormat?wc(t,[1,2]):wc(t,[2,3])}))}}pw.className="GlobalMaxPooling2D",lu(pw);class dw extends hb{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const r=cx(t.layer,n);delete t.layer;const a={layer:r};return Object.assign(a,t),new e(a)}}class hw extends dw{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=rb(e)).length<3)throw new wg(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=rb(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return ho((()=>l_(((e,n)=>[nb(this.layer.call(e,t)),[]]),e=nb(e),[],!1,null,null,!1,!0)[1]))}}hw.className="TimeDistributed",lu(hw);class fw extends dw{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=cx(n),t.goBackwards=!0!==t.goBackwards;const r={};var a;if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=cx(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,a=this.mergeMode,Bg(Qg,"BidirectionalMergeMode",a),e.weights)throw new Mg("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t,n,r,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),this.returnState?(r=a.slice(1),t=a[0]):t=a[0],"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):Ig(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=u_(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);const i=[],s=[];if(null!=n){const e=n.length;if(e%2>0)throw new wg("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,i.push(...n);const r=n.map((e=>new ub({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),s.push(...r)}if(null!=r)throw new Mg("Support for constants in Bidirectional layers is not implemented yet.");const o=i[0]instanceof lb;for(const e of i)if(e instanceof lb!==o)throw new wg("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){const n=[e].concat(i),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return ho((()=>{const n=t.initialState;let r,a,i,s;if(null==n)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{const i=n.slice(0,n.length/2),s=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:s}))}return this.returnState&&(Array.isArray(r)&&(i=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Hp(a,1)),"concat"===this.mergeMode?s=_y([r,a]):"sum"===this.mergeMode?s=wu(r,a):"ave"===this.mergeMode?s=Su(.5,wu(r,a)):"mul"===this.mergeMode?s=Su(r,a):null==this.mergeMode&&(s=[r,a]),this.returnState?null==this.mergeMode?s.concat(i):[s].concat(i):s}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){iy(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),iy(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=cx(t.layer);if(delete t.layer,null!=t.numConstants)throw new Mg("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const r=t;return r.layer=n,new e(r)}}function mw(e){return new mb(e)}function gw(e){return new Pv(e)}function yw(e){return new Yv(e)}function bw(e){return new zv(e)}function xw(e){return new Bv(e)}function vw(e){return new jv(e)}function _w(e){return new Hv(e)}function ww(e){return new a_(e)}function Mw(e){return new Zv(e)}function Nw(e){return new e_(e)}function Sw(e){return new 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Mg("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,-1===["auto","min","max"].indexOf(this.mode)&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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is",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],IN=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}],EN=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],CN=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}],AN=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"t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r=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const a=0===n?0:t.size/n,i=[];ho((()=>{t=Ml(t,[1,n,a]);for(let n=0;n{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);KN(t,e.shape,"TensorList shape mismatch: "),mo(e)})),this.idTensor=Ru(0),this.maxNumElements=r,mo(this.idTensor)}get id(){return this.idTensor.id}copy(){return new eS([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.id)||t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new 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"),mo(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);KN(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());const r=XN(this.elementShape,this.tensors,n);return 0===e.length?Fi([],[0].concat(r)):ho((()=>{const t=e.map((e=>Ml(this.tensors[e],r)));return hd(t,0)}))}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);KN(this.elementShape,t,"TensorList shape mismatch: ");const n=XN(this.elementShape,this.tensors,t);return 0===this.size()?Fi([],[0].concat(n)):ho((()=>{const e=this.tensors.map((e=>Ml(e,n)));return kl(e,0)}))}}function tS(e,t,n){const[r,a]=oN("fusedOps",e,t,n),i="biasadd"===r,s=!i,o="prelu"===a,u="fusedbatchnorm"===r,l=oN("numArgs",e,t,n);if(i){if(o&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and 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a((()=>((e,t,n,r=j)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(oN("x",e,t,n))];case"Acos":return[r.acos(oN("x",e,t,n))];case"Acosh":return[r.acosh(oN("x",e,t,n))];case"Asin":return[r.asin(oN("x",e,t,n))];case"Asinh":return[r.asinh(oN("x",e,t,n))];case"Atan":return[r.atan(oN("x",e,t,n))];case"Atan2":return[r.atan2(oN("x",e,t,n),oN("y",e,t,n))];case"Atanh":return[r.atanh(oN("x",e,t,n))];case"Ceil":return[r.ceil(oN("x",e,t,n))];case"Complex":return[r.complex(oN("real",e,t,n),oN("imag",e,t,n))];case"Cos":return[r.cos(oN("x",e,t,n))];case"Cosh":return[r.cosh(oN("x",e,t,n))];case"Elu":return[r.elu(oN("x",e,t,n))];case"Erf":return[r.erf(oN("x",e,t,n))];case"Exp":return[r.exp(oN("x",e,t,n))];case"Expm1":return[r.expm1(oN("x",e,t,n))];case"Floor":return[r.floor(oN("x",e,t,n))];case"Log":return[r.log(oN("x",e,t,n))];case"Log1p":return[r.log1p(oN("x",e,t,n))];case"Imag":return[r.imag(oN("x",e,t,n))];case"Neg":return[r.neg(oN("x",e,t,n))];case"Reciprocal":return[r.reciprocal(oN("x",e,t,n))];case"Real":return[r.real(oN("x",e,t,n))];case"Relu":return[r.relu(oN("x",e,t,n))];case"Round":return[r.round(oN("x",e,t,n))];case"Selu":return[r.selu(oN("x",e,t,n))];case"Sigmoid":return[r.sigmoid(oN("x",e,t,n))];case"Sin":return[r.sin(oN("x",e,t,n))];case"Sign":return[r.sign(oN("x",e,t,n))];case"Sinh":return[r.sinh(oN("x",e,t,n))];case"Softplus":return[r.softplus(oN("x",e,t,n))];case"Sqrt":return[r.sqrt(oN("x",e,t,n))];case"Square":return[r.square(oN("x",e,t,n))];case"Tanh":return[r.tanh(oN("x",e,t,n))];case"Tan":return[r.tan(oN("x",e,t,n))];case"ClipByValue":return[r.clipByValue(oN("x",e,t,n),oN("clipValueMin",e,t,n),oN("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(oN("x",e,t,n))];case"Rsqrt":return[r.rsqrt(uN(e.inputNames[0],t,n))];case"Prod":return[r.prod(oN("x",e,t,n),oN("axes",e,t,n))];case"LeakyRelu":return[r.leakyRelu(oN("x",e,t,n),oN("alpha",e,t,n))];case"Prelu":return[r.prelu(oN("x",e,t,n),oN("alpha",e,t,n))];case"IsNan":return[r.isNaN(uN(e.inputNames[0],t,n))];default:throw 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a.image.nonMaxSuppressionAsync(r,i,s,o,u)]}case"Where":{const r=a.cast(oN("condition",e,t,n),"bool"),i=[await a.whereAsync(r)];return r.dispose(),i}case"ListDiff":return a.setdiff1dAsync(oN("x",e,t,n),oN("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"evaluation":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"LowerBound":{const a=oN("sortedSequence",e,t,n),i=oN("values",e,t,n);return[r.lowerBound(a,i)]}case"TopKV2":{const a=oN("x",e,t,n),i=oN("k",e,t,n),s=oN("sorted",e,t,n),o=r.topk(a,i,s);return[o.values,o.indices]}case"UpperBound":{const a=oN("sortedSequence",e,t,n),i=oN("values",e,t,n);return[r.upperBound(a,i)]}case"Unique":{const a=oN("x",e,t,n),i=r.unique(a);return[i.values,i.indices]}case"UniqueV2":{const a=oN("x",e,t,n),i=oN("axis",e,t,n),s=r.unique(a,i);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"ResizeBilinear":{const a=oN("images",e,t,n),i=oN("size",e,t,n),s=oN("alignCorners",e,t,n),o=oN("halfPixelCenters",e,t,n);return[r.image.resizeBilinear(a,[i[0],i[1]],s,o)]}case"ResizeNearestNeighbor":{const a=oN("images",e,t,n),i=oN("size",e,t,n),s=oN("alignCorners",e,t,n),o=oN("halfPixelCenters",e,t,n);return[r.image.resizeNearestNeighbor(a,[i[0],i[1]],s,o)]}case"CropAndResize":{const a=oN("image",e,t,n),i=oN("boxes",e,t,n),s=oN("boxInd",e,t,n),o=oN("cropSize",e,t,n),u=oN("method",e,t,n),l=oN("extrapolationValue",e,t,n);return[r.image.cropAndResize(a,i,s,o,u,l)]}case"ImageProjectiveTransformV3":{const a=oN("images",e,t,n),i=oN("transforms",e,t,n),s=oN("outputShape",e,t,n),o=oN("fillValue",e,t,n),u=oN("interpolation",e,t,n),l=oN("fillMode",e,t,n);return[r.image.transform(a,i,u.toLowerCase(),l.toLowerCase(),o,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const a=oN("default",e,t,n);return[uN(e.name,t,n)||a];case"Placeholder":return[uN(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":case"Snapshot":return[hN(oN("x",e,t,n))];case"IdentityN":return oN("x",e,t,n).map((e=>hN(e)));case"Shape":return[r.tensor1d(oN("x",e,t,n).shape,"int32")];case"ShapeN":return oN("x",e,t,n).map((e=>r.tensor1d(e.shape)));case"Size":return[r.scalar(oN("x",e,t,n).size,"int32")];case"Rank":return[r.scalar(oN("x",e,t,n).rank,"int32")];case"NoOp":return[r.scalar(1)];case"Print":const i=oN("x",e,t,n),s=oN("data",e,t,n),o=oN("message",e,t,n),u=oN("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let e=0;e((e,t,n,r=j)=>{switch(e.op){case"Equal":return[r.equal(oN("a",e,t,n),oN("b",e,t,n))];case"NotEqual":return[r.notEqual(oN("a",e,t,n),oN("b",e,t,n))];case"Greater":return[r.greater(oN("a",e,t,n),oN("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(oN("a",e,t,n),oN("b",e,t,n))];case"Less":return[r.less(oN("a",e,t,n),oN("b",e,t,n))];case"LessEqual":return[r.lessEqual(oN("a",e,t,n),oN("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(oN("a",e,t,n),oN("b",e,t,n))];case"LogicalNot":return[r.logicalNot(oN("a",e,t,n))];case"LogicalOr":return[r.logicalOr(oN("a",e,t,n),oN("b",e,t,n))];case"Select":case"SelectV2":return[r.where(oN("condition",e,t,n),oN("a",e,t,n),oN("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(oN("a",e,t,n),oN("b",e,t,n),oN("transposeA",e,t,n),oN("transposeB",e,t,n))];case"Einsum":return[r.einsum(oN("equation",e,t,n),...oN("tensors",e,t,n))];case"Transpose":return[r.transpose(oN("x",e,t,n),oN("perm",e,t,n))];case"_FusedMatMul":const[a,i]=oN("fusedOps",e,t,n),s="biasadd"===a,o="prelu"===i,u=oN("numArgs",e,t,n),l=oN("leakyreluAlpha",e,t,n);if(s){if(o&&2!==u)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&1!==u)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,p]=oN("args",e,t,n);return[r.fused.matMul({a:oN("a",e,t,n),b:oN("b",e,t,n),transposeA:oN("transposeA",e,t,n),transposeB:oN("transposeB",e,t,n),bias:c,activation:i,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(oN("x",e,t,n),oN("axis",e,t,n),oN("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(oN("x",e,t,n),oN("mean",e,t,n),oN("variance",e,t,n),oN("offset",e,t,n),oN("scale",e,t,n),oN("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(oN("x",e,t,n),oN("radius",e,t,n),oN("bias",e,t,n),oN("alpha",e,t,n),oN("beta",e,t,n))];case"Softmax":return[r.softmax(oN("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(oN("x",e,t,n))];case"SparseToDense":return[r.sparseToDense(oN("sparseIndices",e,t,n),oN("outputShape",e,t,n),oN("sparseValues",e,t,n),oN("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Max":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.max(oN("x",e,t,n),a,i)]}case"Mean":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.mean(oN("x",e,t,n),a,i)]}case"Min":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.min(oN("x",e,t,n),a,i)]}case"Sum":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.sum(oN("x",e,t,n),a,i)]}case"All":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.all(oN("x",e,t,n),a,i)]}case"Any":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.any(oN("x",e,t,n),a,i)]}case"ArgMax":{const a=oN("axis",e,t,n);return[r.argMax(oN("x",e,t,n),a)]}case"ArgMin":{const a=oN("axis",e,t,n);return[r.argMin(oN("x",e,t,n),a)]}case"Prod":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.prod(oN("x",e,t,n),a,i)]}case"Cumprod":{const a=oN("axis",e,t,n),i=oN("exclusive",e,t,n),s=oN("reverse",e,t,n);return[r.cumprod(oN("x",e,t,n),a,i,s)]}case"Cumsum":{const a=oN("axis",e,t,n),i=oN("exclusive",e,t,n),s=oN("reverse",e,t,n);return[r.cumsum(oN("x",e,t,n),a,i,s)]}case"Bincount":const a=oN("x",e,t,n),i=oN("weights",e,t,n),s=oN("size",e,t,n);return[r.bincount(a,i,s)];case"DenseBincount":{const a=oN("x",e,t,n),i=oN("weights",e,t,n),s=oN("size",e,t,n),o=oN("binaryOutput",e,t,n);return[r.denseBincount(a,i,s,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"ConcatV2":case"Concat":{const a=oN("n",e,t,n),i=oN("axis",e,t,n);let s=oN("tensors",e,t,n);return s=s.slice(0,a),[r.concat(s,i)]}case"Gather":{const a=oN("x",e,t,n),i=oN("indices",e,t,n);return[r.gather(a,r.cast(i,"int32"),0)]}case"GatherV2":{const a=oN("axis",e,t,n),i=oN("batchDims",e,t,n),s=oN("x",e,t,n),o=oN("indices",e,t,n);return[r.gather(s,r.cast(o,"int32"),a,i)]}case"Reverse":{const a=oN("dims",e,t,n),i=[];for(let e=0;e{const a=oN("axis",e,t,n),i=oN("tensors",e,t,n),s=i[0].shape,o=r.squeeze(i[0]).shape,u=i.map((e=>{const t=de(e.shape,s);if(!t&&!de(r.squeeze(e).shape,o))throw new Error("the input tensors shape does not match");return t?e:r.reshape(e,s)}));return[r.stack(u,a)]}));case"Unpack":{const a=oN("axis",e,t,n),i=oN("tensor",e,t,n);return r.unstack(i,a)}case"Tile":{const a=oN("reps",e,t,n);return[r.tile(oN("x",e,t,n),a)]}case"Split":case"SplitV":{const a=oN("axis",e,t,n),i=oN("numOrSizeSplits",e,t,n),s=oN("x",e,t,n);return r.split(s,i,a)}case"ScatterNd":{const a=oN("indices",e,t,n),i=oN("values",e,t,n),s=oN("shape",e,t,n);return[r.scatterND(a,i,s)]}case"GatherNd":{const a=oN("x",e,t,n),i=oN("indices",e,t,n);return[r.gatherND(a,i)]}case"SparseToDense":{const a=oN("sparseIndices",e,t,n),i=oN("outputShape",e,t,n),s=oN("sparseValues",e,t,n),o=oN("defaultValue",e,t,n);return[r.sparseToDense(a,s,i,s.dtype===o.dtype?o:r.cast(o,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:a,outputValues:i,emptyRowIndicator:s,reverseIndexMap:o}=r.sparse.sparseFillEmptyRows(oN("indices",e,t,n),oN("values",e,t,n),oN("denseShape",e,t,n),oN("defaultValue",e,t,n));return[a,i,s,o]}case"SparseReshape":{const{outputIndices:a,outputShape:i}=r.sparse.sparseReshape(oN("inputIndices",e,t,n),oN("inputShape",e,t,n),oN("newShape",e,t,n));return[a,i]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(oN("data",e,t,n),oN("indices",e,t,n),oN("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(oN("data",e,t,n),oN("indices",e,t,n),oN("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"FFT":return[r.fft(oN("x",e,t,n))];case"IFFT":return[r.ifft(oN("x",e,t,n))];case"RFFT":return[r.rfft(oN("x",e,t,n))];case"IRFFT":return[r.irfft(oN("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"StringNGrams":{const{nGrams:a,nGramsSplits:i}=r.string.stringNGrams(oN("data",e,t,n),oN("dataSplits",e,t,n),oN("separator",e,t,n),oN("nGramWidths",e,t,n),oN("leftPad",e,t,n),oN("rightPad",e,t,n),oN("padWidth",e,t,n),oN("preserveShortSequences",e,t,n));return[a,i]}case"StringSplit":{const{indices:a,values:i,shape:s}=r.string.stringSplit(oN("input",e,t,n),oN("delimiter",e,t,n),oN("skipEmpty",e,t,n));return[a,i,s]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(oN("input",e,t,n),oN("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Cast":return[r.cast(oN("x",e,t,n),oN("dtype",e,t,n))];case"ExpandDims":{const a=oN("axis",e,t,n);return[r.expandDims(oN("x",e,t,n),a)]}case"Squeeze":{const a=oN("axis",e,t,n);return[r.squeeze(oN("x",e,t,n),a)]}case"Reshape":return[r.reshape(oN("x",e,t,n),oN("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(oN("x",e,t,n),oN("padding",e,t,n),oN("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(oN("x",e,t,n),oN("padding",e,t,n),oN("constantValue",e,t,n))];case"SpaceToBatchND":{const a=oN("blockShape",e,t,n),i=oN("paddings",e,t,n);return[r.spaceToBatchND(oN("x",e,t,n),a,i)]}case"BatchToSpaceND":{const a=oN("blockShape",e,t,n),i=oN("crops",e,t,n);return[r.batchToSpaceND(oN("x",e,t,n),a,i)]}case"DepthToSpace":{const a=oN("blockSize",e,t,n),i=oN("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(oN("x",e,t,n),a,i)]}case"BroadcastTo":return[r.broadcastTo(oN("x",e,t,n),oN("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(oN("s0",e,t,n),oN("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const a=oN("keyDType",e,t,n),i=oN("valueDType",e,t,n),s=new rS(a,i);return r.addHashTable(e.name,s),[s.handle]}case"LookupTableImport":case"LookupTableImportV2":{const a=oN("tableHandle",e,t,n,r),i=oN("keys",e,t,n),s=oN("values",e,t,n),o=r.getHashTableById(a.id);return[await o.import(i,s)]}case"LookupTableFind":case"LookupTableFindV2":{const a=oN("tableHandle",e,t,n,r),i=oN("keys",e,t,n),s=oN("defaultValue",e,t,n),o=r.getHashTableById(a.id);return[await o.find(i,s)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=oN("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);case"custom":const i=iN(e.op);if(i&&i.customExecutor)return i.customExecutor(new GN(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return qe(i)?i.then((e=>[].concat(e))):[].concat(i)}class iS{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.iterationId}`)).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function sS(e,t,n,r){const a=new Set,i=[];let s=null,o=null;const u=new Set,l=Object.keys(e).map((e=>pN(e)[0]));let c=[];null!=r&&(c=r.map((e=>pN(e.name)[0])));const p=[...t];for(;p.length>0;){const e=p.pop();(cS(e)||pS(e)||dS(e))&&null==s&&(s=e,o=s.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&-1===l.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),p.push(e))})):i.push(e.name))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:i,dynamicNode:s,syncInputs:o}}const oS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],uS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],lS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function cS(e){return oS.indexOf(e.op)>=0}function pS(e){return uS.indexOf(e.op)>=0}function dS(e){return lS.indexOf(e.op)>=0}class hS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new hS(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const 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){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=sS(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:i}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}return function(e,t,n){const{usedNodes:r,inputs:a}=n,i=[],s=Object.keys(a).map((e=>pN(e)[0])).map((t=>e.nodes[t])),o=e.initNodes;s.forEach((e=>{r.has(e.name)&&i.push(e)})),e.weights.forEach((e=>{r.has(e.name)&&i.push(e)})),null!=o&&o.forEach((e=>{r.has(e.name)&&i.push(e)}));const u=new Set,l=[];for(;i.length>0;){const e=i.pop();u.add(e.name),t[e.name]||l.push(e),e.children.forEach((e=>{!u.has(e.name)&&r.has(e.name)&&e.inputs.every((e=>u.has(e.name)))&&i.push(e)}))}return l}(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map((e=>this.graph.nodes[pN(e)[0]])),a=t.map((e=>pN(e)[0]));let i=a.map((e=>this.graph.nodes[e]));this.resetIntermediateTensors(),0===i.length&&(i=this._outputs);const s=this.getCompilationKey(r,i);let o=this.compiledMap.get(s);null==o&&(o=this.compile(e,i),this.compiledMap.set(s,o));const u={},l={};return ho((()=>{const n=new iS(this.weightMap,u,l,this.functionExecutorMap),r=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,a]=pN(t),i=[];i[a]=e[t],r[n]=i}));const i=this.getFrozenTensorIds(r),s={};for(let e=0;euN(e,r,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,i,s){"control"!==t.category&&-1===i.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(s[e.id]=(s[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const i=function(e,t,n){return t[cN(e,n.currentContextId)]}(e.name,n,r);null!=i&&i.forEach((e=>{if(e&&!e.kept&&!a.has(e.id)){const n=s[e.id];if(1===n){if(this.keepTensorForDebug){const[n,a]=lN(t.name,r);this.intermediateTensors[n]||(this.intermediateTensors[n]=[]),this.intermediateTensors[n][a]=e}else e.dispose();delete s[e.id]}else null!=n&&s[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.intermediateTensors&&(Object.keys(this.intermediateTensors).forEach((e=>this.intermediateTensors[e].forEach((e=>e.dispose())))),this.disposeTensorsMap())}disposeTensorsMap(){this.tensorsMap&&Object.keys(this.tensorsMap).forEach((e=>{this.tensorsMap[e].forEach((e=>{!e||e.kept||e.isDisposed||this.keepIds.has(e.id)||e.dispose()}))}))}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(const e in this.intermediateTensors)this.intermediateTensors[e].forEach((e=>e.dispose())),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Ge().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){console.warn(e.message)}this.resetIntermediateTensors();const i=new iS(this.weightMap,r,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,i,t,n);const s=t.map((e=>uN(e,this.tensorsMap,i))),o=s.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id));return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),null==this.parent&&i.dispose(this.keepIds),s}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const a=Object.keys(e),i=a.map((e=>this.graph.nodes[pN(e)[0]])),s=n.map((e=>pN(e)[0]));let o=s.map((e=>this.graph.nodes[e]));0===o.length&&(o=this._outputs);const{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=sS(e,o,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),h=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=pN(t),a=[];a[r]=e[t],h[n]=a}));const f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){const e=this.processStack(i,d,t,h,g,m,s,f,u);await Promise.all(e)}null!=c||r||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=o.filter((e=>!cS(e)&&!uN(e.name,h,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${l}]. ${e}`)}return h}processStack(e,t,n,r,a,i,s,o,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&oN("isConstant",e.node,r,n)&&([c]=lN(e.node.name,n)),null==r[e.node.name]){const p=aS(e.node,r,n,this._resourceManager);c||([c]=lN(e.node.name,n));const d=n.currentContext;qe(p)?l.push(p.then((l=>(r[c]=l,n.currentContext=d,this.checkTensorForDisposal(c,e.node,r,n,i,s,o),this.processChildNodes(e.node,t,n,r,a,u),l)))):(r[c]=p,this.checkTensorForDisposal(c,e.node,r,n,i,s,o),this.processChildNodes(e.node,t,n,r,a,u))}else this.processChildNodes(e.node,t,n,r,a,u)}return l}processChildNodes(e,t,n,r,a,i){e.children.forEach((e=>{const[s]=lN(e.name,n);!a[s]&&i.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!uN(e,r,n)))&&(a[s]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!uN(e,r,n)))&&(a[s]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[r]=pN(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));se(t,(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&se(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]?t[this._signature.inputs[n].name]=e[n]:t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=pN(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]?this._signature.outputs[e].name:e),{})}checkOutputs(e){e.forEach((e=>{const[t]=pN(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class fS{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class mS{constructor(e,t={},n=i){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new fS}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return qe(e)?e.then((e=>this.loadSync(e))):this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(n=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new hS(LN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=LN.Instance.transformGraph(e.modelInitializer);this.initializer=new hS(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)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){const n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){const e={};return(n instanceof ti?[n]:n).forEach(((t,n)=>e[this.structuredOutputKeys[n]]=t)),e}return n}normalizeInputs(e){if(!(e instanceof ti||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function gS(e,t={},n=i){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&"string"==typeof e&&(e=function(e){return e.endsWith("/")||(e+="/"),`${e}model.json?tfjs-format=file`}(e));const r=new mS(e,t,n);return await r.load(),r}function yS(e){if(null==e)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");if(!e.load)throw new Error(`modelUrl IO Handler ${e} has no load function`);const t=new mS(e);return t.load(),t}const bS="3.19.0";function xS(e,t,n=new Map,r=new Set){if(null==e)return null;if("function"==typeof Blob&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse){if(NS(e)){const a=Array.isArray(e)?[]:{};r.add(e);for(const i in e){const s=xS(e[i],t,n,r);a[i]=s}return r.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,a.value),a.value}function vS(e,t=wS){return _S(e,t)}function _S(e,t,n=new Set){const r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse){if(NS(r)){const a=Array.isArray(r)?[]:{};n.add(r);for(const i in r){const r=_S(e.map((e=>e[i])),t,n);a[i]=r}return n.delete(r),a}throw new Error(`Can't recurse into non-iterable type: ${r}`)}return a.value}function wS(e){return null===e?null:NS(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function MS(e,t){const n=new Map;xS(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(qe(t)){const r=await t;n.set(e,r)}}return xS(e,t,n)}function NS(e){let t=!1;if(Ge().get("IS_BROWSER"))t=e instanceof TextDecoder;else{const{StringDecoder:r}=n(4977);t=e instanceof r}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof ti)&&!(e instanceof Promise)&&!t)}function SS(e){return e instanceof ti?{value:e.clone(),recurse:!1}:NS(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class kS{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)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 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class DS extends kS{constructor(){super(DS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=2*this.capacity,t=new Array(e),n=this.length();for(let e=0;e!0===e))}rowMajorBatch(e,t=!0){return new RS(this,e,t)}columnMajorBatch(e,t=!0,n=wS){return this.rowMajorBatch(e,t).map((e=>vS(e,n)))}concatenate(e,t){return new jS(TS([this,e]),t)}take(e){return e<0||null==e?this:new OS(this,e)}skip(e){return e<0||null==e?this:new $S(this,e)}prefetch(e){return new WS(this,e)}shuffle(e,t){return new VS(this,e,t)}serial(){return new LS(this)}}class CS extends ES{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:(t=e,function(e,t){return xS(e,t)}(t,SS)),done:!1};var t}}class AS extends ES{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class LS extends ES{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class $S extends ES{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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()}}class RS extends ES{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class FS extends ES{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;fo(e.value)}}}class YS extends ES{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mi(e.value),n=this.transform(e.value),r=mi(n);for(const e of t)fi(e,r)||e.dispose();return{value:n,done:!1}}}class zS extends ES{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}}class BS extends ES{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mi(e.value),n=await this.transform(e.value),r=mi(n);for(const e of t)fi(e,r)||e.dispose();return{value:n,done:!1}}}class PS extends ES{constructor(){super(),this.outputQueue=new DS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class HS extends PS{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=mi(e.value),n=this.transform(e.value),r=mi(n);this.outputQueue.pushAll(n);for(const e of t)fi(e,r)||e.dispose();return!0}}class jS extends ES{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,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var US;!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(US||(US={}));class qS extends ES{constructor(e,t=US.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;const r=await MS(this.iterators,(function(e){return e instanceof ES?{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}:{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case US.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case US.SHORTEST:return{value:null,done:!0};case US.LONGEST:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class WS extends ES{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new kS(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class VS extends WS{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ep.alea(n||za().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}class GS{constructor(){this.size=null}batch(e,t=!0){const n=this;let r;return se(e>0,(()=>`batchSize needs to be positive, but it is\n ${e}`)),r=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),KS((async()=>(await n.iterator()).columnMajorBatch(e,t,ZS)),r)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,KS((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,KS((async()=>(await t.iterator()).filter((t=>ho((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return KS((async()=>(await t.iterator()).map((t=>ho((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return KS((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return KS((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,KS((async()=>{return n=IS((async()=>({value:await t.iterator(),done:!1}))).take(e),new jS(n,r);var n,r}),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const r=this,a=Ep.alea(t||za().toString());return KS((async()=>{let t=a.int32();return n&&(t+=a.int32()),(await r.iterator()).shuffle(e,t.toString())}),this.size)}take(e){const t=this;let n;return n=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,KS((async()=>(await t.iterator()).take(e)),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}function KS(e,t=null){return new class extends GS{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function JS(e){return KS((async()=>TS(e)),e.length)}function XS(e){if(!NS(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;nfunction(e,t=US.FAIL){return new qS(e,t)}(await MS(e,(e=>{if(e instanceof GS)return{value:e.iterator(),recurse:!1};if(NS(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),US.SHORTEST)),t)}function ZS(e){if(null===e)return null;return null==(t=e[0])||null===(n=t)||"object"!=typeof n&&"function"!=typeof n||Array.isArray(t)||"object"==typeof t&&t instanceof ti||De(t)?{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ti?hd(e):Fi(e)}(e),recurse:!1}:{value:null,recurse:!0};var t,n}GS.MAX_BUFFER_SIZE=1e4;class QS extends GS{constructor(e){super(),this.input=e}async iterator(){const e=(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)));return e}}const ek='"',tk=Symbol("out"),nk=Symbol("field"),rk=Symbol("quote"),ak=Symbol("quoteafterquote"),ik=Symbol("quoteinquote");class sk extends GS{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 QS(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(se(null==t.delimiter,(()=>"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(){const 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&&se(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);const t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(se(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(const e of Object.keys(this.columnConfigs))if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").");this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value;return this.parseRow(n,!1)}return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){const t=this.parseRow(e),n={},r={};for(let a=0;a14||!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=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!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(!Ge().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");const t=new ok(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`)}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,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;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((r=>{const a=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&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){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(ce(t));return n.set(e,n.length-e.length),Fi(n,t)}}class uk extends ES{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=yd([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,r=(1-t)/2,a=n+e,i=t+r;this.cropBox=bd([r,n,i,a],[1,4])}else this.cropBox=bd([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Ge().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new uk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&se("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`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=zo(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return ho((()=>{const t=Ic(Ls(e,"float32"),0);let n;n=Uh.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const r=n.shape;return Ml(n,r.slice(1))}))}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class lk{}class ck extends ES{split(e){return new pk(this,e)}}class pk extends ck{constructor(e,t){super(),this.upstream=e,this.impl=new dk(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class dk extends PS{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}}class hk extends ES{decodeUTF8(){return new fk(this)}}class fk extends ck{constructor(e){super(),this.upstream=e,this.impl=new mk(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class mk extends PS{constructor(e){if(super(),this.upstream=e,Ge().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:e}=n(1601);this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t,n;return!e.done&&(t=e.value,n=Ge().get("IS_BROWSER")?this.decoder.decode(t,{stream:!0}):this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0)}}class gk extends hk{constructor(e,t={}){super(),this.file=e,this.options=t,se(e instanceof Uint8Array||!!Ge().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise(((e,t)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{const r=new FileReader;r.onload=n=>{let a=r.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(a)},r.onabort=e=>t(new Error("Aborted")),r.onerror=e=>t(new Error(e.type));const a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}));return{value:await e,done:!1}}}function yk(e){return"string"==typeof e&&"file://"===e.slice(0,7)}class bk extends lk{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(yk(this.input)&&Ge().get("IS_NODE")){const e=n(7792);this.input=e.readFileSync(this.input.slice(7))}return new gk(this.input,this.options)}}class xk extends lk{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return yk(this.url)?new bk(this.url,this.fileOptions).iterator():async function(e,t={},n){let r,a;var i;"string"==typeof e?r=e:(r=e.url,a={method:(i=e).method,headers:i.headers,body:i.body,mode:i.mode,credentials:i.credentials,cache:i.cache,redirect:i.redirect,referrer:i.referrer,integrity:i.integrity});const s=await(n||Ba)(r,a);if(s.ok){const e=new Uint8Array(await s.arrayBuffer());return new gk(e,t)}throw new Error(s.statusText)}(this.url,this.fileOptions)}}function vk(e,t={}){return new sk(new xk(e),t)}function _k(e){const t=IS(e);return KS((async()=>t))}function wk(e){return KS((async()=>{const t=await e();return IS((()=>t.next()))}))}async function Mk(e,t){return uk.create(e,t)}async function Nk(e){return ok.create(e)}const Sk="3.19.0";function kk(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&se("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}const Dk=Id;class Tk extends J{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new K(this,lo())}nextDataId(){return Tk.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Ge().get("IS_NODE")&&ca("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================"));const r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&Ee(n[0])){const a=n.map((e=>Pa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){this.data.get(e).refCount++}decRef(e){this.data.has(e)&&this.data.get(e).refCount--}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);return"complex64"===t?yf(this.readSync(n.real.dataId),this.readSync(n.imag.dataId)):this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>Ha(e)));return As(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return As(e.shape,e.dtype,t)}makeOutput(e,t,n){return lo().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;const{complexTensorInfos:n}=this.data.get(e);null!=n&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){const t=za();return e(),{kernelMs:za()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){kk([e],"where");const t=this.readSync(e.dataId);return Dk(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}function Ik(e,t,n){return({inputs:r,attrs:a,backend:i})=>{const{x:s}=r;if(kk(s,e),"string"===s.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=i,u=o.data.get(s.dataId).values,l=ce(s.shape),c=n||s.dtype,p=Me(c,l);for(let e=0;e{const{x:s}=r;if(kk(s,e),"string"===s.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=i,u=o.data.get(s.dataId).values,l=n||s.dtype,c=t(u,l,a);return o.makeTensorInfo(s.shape,l,c)}}Tk.nextDataId=0,Mo("cpu",(()=>new Tk),1);const Ck=Ik(Jt,(e=>e>=0?e:Math.exp(e)-1)),Ak={kernelName:Jt,backendName:"cpu",kernelFunc:Ck};function Lk(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const $k={kernelName:fn,backendName:"cpu",kernelFunc:Lk};function Ok(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:i}=r;kk([a],"leakyRelu");const s=ce(a.shape),o=n.data.get(a.dataId).values,u=we("float32",s);for(let e=0;e{const s=Lo(t,n),o=s.length,u=Re(s),l=we(i,ce(s)),c=t.length,p=n.length,d=Re(t),h=Re(n),f=Co(t,s),m=Co(n,s);if(f.length+m.length===0)for(let t=0;ti[e]=0));const s=je(i,c,d),g=n.slice(-p);m.forEach((e=>g[e]=0));const y=je(g,p,h);l[t]=e(r[s],a[y])}return[l,s]}}const Yk=Fk(((e,t)=>e<0?t*e:e));function zk(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t;kk([r,a],"prelu");const i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,[o,u]=Yk(r.shape,a.shape,i,s,"float32");return n.makeTensorInfo(u,"float32",o)}const Bk={kernelName:ir,backendName:"cpu",kernelFunc:zk},Pk=Ik(cr,(e=>Math.max(0,e))),Hk={kernelName:cr,backendName:"cpu",kernelFunc:Pk},jk=Ik(gr,(e=>Math.min(Math.max(0,e),6))),Uk={kernelName:gr,backendName:"cpu",kernelFunc:jk};function qk(e){return(t,n,r)=>{const a=we(n,t.length);for(let n=0;n1/(1+Math.exp(-e)))),Vk=Ik(Tr,(e=>1/(1+Math.exp(-e)))),Gk={kernelName:Tr,backendName:"cpu",kernelFunc:Vk};function Kk(e,t,n,r,a){if("linear"===n)return Lk({inputs:{x:t},backend:e});if("relu"===n)return Pk({inputs:{x:t},backend:e});if("elu"===n)return Ck({inputs:{x:t},backend:e});if("relu6"===n)return jk({inputs:{x:t},backend:e});if("prelu"===n)return zk({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return Ok({inputs:{x:t},backend:e,attrs:{alpha:a}});if("sigmoid"===n)return Vk({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Jk(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",i),imag:n.makeTensorInfo(a.shape,"float32",s)},o}const Xk={kernelName:St,backendName:"cpu",kernelFunc:Jk};function Zk(e,t,n="float32"){if("complex64"===n)return Jk({inputs:{real:Zk(e,t,"float32"),imag:Zk(e,t,"float32")},backend:e});const r=Be(ce(t),n);return e.makeTensorInfo(t,n,r)}function Qk(e){const{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,i=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,i)}const eD={kernelName:ur,backendName:"cpu",kernelFunc:Qk};function tD(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:i}=r;if("complex64"===i){if("complex64"===a.dtype)return Lk({inputs:{x:a},backend:n});const e=Zk(n,a.shape,a.dtype),t=tD({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),r=Jk({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),r}if("complex64"===a.dtype){const e=Qk({inputs:{input:a},backend:n}),t=tD({inputs:{x:e},backend:n,attrs:{dtype:i}});return n.disposeIntermediateTensorInfo(e),t}if(!ke(a.dtype,i)){const e=Lk({inputs:{x:a},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:i}}if("int32"===i){const e=n.data.get(a.dataId).values,t=Int32Array.from(e);return n.makeTensorInfo(a.shape,"int32",t)}if("bool"===i){const e=n.data.get(a.dataId).values,t=Ya([0],a.dtype),[r,i]=Fk(((e,t)=>e!==t?1:0))(a.shape,[],e,t,"bool");return n.makeTensorInfo(i,"bool",r)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${i}`)}const nD={kernelName:wt,backendName:"cpu",kernelFunc:tD};function rD(e,t,n,r){return null==n?({inputs:n,backend:a})=>{const{a:i,b:s}=n,o=a;kk([i,s],e);const u=o.data.get(i.dataId).values,l=o.data.get(s.dataId).values,c="string"===i.dtype?Gf(u):u,p="string"===i.dtype?Gf(l):l,d=r||i.dtype,[h,f]=t(i.shape,s.shape,c,p,d);return o.makeTensorInfo(f,d,h)}:({inputs:e,backend:a})=>{const{a:i,b:s}=e,o=a;if("complex64"===i.dtype||"complex64"===s.dtype){const e=tD({inputs:{x:i},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),r=t.complexTensorInfos.real,a=t.complexTensorInfos.imag,u=o.data.get(r.dataId).values,l=o.data.get(a.dataId).values,c=tD({inputs:{x:s},backend:o,attrs:{dtype:"complex64"}}),p=o.data.get(c.dataId),d=p.complexTensorInfos.real,h=p.complexTensorInfos.imag,f=o.data.get(d.dataId).values,m=o.data.get(h.dataId).values,[g,y,b]=n(i.shape,s.shape,u,l,f,m),x=o.makeTensorInfo(b,"float32",g),v=o.makeTensorInfo(b,"float32",y),_=Jk({inputs:{real:x,imag:v},backend:o});return o.disposeIntermediateTensorInfo(e),o.disposeIntermediateTensorInfo(c),o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(v),_}{const e=o.data.get(i.dataId).values,n=o.data.get(s.dataId).values,a=r||i.dtype,[u,l]=t(i.shape,s.shape,e,n,a);return o.makeTensorInfo(l,a,u)}}}function aD(e){return(t,n,r,a,i,s)=>{const o=Lo(t,n),u=ce(o),l=o.length,c=Re(o),p=we("float32",u),d=we("float32",u),h=Co(t,o),f=Co(n,o),m=yf(r,a),g=yf(i,s),y=t.length,b=Re(t),x=n.length,v=Re(n);if(h.length+f.length===0)for(let t=0;tr[e]=0));const a=je(r,y,b),i=n.slice(-x);f.forEach((e=>i[e]=0));const s=je(i,x,v),o=e(m[2*a],m[2*a+1],g[2*s],g[2*s+1]);p[t]=o.real,d[t]=o.imag}return[p,d,o]}}const iD=Fk(((e,t)=>e+t)),sD=aD(((e,t,n,r)=>({real:e+n,imag:t+r}))),oD=rD(nt,iD,sD),uD={kernelName:nt,backendName:"cpu",kernelFunc:oD};function lD(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:i}=r,s=ce(a.shape),o=xe(i,s),u=ce(o);se(s===u,(()=>`The new shape (${o}) has ${u} elements and the old shape (${a.shape}) has ${s} elements. 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Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);let c;if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))c=Lk({inputs:{x:a},backend:n});else{const e=n.data.get(a.dataId).values,t=Re(a.shape),r=zD(e,a.shape,a.dtype,t,l,"avg");c=n.makeTensorInfo(l.outShape,a.dtype,r.values)}return c}},jD={kernelName:mt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dimRoundingMode:u,dataFormat:l}=r;kk(a,"avgPool3d");const c=pl(a.shape,i,s,1,o,u,l),p=PD(n.data.get(a.dataId).values,a.shape,a.dtype,Re(a.shape),c,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}},UD={kernelName:gt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,{filterSize:s,strides:o,pad:u,dimRoundingMode:l}=r;kk([a,i],"avgPool3DGrad");const c=pl(i.shape,s,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,x=c.dilationWidth,v=c.effectiveFilterDepth,_=c.effectiveFilterHeight,w=c.effectiveFilterWidth,M=v-1-c.padInfo.front,N=w-1-c.padInfo.left,S=_-1-c.padInfo.top,k=As(i.shape,"float32"),D=1/(f*m*g),T=n.bufferSync(a);for(let e=0;e=c.outDepth||Math.floor(r)!==r))for(let n=0;n<_;n+=b){const a=(s+n)/d;if(!(a<0||a>=c.outHeight||Math.floor(a)!==a))for(let n=0;n=c.outWidth||Math.floor(i)!==i||(u+=T.get(e,r,a,i,t))}}}k.set(u*D,e,n,r,a,t)}return 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n=0;n=a||(s[r]+=i>0?t[n]:1)}return s}function ZD(e,t,n,r=!1){const a=e.shape[0],i=e.shape[1],s=As([a,n],t.dtype);for(let o=0;o=n||(r?s.set(1,o,i):t.size>0?s.set(s.get(o,i)+t.get(o,a),o,i):s.set(s.get(o,i)+1,o,i))}return s}const QD={kernelName:xt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s}=r,o=XD(n.data.get(a.dataId).values,n.data.get(i.dataId).values,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,o)}},eT={kernelName:_t,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,o=Lo(Array.from(i),Array.from(s));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},tT=qk((e=>Math.ceil(e))),nT=Ek(Mt,tT),rT={kernelName:Mt,backendName:"cpu",kernelFunc:nT},aT=Ik(Nt,((e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e{const{x:t}=e.inputs,n=e.backend,r=new 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e=0;e=d.inHeight)continue;const i=e*w[0],s=t+n*N;for(let e=0;e=d.inWidth)continue;const a=s+r*S;let o=i+e*w[1];for(let e=0;e=l.inDepth)continue;const i=e*S[0],s=t+n*N[1];for(let e=0;e=l.inHeight)continue;const a=i+e*S[1],o=s+r*N[2];for(let e=0;e=l.inWidth)continue;const i=a+e*S[2],s=o+t*l.inChannels;let u=i;for(let e=0;eMath.cos(e))),vT={kernelName:$t,backendName:"cpu",kernelFunc:xT},_T=Ik(Ot,(e=>Math.cosh(e))),wT={kernelName:Ot,backendName:"cpu",kernelFunc:_T},MT={kernelName:Yt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:i,boxInd:s}=t,{cropSize:o,method:u,extrapolationValue:l}=r,[c,p,d,h]=a.shape,f=i.shape[0],[m,g]=o,y=As([f,m,g,h],"float32"),b=n.data.get(i.dataId).values,x=n.data.get(s.dataId).values,v=n.data.get(a.dataId).values,_=Re(a.shape),w=Re(y.shape);for(let e=0;e=c)continue;const o=m>1?(a-n)*(p-1)/(m-1):0,f=g>1?(i-r)*(d-1)/(g-1):0;for(let t=0;t1?n*(p-1)+t*o:.5*(n+a)*(p-1);if(c<0||c>p-1)for(let 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d}},jT={kernelName:Xt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{dy:r,y:a}=t;kk([r,a],"eluGrad");const i=new Float32Array(ce(a.shape)),s=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let e=0;e=1?o[e]:o[e]*(t+1)}return n.makeTensorInfo(a.shape,"float32",i)}},UT=Fk(((e,t)=>e===t?1:0)),qT=rD(Qt,UT,null,"bool"),WT={kernelName:Qt,backendName:"cpu",kernelFunc:qT},VT=pf,GT=df,KT=hf,JT=ff,XT=mf,ZT=gf,QT=Ik(Zt,(e=>{const t=Math.sign(e),n=Math.abs(e),r=1/(1+VT*n);return t*(1-((((ZT*r+XT)*r+JT)*r+KT)*r+GT)*r*Math.exp(-n*n))})),eI={kernelName:Zt,backendName:"cpu",kernelFunc:QT},tI=qk((e=>Math.exp(e))),nI=Ek(en,tI,"float32"),rI={kernelName:en,backendName:"cpu",kernelFunc:nI};function aI(e){const{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:i}=r,s=a.shape.length,o=a.shape.slice();let u=i;return i<0&&(se(-(s+1)<=i,(()=>`Axis must be in the interval [${-(s+1)}, ${s}]`)),u=s+i+1),o.splice(u,0,1),lD({inputs:{x:a},backend:n,attrs:{shape:o}})}const iI={kernelName:tn,backendName:"cpu",kernelFunc:aI},sI=qk((e=>Math.expm1(e))),oI=Ek(nn,sI),uI={kernelName:nn,backendName:"cpu",kernelFunc:oI},lI=Fk(((e,t)=>e/t)),cI=rD(Gt,lI),pI={kernelName:Gt,backendName:"cpu",kernelFunc:cI},dI=Fk(((e,t)=>e-t)),hI=aD(((e,t,n,r)=>({real:e-n,imag:t-r}))),fI=rD(Wr,dI,hI),mI={kernelName:Wr,backendName:"cpu",kernelFunc:fI};function gI(e,t,n){const r=e.shape,a=r[0],i=r[1],s=n.data.get(e.dataId),o=s.complexTensorInfos.real,u=s.complexTensorInfos.imag,l=[a,i],c=ce(l),p=we("float32",c),d=we("float32",c);for(let e=0;e{const{image:r}=e,a=n,i=we(r.dtype,ce(r.shape)),[s,o,u,l]=r.shape,c=a.data.get(r.dataId).values;for(let e=0;e=0&&sMath.floor(e))),NI=Ek(on,MI),SI={kernelName:on,backendName:"cpu",kernelFunc:NI},kI=Fk(((e,t)=>Math.floor(e/t))),DI=rD(un,kI,null,"int32"),TI={kernelName:un,backendName:"cpu",kernelFunc:DI},II={kernelName:ua,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r;let m=dT({inputs:{x:a,filter:i},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d}});if(s){const e=m;if("NCHW"===c&&1===s.shape.length&&1!==s.shape[0]){const e=lD({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}});m=oD({inputs:{a:m,b:e},backend:n}),n.disposeIntermediateTensorInfo(e)}else m=oD({inputs:{a:m,b:s},backend:n});n.disposeIntermediateTensorInfo(e)}if(h){const e=m;if("NCHW"===c&&"prelu"===h&&1===o.shape.length&&1!==o.shape[0]){const 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2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n",{getBroadcastDims:wL}=g;function ML(e,t,n){const r=[];if(e.forEach((e=>{const t=ce(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform?r.push(`uniform float ${e.name}${t>1?`[${t}]`:""};`):(r.push(`uniform sampler2D ${e.name};`),r.push(`uniform int offset${e.name};`)),n.enableShapeUniforms){const{uniformShape:t}=LL(n.packedInputs,e.shapeInfo.logicalShape,e.shapeInfo.texShape);switch(t.length){case 1:r.push(`uniform int ${e.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${e.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${e.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${e.name}Shape;`)}r.push(`uniform ivec2 ${e.name}TexShape;`)}})),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;")}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach((e=>{r.push(`uniform ${e.type} ${e.name}${e.arrayIndex?`[${e.arrayIndex}]`:""};`)}));const a=r.join("\n"),i=e.map((e=>function(e,t,n=!1,r){let a="";a+=n?SL(e,r):NL(e,r);const i=e.shapeInfo.logicalShape,s=t.logicalShape;return i.length<=s.length&&(a+=n?function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",i=e.shapeInfo.logicalShape.length,s=t.logicalShape.length,o=wL(e.shapeInfo.logicalShape,t.logicalShape),u=AL(s),l=s-i;let c;const p=["x","y","z","w","u","v"];c=0===i?"":s<2&&o.length>=1?"coords = 0;":o.map((e=>`coords.${p[e+l]} = 0;`)).join("\n");let d="";d=s<2&&i>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${p[t+l]}`)).join(", ");let h="return outputValue;";const f=1===ce(e.shapeInfo.logicalShape),m=1===ce(t.logicalShape);if(1!==i||f||m){if(f&&!m)h=1===s?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(o.length){const e=i-2,t=i-1;o.indexOf(e)>-1&&o.indexOf(t)>-1?h="return vec4(outputValue.x);":o.indexOf(e)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(t)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}}else h="\n return vec4(outputValue.xy, outputValue.xy);\n ";return`\n vec4 ${a}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${r}(${d});\n ${h}\n }\n `}(e,t):function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",i=t.texShape,s=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&null==e.shapeInfo.flatOffset&&de(s,i))return`\n float ${a}() {\n return sampleTexture(${n}, resultUV);\n }\n `;const l=AL(u),c=wL(e.shapeInfo.logicalShape,t.logicalShape),p=u-o;let d;const h=["x","y","z","w","u","v"];d=0===o?"":u<2&&c.length>=1?"coords = 0;":c.map((e=>`coords.${h[e+p]} = 0;`)).join("\n");let f="";return f=u<2&&o>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${h[t+p]}`)).join(", "),`\n float ${a}() {\n ${l} coords = getOutputCoords();\n ${d}\n return get${r}(${f});\n }\n `}(e,t)),a}(e,t,n.packedInputs,n.enableShapeUniforms))).join("\n"),s=t.texShape,o=yL(),u=function(e){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${e.texture2D}(textureSampler, uv).r;\n }\n `}(o);let l,c,p=function(e){return`${e.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${e.varyingFs} vec2 resultUV;\n ${e.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${e.defineSpecialNaN}\n ${e.defineSpecialInf}\n ${e.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${kL}\n ${DL}\n ${TL}\n `}(o);return t.isPacked?(l=function(e,t,n){switch(e.length){case 0:return"\n int getOutputCoords() {\n return 0;\n }\n ";case 1:return function(e,t,n){const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===r[0]?n?"\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n ":`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${r[1]}.0);\n }\n `:1===r[1]?n?"\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n ":`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${r[0]}.0);\n }\n `:n?"\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n ":`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);\n }\n `}(0,t,n);case 2:return function(e,t,n){const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(de(e,t))return n?"\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n }\n ":`\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));\n }\n `;const a=Math.ceil(e[1]/2);return n?"\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec2(r, c);\n }\n ":`\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n\n int index = resTexRC.x * ${r[1]} + resTexRC.y;\n int r = 2 * (index / ${a});\n int c = imod(index, ${a}) * 2;\n\n return ivec2(r, c);\n }\n `}(e,t,n);case 3:return function(e,t,n){if(n)return"\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),i=a*Math.ceil(e[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n int b = index / ${i};\n index -= b * ${i};\n\n int r = 2 * (index / ${a});\n int c = imod(index, ${a}) * 2;\n\n return ivec3(b, r, c);\n }\n `}(e,t,n);default:return function(e,t,n){if(n)return"\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),i=a*Math.ceil(e[e.length-2]/2);let s=i,o="",u="b, r, c";for(let t=2;t1&&!de(t,n)&&r.lengthe[t])).join(", ")}function RL(e,t,n){const r={},a={},i={},s=[];let o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),1===Ge().getNumber("WEBGL_VERSION")&&(c=e.getUniformLocation(n,"INFINITY",!1));const d=!1;for(let s=0;s{s[r]=e.getUniformLocation(n,t.name,d)})),{uniformLocations:r,customUniformLocations:s,infLoc:c,nanLoc:p,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function FL(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach(((e,n)=>{const r=e.logicalShape,a=t[n],i=a.shape;if(!de(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(e.isUniform&&a.isUniform)return;const s=e.texShape,o=a.isUniform?null:a.texData.texShape;if(!de(s,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${s} and ${o} must match`)}))}function YL(e){return Ge().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}class zL{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=BA.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?xL(["r","c","d"],e):bL(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${t.output} = result;\n }\n `}}class BL{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=BA.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?xL(["r","c","d"],e):bL(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}}class PL{constructor(e){this.variableNames=["A"],this.outTexUsage=PA.DOWNLOAD;const t=yL();this.outputShape=e,this.userCode=`\n ${_L}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}}class HL{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=PA.DOWNLOAD;const t=yL();this.outputShape=e,this.userCode=`\n ${_L}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}}class jL{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`\n ${this.enableShapeUniforms?"\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n":vL(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n vec4 values = ${n.texture2D}(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n ${n.output} = vec4(${r}, 0., 0., 0.);\n }\n `}}class UL{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);let r="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let t=0;t<=1;t++)for(let a=0;a<=1;a++){const i=2*t+a;r+=`\n localCoords = coords;\n if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {\n localCoords[2] += ${a};\n if (localCoords[1] + ${t} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n localCoords[1] += ${t};\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n values = ${n.texture2D}(A, uv);\n\n if (offset == 0) {\n result[${i}] = values[0];\n } else if (offset == 1) {\n result[${i}] = values[1];\n } else if (offset == 2) {\n result[${i}] = values[2];\n } else {\n result[${i}] = values[3];\n }\n }\n }\n `}this.userCode=`\n ${this.enableShapeUniforms?"\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n":vL(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n vec4 result = vec4(0.);\n int flatIndex, r, c, offset;\n ivec3 localCoords;\n vec2 uv;\n vec4 values;\n\n ${r}\n\n ${n.output} = ${a};\n }\n `}}function qL(e,t,n,r,a,i){!function(e,t){const n=Ge().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const s=function(e){return aL(e,(()=>e.createTexture()),"Unable to create WebGLTexture.")}(e),o=e.TEXTURE_2D;return VA(e,(()=>e.bindTexture(o,s))),VA(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),VA(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),VA(e,(()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST))),VA(e,(()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST))),1===Ge().getNumber("WEBGL_VERSION")?VA(e,(()=>e.texImage2D(o,0,r,t,n,0,a,i,null))):VA(e,(()=>e.texStorage2D(o,1,r,t,n))),VA(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),{texture:s,texShape:[n,t]}}function WL(e){return e.internalFormatFloat}function VL(e){return e.internalFormatHalfFloat}function GL(e){return e.downloadTextureFormat}function KL(e){return e.internalFormatPackedFloat}function JL(e){return e.internalFormatPackedHalfFloat}class XL{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=Ge().getNumber("WEBGL_VERSION");null!=e?(this.gl=e,function(e,t){FA[e]=t}(t,e)):this.gl=zA(t);let n="WEBGL_color_buffer_float";const r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),1===Ge().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=KA(this.gl,e),dL(this.gl,t))this.textureHalfFloatExtension=KA(this.gl,t);else if(Ge().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),dL(this.gl,r))this.colorBufferHalfFloatExtension=KA(this.gl,r);else if(Ge().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",dL(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!dL(this.gl,r))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(r)}this.vertexBuffer=function(e){return function(e,t){const n=aL(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return VA(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),VA(e,(()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}(this.gl),this.indexBuffer=function(e){return function(e,t){const n=aL(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return VA(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n))),VA(e,(()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Uint16Array([0,1,2,2,1,3]))}(this.gl),this.framebuffer=function(e){return 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qL(e,a,i,GL(r),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){VA(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),n.data instanceof Uint8Array?2===Ge().getNumber("WEBGL_VERSION")?VA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data))):VA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data))):2===Ge().getNumber("WEBGL_VERSION")?VA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n))):VA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n))),VA(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),function(e,t,n,r,a,i){let s,o,u;VA(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),a instanceof Uint8Array?(s=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(s=new Float32Array(n*r*4),o=e.FLOAT,u=i.internalFormatPackedFloat),s.set(a),2===Ge().getNumber("WEBGL_VERSION")?VA(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,o,s))):VA(e,(()=>e.texImage2D(e.TEXTURE_2D,0,u,n,r,0,e.RGBA,o,s))),VA(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=qA(t,n);return qL(e,a,i,JL(r),e.RGBA,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=qA(t,n);return qL(e,a,i,KL(r),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(nL(this.gl,this.framebuffer),this.outputTexture=null),VA(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n,r){const[a,i]=jA(t,n),s=new Uint8Array(t*n*4);return VA(e,(()=>e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s))),new Float32Array(s.buffer)}(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,a,i){return function(e,t,n,r,a,i,s,o){const u=e,l=new Float32Array(function(e,t){const[n,r]=qA(e,t);return n*r*4}(i,s));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}(this.gl,e,0,0,0,a,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=function(e,t,n,r){const a=e.createBuffer();VA(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a)));const i=16*t*n;return VA(e,(()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ))),VA(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0))),VA(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null))),a}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Ge().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(a,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=a}else Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n){const r=new Float32Array(t*n*4);return VA(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r))),r}(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=function(e){const t=yL();return function(e,t){const n=aL(e,(()=>e.createShader(e.VERTEX_SHADER)),"Unable to create vertex WebGLShader.");if(VA(e,(()=>e.shaderSource(n,t))),VA(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`)}(t));const n=function(e){return aL(e,(()=>e.createProgram()),"Unable to create WebGLProgram.")}(t);return VA(t,(()=>t.attachShader(n,this.vertexShader))),VA(t,(()=>t.attachShader(n,e))),function(e,t){if(VA(e,(()=>e.linkProgram(t))),!Ge().get("ENGINE_COMPILE_ONLY")&&!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,n),this.debug&&ZA(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=function(e,t,n){return VA(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),QA(e,t,"clipSpacePos",n,3,20,0)&&QA(e,t,"uv",n,2,20,12)}(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&VA(this.gl,(()=>this.gl.deleteProgram(e)))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&ZA(this.gl,this.program),VA(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return aL(e,(()=>e.getUniformLocation(t,n)),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),VA(this.gl,(()=>this.gl.getAttribLocation(e,t)))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),eL(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,a]=qA(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&ZA(this.gl,this.program),rL(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),VA(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),VA(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=KA(this.gl,2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await be((()=>this.disposed||this.isQueryAvailable(e,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=function(e){let t=0;for(;te.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||be((()=>(this.pollItems(),0===this.itemsToPoll.length)))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),tL(this.gl,e,this.framebuffer),this.debug&&rL(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(tL(this.gl,this.outputTexture,this.framebuffer),this.debug&&rL(this.gl)):nL(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const r=this.gl;tL(r,e,this.framebuffer),this.debug&&rL(r),this.outputTexture=e,VA(r,(()=>r.viewport(0,0,t,n))),VA(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),VA(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new 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j${constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=YL(this.outputShape.length),0===this.rank)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{const e=H$("rc",this.rank),t=AL(this.rank),n=this.getOutOfBoundsCondition(e),r=this.getSetup(e),a=this.getOutput(e);this.userCode=`\n void main() {\n ${t} rc = getOutputCoords();\n\n if(${n}) {\n setOutput(vec4(0));\n } else {\n ${r}\n\n setOutput(vec4(${a}));\n }\n }\n `}}getSourceCoordsArr(e){const t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let a=`${0===n?"r":"rp1"}, ${0===r?"c":"cp1"}`;for(let t=2;t ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${r};\n `}getOutput(e){const t=this.getSourceCoordsArr(e);return 1===this.rank?`getA(rc), (rc + 1 >= 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q${constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const r=V$(t,n),a=G$(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);const i=W$(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();const e=this.freeTextures[a].shift();return this.usedTextures[a].push(e),e}let s;return r===HA.PACKED_2X2_FLOAT32?s=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===HA.PACKED_2X2_FLOAT16?s=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===HA.UNPACKED_FLOAT32?s=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===HA.UNPACKED_FLOAT16?s=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===HA.PACKED_4X1_UNSIGNED_BYTE&&(s=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(s),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),s}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;const a=V$(n,r),i=G$(t,a,r);i in this.freeTextures||(this.freeTextures[i]=[]);const s=W$(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=Ge().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=s):(this.freeTextures[i].push(e),this.numFreeTextures++,this._numBytesFree+=s),this.numUsedTextures--;const u=this.usedTextures[i],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function W$(e,t,n,r,a){const i=function(e,t){switch(e){case HA.PACKED_2X2_FLOAT32:return KL(t);case HA.PACKED_2X2_FLOAT16:return JL(t);case HA.UNPACKED_FLOAT32:return WL(t);case HA.UNPACKED_FLOAT16:return VL(t);case HA.PACKED_4X1_UNSIGNED_BYTE:return GL(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);let s;if(a){const[t,n]=qA(e[0],e[1]);s=t*n}else{const[t,n]=jA(e[0],e[1]);s=t*n}const o=function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,i);return s*o}function V$(e,t){if(e===PA.UPLOAD)return HA.PACKED_2X2_FLOAT32;if(e===PA.RENDER||null==e)return function(e){return Ge().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?HA.PACKED_2X2_FLOAT32:HA.UNPACKED_FLOAT32:e?HA.PACKED_2X2_FLOAT16:HA.UNPACKED_FLOAT16}(t);if(e===PA.DOWNLOAD||e===PA.PIXELS)return HA.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function G$(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class K${constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}const J$="return abs(x);",X$="return x;";class Z${constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}class Q${constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);const t=e.length,n=H$("rc",t),r=AL(t),a=function(e,t){if(1===e)return"rc";let n="";for(let r=0;rt.push(e)))}const t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:i,complexTensorInfos:s,isPacked:o}=t;if(null!=a){let t;t=o?new Z$(r,X$):new K$(r,X$);const n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:i}],i),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if(Ge().getBool("DEBUG")&&!Ge().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===Ge().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u,l,c=null;if("complex64"!==i&&Ge().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);const t=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(t.texture.texture,...UA(r))}if(this.pendingRead.set(e,[]),"complex64"!==i&&await this.gpgpu.createAndWaitForFence(),"complex64"===i){const e=await Promise.all([this.read(s.real.dataId),this.read(s.imag.dataId)]);l=yf(e[0],e[1])}else if(null==c)l=this.getValuesFromTexture(e);else{const e=ce(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}if(null!=u&&this.disposeIntermediateTensorInfo(u),null!=c){const e=this.gpgpu.gl;VA(e,(()=>e.deleteBuffer(c)))}const p=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach((e=>e(p))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&lo().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){const n=this.texData.get(e),{values:r,shape:a,slice:i,dtype:s,isPacked:o,texture:u}=n;if("complex64"===s)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=i){let n;n=o?new Z$(a,X$):new K$(a,X$);const r=this.runWebGLProgram(n,[{dataId:e,shape:a,dtype:s}],s),i=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),i}if(null==u)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const l=this.decode(e,t.customTexShape),c=lo().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return Object.assign({tensorRef:c},p.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>Ha(e)));return As(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return As(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t0}time(e){const t=this.activeTimers,n=[];let r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();const a=le(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),i=le(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);const s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(a);s.kernelMs=re(e),s.getExtraProfileInfo=()=>e.map(((e,t)=>({name:i[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else s.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,s})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:za(),endMs:null}}endTimer(e){return Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=za(),e)}async getQueryTime(e){if(Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:a,isPacked:i,slice:s}=this.texData.get(e),o=s&&s.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,i)));const l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=nO){return Ge().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&ce(e.shape)0&&Ee(n[0])){const a=n.map((e=>Pa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return lo().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new Q$(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new j$(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[iL(e.shape),...sL(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[iL(t),...sL(t)],i=new U$(a,n),s=[n],o=this.runWebGLProgram(i,[r],e.dtype,s,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:r,shape:a,dtype:i}=n;null!=t&&se(ce(a)<=t[0]*t[1]*4,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."));const s=oL(a);let o;o=r?new BL(s):new zL(s);const u=[null!=t?t:UA(s)];return{dtype:i,shape:a,dataId:this.runWebGLProgram(o,[{shape:s,dtype:i,dataId:e}],i,u,!0,t).dataId}}runWebGLProgram(e,t,n,r,a=!1,i){const s=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(s.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===BA.DENSE){const t=null!=i?i:UA(e.outputShape);o.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(o.usage=e.outTexUsage),0===ce(s.shape))return o.values=we(s.dtype,0),s;const u=[],l=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&ce(t.shape)<=Ge().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),u.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!lL(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),u.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(s.dataId);const c={shape:s.shape,texData:o,isUniform:!1},p=function(e,t,n){let r="";t.concat(n).forEach((t=>{const a=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const i=t.texData.texShape,{useSqueezeShape:s,uniformShape:o,keptDims:u}=LL(e.packedInputs,t.shape,i);let l="",c="",p="";if(1===o.length&&e.packedInputs){const e=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];l=`${e[0]>1}_${e[1]>1}`}else if(2!==o.length||e.packedInputs){if(o.length>2&&!e.packedInputs){const e=Re(o);p=`${e[0]===i[1]}_${e[e.length-1]===i[1]}`}}else c=`${o[0]>1}_${o[1]>1}`;const d=t.shape.length,h=2===o.length&&de(t.shape,i),f=1===ce(t.shape),m=Co(t.shape,n.shape),g=!e.packedInputs&&d===n.shape.length&&de(i,n.texData.texShape),y=e.packedInputs||o.length>2?"":`${i[0]>1}_${i[1]>1}`;r+=`${d}_${g}_${s?u:""}_${o.length}_${f}_${m}_${h}_${l}_${c}_${p}_${y}_${a}`}else{const e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${a}`}}));const a=e.userCode;let i=e.constructor.name;return i+="_"+r+"_"+a+`${Ge().getNumber("WEBGL_VERSION")}`,i}(e,l,c),d=this.getAndSaveBinary(p,(()=>function(e,t,n,r){const a=n.map(((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),i=a.map((e=>e.shapeInfo)),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},o=ML(a,s,t),u=function(e,t){const n=aL(e,(()=>e.createShader(e.FRAGMENT_SHADER)),"Unable to create fragment WebGLShader.");if(VA(e,(()=>e.shaderSource(n,t))),VA(e,(()=>e.compileShader(n))),Ge().get("ENGINE_COMPILE_ONLY"))return n;if(!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw XA(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}(e.gl,o),l=e.createProgram(u);return Ge().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:s,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:s},RL(e,t,l))}(this.gpgpu,e,l,c))),h=null!=this.activeTimers;let f;h&&(f=this.startTimer()),Ge().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,a){t.program.enableShapeUniforms||(FL(t.inShapeInfos,n),FL([t.outShapeInfo],[r]));const i=r.texData.texture,s=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(i.texture,s[0],s[1]):e.setOutputMatrixTexture(i.texture,s[0],s[1]),e.setProgram(t.webGLProgram),1===Ge().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach(((n,r)=>{const a=t.program.variableNames[r],i=t.uniformLocations[a],s=t.uniformLocations[`offset${a}`],o=t.inShapesLocations[`${a}Shape`],u=t.inTexShapesLocations[`${a}TexShape`];if(o){const{uniformShape:r}=LL(t.program.packedInputs,n.shape,n.texData.texShape);switch(r.length){case 1:e.gl.uniform1iv(o,new Int32Array(r));break;case 2:e.gl.uniform2iv(o,new Int32Array(r));break;case 3:e.gl.uniform3iv(o,new Int32Array(r));break;case 4:e.gl.uniform4iv(o,new Int32Array(r))}}if(u&&e.gl.uniform2i(u,n.texData.texShape[0],n.texData.texShape[1]),null!=i)if(n.isUniform)if(ce(n.shape)<2)e.gl.uniform1f(i,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(i,t)}else null!=n.texData.slice&&null!=s&&e.gl.uniform1i(s,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture.texture,i,r)}));const o=t.outShapeLocation;if(o)switch(r.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(r.shape))}if(t.outShapeStridesLocation){const n=Re(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach(((n,r)=>{const i=t.customUniformLocations[r],s=a[r];if("float"===n.type)e.gl.uniform1fv(i,s);else if("vec2"===n.type)e.gl.uniform2fv(i,s);else if("vec3"===n.type)e.gl.uniform3fv(i,s);else if("vec4"===n.type)e.gl.uniform4fv(i,s);else if("int"===n.type)e.gl.uniform1iv(i,s);else if("ivec2"===n.type)e.gl.uniform2iv(i,s);else if("ivec3"===n.type)e.gl.uniform3iv(i,s);else{if("ivec4"!==n.type)throw Error(`uniform type ${n.type} is not supported yet.`);e.gl.uniform4iv(i,s)}})),e.executeProgram()}(this.gpgpu,d,l,c,r),u.forEach((e=>this.disposeIntermediateTensorInfo(e))),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));const m=Ge().get("WEBGL_FLUSH_THRESHOLD");if(m>0){const e=za();e-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!Ge().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&!1===a){const e=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),e}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Ge().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})),this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=ho((()=>{if(!Ge().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=Ge().getBool("DEBUG");Ge().set("DEBUG",!1);const t=this.abs(Ru(1e-8)).dataSync()[0];if(Ge().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:i,usage:s,isPacked:o}=t;if(null!=i)return;const u=null!=this.activeTimers;let l;u&&(l=za());let c=t.texShape;if(null==c&&(c=function(e,t=!1){let n=Ge().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,e=e.map(((t,n)=>n>=e.length-2?te(e[n]):e[n])),1===e.length&&(e=[2,e[0]])),2!==e.length){const t=_e(e);e=t.newShape}let r=ce(e);if(e.length<=1&&r<=n)return[1,r];if(2===e.length&&e[0]<=n&&e[1]<=n)return e;if(3===e.length&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(3===e.length&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const t=iL(e);let n=2,a=2;return e.length&&([n,a]=sL(e)),r=t*(n/2)*(a/2),me(r).map((e=>2*e))}return me(r)}(n,o),t.texShape=c),null!=a){const e=oL(n);let i,s=c[1],p=c[0];const d=a instanceof Uint8Array||a instanceof Uint8ClampedArray;!o&&d||([s,p]=qA(c[0],c[1])),i=o?new UL(e,d):new jL(e,d);const h=d?[p,s]:c,f=this.makeTensorInfo(h,r),m=this.texData.get(f.dataId);m.usage=d?PA.PIXELS:PA.UPLOAD,m.texShape=h,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(f.dataId),s,p,a);const g=[[p,s]],y=!0,b=this.runWebGLProgram(i,[f],r,g,y),x=this.texData.get(b.dataId);t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,Ge().get("ENGINE_COMPILE_ONLY")?this.disposeData(b.dataId):(t.texture=x.texture,t.values=null,this.texData.delete(b.dataId)),this.disposeIntermediateTensorInfo(f),u&&(this.uploadWaitMs+=za()-l)}else{const e=this.acquireTexture(c,s,r,o);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*Te(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(const[,t]of Object.entries(this.binaryCache)){const n=new Promise((e=>{try{this.checkCompletion_(t),e(!0)}catch(e){throw e}}));e.push(n)}return Promise.all(e)}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Xh(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)){if(console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS))throw XA(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.");throw new Error("Failed to link vertex and fragment shaders.")}return!0}getUniformLocations(){for(const[,e]of Object.entries(this.binaryCache)){const{uniformLocations:t,customUniformLocations:n,infLoc:r,nanLoc:a,inShapesLocations:i,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=RL(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=a,e.inShapesLocations=i,e.inTexShapesLocations=s,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}}rO.nextDataId=0,ki()&&Mo("webgl",(()=>new rO),2);class aO{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=Lo(t,n),this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}}class iO{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Lo(t,n);const a=this.outputShape.length;this.enableShapeUniforms=YL(a);let i="";if(r)if(0===a||1===ce(this.outputShape))i="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else if(i=`\n ${AL(a)} coords = getOutputCoords();\n `,1===a)this.enableShapeUniforms?i+="\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n ":i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{const e=H$("coords",a);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= outShape[${a} - 2];\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= outShape[${a} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}}function sO(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const oO={kernelName:fn,backendName:"webgl",kernelFunc:sO};function uO(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,i=n.makeTensorInfo(r.shape,"complex64"),s=n.texData.get(i.dataId),o=sO({inputs:{x:r},backend:n}),u=sO({inputs:{x:a},backend:n});return s.complexTensorInfos={real:o,imag:u},i}const lO={kernelName:St,backendName:"webgl",kernelFunc:uO},cO="return (a < 0.) ? b * a : a;",pO="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",dO={kernelName:vn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:i}=r,s=n.makeTensorInfo([],"float32",Fa(i,"float32")),o=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new iO(pO,a.shape,s.shape):new aO(cO,a.shape,s.shape),u=n.runWebGLProgram(o,[a,s],"float32");return n.disposeIntermediateTensorInfo(s),u}},hO="return (a < 0.) ? b * a : a;",fO="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",mO={kernelName:ir,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t,i=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new iO(fO,r.shape,a.shape):new aO(hO,r.shape,a.shape);return n.runWebGLProgram(i,[r,a],"float32")}};function gO({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:i})=>{const{x:s}=a,o=i,u=r||s.dtype;if(o.shouldExecuteOnCPU([s])&&null!=n){const e=o.texData.get(s.dataId),t=n(e.values,u);return o.makeTensorInfo(s.shape,u,t)}let l;return l=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new Z$(s.shape,t):new K$(s.shape,e),o.runWebGLProgram(l,[s],u)}}function yO({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:i}){return({inputs:s,backend:o})=>{const{a:u,b:l}=s,c=o;if(r&&"complex64"===u.dtype){const t=c.texData.get(u.dataId),n=c.texData.get(l.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:u.shape},i={dataId:r.dataId,dtype:r.dtype,shape:l.shape},s=new aO(e,u.shape,l.shape);return c.runWebGLProgram(s,[a,i],ci(n.dtype,r.dtype))})),i=uO({inputs:{real:r,imag:a},backend:c});return c.disposeIntermediateTensorInfo(r),c.disposeIntermediateTensorInfo(a),i}const p=i||ci(u.dtype,l.dtype);if(("string"===u.dtype||"string"===l.dtype||c.shouldExecuteOnCPU([u,l]))&&null!=a){const e=c.texData.get(u.dataId).values,t=c.texData.get(l.dataId).values,n="string"===u.dtype?Gf(e):e,r="string"===u.dtype?Gf(t):t,[i,s]=a(u.shape,l.shape,n,r,p),o=c.makeTensorInfo(s,p);return c.texData.get(o.dataId).values=i,o}let d;return d=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new iO(t,u.shape,l.shape,n):new aO(e,u.shape,l.shape),c.runWebGLProgram(d,[u,l],p)}}function bO(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n";if("elu"===e)return t?"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n":"return (x >= 0.0) ? x : (exp(x) - 1.0);";if("relu6"===e)return t?"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n";if("prelu"===e)return t?fO:hO;if("leakyrelu"===e)return t?pO:cO;if("sigmoid"===e)return"return 1.0 / (1.0 + exp(-1.0 * x));";throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class xO{constructor(e,t,n,r=!1,a=!1,i=!1,s=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=YL(this.outputShape.length);const l=r?e[1]:e[2],c=Math.ceil(l/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";s&&(m=o?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${s}\n }`:u?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${s}\n }`:`vec4 activation(vec4 x) {\n ${s}\n }`,g="result = activation(result);");const y=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]`The new shape (${u}) has ${l} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`));const c=s.texData.get(a.dataId);return!c.isPacked||lL(a.shape,u)||null!==c.texture&&lL(c.shape,u)?(s.incRef(a.dataId),{dataId:a.dataId,shape:u,dtype:a.dtype}):function(e,t,n){const r=[iL(e.shape),...sL(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},i=[iL(t),...sL(t)],s=new U$(i,r),o=[r],u=n.runWebGLProgram(s,[a],e.dtype,o,!0);return{dataId:u.dataId,shape:t,dtype:u.dtype}}(a,u,s)}const SO={kernelName:pr,backendName:"webgl",kernelFunc:NO};class kO{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:i}=e;this.outputShape=[r,i];const s=4*Math.floor(n/4),o=n%4;let u="sumValue += dot(values, ones);";if(null!=t){const e=1/t;u=`sumValue += dot(values * ${he(e)?e.toPrecision(2):e}, ones);`}let l="";a%n>0&&(l=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${l}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${s}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${s};\n if (${1===o}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${2===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${3===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}}class DO{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:i}=e;this.outputShape=[r,i];let s="0.0",o="";"prod"===t?s="1.0":"min"===t?(s="1.0 / 1e-20",o="min"):"max"===t&&(s="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");const l=4*Math.floor(n/4),c=n%4;let p=`\n if (${"sum"===t}) {\n sumValue += dot(values, ones);\n } else if (${"prod"===t}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${o}(values, minMaxValue);\n if (${"min"===t} || ${"max"===t}) {\n minMaxValue = ${o}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d="vec4";"all"===t?(s="1.0",p="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",d="bvec4"):"any"===t&&(s="0.0",p="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",d="bvec4");let h="";a%n>0&&(h=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${s};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${h}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n vec4 minMaxValue = vec4(${s});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${l}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${p}\n }\n\n int inIdx = inOffset + ${l};\n if (${1===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${2===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${3===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${p}\n }\n setOutput(${u});\n }\n `}}function TO(e,t,n,r){const a=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],r=tf(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let i=e;for(let s=0;s6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let t=0;t6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=AL(this.rank),a=P$("rc",this.rank),i=new Array(this.rank);for(let e=0;e`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));const v=n?[y,p,h]:[y,h,p],_=r?[b,f,d]:[b,d,f],w=NO({inputs:{x:e},backend:a,attrs:{shape:v}}),M=NO({inputs:{x:t},backend:a,attrs:{shape:_}}),N=[w,M],S=Math.max(y,b),k=n?w.shape[1]:w.shape[2],D=null!=i,T=null!=s,I="leakyrelu"===u,E=null!=u?bO(u,!0):null;let C;if((1===h||1===f)&&k>1e3&&!1===(D||T||I||null!=E)){let e=w,t=M;n&&(e=$O({inputs:{x:w},backend:a,attrs:{perm:[0,2,1]}}),N.push(e)),r&&(t=$O({inputs:{x:M},backend:a,attrs:{perm:[0,2,1]}}),N.push(t));const i=1===f;let s=e;1!==f&&(s=NO({inputs:{x:e},backend:a,attrs:{shape:[S,k,1]}}),N.push(s));const o=1===f?2:1;let u=t;i&&(u=NO({inputs:{x:t},backend:a,attrs:{shape:[S,1,k]}}),N.push(u));const l=wO({inputs:{a:s,b:u},backend:a});C=AO({inputs:{x:l},backend:a,attrs:{axis:o,keepDims:!0}}),N.push(l)}else{const u=ci(e.dtype,t.dtype),l=new xO(v,_,[S,h,f],n,r,D,E,T,I),c=[w,M];if(null!=i&&c.push(i),T&&c.push(s),I){const e=a.makeTensorInfo([],"float32",Fa(o,"float32"));c.push(e),N.push(e)}C=a.runWebGLProgram(l,c,u)}const A=NO({inputs:{x:C},backend:a,attrs:{shape:x}});N.push(C);for(const e of N)a.disposeIntermediateTensorInfo(e);return A}const FO={kernelName:oa,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:i,bias:s,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=r;return RO({a,b:i,transposeA:u,transposeB:l,backend:n,bias:s,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}},YO="return abs(x);",zO={kernelName:Qe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=k$(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let a;return a=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new Z$(r.shape,YO):new K$(r.shape,YO),n.runWebGLProgram(a,[r],r.dtype)}},BO=gO({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n"}),PO={kernelName:et,backendName:"webgl",kernelFunc:BO},HO=gO({opSnippet:"if (isnan(x)) return x;\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),jO={kernelName:tt,backendName:"webgl",kernelFunc:HO},UO="return a + b;",qO=yO({opSnippet:UO,packedOpSnippet:UO,supportsComplex:!0,cpuKernelImpl:ZL}),WO={kernelName:nt,backendName:"webgl",kernelFunc:qO};class VO{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n float result = ${r};\n setOutput(result);\n }\n `}}class GO{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n vec4 result = ${r};\n setOutput(result);\n }\n `}}const KO={kernelName:rt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,a=n;if(1===a.length)return sO({inputs:{x:a[0]},backend:r});if(a.length>Ge().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:r}),i=e({inputs:a.slice(t),backend:r});return e({inputs:[n,i],backend:r})}const i=a.map((e=>e.dtype)).reduce(((e,t)=>ci(e,t))),s=a.map((e=>e.shape)),o=Ge().getBool("WEBGL_PACK")?new GO(a[0].shape,s):new VO(a[0].shape,s);return r.runWebGLProgram(o,a,i)}},JO={kernelName:at,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=ve(i,a.shape);let l=u;const c=xc(l,o);let p=a;null!=c&&(p=$O({inputs:{x:a},backend:n,attrs:{perm:c}}),l=_c(l.length,o)),bc("all",l,o);const[d,h]=gc(p.shape,l),f=NO({inputs:{x:p},backend:n,attrs:{shape:[-1,ce(h)]}}),m=TO(f,f.dtype,"all",n);let g;return g=NO(s?{inputs:{x:m},backend:n,attrs:{shape:yc(d,u)}}:{inputs:{x:m},backend:n,attrs:{shape:d}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(p),g}},XO={kernelName:it,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=ve(i,a.shape);let l=u;const c=xc(l,o);let p=a;null!=c&&(p=$O({inputs:{x:a},backend:n,attrs:{perm:c}}),l=_c(l.length,o)),bc("any",l,o);const[d,h]=gc(p.shape,l),f=NO({inputs:{x:p},backend:n,attrs:{shape:[-1,ce(h)]}}),m=TO(f,f.dtype,"any",n);let g;return g=NO(s?{inputs:{x:m},backend:n,attrs:{shape:yc(d,u)}}:{inputs:{x:m},backend:n,attrs:{shape:d}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(p),g}};class ZO{constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:a,outSize:i}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,i];const s="max"===t?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${r};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${r}; i++) {\n int inIdx = ${o};\n float candidate = getA(batch, inIdx);\n if (candidate ${s} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}}class QO{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,se(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const a=e[e.length-1],i=Math.ceil(a/t);this.outputShape=e.slice(0,-1),i>1&&this.outputShape.push(i),r||this.variableNames.push("bestIndicesA");const s=this.outputShape,o=s.length,u=AL(o),l=H$("coords",o);let c,p;if(1===i){p=o+1;const e=AL(p);c=`\n ${e} sourceLocR = ${e}(${l.join()}, 0);\n ++${l[o-1]};\n ${e} sourceLocG = ${e}(${l.join()}, 0);\n ++${l[o-2]};\n ${e} sourceLocA = ${e}(${l.join()}, 0);\n --${l[o-1]};\n ${e} sourceLocB = ${e}(${l.join()}, 0);\n --${l[o-2]};`}else p=o,c=`\n ${u} sourceLocR = coords;\n ++${l[o-1]};\n ${u} sourceLocG = coords;\n ++${l[o-2]};\n ${u} sourceLocA = coords;\n --${l[o-1]};\n ${u} sourceLocB = coords;\n --${l[o-2]};`;const d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map((e=>"int "+e)),m=H$("sourceLocR",p-1).concat("inIdx.r"),g=H$("sourceLocG",p-1).concat("inIdx.g"),y=H$("sourceLocB",p-1).concat("inIdx.b"),b=H$("sourceLocA",p-1).concat("inIdx.a"),x="max"===n?"greaterThan":"lessThan",v=r?"":`\n inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${y.join()}),\n getBestIndicesAChannel(${b.join()})));`,_=`vec4(\n getAChannel(${m.join()}),\n hasNextCol ? getAChannel(${g.join()}) : 0.,\n hasNextRow ? getAChannel(${y.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,w=r?"":`\n float getBestIndicesAChannel(${f.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${f.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${w}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${l[o-1]} < ${s[o-1]-1};\n bool hasNextRow = ${l[o-2]} < ${s[o-2]-1};\n ${c}\n ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n sourceLocB${h}, sourceLocA${h}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${v}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}}function eR(e,t,n,r=null){let a=t.shape[0],i=t.shape[1];null!=r&&(a=r.shape[0],i=r.shape[1]);const s=tf(i),o={windowSize:s,inSize:i,batchSize:a,outSize:Math.ceil(i/s)},u=new ZO(o,n,null==r),l=[t];null!=r&&l.push(r);const c=e.runWebGLProgram(u,l,"int32");if(1===c.shape[1])return c;const p=eR(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function tR(e,t,n,r=null){const a=null!=r?r.shape:t.shape,i=tf(a[a.length-1]),s=new QO(a,i,n,null==r),o=null==r?[t]:[t,r],u=e.runWebGLProgram(s,o,"int32");if(u.shape.length===t.shape.length){const r=tR(e,t,n,u);return e.disposeIntermediateTensorInfo(u),r}return u}function nR(e,t,n,r){const a=[n];if(bc("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Ge().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],i=e.texData.get(t.dataId);let s=t;null!==i&&i.isPacked&&(s=e.unpackTensor(t),n.push(s));const[o,u]=gc(s.shape,a),l=ce(u),c=NO({inputs:{x:s},backend:e,attrs:{shape:[-1,l]}});n.push(c);const p=eR(e,c,r);n.push(p);const d=NO({inputs:{x:p},backend:e,attrs:{shape:o}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),d}return tR(e,t,r)}const rR={kernelName:st,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i}=r;let s=ve(i,a.shape);const o=xc(s,a.shape.length);let u=a;const l=[];null!=o&&(u=$O({inputs:{x:a},backend:n,attrs:{perm:o}}),l.push(u),s=_c(s.length,u.shape.length)),bc("argMax",[s[0]],u.shape.length);const c=nR(n,u,s[0],"max");return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},aR={kernelName:ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i}=r;let s=ve(i,a.shape);const o=xc(s,a.shape.length);let u=a;const l=[];null!=o&&(u=$O({inputs:{x:a},backend:n,attrs:{perm:o}}),l.push(u),s=_c(s.length,u.shape.length)),bc("argMin",[s[0]],u.shape.length);const c=nR(n,u,s[0],"min");return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},iR=gO({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n"}),sR={kernelName:ut,backendName:"webgl",kernelFunc:iR},oR=gO({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),uR={kernelName:lt,backendName:"webgl",kernelFunc:oR},lR=gO({opSnippet:"if (isnan(x)) return x;\n return atan(x);\n"}),cR={kernelName:ct,backendName:"webgl",kernelFunc:lR},pR=yO({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",packedOpSnippet:"\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),dR={kernelName:dt,backendName:"webgl",kernelFunc:pR},hR=gO({opSnippet:"if (isnan(x)) return x;\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),fR={kernelName:pt,backendName:"webgl",kernelFunc:hR};class mR{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,s=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec2 strides = ivec2(${s}, ${o});\n const ivec2 pads = ivec2(${d}, ${h});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${p};\n wC += ${l}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?m:g:`wR * ${p} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / count");const x=4*Math.floor(i/4),v=i%4,_=`\n if (${f}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${o});\n const ivec2 pads = ivec2(${d}, ${h});\n const float initializationValue = ${y};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${y});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${x}; wC += 4) {\n int xC = xCCorner + wC * ${l};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n getValue(batch, xR, xC + 3 * ${l}, d)\n );\n\n ${_}\n }\n\n int xC = xCCorner + ${x};\n if (${1===v}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${2===v}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${3===v}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n initializationValue\n );\n\n ${_}\n }\n }\n setOutput(${b});\n }\n `}}class gR{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,s=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let x="0.0";if(b||(x="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec3 strides =\n ivec3(${s}, ${o}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${h};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f};\n wC += ${p}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n wR * ${f} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(v="avgValue / count");const _=4*Math.floor(i/4),w=i%4,M=`\n if (${b}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${s}, ${o}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n const float initializationValue = ${x};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${x});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${h};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${_}; wC += 4) {\n int xC = xCCorner + wC * ${p};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n );\n\n ${M}\n }\n\n int xC = xCCorner + ${_};\n if (${1===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${M}\n } else if (${2===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n initializationValue,\n initializationValue\n );\n\n ${M}\n } else if (${3===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n initializationValue\n );\n\n ${M}\n }\n }\n setOutput(${v});\n }\n }\n `}}const yR={kernelName:ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;mL(a,"avgPool");const{filterSize:i,strides:s,pad:o,dimRoundingMode:u}=r;se(vl(s,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))return sO({inputs:{x:a},backend:n});const c=new mR(l,"avg",!1);return n.runWebGLProgram(c,[a],"float32")}},bR={kernelName:mt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dimRoundingMode:u,dataFormat:l}=r,c=pl(a.shape,i,s,[1,1,1],o,u,l),p=new gR(c,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}};class xR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n const ivec2 pads = ivec2(${l}, ${c});\n const float avgMultiplier = float(${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${o};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${s}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}}class vR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n const ivec3 pads = ivec3(${h}, ${f}, ${m});\n const float avgMultiplier = float(${g});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${c};\n wD += ${o}) {\n float dyD = float(dyDCorner + wD) / ${a}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${p};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${l}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const _R={kernelName:gt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=r,p=pl(s.shape,o,u,[1,1,1],l,c),d=new vR(p);return n.runWebGLProgram(d,[a],s.dtype)}},wR={kernelName:ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i;mL([a,i],"avgPoolGrad");const{filterSize:o,strides:u,pad:l}=r,c=cl(s.shape,o,u,1,l),p=new xR(c);return n.runWebGLProgram(p,[a],s.dtype)}},MR={kernelName:yt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:i}=t,{transposeA:s,transposeB:o}=r;return RO({a,b:i,transposeA:s,transposeB:o,backend:n})}};class NR{constructor(e,t,n,r,a,i){this.outputShape=[],this.variableNames=["x","mean","variance"],Lo(e,t),Lo(e,n);let s="0.0";null!=r&&(Lo(e,r),this.variableNames.push("offset"),s="getOffsetAtOutCoords()");let o="1.0";null!=a&&(Lo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${s};\n float scale = ${o};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}}class SR{constructor(e,t,n,r,a,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Lo(e,t),Lo(e,n);let s="vec4(0.0)";null!=r&&(Lo(e,r),this.variableNames.push("offset"),s="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=a&&(Lo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${s};\n vec4 scale = ${o};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}}const kR={kernelName:ln,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,mean:a,variance:i,offset:s,scale:o}=e;se(a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),se(null==s||a.shape.length===s.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),se(null==o||a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:u}=n;null==u&&(u=.001);const l=[r,a,i];let c=null;null!=s&&(c=s.shape,l.push(s));let p=null;null!=o&&(p=o.shape,l.push(o));const d=Ge().getBool("WEBGL_PACK_NORMALIZATION")?new SR(r.shape,a.shape,i.shape,c,p,u):new NR(r.shape,a.shape,i.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)}};class DR{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=AL(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const n=function(e){if(1===e)return"sourceLoc";if(e<=6)return TR.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let r;const a=e.map(((e,t)=>`sourceLoc.${TR[t]} = start[${t}] + coords.${TR[t]};`));r=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${a.join("\n")}\n `,this.userCode=`\n void main() {\n ${r}\n setOutput(getSource(${n}));\n }\n `}}const TR=["x","y","z","w","u","v"];class IR{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const t=AL(this.rank),n=H$("coords",this.rank),r=H$("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,i=`getChannel(getSource(${r.join()}), ${a})`,s=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.y = ${i};\n --${r[this.rank-1]};\n }\n `,o=1===this.rank?"":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n ++${r[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.w = ${i};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${s}\n ${o}\n setOutput(result);\n }\n `}}function ER(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:i,size:s}=r,[o,u]=au(a,i,s);if(Uo(a,o,u),0===ce(u))return n.makeTensorInfo(u,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=D$(e.values,o,u,a.shape,a.dtype);return n.makeTensorInfo(u,a.dtype,t)}const{isPacked:l}=n.texData.get(a.dataId),c=nu(a.shape,o,u);if(l||!c){const e=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IR(u):new DR(u),t=[o];return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){const a=r.texData.get(e.dataId),i=r.makeTensorInfo(n,e.dtype),s=r.texData.get(i.dataId);Object.assign(s,a),s.refCount=1,s.shape=n,s.dtype=e.dtype;let o=ru(t,Re(e.shape));a.slice&&(o+=a.slice.flatOffset),s.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};const u=r.dataRefCount.get(s.slice.origDataId)||1;return r.dataRefCount.set(s.slice.origDataId,u+1),i}(a,o,u,n)}const CR={kernelName:Nr,backendName:"webgl",kernelFunc:ER},AR={kernelName:bt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:i,crops:s}=r;se(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const o=i.reduce(((e,t)=>e*t)),u=rf(a.shape,i,o),l=af(u.length,i.length),c=sf(a.shape,i,o),p=of(s,i.length),d=uf(c,s,i.length),h=[],f=NO({inputs:{x:a},backend:n,attrs:{shape:u}}),m=$O({inputs:{x:f},backend:n,attrs:{perm:l}}),g=NO({inputs:{x:m},backend:n,attrs:{shape:c}}),y=ER({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}},LR={kernelName:xt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s}=r,o=n.readSync(a.dataId),u=n.readSync(i.dataId),l=QL(o,u,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,l)}},$R={kernelName:_t,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,i=n.readSync(r.dataId),s=n.readSync(a.dataId),o=Lo(Array.from(i),Array.from(s));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},OR=yO({opSnippet:"return float(a != b);",cpuKernelImpl:v$,dtype:"bool"}),RR={kernelName:Kn,backendName:"webgl",kernelFunc:OR};function FR(e){const{inputs:t,backend:n}=e,{input:r}=t;return sO({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}const YR={kernelName:ur,backendName:"webgl",kernelFunc:FR},zR={kernelName:wt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r,attrs:a}=t,{x:i}=n,{dtype:s}=a;if("complex64"===s){if("complex64"===i.dtype)return sO({inputs:{x:i},backend:r});const t=up(i.shape),n=e({inputs:{x:i},backend:r,attrs:{dtype:"float32"}}),a=uO({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===i.dtype){const t=FR({inputs:{input:i},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(t),n}if(!ke(i.dtype,s)){const e=sO({inputs:{x:i},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:s}}if("int32"===s)return function(e,t){const n=new K$(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(i,r);if("bool"===s){const e=r.makeTensorInfo([],"bool",we("bool",1)),t=OR({inputs:{a:i,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${s}`)}},BR="return ceil(x);",PR=gO({opSnippet:BR,packedOpSnippet:BR,cpuKernelImpl:t$}),HR={kernelName:Mt,backendName:"webgl",kernelFunc:PR};class jR{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n "}}class UR{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n "}}const qR={kernelName:Nt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:i,clipValueMax:s}=r;let o;o=Ge().getBool("WEBGL_PACK_CLIP")?new UR(a.shape):new jR(a.shape);const u=[[i],[s]];return n.runWebGLProgram(o,[a],a.dtype,u)}};class WR{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n "}}function VR(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const GR={kernelName:kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),i=new WR(r.shape),s=[VR(r,a.complexTensorInfos.real),VR(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(i,s,s[0].dtype)}};class KR{constructor(e){this.outputShape=[],this.outputShape=Qh(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));const t=new Array(e.length-1);t[0]=e[0][1];for(let n=1;n`T${t}`));const o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n= ${o[e-1]}) {\n return getChannel(\n getT${e}(${XR(s,u,t)}),\n vec2(${XR(l,u,t)}));\n }`}const d=o.length,h=o[o.length-1];p+=`\n return getChannel(\n getT${d}(${XR(s,u,h)}),\n vec2(${XR(l,u,h)}));`,this.userCode=`\n float getValue(${s.map((e=>"int "+e))}) {\n ${p}\n }\n\n void main() {\n ${a} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[r-1]} = ${i[r-1]} + 1;\n if (${i[r-1]} < ${n[r-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[r-2]} = ${i[r-2]} + 1;\n if (${i[r-2]} < ${n[r-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[r-1]} = ${i[r-1]} - 1;\n if (${i[r-2]} < ${n[r-2]} &&\n ${i[r-1]} < ${n[r-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}}function XR(e,t,n){const r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function ZR(e){const{inputs:t,backend:n}=e,{input:r}=t;return sO({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const QR={kernelName:gn,backendName:"webgl",kernelFunc:ZR};function eF(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map((e=>FR({inputs:{input:e},backend:n}))),a=e.map((e=>ZR({inputs:{input:e},backend:n}))),i=eF(r,t,n),s=eF(a,t,n),o=uO({inputs:{real:i,imag:s},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),a.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(s),o}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){const a=e.map((e=>{const r=ce(e.shape.slice(t));return NO({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})})),i=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),s=Qh(a.map((e=>e.shape)),1),o=1===a[0].shape[0],u=n$(i,s,r,o),l=Qh(e.map((e=>e.shape)),t),c=n.makeTensorInfo(l,r,u);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}const i=Ge().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>i){const r=[];for(let a=0;a1){const a=new JR(e.map((e=>e.shape)),t);return n.runWebGLProgram(a,e,r)}const{tensors2D:s,outShape:o}=function(e,t,n){const r=Qh(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>NO({inputs:{x:e},attrs:{shape:[-1,ce(e.shape.slice(t))]},backend:n}))),outShape:r}}(e,t,n),u=new KR(s.map((e=>e.shape))),l=n.runWebGLProgram(u,s,r);s.forEach((e=>n.disposeIntermediateTensorInfo(e)));const c=NO({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function tF(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,i=ve(a,t[0].shape)[0],s=Qh(t.map((e=>e.shape)),i);if(0===ce(s))return n.makeTensorInfo(s,t[0].dtype,[]);const o=t.filter((e=>ce(e.shape)>0));return 1===o.length?sO({inputs:{x:o[0]},backend:n}):(Zh(o.map((e=>e.shape)),i),eF(o,i,n))}const nF={kernelName:Dt,backendName:"webgl",kernelFunc:tF};class rF{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,s=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let x="",v="";n&&(x=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,v="result = activation(result);");const _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${x}\n\n const ivec2 strides = ivec2(${o}, ${u});\n const ivec2 pads = ivec2(${i}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${b}];\n\n ivec2 xRCCorner =\n ivec2(coords[${g}], coords[${y}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${h}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${m}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${1===f}) {\n\n if (${m}) {\n dotProd +=\n getX(batch, xR, xC, ${h}) *\n getW(wR, wC, ${h}, d2);\n } else {\n dotProd +=\n getX(batch, ${h}, xR, xC) *\n getW(wR, wC, ${h}, d2);\n }\n\n } else if (${2===f}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${h}, d2),\n getW(wR, wC, ${h} + 1, d2)\n );\n\n if (${m}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${h}),\n getX(batch, xR, xC, ${h} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${h}, xR, xC),\n getX(batch, ${h} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${3===f}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${h}, d2),\n getW(wR, wC, ${h} + 1, d2),\n getW(wR, wC, ${h} + 2, d2)\n );\n\n if (${m}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${h}),\n getX(batch, xR, xC, ${h} + 1),\n getX(batch, xR, xC, ${h} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${h}, xR, xC),\n getX(batch, ${h} + 1, xR, xC),\n getX(batch, ${h} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${v}\n setOutput(result);\n }\n `}}class aF{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${a}, ${i}, ${s});\n const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${c}; wF++) {\n int xF = xFCorner + wF * ${o};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${h}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${1===f}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${h}) *\n getW(wF, wR, wC, ${h}, d2);\n } else if (${2===f}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${h}),\n getX(batch, xF, xR, xC, ${h} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${h}, d2),\n getW(wF, wR, wC, ${h} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${3===f}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${h}),\n getX(batch, xF, xR, xC, ${h} + 1),\n getX(batch, xF, xR, xC, ${h} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${h}, d2),\n getW(wF, wR, wC, ${h} + 1, d2),\n getW(wF, wR, wC, ${h} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class iF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);const{dataFormat:n}=t,r=yL(),a="channelsLast"===n,i=a?1:2,s=a?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let u="";for(let e=0;e<=1;e++)for(let t=0;t<=1;t++)u+=`\n blockIndex = rc.z + ${t};\n pos = rc.y + ${e};\n\n ${o}\n offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n if(d0 < inputShape[${i}] && d0 >= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${s}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${a}) {\n innerDims = vec2(d1, ch);\n result[${2*e+t}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${2*e+t}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${r.output} = result;\n }\n `}}function sF(e,t){const n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function oF({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:s=0,activation:o=null}){const u=e.shape,l=r.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h="channelsLast"===n.dataFormat;let f;const m=[];if(null!=i){const e=sF(i.shape,h);null!=e&&(i=NO({inputs:{x:i},backend:r,attrs:{shape:e}}),m.push(i))}if(null!=a){const e=sF(a.shape,h);null!=e&&(a=NO({inputs:{x:a},backend:r,attrs:{shape:e}}),m.push(a))}if((1!==p&&1!==d||!(c>1e3))&&l.isPacked&&h&&null!=l.texture&&u[2]%2!=0&&de(l.shape.slice(-3),u.slice(-3))){const c=u[0]*u[1]*(u[2]+1),p={dataId:e.dataId,shape:[1,c,n.inChannels],dtype:e.dtype},d=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,se(lL(l.shape,p.shape),(()=>`packed reshape ${l.shape} to ${p.shape} isn't free`));const h=NO({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});m.push(h);const g=RO({a:p,b:h,backend:r,transposeA:!1,transposeB:!1,bias:a,activation:o,preluActivationWeights:i,leakyreluAlpha:s}),y=r.texData.get(g.dataId);se(y.isPacked,(()=>"batchMatMul result is expected to be packed")),l.shape=d,y.shape=n.outShape,f=sO({inputs:{x:g},backend:r}),f.shape=n.outShape,m.push(g)}else{const u=n.outHeight*n.outWidth,l=NO({inputs:{x:e},backend:r,attrs:{shape:h?[n.batchSize,u,n.inChannels]:[n.batchSize,n.inChannels,u]}}),c=NO({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),p=RO({a:h?l:c,b:h?c:l,transposeA:!h,transposeB:!1,backend:r,bias:a,activation:o,preluActivationWeights:i,leakyreluAlpha:s});f=NO({inputs:{x:p},backend:r,attrs:{shape:n.outShape}}),m.push(l),m.push(c),m.push(p)}for(const e of m)r.disposeIntermediateTensorInfo(e);return f}function uF({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:s=0,activation:o=null}){const{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f="channelsLast"===h,m=u*l*c,g=d*p,y=[n.batchSize,m,g],b=[];if(null!=i){const e=sF(i.shape,f);null!=e&&(i=NO({inputs:{x:i},backend:r,attrs:{shape:e}}),b.push(i))}if(null!=a){const e=sF(a.shape,f);null!=e&&(a=NO({inputs:{x:a},backend:r,attrs:{shape:e}}),b.push(a))}const x=NO({inputs:{x:t},backend:r,attrs:{shape:[1,m,ce(t.shape)/m]}});b.push(x);const v=new iF(y,n),_=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],w=r.runWebGLProgram(v,[e],"float32",_),M=NO({inputs:{x:w},backend:r,attrs:{shape:y}});b.push(w),b.push(M);const N=null!=a,S=null!=i,k="leakyrelu"===o,D=o?bO(o,!0):null,T=new xO(f?M.shape:x.shape,f?x.shape:M.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],!0,!1,N,D,S,k),I=f?[M,x]:[x,M];if(a&&I.push(a),S&&I.push(i),k){const e=r.makeTensorInfo([],"float32",Fa(s,"float32"));I.push(e),b.push(e)}const E=r.runWebGLProgram(T,I,"float32"),C=NO({inputs:{x:E},backend:r,attrs:{shape:n.outShape}});b.push(E);for(const e of b)r.disposeIntermediateTensorInfo(e);return C}const lF={kernelName:Tt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=r,p=_l(u),d=dl(a.shape,i.shape,s,l,o,c,!1,p);let h;if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type)if(Ge().getBool("WEBGL_CONV_IM2COL"))h=uF({x:a,filter:i,convInfo:d,backend:n});else{const e=new rF(d);h=n.runWebGLProgram(e,[a,i],"float32")}else h=oF({x:a,filter:i,convInfo:d,backend:n});const f=NO({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}};class cF{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,i="channelsLast"===e.dataFormat;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${i}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class pF{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i="channelsLast"===e.dataFormat,s=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=i?1:2,l=i?2:3,c=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${s}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${c}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class dF{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,i=e.padInfo.top,s=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${a};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${r} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class hF{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${a}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${r}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${r} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const fF={kernelName:It,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=r,p=_l(u),d=dl(a.shape,c,s,1,o,l,!1,p),h=new cF(d);return n.runWebGLProgram(h,[a,i],"float32")}},mF={kernelName:Et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{inputShape:s,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=r,p=_l(l),d=dl(s,i.shape,o,1,u,c,!1,p),h=new pF(d);return n.runWebGLProgram(h,[a,i],"float32")}},gF={kernelName:Ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dilations:u}=r,l=hl(a.shape,i.shape,s,u,o),c=new aF(l);return n.runWebGLProgram(c,[a,i],"float32")}},yF={kernelName:At,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,pad:o,filterShape:u}=r,l=hl(a.shape,u,s,1,o),c=new dF(l);return n.runWebGLProgram(c,[a,i],"float32")}},bF={kernelName:Lt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{pad:s,strides:o,inputShape:u}=r,l=hl(u,i.shape,o,1,s),c=new hF(l);return n.runWebGLProgram(c,[a,i],"float32")}},xF=gO({opSnippet:"if (isnan(x)) return x;\n return cos(x);\n"}),vF={kernelName:$t,backendName:"webgl",kernelFunc:xF},_F=gO({opSnippet:"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n"}),wF={kernelName:Ot,backendName:"webgl",kernelFunc:_F};class MF{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[i,s,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];const d="bilinear"===r?1:0,[h,f]=[s-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(s-1)/(c-1),"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=p>1?[""+(o-1)/(p-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n const float height_ratio = float(${m});\n const float width_ratio = float(${b});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${g};\n float width_scale = ${x};\n\n float in_y = ${y};\n if( in_y < 0.0 || in_y > ${h} ) {\n setOutput(float(${a}));\n return;\n }\n float in_x = ${v};\n if( in_x < 0.0 || in_x > ${f} ) {\n setOutput(float(${a}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}}const NF={kernelName:Yt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:i,boxInd:s}=t,{cropSize:o,method:u,extrapolationValue:l}=r,c=new MF(a.shape,i.shape,o,u,l);return n.runWebGLProgram(c,[a,i,s],"float32")}};var SF;!function(e){e.Prod="*",e.Sum="+"}(SF||(SF={}));class kF{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const a=this.outputShape.length,i=this.op===SF.Prod?"1.0":"0.0",s=n?i:`getX(${DF(a,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1];let u="",l="";n?(u=r?"end != "+(o-1):"end != 0",l=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${o}`:"end >= pow2",l=r?"end + pow2":"end - pow2"),this.userCode=`\n void main() {\n ${AL(a)} coords = getOutputCoords();\n int end = ${TF(a,"coords",this.op)};\n float val = ${s};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${l};\n ${TF(a,"coords",this.op)} = idx;\n val ${this.op}= getX(${DF(a,"coords",this.op)});\n }\n setOutput(val);\n }\n `}}function DF(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function TF(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function IF(e,t,n,r,a,i){const s=t.shape.length,o=xc([r],s);let u=t;null!=o&&(u=$O({inputs:{x:t},backend:n,attrs:{perm:o}}));const l=_c(1,s)[0];if(l!==s-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);const c=u.shape[l];let p=sO({inputs:{x:u},backend:n});for(let t=0;t<=Math.ceil(Math.log2(c))-1;t++){const r=new kF(e,u.shape,!1,i),a=[[t]],s=p;p=n.runWebGLProgram(r,[p],p.dtype,a),n.disposeIntermediateTensorInfo(s)}if(a){const t=new kF(e,u.shape,a,i),r=p;p=n.runWebGLProgram(t,[p],p.dtype),n.disposeIntermediateTensorInfo(r)}if(null!=o){const e=$O({inputs:{x:p},backend:n,attrs:{perm:vc(o)}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),e}return p}const EF={kernelName:Rt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,exclusive:s,reverse:o}=r;return IF(SF.Prod,a,n,i,s,o)}},CF={kernelName:Ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,exclusive:s,reverse:o}=r;return IF(SF.Sum,a,n,i,s,o)}},AF={kernelName:zt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s,binaryOutput:o}=r;if(1===a.shape.length){const e=n.readSync(a.dataId),t=n.readSync(i.dataId),r=QL(e,t,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,r)}if(2===a.shape.length){const e=n.bufferSync(a),t=n.bufferSync(i),r=e$(e,t,s,o);return n.makeTensorInfo(r.shape,i.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};class LF{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const $F={kernelName:Bt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:i,dataFormat:s}=r,o=a.shape[0],u=("NHWC"===s?a.shape[1]:a.shape[2])*i,l=("NHWC"===s?a.shape[2]:a.shape[3])*i,c=("NHWC"===s?a.shape[3]:a.shape[1])/(i*i),p=new LF("NHWC"===s?[o,u,l,c]:[o,c,u,l],i,s);return n.runWebGLProgram(p,[a],a.dtype)}};class OF{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=YL(this.outputShape.length);const i=e.filterHeight,s=e.filterWidth,o=e.outChannels/e.inChannels;let u="",l="";n&&(u=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,l="result = activation(result);");const c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${o};\n int q = d2 - d1 * ${o};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${s}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${c}\n ${l}\n setOutput(result);\n }\n `}}class RF{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=YL(this.outputShape.length);const i=e.outChannels/e.inChannels,s=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c;let d="\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e=0 && xR < inDims[0]) {\n ";for(let e=0;e<(p+1)/2;e++){const t=2*e;if(d+=`\n xC = xCCorner + ${t*u};\n `,1===o){if(t= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n `,d+=1===u&&t>0?`\n xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n `:`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n } else {\n xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xC${t} = xTexelC${t};\n `,t+1= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n `,u>1&&(d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n xTexelC${t}Ready = 1;\n }\n `),d+=`\n xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);\n `):d+=1===e?`\n xC${t+1} = xTexelC${t};\n `:`\n xCOffset = xC + ${e};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t+1} = xTexelC${t+1};\n `}}else t= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n `,t+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(\n xTexelC${t}.xy, xTexelC${t+1}.xy);\n `,t+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`));const p=dl(a.shape,i.shape,s,c,o,l,!0);let d;d=Ge().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?new RF(p):new OF(p);const h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[a,i],"float32",h)}};class YF{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class zF{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i=t-1-e.padInfo.top,s=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${o}; dm++) {\n int d2 = d1 * ${o} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const BF={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=r,p=dl(a.shape,c,s,o,u,l,!0),d=new YF(p);return n.runWebGLProgram(d,[a,i],"float32")}},PF={kernelName:jt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{strides:s,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=r,p=dl(c,i.shape,s,o,u,l,!0),d=new zF(p);return n.runWebGLProgram(d,[a,i],"float32")}};class HF{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n "}}const jF={kernelName:Ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],i=ce(r.shape),s=NO({inputs:{x:r},backend:n,attrs:{shape:[i]}}),o=new HF(i),u=n.runWebGLProgram(o,[s],s.dtype),l=NO({inputs:{x:u},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(u),l}};class UF{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:i,filterHeight:s,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=r;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${i});\n const ivec2 pads = ivec2(${c}, ${p});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${s}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${o}; w++) {\n int wIn = wBeg + w * ${l};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}}const qF={kernelName:qt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dilations:u}=r,l=ll(a.shape,i.shape,s,o,"NHWC",u);let c;const p=new UF(l);c=n.runWebGLProgram(p,[a,i],"float32");const d=NO({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}},WF={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,i=t,{allDims:s,summedDims:o,idDims:u}=kf(a,i.length);Tf(s.length,u,i);const{path:l,steps:c}=If(o,u),p=c.length;let d=null,h=s.length;const f=[];for(let e=0;e=0&&(d=AO({inputs:{x:d},backend:n,attrs:{axis:l[e]-(s.length-h),keepDims:!1}}),f.push(d)),h--)}for(const e of f)e!==d&&n.disposeIntermediateTensorInfo(e);return d}},VF=gO({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n"}),GF={kernelName:Jt,backendName:"webgl",kernelFunc:VF},KF={kernelName:Xt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:r,y:a}=t,i=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new iO("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",r.shape,a.shape):new aO("return (b >= 1.0) ? a : a * (b + 1.0);",r.shape,a.shape);return n.runWebGLProgram(i,[r,a],r.dtype)}},JF=yO({opSnippet:"return float(a == b);",packedOpSnippet:"\n return vec4(equal(a, b));\n",dtype:"bool",cpuKernelImpl:r$}),XF={kernelName:Qt,backendName:"webgl",kernelFunc:JF},ZF=gO({opSnippet:`\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = ${pf};\n float a1 = ${df};\n float a2 = ${hf};\n float a3 = ${ff};\n float a4 = ${mf};\n float a5 = ${gf};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),QF={kernelName:Zt,backendName:"webgl",kernelFunc:ZF},eY=gO({opSnippet:"if (isnan(x)) return x;\n return exp(x);\n",packedOpSnippet:"\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",cpuKernelImpl:a$,dtype:"float32"}),tY={kernelName:en,backendName:"webgl",kernelFunc:eY};function nY(e){const{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:i}=t,s=i.shape.length,o=i.shape.slice();let u=a;return a<0&&(se(-(s+1)<=a,(()=>`Axis must be in the interval [${-(s+1)}, ${s}]`)),u=s+a+1),o.splice(u,0,1),NO({inputs:{x:i},backend:r,attrs:{shape:o}})}const rY={kernelName:tn,backendName:"webgl",kernelFunc:nY},aY="return exp(x) - 1.0;",iY=gO({opSnippet:aY,packedOpSnippet:aY,cpuKernelImpl:i$}),sY={kernelName:nn,backendName:"webgl",kernelFunc:iY};class oY{constructor(e,t,n){this.variableNames=["real","imag"];const r=t[1];this.outputShape=t;const a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${r}.0`:"1.0";let s;if("real"===e)s="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);s="return real * expI + imag * expR;"}this.userCode=`\n const float exponentMultiplier = ${a};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${s}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${r});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${r}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}}function uY(e,t,n){const r=n.texData.get(e.dataId),a=ce(e.shape),i=e.shape[e.shape.length-1],s=NO({inputs:{x:e},backend:n,attrs:{shape:[a/i,i]}}),o=s.shape,u=new oY("real",o,t),l=new oY("imag",o,t),c=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:o},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:o}],p=n.runWebGLProgram(u,c,"float32"),d=n.runWebGLProgram(l,c,"float32"),h=uO({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);const f=NO({inputs:{x:h},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(h),f}const lY={kernelName:rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return uY(r,!1,n)}};class cY{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n "}}function pY(e){const{backend:t,attrs:n}=e,{shape:r,value:a}=n;let{dtype:i}=n;if(i=i||Le(a),"string"===i){const e=Me(i,ce(r));return e.fill(a),t.makeTensorInfo(r,i,e)}{const e=new cY(r,a),n=[[a]];return t.runWebGLProgram(e,[],i,n)}}const dY={kernelName:an,backendName:"webgl",kernelFunc:pY};class hY{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const fY={kernelName:sn,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,a=new hY(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},mY="return floor(x);",gY=gO({opSnippet:mY,packedOpSnippet:mY,cpuKernelImpl:s$}),yY={kernelName:on,backendName:"webgl",kernelFunc:gY},bY=yO({opSnippet:"\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",packedOpSnippet:"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n",dtype:"int32"}),xY={kernelName:un,backendName:"webgl",kernelFunc:bY};class vY{constructor(e){this.variableNames=["A"];const t=yL(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}}class _Y{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=yL(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${r}.0, ${n}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}}const wY={kernelName:ia,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:a}=t;const{numChannels:i}=r,s="undefined"!=typeof HTMLVideoElement&&a instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&a instanceof HTMLImageElement,[u,l]=s?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[l,u],p=[l,u,i];(o||s)&&(null==MY&&(MY=document.createElement("canvas").getContext("2d")),MY.canvas.width=u,MY.canvas.height=l,MY.drawImage(a,0,0,u,l),a=MY.canvas);const d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=PA.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);const h=Ge().getBool("WEBGL_PACK")?new _Y(p):new vY(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}};let MY;const NY={kernelName:ua,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=_l(c),g=dl(a.shape,i.shape,u,p,l,d,!1,m);let y;const b=[];if(1!==g.filterHeight||1!==g.filterWidth||1!==g.dilationHeight||1!==g.dilationWidth||1!==g.strideHeight||1!==g.strideWidth||"SAME"!==g.padInfo.type&&"VALID"!==g.padInfo.type)if(Ge().getBool("WEBGL_CONV_IM2COL"))y=uF({x:a,filter:i,convInfo:g,backend:n,bias:s,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{const e=null!=s,t=null!=o,r="leakyrelu"===h,u=h?bO(h,!1):null,l=new rF(g,e,u,t,r),p=[a,i],d=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=NO({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return b.push(t),t}return e};if(e&&p.push(d(s,c)),t&&p.push(d(o,c)),r){const e=n.makeTensorInfo([],"float32",Fa(f,"float32"));p.push(e),b.push(e)}y=n.runWebGLProgram(l,p,"float32")}else y=oF({x:a,filter:i,convInfo:g,backend:n,bias:s,activation:h,preluActivationWeights:o,leakyreluAlpha:f});const x=NO({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach((e=>n.disposeIntermediateTensorInfo(e))),x}},SY={kernelName:la,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=[];let m=c;null==m&&(m=[1,1]),se(vl(u,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`));const g=dl(a.shape,i.shape,u,m,l,p,!0),y=Ge().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?bO(d,y):null,x=[a,i],v=null!=s,_=null!=o,w="leakyrelu"===d;if(v&&x.push(s),_&&x.push(o),w){const e=n.makeTensorInfo([],"float32",Fa(h,"float32"));x.push(e),f.push(e)}let M;M=y?new RF(g,v,b,_,w):new OF(g,v,b,_,w);const N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],S=n.runWebGLProgram(M,x,"float32",N);return f.forEach((e=>n.disposeIntermediateTensorInfo(e))),S}};class kY{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;const a=AL(t.length),i=AL(n.length),s=this.sliceDim>1?"strides[j]":"strides",o=AL(r.length),u=r.length>1?"paramsShape[j]":"paramsShape";this.userCode=`\n ${a} strides = ${a}(${this.strides});\n ${o} paramsShape = ${o}(${this.paramsShape});\n void main() {\n ${i} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n for (int j = 0; j < ${this.sliceDim}; j++) {\n int index = round(getIndices(coords[0], j));\n out_of_bounds = out_of_bounds || index < 0;\n out_of_bounds = out_of_bounds || index >= ${u};\n flattenIndex += index * ${s};\n }\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}}const DY={kernelName:pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,i=a.shape,s=i[i.length-1],o=ce(r.shape),[u,l,c,p]=Bo(r,a),d=NO({inputs:{x:a},backend:n,attrs:{shape:[l,s]}}),h=NO({inputs:{x:r},backend:n,attrs:{shape:[ce(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){const e=n.readSync(a.dataId),t=n.bufferSync(r),i=o$(e,t,r.dtype,l,s,c,p,r.shape,o);return n.makeTensorInfo(u,r.dtype,i.values)}const f=new kY(s,p,[l,c],r.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=NO({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}};class TY{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=AL(this.rank),r=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let t=0;t= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${r}));\n }\n `}}function IY(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,indices:i}=t,{axis:s,batchDims:o}=r,u=ve(s,a.shape)[0];if(Ge().get("DEBUG")){const e=n.readSync(i.dataId),t=a.shape[u];for(let n=0;n=0,(()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`))}}const l=Vf(a,i,u,o),c=ce(i.shape),p=[],d=NO({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=NO({inputs:{x:i},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);const f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([a,i])||"string"===a.dtype){const e=n.bufferSync(h),t=n.bufferSync(d),r=u$(t,e,f);return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(l.outputShape,r.dtype,r.values)}const m=new TY(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);const y=NO({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}const EY={kernelName:cn,backendName:"webgl",kernelFunc:IY},CY=yO({opSnippet:"return float(a > b);",packedOpSnippet:"\n return vec4(greaterThan(a, b));\n",cpuKernelImpl:l$,dtype:"bool"}),AY={kernelName:dn,backendName:"webgl",kernelFunc:CY},LY=yO({opSnippet:"return float(a >= b);",packedOpSnippet:"\n return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:c$}),$Y={kernelName:hn,backendName:"webgl",kernelFunc:LY},OY={kernelName:mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return uY(r,!0,n)}},RY=gO({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),FY={kernelName:yn,backendName:"webgl",kernelFunc:RY},YY=gO({opSnippet:"return float(isinf(x));",dtype:"bool"}),zY={kernelName:bn,backendName:"webgl",kernelFunc:YY},BY=gO({opSnippet:"return float(isnan(x));",dtype:"bool"}),PY={kernelName:xn,backendName:"webgl",kernelFunc:BY},HY=yO({opSnippet:"return float(a < b);",packedOpSnippet:"\n return vec4(lessThan(a, b));\n",cpuKernelImpl:p$,dtype:"bool"}),jY={kernelName:_n,backendName:"webgl",kernelFunc:HY},UY=yO({opSnippet:"return float(a <= b);",packedOpSnippet:"\n return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:d$,dtype:"bool"}),qY={kernelName:wn,backendName:"webgl",kernelFunc:UY},WY={kernelName:Mn,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,num:i}=n,s=h$(r,a,i);return t.makeTensorInfo([s.length],"float32",s)}},VY=gO({opSnippet:"if (isnan(x)) return x;\n return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n",cpuKernelImpl:f$}),GY={kernelName:Nn,backendName:"webgl",kernelFunc:VY},KY=gO({opSnippet:"if (isnan(x)) return x;\n return log(1.0 + x);\n"}),JY={kernelName:Sn,backendName:"webgl",kernelFunc:KY},XY=yO({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),ZY={kernelName:kn,backendName:"webgl",kernelFunc:XY},QY=gO({opSnippet:"return float(!(x >= 1.0));"}),ez={kernelName:Dn,backendName:"webgl",kernelFunc:QY},tz=yO({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n",dtype:"bool"}),nz={kernelName:Tn,backendName:"webgl",kernelFunc:tz};class rz{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];const i=t,s=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${s}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${o};\n setOutput(val);\n }\n `}}class az{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const i=t,s=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${s}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${o};\n setOutput(result);\n }\n `}}const iz={kernelName:An,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:i,bias:s,alpha:o,beta:u}=r,l=Ge().getBool("WEBGL_PACK_NORMALIZATION")?new az(a.shape,i,s,o,u):new rz(a.shape,i,s,o,u);return n.runWebGLProgram(l,[a],a.dtype)}};class sz{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${r}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${r})\n * float(${a})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${a});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}}const oz={kernelName:Ln,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a,y:i,dy:s}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=r,p=new sz(a.shape,o,u,l,c);return n.runWebGLProgram(p,[a,i,s],a.dtype)}};function uz(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:i,keepDims:s}=r,o=a.shape.length,u=ve(i,a.shape);let l=u;const c=xc(l,o),p=null!=c,d=n.shouldExecuteOnCPU([a]);let h=a;if(p){if(d){const e=n.texData.get(h.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:g$}),pz={kernelName:On,backendName:"webgl",kernelFunc:cz},dz={kernelName:Rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;mL(a,"maxPool");const{filterSize:i,strides:s,pad:o,dimRoundingMode:u}=r;se(vl(s,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))return sO({inputs:{x:a},backend:n});const c=new mR(l,"max",!1);return n.runWebGLProgram(c,[a],a.dtype)}},hz={kernelName:Yn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dataFormat:u,dimRoundingMode:l}=r,c=pl(a.shape,i,s,[1,1,1],o,l,u),p=new gR(c,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}};class fz{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,i=e.effectiveFilterWidth,s=a-1-e.padInfo.top,o=i-1-e.padInfo.left,u=a*i-1;this.userCode=`\n const ivec2 pads = ivec2(${s}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${a};\n wR += ${r}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}}class mz{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,s=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${o};\n wD += ${a}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC += ${s}) {\n float dyC = float(dyCCorner + wC) / ${r}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${h} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${l} +\n wR * ${l} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const gz={kernelName:zn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=r,p=pl(s.shape,o,u,[1,1,1],l,c),d=new gR(p,"max",!0),h=n.runWebGLProgram(d,[s],s.dtype),f=new mz(p),m=n.runWebGLProgram(f,[a,h],s.dtype);return n.disposeIntermediateTensorInfo(h),m}},yz={kernelName:Fn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i,output:s}=t,o=i;mL([i,s],"maxPoolGrad");const{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r,d=cl(o.shape,u,l,1,c,p),h=new mR(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new fz(d),g=n.runWebGLProgram(m,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}},bz={kernelName:Bn,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:a,strides:i,pad:s,includeBatchInIndex:o}=t,u=n;se(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));const l=[1,1];se(vl(i,l),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`));const c=cl(r.shape,a,i,l,s),[p,d]=function(e,t,n,r){let a=new mR(n,"max",!1);const i=r.runWebGLProgram(a,[e],"float32");return a=new mR(n,"max",!0,!0,t),[i,r.runWebGLProgram(a,[e],"float32")]}(r,o,c,u);return[p,d]}},xz={kernelName:Pn,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:a,axis:i}=t,s=n,o=r.shape.length,u=ve(i,r.shape);let l=u;const c=xc(l,o),p=null!=c,d=s.shouldExecuteOnCPU([r]),h=[];let f=r;if(p){if(d){const e=s.texData.get(f.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:y$}),wz={kernelName:jn,backendName:"webgl",kernelFunc:_z};class Mz{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=AL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u="reflect"===n?0:1;this.userCode=1!==r?`\n ${a} start = ${a}(${i});\n ${a} end = ${a}(${s});\n\n void main() {\n ${a} outC = getOutputCoords();\n for (int i = 0; i < ${r}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${u};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n }\n }\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n `:`\n int start = ${i};\n int end = ${s};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `}}class Nz{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=AL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=H$("rc",r),u=H$("source",r),l=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p="reflect"===n?0:1;let d="";if(1===r){const e=`\n ${a} source = rc;\n if (source < start) {\n source = start * 2 - source - ${p};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${p};\n }\n source -= start;\n `;d=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n `}else{const e=`\n ${a} source = rc;\n ${a} lt = ${a}(lessThan(source, start));\n ${a} gte = ${a}(greaterThanEqual(source, end));\n ${a} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${p}) +\n gte * ((end - 1) * 2 - source + ${p});\n source -= start;\n `;d=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {\n ${e}\n result[2] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[3] = getChannel(getX(${u.join()}), ${c});\n }\n }\n `}this.userCode=`\n const ${a} start = ${a}(${i});\n const ${a} end = ${a}(${s});\n\n void main() {\n ${a} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${d}\n setOutput(result);\n }\n `}}const Sz={kernelName:Un,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r}=e,{paddings:a,mode:i}=n,s=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nz(r.shape,a,i):new Mz(r.shape,a,i);return t.runWebGLProgram(s,[r],r.dtype)}},kz=yO({opSnippet:"if (b == 0.0) return NAN;\n return mod(a, b);",packedOpSnippet:"\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),Dz={kernelName:qn,backendName:"webgl",kernelFunc:kz};class Tz{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${t-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${t-1}));\n }\n `}}const Iz=yO({opSnippet:"\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n",checkOutOfBounds:!0}),Ez={kernelName:Gt,backendName:"webgl",kernelFunc:Iz},Cz="return a - b;",Az=yO({opSnippet:Cz,packedOpSnippet:Cz,supportsComplex:!0,cpuKernelImpl:R$}),Lz={kernelName:Wr,backendName:"webgl",kernelFunc:Az};function $z(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:i}=r,s=ve([i],a.shape),o=uz({inputs:{x:a},backend:n,attrs:{reductionIndices:s,keepDims:!1}}),u=yc(o.shape,s),l=NO({inputs:{x:o},backend:n,attrs:{shape:u}}),c=Az({inputs:{a,b:l},backend:n}),p=eY({inputs:{x:c},backend:n}),d=AO({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:!1}}),h=NO({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Iz({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}const Oz={kernelName:$r,backendName:"webgl",kernelFunc:$z},Rz={kernelName:Wn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:i,seed:s,normalized:o}=r,u=o?a:$z({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new Tz(l,c,i),d=[[s]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}},Fz={kernelName:Gn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){const e=n.texData.get(r.dataId),[t,a]=x$(e.values,r.shape,r.dtype);return n.makeTensorInfo(a,r.dtype,t)}let a;return a=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new Z$(r.shape,"\n vec4 result = -x;\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"):new K$(r.shape,"if (isnan(x)) return x;\n return -x;\n"),n.runWebGLProgram(a,[r],r.dtype)}},Yz=uh,zz={kernelName:Jn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u}=r,l=n.readSync(a.dataId),c=n.readSync(i.dataId),{selectedIndices:p}=Yz(l,c,s,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}},Bz=lh,Pz={kernelName:Xn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=r,c=n.readSync(a.dataId),p=n.readSync(i.dataId),{selectedIndices:d,validOutputs:h}=Bz(c,p,s,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}},Hz=ch,jz={kernelName:Zn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=r,c=n.readSync(a.dataId),p=n.readSync(i.dataId),d=s,h=o,f=u,m=l,{selectedIndices:g,selectedScores:y}=Hz(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class Uz{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${r}), float(${n}),\n float(index == coords.y)));\n }\n `}}const qz={kernelName:er,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:i,onValue:s,offValue:o}=r,u=ce(a.shape),l=new Uz(u,i,s,o),c=NO({inputs:{x:a},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],a.dtype);n.disposeIntermediateTensorInfo(c);const d=NO({inputs:{x:p},backend:n,attrs:{shape:[...a.shape,i]}});return n.disposeIntermediateTensorInfo(p),d}};function Wz(e){const{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){const e=FR({inputs:{input:r},backend:n}),t=Wz({inputs:{x:e},backend:n}),a=ZR({inputs:{input:r},backend:n}),i=Wz({inputs:{x:a},backend:n}),s=uO({inputs:{real:t,imag:i},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),s}return pY({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const Vz={kernelName:ra,backendName:"webgl",kernelFunc:Wz},Gz={kernelName:Qn,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===a.dtype){const t=FR({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),i=ZR({inputs:{input:a},backend:r}),s=Wz({inputs:{x:i},backend:r}),o=uO({inputs:{real:n,imag:s},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(s),o}return pY({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}},Kz={kernelName:tr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return nY({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const i=t[0].shape,s=t[0].dtype;t.forEach((e=>{oe(i,e.shape,"All tensors passed to stack must have matching shapes"),se(s===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],u=tF({inputs:t.map((e=>{const t=nY({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),u}};class Jz{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=AL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n ${a} start = ${a}(${i});\n ${a} end = ${a}(${s});\n\n void main() {\n ${a} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n }\n `:`\n int start = ${i};\n int end = ${s};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `}}class Xz{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=AL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=H$("rc",r),u=H$("source",r),l=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;\n if(${l}) {\n `,1===r?"":`}\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":` ${o[r-1]} += 1;\n if(${l}) {`],d=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let h="";for(let e=0,t=1===r?2:4;e{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:i,constantValue:s}=r;if(0===ce(a.shape)){const e=i.map(((e,t)=>e[0]+a.shape[t]+e[1]));return pY({backend:n,attrs:{shape:e,value:s,dtype:a.dtype}})}const o=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xz(a.shape,i,s):new Jz(a.shape,i,s),u=[[s]];return n.runWebGLProgram(o,[a],a.dtype,u)},Qz={kernelName:nr,backendName:"webgl",kernelFunc:Zz},eB=yO({opSnippet:"\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),tB={kernelName:ar,backendName:"webgl",kernelFunc:eB},nB={kernelName:sr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=[],l=ve(i,a.shape);let c=l;const p=xc(c,o);let d,h=a;if(null!=p&&(h=$O({inputs:{x:a},backend:n,attrs:{perm:p}}),c=_c(c.length,o),u.push(h)),bc("prod",c,o),n.shouldExecuteOnCPU([h])){const e=n.texData.get(h.dataId).values,{outVals:t,outShape:r,outDtype:a}=_$(h.shape,h.dtype,e,c);d=n.makeTensorInfo(r,a,t)}else{const[e,t]=gc(h.shape,c),r=ce(t),i=NO({inputs:{x:h},backend:n,attrs:{shape:[-1,r]}}),s=TO(i,pi(a.dtype),"prod",n);d=NO({inputs:{x:s},backend:n,attrs:{shape:e}}),u.push(i),u.push(s)}if(s){u.push(d);const e=yc(d.shape,l);d=NO({inputs:{x:d},backend:n,attrs:{shape:e}})}return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}},rB=e=>{const{backend:t,attrs:n}=e,{start:r,stop:a,step:i,dtype:s}=n,o=w$(r,a,i,s);return t.makeTensorInfo([o.length],s,o)},aB={kernelName:or,backendName:"webgl",kernelFunc:rB},iB=gO({opSnippet:"return 1.0 / x;"}),sB={kernelName:lr,backendName:"webgl",kernelFunc:iB},oB=gO({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),uB={kernelName:cr,backendName:"webgl",kernelFunc:oB},lB=gO({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),cB={kernelName:gr,backendName:"webgl",kernelFunc:lB};class pB{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${s}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}}class dB{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${s}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}}const hB={kernelName:fr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:i,halfPixelCenters:s,size:o}=r,[u,l]=o,c=Ge().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dB(a.shape,u,l,i,s):new pB(a.shape,u,l,i,s);return n.runWebGLProgram(c,[a],"float32")}};class fB{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,i,s]=e,o=[n&&i>1?r-1:r,n&&s>1?a-1:a],u=[n&&i>1?i-1:i,n&&s>1?s-1:s],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=2*Math.ceil(p)+2,f=2*Math.ceil(d)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${h});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${s}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const mB={kernelName:mr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:i}=t,{alignCorners:s}=r,o=new fB(i.shape,a.shape,s);return n.runWebGLProgram(o,[i],i.dtype)}};class gB{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let d;d=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${s}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}}class yB{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let d;d=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${s}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}}const bB={kernelName:dr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:i,halfPixelCenters:s,size:o}=r,[u,l]=o,c=Ge().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yB(a.shape,u,l,i,s):new gB(a.shape,u,l,i,s);return n.runWebGLProgram(c,[a],a.dtype)}};class xB{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,i,s]=e,o=[n&&i>1?r-1:r,n&&s>1?a-1:a],u=[n&&i>1?i-1:i,n&&s>1?s-1:s],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=2*Math.ceil(p)+2,f=2*Math.ceil(d)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${h});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${s}) {\n continue;\n }\n\n float sourceFracRow =\n float(${o[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${o[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${r}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${a}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const vB={kernelName:hr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:i}=t,{alignCorners:s}=r,o=new xB(i.shape,a.shape,s);return n.runWebGLProgram(o,[i],i.dtype)}};class _B{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `);const r=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r))).join(","),a=AL(n);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${r}));\n }\n `}}class wB{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const r=H$("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,s=AL(n);function o(n){const r=e.map(((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(a,n)));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${a}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:`\n void main() {\n ${s} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${function(e){return o(e)}(r.slice())};\n if(${a}){\n result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(r.slice())};\n }\n if(${i}) {\n result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n if(${a}) {\n result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n }\n }\n setOutput(result);\n }\n `}}const MB={kernelName:yr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:i}=r,s=a.shape.length,o=ve(i,a.shape);if(0===s)return sO({inputs:{x:a},backend:n});const u=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wB(a.shape,o):new _B(a.shape,o);return n.runWebGLProgram(u,[a],a.dtype)}};class NB{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const n=e[1],r=e[2];this.outputShape=e;let a="";a="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n vec3 fill = vec3(${t.join(",")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${a}\n if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const SB={kernelName:sa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,{radians:a,fillValue:i,center:s}=t,o=n,u=new NB(r.shape,i),[l,c]=nf(s,r.shape[1],r.shape[2]),p=[[l,c,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(u,[r],r.dtype,p)}},kB=gO({opSnippet:"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n"}),DB={kernelName:br,backendName:"webgl",kernelFunc:kB},TB=gO({opSnippet:"return inversesqrt(x);",cpuKernelImpl:M$}),IB={kernelName:xr,backendName:"webgl",kernelFunc:TB};class EB{constructor(e,t,n,r,a,i,s=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;const o=AL(a.length),u=AL(i.length);let l="";1===n?l="i":2===n&&(l="i, j");const c=`getIndices(${l})`;let p="";1===r?p="i":2===r&&(p="i, coords[1]");const d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`\n ${o} strides = ${o}(${a});\n\n void main() {\n ${u} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${c});\n flattenedIndex += index * ${h};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `}}const CB={kernelName:vr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:i}=t,{shape:s}=r,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=jo(0,a,s),d=[p/l,l];if(0===p)return n.makeTensorInfo(s,a.dtype);const h=NO({inputs:{x:a},backend:n,attrs:{shape:[u,o]}}),f=NO({inputs:{x:i},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new EB(u,o,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),b=NO({inputs:{x:y},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class AB{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];const a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=2===Ge().getNumber("WEBGL_VERSION")?"while (left < right) {":a,s="left"===r?"<":"<=";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${s} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}}const LB={kernelName:_r,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sortedSequence:a,values:i}=t,{side:s}=r,o=new AB(a.shape[0],a.shape[1],i.shape[1],s),u=[[a.shape[1]]];return n.runWebGLProgram(o,[a,i],"int32",u)}};class $B{constructor(e,t,n){let r,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",r="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[];for(let r=0;r= 1.0) {\n setOutput(getA(${a}));\n } else {\n setOutput(getB(${a}));\n }\n }\n `}}const OB={kernelName:wr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:r,t:a,e:i}=t,s=new $B(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(s,[r,a,i],ci(a.dtype,i.dtype))}},RB=gO({opSnippet:`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${lf};\n float scale = ${cf};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),FB={kernelName:Mr,backendName:"webgl",kernelFunc:RB},YB=gO({opSnippet:"if 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(too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n"}),VB={kernelName:Ir,backendName:"webgl",kernelFunc:WB},GB={kernelName:Ar,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:i,paddings:s}=r;se(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const o=i.reduce(((e,t)=>e*t)),u=[[0,0]];u.push(...s);for(let e=1+i.length;en.disposeIntermediateTensorInfo(e))),g}},KB={kernelName:Or,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:i,defaultValue:s}=t;if(1!==i.shape.length)throw new Error(`Dense shape must be a vector, saw:\n ${i.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n ${a.shape}`);if(0!==s.shape.length)throw new Error(`Default value must be a scalar, saw:\n ${s.shape}`);const o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=n.readSync(i.dataId),c=n.readSync(s.dataId)[0],[p,d,h,f,m]=T$(o,r.shape,r.dtype,u,a.dtype,l,c);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],a.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map((e=>Number(e))))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}},JB={kernelName:Rr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:i}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(1!==i.shape.length)throw new Error(`Target shape should be a vector but received shape ${i.shape}`);const s=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),u=Array.from(n.readSync(i.dataId)),[l,c,p]=I$(o,r.shape,r.dtype,s,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([p.length],i.dtype,new Int32Array(p))]}},XB={kernelName:Fr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:i}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n ${a.shape}`);if(1!==i.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${i.shape}`);const s=n.readSync(r.dataId),o=n.readSync(a.dataId),u=n.readSync(i.dataId),[l,c]=E$(s,r.shape,r.dtype,o,u,!0);return n.makeTensorInfo(c,r.dtype,l)}},ZB={kernelName:Yr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:i}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n ${a.shape}`);if(1!==i.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${i.shape}`);const s=n.readSync(r.dataId),o=n.readSync(a.dataId),u=n.readSync(i.dataId),[l,c]=E$(s,r.shape,r.dtype,o,u);return n.makeTensorInfo(c,r.dtype,l)}},QB={kernelName:zr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:i,defaultValue:s}=t,{outputShape:o}=r,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=jo(0,a,o);if("string"===i.dtype){const e=n.bufferSync(a),t=n.bufferSync(i),r=Ha(n.readSync(s.dataId)[0]),h=N$(e,t,o,d,c,l,u,p,r,!1);return n.makeTensorInfo(o,h.dtype,h.values)}const h=new EB(l,u,a.shape.length,i.shape.length,p,[d,1],!1),f=n.runWebGLProgram(h,[i,a,s],i.dtype),m=NO({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}},eP={kernelName:Lr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:i,axis:s}=r,o=ve(s,a.shape)[0],u=Af(a,i,o),l=a.shape.length,c=new Array(l).fill(0),p=a.shape.slice();return u.map((e=>{const t=[...p];t[o]=e;const r=ER({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,r}))}},tP="return sqrt(x);",nP=gO({opSnippet:tP,packedOpSnippet:tP,cpuKernelImpl:C$}),rP={kernelName:Er,backendName:"webgl",kernelFunc:nP},aP=gO({opSnippet:"return x * x;"}),iP={kernelName:Pr,backendName:"webgl",kernelFunc:aP},sP="return (a - b) * (a - b);",oP=yO({opSnippet:sP,packedOpSnippet:sP}),uP={kernelName:Br,backendName:"webgl",kernelFunc:oP},lP={kernelName:aa,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:r}=e,a=`if (isnan(x)) return x;\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,i=new K$(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}};class cP{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const r=n.length,a=AL(n.length),i=AL(n.length);let s="";if(1===r)s="coords * strides + begin";else{let e=0;s=n.map(((t,r)=>(e++,1===n.length?`coords * strides[${r}] + begin[${r}]`:`coords[${e-1}] * strides[${r}] + begin[${r}]`))).join(",")}this.userCode=`\n ${a} begin = ${a}(${e});\n ${a} strides = ${a}(${t});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}}const pP={kernelName:Hr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:i,end:s,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:v}=iu(a.shape,i,s,o,u,l,c,p,d);let _;if(m)_=NO({inputs:{x:a},backend:n,attrs:{shape:f}});else if(g||y){se(a.shape.length>=1,(()=>`Input must have rank at least 1, got: ${a.shape.length}`));const e=Wo(b,x,v),t=ER({inputs:{x:a},backend:n,attrs:{begin:b,size:e}});_=NO({inputs:{x:t},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(t)}else if(n.shouldExecuteOnCPU([a])){const e=n.readSync(a.dataId),t=As(a.shape,a.dtype,e),r=A$(h,t,v,b);_=n.makeTensorInfo(f,a.dtype,r.values)}else{const e=new cP(b,v,h);_=n.runWebGLProgram(e,[a],a.dtype)}const w=NO({inputs:{x:_},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(_),w}},dP={kernelName:jr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:a,nGramWidths:i,leftPad:s,rightPad:o,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=L$(d,h,a,i,s,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}},hP={kernelName:Ur,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:a}=r,{input:i,delimiter:s}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(1!==i.shape.length)throw new Error(`Input must be a vector, got shape: ${i.shape}`);if(0!==s.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${s.shape}`);const o=n.readSync(i.dataId),u=n.readSync(s.dataId)[0],[l,c,p]=$$(o,u,a),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}},fP={kernelName:qr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:a}=r,{input:i}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");const s=n.readSync(i.dataId),o=O$(s,a);return n.makeTensorInfo(i.shape,"int32",o)}},mP=gO({opSnippet:"return tan(x);"}),gP={kernelName:Vr,backendName:"webgl",kernelFunc:mP},yP=gO({opSnippet:"\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),bP={kernelName:Gr,backendName:"webgl",kernelFunc:yP};class xP{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let r=0;r5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let t=0;t5){const e=n.readSync(a.dataId),t="string"===a.dtype?e.map((e=>Ha(e))):e,r=As(a.shape,a.dtype,t),s=F$(r,i);return n.makeTensorInfo(s.shape,s.dtype,s.values)}const s=new xP(a.shape,i);return n.runWebGLProgram(s,[a],a.dtype)}const _P={kernelName:Kr,backendName:"webgl",kernelFunc:vP};class wP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n "}}class MP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode="\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n "}}function NP(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function SP(e){let t=1;for(;tu){const e=n.readSync(a.dataId),[t,r]=Y$(e,l,a.dtype,i,s);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(r.shape,r.dtype,r.values)]}if(0===i)return l[l.length-1]=0,[n.makeTensorInfo(l,a.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(1===c)return[a,pY({attrs:{shape:l,dtype:"int32",value:0},backend:n})];const p=n.texData.get(a.dataId),d=null!==p&&p.isPacked,h=d?n.unpackTensor(a):a,f=ce(l)/c,m=NO({inputs:{x:h},attrs:{shape:[f,c]},backend:n});d&&NP(n,h);const g=SP(i),y=SP(c);let b=null;const x=()=>null===b?[m,m]:[m,b],v=(e,t,r)=>{const a=x(),i=new wP(r),s=[[c],[null===b?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],o=b;b=n.runWebGLProgram(i,a,"int32",s),NP(n,o)};for(let e=1;e=1;n/=2)v(t,n,[f,y])}for(let e=y;e>g;e/=2){const t=x(),r=new MP([f,e/2]),a=[[c],[null===b?1:0],[g]],i=b;b=n.runWebGLProgram(r,t,"int32",a),NP(n,i);const s=g/2,o=2*s;for(let e=s;e>=1;e/=2)v(o,e,b.shape)}let _=b;b=ER({inputs:{x:b},backend:n,attrs:{begin:0,size:[f,i]}}),NP(n,_);let w=IY({inputs:{x:m,indices:b},backend:n,attrs:{axis:1,batchDims:1}});NP(n,m);const M=l.slice(0,-1);M.push(i),_=b,b=NO({inputs:{x:b},attrs:{shape:M},backend:n}),NP(n,_);const N=w;return w=NO({inputs:{x:w},attrs:{shape:M},backend:n}),NP(n,N),[w,b]}};class DP{constructor(e,t,n,r,a,i){this.variableNames=["Image","Transforms"],this.outputShape=i;const s="nearest"===n?1:2;let o;switch(r){case"constant":default:o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${o} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${o} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${o} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${a});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${a});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${t}));\n float mapY = mapCoord(inY, float(${e}));\n\n if (${s} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}}const TP={kernelName:Xr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:i}=t,{interpolation:s,fillMode:o,fillValue:u,outputShape:l}=r,[c,p,d,h]=a.shape,[f,m]=null!=l?l:[p,d],g=new DP(p,d,s,o,u,[c,f,m,h]);return n.runWebGLProgram(g,[a,i],"float32")}},IP={kernelName:Qr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:i}=t;mL(i,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const s=r.readSync(i.dataId),{outputValues:o,outputShape:u,indices:l}=B$(s,a,i.shape,i.dtype);return[r.makeTensorInfo(u,i.dtype,o),r.makeTensorInfo([l.length],"int32",l)]}},EP={kernelName:ea,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{value:a}=t;let{axis:i}=r;i<0&&(i+=a.shape.length);const s=a,o=s.shape.length,u=a.shape[i],l=new Array(o-1);let c=0;for(let e=0;en.disposeIntermediateTensorInfo(e))),f}};class CP{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,r=e.batchSize,a=e.inSize,i=e.numSegments,s=i*Math.ceil(a/n);this.outputShape=[r,s];const o=4*Math.floor(n/4),u=n%4,l="\n sumValue += dot(values, segFilter);\n ";let c="";a%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return initializationValue;\n }\n `);let p="";a%n>0&&(p=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${p}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${o}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 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t(V7,{string:function(e){var t=Rq();return n(e).compile().evaluate(t)},"string, Map | Object":function(e,t){return n(e).compile().evaluate(t)},"Array | Matrix":function(e){var t=Rq();return Gq(e,(function(e){return n(e).compile().evaluate(t)}))},"Array | Matrix, Map | Object":function(e,t){return Gq(e,(function(e){return n(e).compile().evaluate(t)}))}})})),K7={name:"e",category:"Constants",syntax:["e"],description:"Euler's number, the base of the natural logarithm. Approximately equal to 2.71828",examples:["e","e ^ 2","exp(2)","log(e)"],seealso:["exp"]},J7={name:"pi",category:"Constants",syntax:["pi"],description:"The number pi is a mathematical constant that is the ratio of a circle's circumference to its diameter, and is approximately equal to 3.14159",examples:["pi","sin(pi/2)"],seealso:["tau"]},X7={bignumber:{name:"bignumber",category:"Construction",syntax:["bignumber(x)"],description:"Create a big number from a number or string.",examples:["0.1 + 0.2","bignumber(0.1) + bignumber(0.2)",'bignumber("7.2")','bignumber("7.2e500")',"bignumber([0.1, 0.2, 0.3])"],seealso:["boolean","complex","fraction","index","matrix","string","unit"]},boolean:{name:"boolean",category:"Construction",syntax:["x","boolean(x)"],description:"Convert a string or number into a boolean.",examples:["boolean(0)","boolean(1)","boolean(3)",'boolean("true")','boolean("false")',"boolean([1, 0, 1, 1])"],seealso:["bignumber","complex","index","matrix","number","string","unit"]},complex:{name:"complex",category:"Construction",syntax:["complex()","complex(re, im)","complex(string)"],description:"Create a complex number.",examples:["complex()","complex(2, 3)",'complex("7 - 2i")'],seealso:["bignumber","boolean","index","matrix","number","string","unit"]},createUnit:{name:"createUnit",category:"Construction",syntax:["createUnit(definitions)","createUnit(name, definition)"],description:"Create a user-defined unit and register it with the Unit type.",examples:['createUnit("foo")','createUnit("knot", {definition: "0.514444444 m/s", aliases: ["knots", "kt", "kts"]})','createUnit("mph", "1 mile/hour")'],seealso:["unit","splitUnit"]},fraction:{name:"fraction",category:"Construction",syntax:["fraction(num)","fraction(matrix)","fraction(num,den)","fraction({n: num, d: den})"],description:"Create a fraction from a number or from integer numerator and denominator.",examples:["fraction(0.125)","fraction(1, 3) + fraction(2, 5)","fraction({n: 333, d: 53})","fraction([sqrt(9), sqrt(10), sqrt(11)])"],seealso:["bignumber","boolean","complex","index","matrix","string","unit"]},index:{name:"index",category:"Construction",syntax:["[start]","[start:end]","[start:step:end]","[start1, start 2, ...]","[start1:end1, start2:end2, ...]","[start1:step1:end1, start2:step2:end2, ...]"],description:"Create an index to get or replace a subset of a matrix",examples:["[]","[1, 2, 3]","A = [1, 2, 3; 4, 5, 6]","A[1, :]","A[1, 2] = 50","A[0:2, 0:2] = ones(2, 2)"],seealso:["bignumber","boolean","complex","matrix,","number","range","string","unit"]},matrix:{name:"matrix",category:"Construction",syntax:["[]","[a1, b1, ...; a2, b2, ...]","matrix()",'matrix("dense")',"matrix([...])"],description:"Create a matrix.",examples:["[]","[1, 2, 3]","[1, 2, 3; 4, 5, 6]","matrix()","matrix([3, 4])",'matrix([3, 4; 5, 6], "sparse")','matrix([3, 4; 5, 6], "sparse", "number")'],seealso:["bignumber","boolean","complex","index","number","string","unit","sparse"]},number:{name:"number",category:"Construction",syntax:["x","number(x)","number(unit, valuelessUnit)"],description:"Create a number or convert a string or boolean into a number.",examples:["2","2e3","4.05","number(2)",'number("7.2")',"number(true)","number([true, false, true, true])",'number(unit("52cm"), "m")'],seealso:["bignumber","boolean","complex","fraction","index","matrix","string","unit"]},sparse:{name:"sparse",category:"Construction",syntax:["sparse()","sparse([a1, b1, ...; a1, b2, ...])",'sparse([a1, b1, ...; a1, b2, ...], "number")'],description:"Create a sparse matrix.",examples:["sparse()","sparse([3, 4; 5, 6])",'sparse([3, 0; 5, 0], "number")'],seealso:["bignumber","boolean","complex","index","number","string","unit","matrix"]},splitUnit:{name:"splitUnit",category:"Construction",syntax:["splitUnit(unit: Unit, parts: Unit[])"],description:"Split a unit in an array of units whose sum is equal to the original unit.",examples:['splitUnit(1 m, ["feet", "inch"])'],seealso:["unit","createUnit"]},string:{name:"string",category:"Construction",syntax:['"text"',"string(x)"],description:"Create a string or convert a value to a string",examples:['"Hello World!"',"string(4.2)","string(3 + 2i)"],seealso:["bignumber","boolean","complex","index","matrix","number","unit"]},unit:{name:"unit",category:"Construction",syntax:["value unit","unit(value, unit)","unit(string)"],description:"Create a unit.",examples:["5.5 mm","3 inch",'unit(7.1, "kilogram")','unit("23 deg")'],seealso:["bignumber","boolean","complex","index","matrix","number","string"]},e:K7,E:K7,false:{name:"false",category:"Constants",syntax:["false"],description:"Boolean value false",examples:["false"],seealso:["true"]},i:{name:"i",category:"Constants",syntax:["i"],description:"Imaginary unit, defined as i*i=-1. A complex number is described as a + b*i, where a is the real part, and b is the imaginary part.",examples:["i","i * i","sqrt(-1)"],seealso:[]},Infinity:{name:"Infinity",category:"Constants",syntax:["Infinity"],description:"Infinity, a number which is larger than the maximum number that can be handled by a floating point number.",examples:["Infinity","1 / 0"],seealso:[]},LN2:{name:"LN2",category:"Constants",syntax:["LN2"],description:"Returns the natural logarithm of 2, approximately equal to 0.693",examples:["LN2","log(2)"],seealso:[]},LN10:{name:"LN10",category:"Constants",syntax:["LN10"],description:"Returns the natural logarithm of 10, approximately equal to 2.302",examples:["LN10","log(10)"],seealso:[]},LOG2E:{name:"LOG2E",category:"Constants",syntax:["LOG2E"],description:"Returns the base-2 logarithm of E, approximately equal to 1.442",examples:["LOG2E","log(e, 2)"],seealso:[]},LOG10E:{name:"LOG10E",category:"Constants",syntax:["LOG10E"],description:"Returns the base-10 logarithm of E, approximately equal to 0.434",examples:["LOG10E","log(e, 10)"],seealso:[]},NaN:{name:"NaN",category:"Constants",syntax:["NaN"],description:"Not a number",examples:["NaN","0 / 0"],seealso:[]},null:{name:"null",category:"Constants",syntax:["null"],description:"Value null",examples:["null"],seealso:["true","false"]},pi:J7,PI:J7,phi:{name:"phi",category:"Constants",syntax:["phi"],description:"Phi is the golden ratio. Two quantities are in the golden ratio if their ratio is the same as the ratio of their sum to the larger of the two quantities. Phi is defined as `(1 + sqrt(5)) / 2` and is approximately 1.618034...",examples:["phi"],seealso:[]},SQRT1_2:{name:"SQRT1_2",category:"Constants",syntax:["SQRT1_2"],description:"Returns the square root of 1/2, approximately equal to 0.707",examples:["SQRT1_2","sqrt(1/2)"],seealso:[]},SQRT2:{name:"SQRT2",category:"Constants",syntax:["SQRT2"],description:"Returns the square root of 2, approximately equal to 1.414",examples:["SQRT2","sqrt(2)"],seealso:[]},tau:{name:"tau",category:"Constants",syntax:["tau"],description:"Tau is the ratio constant of a circle's circumference to radius, equal to 2 * pi, approximately 6.2832.",examples:["tau","2 * pi"],seealso:["pi"]},true:{name:"true",category:"Constants",syntax:["true"],description:"Boolean value true",examples:["true"],seealso:["false"]},version:{name:"version",category:"Constants",syntax:["version"],description:"A string with the version number of math.js",examples:["version"],seealso:[]},speedOfLight:{description:"Speed of light in vacuum",examples:["speedOfLight"]},gravitationConstant:{description:"Newtonian constant of gravitation",examples:["gravitationConstant"]},planckConstant:{description:"Planck constant",examples:["planckConstant"]},reducedPlanckConstant:{description:"Reduced Planck constant",examples:["reducedPlanckConstant"]},magneticConstant:{description:"Magnetic constant (vacuum permeability)",examples:["magneticConstant"]},electricConstant:{description:"Electric constant (vacuum permeability)",examples:["electricConstant"]},vacuumImpedance:{description:"Characteristic impedance of vacuum",examples:["vacuumImpedance"]},coulomb:{description:"Coulomb's constant",examples:["coulomb"]},elementaryCharge:{description:"Elementary charge",examples:["elementaryCharge"]},bohrMagneton:{description:"Borh magneton",examples:["bohrMagneton"]},conductanceQuantum:{description:"Conductance quantum",examples:["conductanceQuantum"]},inverseConductanceQuantum:{description:"Inverse conductance quantum",examples:["inverseConductanceQuantum"]},magneticFluxQuantum:{description:"Magnetic flux quantum",examples:["magneticFluxQuantum"]},nuclearMagneton:{description:"Nuclear magneton",examples:["nuclearMagneton"]},klitzing:{description:"Von Klitzing constant",examples:["klitzing"]},bohrRadius:{description:"Borh radius",examples:["bohrRadius"]},classicalElectronRadius:{description:"Classical electron radius",examples:["classicalElectronRadius"]},electronMass:{description:"Electron mass",examples:["electronMass"]},fermiCoupling:{description:"Fermi coupling constant",examples:["fermiCoupling"]},fineStructure:{description:"Fine-structure constant",examples:["fineStructure"]},hartreeEnergy:{description:"Hartree energy",examples:["hartreeEnergy"]},protonMass:{description:"Proton mass",examples:["protonMass"]},deuteronMass:{description:"Deuteron Mass",examples:["deuteronMass"]},neutronMass:{description:"Neutron mass",examples:["neutronMass"]},quantumOfCirculation:{description:"Quantum of circulation",examples:["quantumOfCirculation"]},rydberg:{description:"Rydberg constant",examples:["rydberg"]},thomsonCrossSection:{description:"Thomson cross section",examples:["thomsonCrossSection"]},weakMixingAngle:{description:"Weak mixing angle",examples:["weakMixingAngle"]},efimovFactor:{description:"Efimov factor",examples:["efimovFactor"]},atomicMass:{description:"Atomic mass constant",examples:["atomicMass"]},avogadro:{description:"Avogadro's number",examples:["avogadro"]},boltzmann:{description:"Boltzmann constant",examples:["boltzmann"]},faraday:{description:"Faraday constant",examples:["faraday"]},firstRadiation:{description:"First radiation constant",examples:["firstRadiation"]},loschmidt:{description:"Loschmidt constant at T=273.15 K and p=101.325 kPa",examples:["loschmidt"]},gasConstant:{description:"Gas constant",examples:["gasConstant"]},molarPlanckConstant:{description:"Molar Planck constant",examples:["molarPlanckConstant"]},molarVolume:{description:"Molar volume of an ideal gas at T=273.15 K and p=101.325 kPa",examples:["molarVolume"]},sackurTetrode:{description:"Sackur-Tetrode constant at T=1 K and p=101.325 kPa",examples:["sackurTetrode"]},secondRadiation:{description:"Second radiation constant",examples:["secondRadiation"]},stefanBoltzmann:{description:"Stefan-Boltzmann constant",examples:["stefanBoltzmann"]},wienDisplacement:{description:"Wien displacement law constant",examples:["wienDisplacement"]},molarMass:{description:"Molar mass constant",examples:["molarMass"]},molarMassC12:{description:"Molar mass constant of carbon-12",examples:["molarMassC12"]},gravity:{description:"Standard acceleration of gravity (standard acceleration of free-fall on Earth)",examples:["gravity"]},planckLength:{description:"Planck length",examples:["planckLength"]},planckMass:{description:"Planck mass",examples:["planckMass"]},planckTime:{description:"Planck time",examples:["planckTime"]},planckCharge:{description:"Planck charge",examples:["planckCharge"]},planckTemperature:{description:"Planck temperature",examples:["planckTemperature"]},derivative:{name:"derivative",category:"Algebra",syntax:["derivative(expr, variable)","derivative(expr, variable, {simplify: boolean})"],description:"Takes the derivative of an expression expressed in parser Nodes. The derivative will be taken over the supplied variable in the second parameter. If there are multiple variables in the expression, it will return a partial derivative.",examples:['derivative("2x^3", "x")','derivative("2x^3", "x", {simplify: false})','derivative("2x^2 + 3x + 4", "x")','derivative("sin(2x)", "x")','f = parse("x^2 + x")','x = parse("x")',"df = derivative(f, x)","df.evaluate({x: 3})"],seealso:["simplify","parse","evaluate"]},lsolve:{name:"lsolve",category:"Algebra",syntax:["x=lsolve(L, b)"],description:"Finds one solution of the linear system L * x = b where L is an [n x n] lower triangular matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lsolve(a, b)"],seealso:["lsolveAll","lup","lusolve","usolve","matrix","sparse"]},lsolveAll:{name:"lsolveAll",category:"Algebra",syntax:["x=lsolveAll(L, b)"],description:"Finds all solutions of the linear system L * x = b where L is an [n x n] lower triangular matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lsolve(a, b)"],seealso:["lsolve","lup","lusolve","usolve","matrix","sparse"]},lup:{name:"lup",category:"Algebra",syntax:["lup(m)"],description:"Calculate the Matrix LU decomposition with partial pivoting. Matrix A is decomposed in three matrices (L, U, P) where P * A = L * U",examples:["lup([[2, 1], [1, 4]])","lup(matrix([[2, 1], [1, 4]]))","lup(sparse([[2, 1], [1, 4]]))"],seealso:["lusolve","lsolve","usolve","matrix","sparse","slu","qr"]},lusolve:{name:"lusolve",category:"Algebra",syntax:["x=lusolve(A, b)","x=lusolve(lu, b)"],description:"Solves the linear system A * x = b where A is an [n x n] matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lusolve(a, b)"],seealso:["lup","slu","lsolve","usolve","matrix","sparse"]},leafCount:{name:"leafCount",category:"Algebra",syntax:["leafCount(expr)"],description:"Computes the number of leaves in the parse tree of the given expression",examples:['leafCount("e^(i*pi)-1")','leafCount(parse("{a: 22/7, b: 10^(1/2)}"))'],seealso:["simplify"]},resolve:{name:"resolve",category:"Algebra",syntax:["resolve(node, scope)"],description:"Recursively substitute variables in an expression tree.",examples:['resolve(parse("1 + x"), { x: 7 })','resolve(parse("size(text)"), { text: "Hello World" })','resolve(parse("x + y"), { x: parse("3z") })','resolve(parse("3x"), { x: parse("y+z"), z: parse("w^y") })'],seealso:["simplify","evaluate"],mayThrow:["ReferenceError"]},simplify:{name:"simplify",category:"Algebra",syntax:["simplify(expr)","simplify(expr, rules)"],description:"Simplify an expression tree.",examples:['simplify("3 + 2 / 4")','simplify("2x + x")','f = parse("x * (x + 2 + x)")',"simplified = simplify(f)","simplified.evaluate({x: 2})"],seealso:["simplifyCore","derivative","evaluate","parse","rationalize","resolve"]},simplifyCore:{name:"simplifyCore",category:"Algebra",syntax:["simplifyCore(node)"],description:"Perform simple one-pass simplifications on an expression tree.",examples:['simplifyCore(parse("0*x"))','simplifyCore(parse("(x+0)*2"))'],seealso:["simplify","evaluate"]},symbolicEqual:{name:"symbolicEqual",category:"Algebra",syntax:["symbolicEqual(expr1, expr2)","symbolicEqual(expr1, expr2, options)"],description:"Returns true if the difference of the expressions simplifies to 0",examples:['symbolicEqual("x*y","y*x")','symbolicEqual("abs(x^2)", "x^2")','symbolicEqual("abs(x)", "x", {context: {abs: {trivial: true}}})'],seealso:["simplify","evaluate"]},rationalize:{name:"rationalize",category:"Algebra",syntax:["rationalize(expr)","rationalize(expr, scope)","rationalize(expr, scope, detailed)"],description:"Transform a rationalizable expression in a rational fraction. If rational fraction is one variable polynomial then converts the numerator and denominator in canonical form, with decreasing exponents, returning the coefficients of numerator.",examples:['rationalize("2x/y - y/(x+1)")','rationalize("2x/y - y/(x+1)", true)'],seealso:["simplify"]},slu:{name:"slu",category:"Algebra",syntax:["slu(A, order, threshold)"],description:"Calculate the Matrix LU decomposition with full pivoting. Matrix A is decomposed in two matrices (L, U) and two permutation vectors (pinv, q) where P * A * Q = L * U",examples:["slu(sparse([4.5, 0, 3.2, 0; 3.1, 2.9, 0, 0.9; 0, 1.7, 3, 0; 3.5, 0.4, 0, 1]), 1, 0.001)"],seealso:["lusolve","lsolve","usolve","matrix","sparse","lup","qr"]},usolve:{name:"usolve",category:"Algebra",syntax:["x=usolve(U, b)"],description:"Finds one solution of the linear system U * x = b where U is an [n x n] upper triangular matrix and b is a [n] column vector.",examples:["x=usolve(sparse([1, 1, 1, 1; 0, 1, 1, 1; 0, 0, 1, 1; 0, 0, 0, 1]), [1; 2; 3; 4])"],seealso:["usolveAll","lup","lusolve","lsolve","matrix","sparse"]},usolveAll:{name:"usolveAll",category:"Algebra",syntax:["x=usolve(U, b)"],description:"Finds all solutions of the linear system U * x = b where U is an [n x n] upper triangular matrix and b is a [n] column vector.",examples:["x=usolve(sparse([1, 1, 1, 1; 0, 1, 1, 1; 0, 0, 1, 1; 0, 0, 0, 1]), [1; 2; 3; 4])"],seealso:["usolve","lup","lusolve","lsolve","matrix","sparse"]},qr:{name:"qr",category:"Algebra",syntax:["qr(A)"],description:"Calculates the Matrix QR decomposition. Matrix `A` is decomposed in two matrices (`Q`, `R`) where `Q` is an orthogonal matrix and `R` is an upper triangular matrix.",examples:["qr([[1, -1, 4], [1, 4, -2], [1, 4, 2], [1, -1, 0]])"],seealso:["lup","slu","matrix"]},abs:{name:"abs",category:"Arithmetic",syntax:["abs(x)"],description:"Compute the absolute value.",examples:["abs(3.5)","abs(-4.2)"],seealso:["sign"]},add:{name:"add",category:"Operators",syntax:["x + y","add(x, y)"],description:"Add two values.",examples:["a = 2.1 + 3.6","a - 3.6","3 + 2i","3 cm + 2 inch",'"2.3" + "4"'],seealso:["subtract"]},cbrt:{name:"cbrt",category:"Arithmetic",syntax:["cbrt(x)","cbrt(x, allRoots)"],description:"Compute the cubic root value. If x = y * y * y, then y is the cubic root of x. When `x` is a number or complex number, an optional second argument `allRoots` can be provided to return all three cubic roots. If not provided, the principal root is returned",examples:["cbrt(64)","cube(4)","cbrt(-8)","cbrt(2 + 3i)","cbrt(8i)","cbrt(8i, true)","cbrt(27 m^3)"],seealso:["square","sqrt","cube","multiply"]},ceil:{name:"ceil",category:"Arithmetic",syntax:["ceil(x)"],description:"Round a value towards plus infinity. If x is complex, both real and imaginary part are rounded towards plus infinity.",examples:["ceil(3.2)","ceil(3.8)","ceil(-4.2)"],seealso:["floor","fix","round"]},cube:{name:"cube",category:"Arithmetic",syntax:["cube(x)"],description:"Compute the cube of a value. The cube of x is x * x * x.",examples:["cube(2)","2^3","2 * 2 * 2"],seealso:["multiply","square","pow"]},divide:{name:"divide",category:"Operators",syntax:["x / y","divide(x, y)"],description:"Divide two values.",examples:["a = 2 / 3","a * 3","4.5 / 2","3 + 4 / 2","(3 + 4) / 2","18 km / 4.5"],seealso:["multiply"]},dotDivide:{name:"dotDivide",category:"Operators",syntax:["x ./ y","dotDivide(x, y)"],description:"Divide two values element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","b = [2, 1, 1; 3, 2, 5]","a ./ b"],seealso:["multiply","dotMultiply","divide"]},dotMultiply:{name:"dotMultiply",category:"Operators",syntax:["x .* y","dotMultiply(x, y)"],description:"Multiply two values element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","b = [2, 1, 1; 3, 2, 5]","a .* b"],seealso:["multiply","divide","dotDivide"]},dotPow:{name:"dotPow",category:"Operators",syntax:["x .^ y","dotPow(x, y)"],description:"Calculates the power of x to y element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","a .^ 2"],seealso:["pow"]},exp:{name:"exp",category:"Arithmetic",syntax:["exp(x)"],description:"Calculate the exponent of a value.",examples:["exp(1.3)","e ^ 1.3","log(exp(1.3))","x = 2.4","(exp(i*x) == cos(x) + i*sin(x)) # Euler's formula"],seealso:["expm","expm1","pow","log"]},expm:{name:"expm",category:"Arithmetic",syntax:["exp(x)"],description:"Compute the matrix exponential, expm(A) = e^A. The matrix must be square. Not to be confused with exp(a), which performs element-wise exponentiation.",examples:["expm([[0,2],[0,0]])"],seealso:["exp"]},expm1:{name:"expm1",category:"Arithmetic",syntax:["expm1(x)"],description:"Calculate the value of subtracting 1 from the exponential value.",examples:["expm1(2)","pow(e, 2) - 1","log(expm1(2) + 1)"],seealso:["exp","pow","log"]},fix:{name:"fix",category:"Arithmetic",syntax:["fix(x)"],description:"Round a value towards zero. If x is complex, both real and imaginary part are rounded towards zero.",examples:["fix(3.2)","fix(3.8)","fix(-4.2)","fix(-4.8)"],seealso:["ceil","floor","round"]},floor:{name:"floor",category:"Arithmetic",syntax:["floor(x)"],description:"Round a value towards minus infinity.If x is complex, both real and imaginary part are rounded towards minus infinity.",examples:["floor(3.2)","floor(3.8)","floor(-4.2)"],seealso:["ceil","fix","round"]},gcd:{name:"gcd",category:"Arithmetic",syntax:["gcd(a, b)","gcd(a, b, c, ...)"],description:"Compute the greatest common divisor.",examples:["gcd(8, 12)","gcd(-4, 6)","gcd(25, 15, -10)"],seealso:["lcm","xgcd"]},hypot:{name:"hypot",category:"Arithmetic",syntax:["hypot(a, b, c, ...)","hypot([a, b, c, ...])"],description:"Calculate the hypotenusa of a list with values. ",examples:["hypot(3, 4)","sqrt(3^2 + 4^2)","hypot(-2)","hypot([3, 4, 5])"],seealso:["abs","norm"]},lcm:{name:"lcm",category:"Arithmetic",syntax:["lcm(x, y)"],description:"Compute the least common multiple.",examples:["lcm(4, 6)","lcm(6, 21)","lcm(6, 21, 5)"],seealso:["gcd"]},log:{name:"log",category:"Arithmetic",syntax:["log(x)","log(x, base)"],description:"Compute the logarithm of a value. If no base is provided, the natural logarithm of x is calculated. If base if provided, the logarithm is calculated for the specified base. log(x, base) is defined as log(x) / log(base).",examples:["log(3.5)","a = log(2.4)","exp(a)","10 ^ 4","log(10000, 10)","log(10000) / log(10)","b = log(1024, 2)","2 ^ b"],seealso:["exp","log1p","log2","log10"]},log2:{name:"log2",category:"Arithmetic",syntax:["log2(x)"],description:"Calculate the 2-base of a value. This is the same as calculating `log(x, 2)`.",examples:["log2(0.03125)","log2(16)","log2(16) / log2(2)","pow(2, 4)"],seealso:["exp","log1p","log","log10"]},log1p:{name:"log1p",category:"Arithmetic",syntax:["log1p(x)","log1p(x, base)"],description:"Calculate the logarithm of a `value+1`",examples:["log1p(2.5)","exp(log1p(1.4))","pow(10, 4)","log1p(9999, 10)","log1p(9999) / log(10)"],seealso:["exp","log","log2","log10"]},log10:{name:"log10",category:"Arithmetic",syntax:["log10(x)"],description:"Compute the 10-base logarithm of a value.",examples:["log10(0.00001)","log10(10000)","10 ^ 4","log(10000) / log(10)","log(10000, 10)"],seealso:["exp","log"]},mod:{name:"mod",category:"Operators",syntax:["x % y","x mod y","mod(x, y)"],description:"Calculates the modulus, the remainder of an integer division.",examples:["7 % 3","11 % 2","10 mod 4","isOdd(x) = x % 2","isOdd(2)","isOdd(3)"],seealso:["divide"]},multiply:{name:"multiply",category:"Operators",syntax:["x * y","multiply(x, y)"],description:"multiply two values.",examples:["a = 2.1 * 3.4","a / 3.4","2 * 3 + 4","2 * (3 + 4)","3 * 2.1 km"],seealso:["divide"]},norm:{name:"norm",category:"Arithmetic",syntax:["norm(x)","norm(x, p)"],description:"Calculate the norm of a number, vector or matrix.",examples:["abs(-3.5)","norm(-3.5)","norm(3 - 4i)","norm([1, 2, -3], Infinity)","norm([1, 2, -3], -Infinity)","norm([3, 4], 2)","norm([[1, 2], [3, 4]], 1)",'norm([[1, 2], [3, 4]], "inf")','norm([[1, 2], [3, 4]], "fro")']},nthRoot:{name:"nthRoot",category:"Arithmetic",syntax:["nthRoot(a)","nthRoot(a, root)"],description:'Calculate the nth root of a value. The principal nth root of a positive real number A, is the positive real solution of the equation "x^root = A".',examples:["4 ^ 3","nthRoot(64, 3)","nthRoot(9, 2)","sqrt(9)"],seealso:["nthRoots","pow","sqrt"]},nthRoots:{name:"nthRoots",category:"Arithmetic",syntax:["nthRoots(A)","nthRoots(A, root)"],description:'Calculate the nth roots of a value. An nth root of a positive real number A, is a positive real solution of the equation "x^root = A". This function returns an array of complex values.',examples:["nthRoots(1)","nthRoots(1, 3)"],seealso:["sqrt","pow","nthRoot"]},pow:{name:"pow",category:"Operators",syntax:["x ^ y","pow(x, y)"],description:"Calculates the power of x to y, x^y.",examples:["2^3","2*2*2","1 + e ^ (pi * i)","math.pow([[1, 2], [4, 3]], 2)","math.pow([[1, 2], [4, 3]], -1)"],seealso:["multiply","nthRoot","nthRoots","sqrt"]},round:{name:"round",category:"Arithmetic",syntax:["round(x)","round(x, n)"],description:"round a value towards the nearest integer.If x is complex, both real and imaginary part are rounded towards the nearest integer. When n is specified, the value is rounded to n decimals.",examples:["round(3.2)","round(3.8)","round(-4.2)","round(-4.8)","round(pi, 3)","round(123.45678, 2)"],seealso:["ceil","floor","fix"]},sign:{name:"sign",category:"Arithmetic",syntax:["sign(x)"],description:"Compute the sign of a value. The sign of a value x is 1 when x>1, -1 when x<0, and 0 when x=0.",examples:["sign(3.5)","sign(-4.2)","sign(0)"],seealso:["abs"]},sqrt:{name:"sqrt",category:"Arithmetic",syntax:["sqrt(x)"],description:"Compute the square root value. If x = y * y, then y is the square root of x.",examples:["sqrt(25)","5 * 5","sqrt(-1)"],seealso:["square","sqrtm","multiply","nthRoot","nthRoots","pow"]},sqrtm:{name:"sqrtm",category:"Arithmetic",syntax:["sqrtm(x)"],description:"Calculate the principal square root of a square matrix. The principal square root matrix `X` of another matrix `A` is such that `X * X = A`.",examples:["sqrtm([[1, 2], [3, 4]])"],seealso:["sqrt","abs","square","multiply"]},square:{name:"square",category:"Arithmetic",syntax:["square(x)"],description:"Compute the square of a value. The square of x is x * x.",examples:["square(3)","sqrt(9)","3^2","3 * 3"],seealso:["multiply","pow","sqrt","cube"]},subtract:{name:"subtract",category:"Operators",syntax:["x - y","subtract(x, y)"],description:"subtract two values.",examples:["a = 5.3 - 2","a + 2","2/3 - 1/6","2 * 3 - 3","2.1 km - 500m"],seealso:["add"]},unaryMinus:{name:"unaryMinus",category:"Operators",syntax:["-x","unaryMinus(x)"],description:"Inverse the sign of a value. Converts booleans and strings to numbers.",examples:["-4.5","-(-5.6)",'-"22"'],seealso:["add","subtract","unaryPlus"]},unaryPlus:{name:"unaryPlus",category:"Operators",syntax:["+x","unaryPlus(x)"],description:"Converts booleans and strings to numbers.",examples:["+true",'+"2"'],seealso:["add","subtract","unaryMinus"]},xgcd:{name:"xgcd",category:"Arithmetic",syntax:["xgcd(a, b)"],description:"Calculate the extended greatest common divisor for two values. The result is an array [d, x, y] with 3 entries, where d is the greatest common divisor, and d = x * a + y * b.",examples:["xgcd(8, 12)","gcd(8, 12)","xgcd(36163, 21199)"],seealso:["gcd","lcm"]},invmod:{name:"invmod",category:"Arithmetic",syntax:["invmod(a, b)"],description:"Calculate the (modular) multiplicative inverse of a modulo b. Solution to the equation ax ≣ 1 (mod b)",examples:["invmod(8, 12)=NaN","invmod(7, 13)=2","math.invmod(15151, 15122)=10429"],seealso:["gcd","xgcd"]},bitAnd:{name:"bitAnd",category:"Bitwise",syntax:["x & y","bitAnd(x, y)"],description:"Bitwise AND operation. Performs the logical AND operation on each pair of the corresponding bits of the two given values by multiplying them. If both bits in the compared position are 1, the bit in the resulting binary representation is 1, otherwise, the result is 0",examples:["5 & 3","bitAnd(53, 131)","[1, 12, 31] & 42"],seealso:["bitNot","bitOr","bitXor","leftShift","rightArithShift","rightLogShift"]},bitNot:{name:"bitNot",category:"Bitwise",syntax:["~x","bitNot(x)"],description:"Bitwise NOT operation. Performs a logical negation on each bit of the given value. Bits that are 0 become 1, and those that are 1 become 0.",examples:["~1","~2","bitNot([2, -3, 4])"],seealso:["bitAnd","bitOr","bitXor","leftShift","rightArithShift","rightLogShift"]},bitOr:{name:"bitOr",category:"Bitwise",syntax:["x | y","bitOr(x, y)"],description:"Bitwise OR operation. Performs the logical inclusive OR operation on each pair of corresponding bits of the two given values. The result in each position is 1 if the first bit is 1 or the second bit is 1 or both bits are 1, otherwise, the result is 0.",examples:["5 | 3","bitOr([1, 2, 3], 4)"],seealso:["bitAnd","bitNot","bitXor","leftShift","rightArithShift","rightLogShift"]},bitXor:{name:"bitXor",category:"Bitwise",syntax:["bitXor(x, y)"],description:"Bitwise XOR operation, exclusive OR. Performs the logical exclusive OR operation on each pair of corresponding bits of the two given values. The result in each position is 1 if only the first bit is 1 or only the second bit is 1, but will be 0 if both are 0 or both are 1.",examples:["bitOr(1, 2)","bitXor([2, 3, 4], 4)"],seealso:["bitAnd","bitNot","bitOr","leftShift","rightArithShift","rightLogShift"]},leftShift:{name:"leftShift",category:"Bitwise",syntax:["x << y","leftShift(x, y)"],description:"Bitwise left logical shift of a value x by y number of bits.",examples:["4 << 1","8 >> 1"],seealso:["bitAnd","bitNot","bitOr","bitXor","rightArithShift","rightLogShift"]},rightArithShift:{name:"rightArithShift",category:"Bitwise",syntax:["x >> y","rightArithShift(x, y)"],description:"Bitwise right arithmetic shift of a value x by y number of bits.",examples:["8 >> 1","4 << 1","-12 >> 2"],seealso:["bitAnd","bitNot","bitOr","bitXor","leftShift","rightLogShift"]},rightLogShift:{name:"rightLogShift",category:"Bitwise",syntax:["x >>> y","rightLogShift(x, y)"],description:"Bitwise right logical shift of a value x by y number of bits.",examples:["8 >>> 1","4 << 1","-12 >>> 2"],seealso:["bitAnd","bitNot","bitOr","bitXor","leftShift","rightArithShift"]},bellNumbers:{name:"bellNumbers",category:"Combinatorics",syntax:["bellNumbers(n)"],description:"The Bell Numbers count the number of partitions of a set. A partition is a pairwise disjoint subset of S whose union is S. `bellNumbers` only takes integer arguments. The following condition must be enforced: n >= 0.",examples:["bellNumbers(3)","bellNumbers(8)"],seealso:["stirlingS2"]},catalan:{name:"catalan",category:"Combinatorics",syntax:["catalan(n)"],description:"The Catalan Numbers enumerate combinatorial structures of many different types. catalan only takes integer arguments. The following condition must be enforced: n >= 0.",examples:["catalan(3)","catalan(8)"],seealso:["bellNumbers"]},composition:{name:"composition",category:"Combinatorics",syntax:["composition(n, k)"],description:"The composition counts of n into k parts. composition only takes integer arguments. The following condition must be enforced: k <= n.",examples:["composition(5, 3)"],seealso:["combinations"]},stirlingS2:{name:"stirlingS2",category:"Combinatorics",syntax:["stirlingS2(n, k)"],description:"he Stirling numbers of the second kind, counts the number of ways to partition a set of n labelled objects into k nonempty unlabelled subsets. `stirlingS2` only takes integer arguments. The following condition must be enforced: k <= n. If n = k or k = 1, then s(n,k) = 1.",examples:["stirlingS2(5, 3)"],seealso:["bellNumbers"]},config:{name:"config",category:"Core",syntax:["config()","config(options)"],description:"Get configuration or change configuration.",examples:["config()","1/3 + 1/4",'config({number: "Fraction"})',"1/3 + 1/4"],seealso:[]},import:{name:"import",category:"Core",syntax:["import(functions)","import(functions, options)"],description:"Import functions or constants from an object.",examples:["import({myFn: f(x)=x^2, myConstant: 32 })","myFn(2)","myConstant"],seealso:[]},typed:{name:"typed",category:"Core",syntax:["typed(signatures)","typed(name, signatures)"],description:"Create a typed function.",examples:['double = typed({ "number": f(x)=x+x })',"double(2)",'double("hello")'],seealso:[]},arg:{name:"arg",category:"Complex",syntax:["arg(x)"],description:"Compute the argument of a complex value. If x = a+bi, the argument is computed as atan2(b, a).",examples:["arg(2 + 2i)","atan2(3, 2)","arg(2 + 3i)"],seealso:["re","im","conj","abs"]},conj:{name:"conj",category:"Complex",syntax:["conj(x)"],description:"Compute the complex conjugate of a complex value. If x = a+bi, the complex conjugate is a-bi.",examples:["conj(2 + 3i)","conj(2 - 3i)","conj(-5.2i)"],seealso:["re","im","abs","arg"]},re:{name:"re",category:"Complex",syntax:["re(x)"],description:"Get the real part of a complex number.",examples:["re(2 + 3i)","im(2 + 3i)","re(-5.2i)","re(2.4)"],seealso:["im","conj","abs","arg"]},im:{name:"im",category:"Complex",syntax:["im(x)"],description:"Get the imaginary part of a complex number.",examples:["im(2 + 3i)","re(2 + 3i)","im(-5.2i)","im(2.4)"],seealso:["re","conj","abs","arg"]},evaluate:{name:"evaluate",category:"Expression",syntax:["evaluate(expression)","evaluate([expr1, expr2, expr3, ...])"],description:"Evaluate an expression or an array with expressions.",examples:['evaluate("2 + 3")','evaluate("sqrt(" + 4 + ")")'],seealso:[]},help:{name:"help",category:"Expression",syntax:["help(object)","help(string)"],description:"Display documentation on a function or data type.",examples:["help(sqrt)",'help("complex")'],seealso:[]},distance:{name:"distance",category:"Geometry",syntax:["distance([x1, y1], [x2, y2])","distance([[x1, y1], [x2, y2]])"],description:"Calculates the Euclidean distance between two points.",examples:["distance([0,0], [4,4])","distance([[0,0], [4,4]])"],seealso:[]},intersect:{name:"intersect",category:"Geometry",syntax:["intersect(expr1, expr2, expr3, expr4)","intersect(expr1, expr2, expr3)"],description:"Computes the intersection point of lines and/or planes.",examples:["intersect([0, 0], [10, 10], [10, 0], [0, 10])","intersect([1, 0, 1], [4, -2, 2], [1, 1, 1, 6])"],seealso:[]},and:{name:"and",category:"Logical",syntax:["x and y","and(x, y)"],description:"Logical and. Test whether two values are both defined with a nonzero/nonempty value.",examples:["true and false","true and true","2 and 4"],seealso:["not","or","xor"]},not:{name:"not",category:"Logical",syntax:["not x","not(x)"],description:"Logical not. Flips the boolean value of given argument.",examples:["not true","not false","not 2","not 0"],seealso:["and","or","xor"]},or:{name:"or",category:"Logical",syntax:["x or y","or(x, y)"],description:"Logical or. Test if at least one value is defined with a nonzero/nonempty value.",examples:["true or false","false or false","0 or 4"],seealso:["not","and","xor"]},xor:{name:"xor",category:"Logical",syntax:["x xor y","xor(x, y)"],description:"Logical exclusive or, xor. Test whether one and only one value is defined with a nonzero/nonempty value.",examples:["true xor false","false xor false","true xor true","0 xor 4"],seealso:["not","and","or"]},concat:{name:"concat",category:"Matrix",syntax:["concat(A, B, C, ...)","concat(A, B, C, ..., dim)"],description:"Concatenate matrices. By default, the matrices are concatenated by the last dimension. The dimension on which to concatenate can be provided as last argument.",examples:["A = [1, 2; 5, 6]","B = [3, 4; 7, 8]","concat(A, B)","concat(A, B, 1)","concat(A, B, 2)"],seealso:["det","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},count:{name:"count",category:"Matrix",syntax:["count(x)"],description:"Count the number of elements of a matrix, array or string.",examples:["a = [1, 2; 3, 4; 5, 6]","count(a)","size(a)",'count("hello world")'],seealso:["size"]},cross:{name:"cross",category:"Matrix",syntax:["cross(A, B)"],description:"Calculate the cross product for two vectors in three dimensional space.",examples:["cross([1, 1, 0], [0, 1, 1])","cross([3, -3, 1], [4, 9, 2])","cross([2, 3, 4], [5, 6, 7])"],seealso:["multiply","dot"]},column:{name:"column",category:"Matrix",syntax:["column(x, index)"],description:"Return a column from a matrix or array.",examples:["A = [[1, 2], [3, 4]]","column(A, 1)","column(A, 2)"],seealso:["row","matrixFromColumns"]},ctranspose:{name:"ctranspose",category:"Matrix",syntax:["x'","ctranspose(x)"],description:"Complex Conjugate and Transpose a matrix",examples:["a = [1, 2, 3; 4, 5, 6]","a'","ctranspose(a)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","zeros"]},det:{name:"det",category:"Matrix",syntax:["det(x)"],description:"Calculate the determinant of a matrix",examples:["det([1, 2; 3, 4])","det([-2, 2, 3; -1, 1, 3; 2, 0, -1])"],seealso:["concat","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},diag:{name:"diag",category:"Matrix",syntax:["diag(x)","diag(x, k)"],description:"Create a diagonal matrix or retrieve the diagonal of a matrix. When x is a vector, a matrix with the vector values on the diagonal will be returned. When x is a matrix, a vector with the diagonal values of the matrix is returned. When k is provided, the k-th diagonal will be filled in or retrieved, if k is positive, the values are placed on the super diagonal. When k is negative, the values are placed on the sub diagonal.",examples:["diag(1:3)","diag(1:3, 1)","a = [1, 2, 3; 4, 5, 6; 7, 8, 9]","diag(a)"],seealso:["concat","det","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},diff:{name:"diff",category:"Matrix",syntax:["diff(arr)","diff(arr, dim)"],description:["Create a new matrix or array with the difference of the passed matrix or array.","Dim parameter is optional and used to indicant the dimension of the array/matrix to apply the difference","If no dimension parameter is passed it is assumed as dimension 0","Dimension is zero-based in javascript and one-based in the parser","Arrays must be 'rectangular' meaning arrays like [1, 2]","If something is passed as a matrix it will be returned as a matrix but other than that all matrices are converted to arrays"],examples:["diff([1, 2, 4, 7, 0])","diff([1, 2, 4, 7, 0], 0)","diff(matrix([1, 2, 4, 7, 0]))","diff([[1, 2], [3, 4]])","diff([[1, 2], [3, 4]], 0)","diff([[1, 2], [3, 4]], 1)","diff([[1, 2], [3, 4]], bignumber(1))","diff(matrix([[1, 2], [3, 4]]), 1)","diff([[1, 2], matrix([3, 4])], 1)"],seealso:["subtract","partitionSelect"]},dot:{name:"dot",category:"Matrix",syntax:["dot(A, B)","A * B"],description:"Calculate the dot product of two vectors. The dot product of A = [a1, a2, a3, ..., an] and B = [b1, b2, b3, ..., bn] is defined as dot(A, B) = a1 * b1 + a2 * b2 + a3 * b3 + ... + an * bn",examples:["dot([2, 4, 1], [2, 2, 3])","[2, 4, 1] * [2, 2, 3]"],seealso:["multiply","cross"]},getMatrixDataType:{name:"getMatrixDataType",category:"Matrix",syntax:["getMatrixDataType(x)"],description:'Find the data type of all elements in a matrix or array, for example "number" if all items are a number and "Complex" if all values are complex numbers. If a matrix contains more than one data type, it will return "mixed".',examples:["getMatrixDataType([1, 2, 3])","getMatrixDataType([[5 cm], [2 inch]])",'getMatrixDataType([1, "text"])',"getMatrixDataType([1, bignumber(4)])"],seealso:["matrix","sparse","typeOf"]},identity:{name:"identity",category:"Matrix",syntax:["identity(n)","identity(m, n)","identity([m, n])"],description:"Returns the identity matrix with size m-by-n. The matrix has ones on the diagonal and zeros elsewhere.",examples:["identity(3)","identity(3, 5)","a = [1, 2, 3; 4, 5, 6]","identity(size(a))"],seealso:["concat","det","diag","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},filter:{name:"filter",category:"Matrix",syntax:["filter(x, test)"],description:"Filter items in a matrix.",examples:["isPositive(x) = x > 0","filter([6, -2, -1, 4, 3], isPositive)","filter([6, -2, 0, 1, 0], x != 0)"],seealso:["sort","map","forEach"]},flatten:{name:"flatten",category:"Matrix",syntax:["flatten(x)"],description:"Flatten a multi dimensional matrix into a single dimensional matrix.",examples:["a = [1, 2, 3; 4, 5, 6]","size(a)","b = flatten(a)","size(b)"],seealso:["concat","resize","size","squeeze"]},forEach:{name:"forEach",category:"Matrix",syntax:["forEach(x, callback)"],description:"Iterates over all elements of a matrix/array, and executes the given callback function.",examples:["numberOfPets = {}","addPet(n) = numberOfPets[n] = (numberOfPets[n] ? numberOfPets[n]:0 ) + 1;",'forEach(["Dog","Cat","Cat"], addPet)',"numberOfPets"],seealso:["map","sort","filter"]},inv:{name:"inv",category:"Matrix",syntax:["inv(x)"],description:"Calculate the inverse of a matrix",examples:["inv([1, 2; 3, 4])","inv(4)","1 / 4"],seealso:["concat","det","diag","identity","ones","range","size","squeeze","subset","trace","transpose","zeros"]},pinv:{name:"pinv",category:"Matrix",syntax:["pinv(x)"],description:"Calculate the Moore–Penrose inverse of a matrix",examples:["pinv([1, 2; 3, 4])","pinv([[1, 0], [0, 1], [0, 1]])","pinv(4)"],seealso:["inv"]},eigs:{name:"eigs",category:"Matrix",syntax:["eigs(x)"],description:"Calculate the eigenvalues and eigenvectors of a real symmetric matrix",examples:["eigs([[5, 2.3], [2.3, 1]])"],seealso:["inv"]},kron:{name:"kron",category:"Matrix",syntax:["kron(x, y)"],description:"Calculates the kronecker product of 2 matrices or vectors.",examples:["kron([[1, 0], [0, 1]], [[1, 2], [3, 4]])","kron([1,1], [2,3,4])"],seealso:["multiply","dot","cross"]},matrixFromFunction:{name:"matrixFromFunction",category:"Matrix",syntax:["math.matrixFromFunction(size, fn)","math.matrixFromFunction(size, fn, format)","math.matrixFromFunction(size, fn, format, datatype)","math.matrixFromFunction(size, format, fn)","math.matrixFromFunction(size, format, datatype, fn)"],description:"Create a matrix by evaluating a generating function at each index.",examples:["f(I) = I[1] - I[2]","matrixFromFunction([3,3], f)","g(I) = I[1] - I[2] == 1 ? 4 : 0",'matrixFromFunction([100, 100], "sparse", g)',"matrixFromFunction([5], random)"],seealso:["matrix","matrixFromRows","matrixFromColumns","zeros"]},matrixFromRows:{name:"matrixFromRows",category:"Matrix",syntax:["math.matrixFromRows(...arr)","math.matrixFromRows(row1, row2)","math.matrixFromRows(row1, row2, row3)"],description:"Create a dense matrix from vectors as individual rows.",examples:["matrixFromRows([1, 2, 3], [[4],[5],[6]])"],seealso:["matrix","matrixFromColumns","matrixFromFunction","zeros"]},matrixFromColumns:{name:"matrixFromColumns",category:"Matrix",syntax:["math.matrixFromColumns(...arr)","math.matrixFromColumns(row1, row2)","math.matrixFromColumns(row1, row2, row3)"],description:"Create a dense matrix from vectors as individual columns.",examples:["matrixFromColumns([1, 2, 3], [[4],[5],[6]])"],seealso:["matrix","matrixFromRows","matrixFromFunction","zeros"]},map:{name:"map",category:"Matrix",syntax:["map(x, callback)"],description:"Create a new matrix or array with the results of the callback function executed on each entry of the matrix/array.",examples:["map([1, 2, 3], square)"],seealso:["filter","forEach"]},ones:{name:"ones",category:"Matrix",syntax:["ones(m)","ones(m, n)","ones(m, n, p, ...)","ones([m])","ones([m, n])","ones([m, n, p, ...])"],description:"Create a matrix containing ones.",examples:["ones(3)","ones(3, 5)","ones([2,3]) * 4.5","a = [1, 2, 3; 4, 5, 6]","ones(size(a))"],seealso:["concat","det","diag","identity","inv","range","size","squeeze","subset","trace","transpose","zeros"]},partitionSelect:{name:"partitionSelect",category:"Matrix",syntax:["partitionSelect(x, k)","partitionSelect(x, k, compare)"],description:"Partition-based selection of an array or 1D matrix. Will find the kth smallest value, and mutates the input array. Uses Quickselect.",examples:["partitionSelect([5, 10, 1], 2)",'partitionSelect(["C", "B", "A", "D"], 1)'],seealso:["sort"]},range:{name:"range",category:"Type",syntax:["start:end","start:step:end","range(start, end)","range(start, end, step)","range(string)"],description:"Create a range. Lower bound of the range is included, upper bound is excluded.",examples:["1:5","3:-1:-3","range(3, 7)","range(0, 12, 2)",'range("4:10")',"a = [1, 2, 3, 4; 5, 6, 7, 8]","a[1:2, 1:2]"],seealso:["concat","det","diag","identity","inv","ones","size","squeeze","subset","trace","transpose","zeros"]},resize:{name:"resize",category:"Matrix",syntax:["resize(x, size)","resize(x, size, defaultValue)"],description:"Resize a matrix.",examples:["resize([1,2,3,4,5], [3])","resize([1,2,3], [5])","resize([1,2,3], [5], -1)","resize(2, [2, 3])",'resize("hello", [8], "!")'],seealso:["size","subset","squeeze","reshape"]},reshape:{name:"reshape",category:"Matrix",syntax:["reshape(x, sizes)"],description:"Reshape a multi dimensional array to fit the specified dimensions.",examples:["reshape([1, 2, 3, 4, 5, 6], [2, 3])","reshape([[1, 2], [3, 4]], [1, 4])","reshape([[1, 2], [3, 4]], [4])"],seealso:["size","squeeze","resize"]},rotate:{name:"rotate",category:"Matrix",syntax:["rotate(w, theta)","rotate(w, theta, v)"],description:"Returns a 2-D rotation matrix (2x2) for a given angle (in radians). Returns a 2-D rotation matrix (3x3) of a given angle (in radians) around given axis.",examples:["rotate([1, 0], math.pi / 2)",'rotate(matrix([1, 0]), unit("35deg"))','rotate([1, 0, 0], unit("90deg"), [0, 0, 1])','rotate(matrix([1, 0, 0]), unit("90deg"), matrix([0, 0, 1]))'],seealso:["matrix","rotationMatrix"]},rotationMatrix:{name:"rotationMatrix",category:"Matrix",syntax:["rotationMatrix(theta)","rotationMatrix(theta, v)","rotationMatrix(theta, v, format)"],description:"Returns a 2-D rotation matrix (2x2) for a given angle (in radians). Returns a 2-D rotation matrix (3x3) of a given angle (in radians) around given axis.",examples:["rotationMatrix(pi / 2)",'rotationMatrix(unit("45deg"), [0, 0, 1])','rotationMatrix(1, matrix([0, 0, 1]), "sparse")'],seealso:["cos","sin"]},row:{name:"row",category:"Matrix",syntax:["row(x, index)"],description:"Return a row from a matrix or array.",examples:["A = [[1, 2], [3, 4]]","row(A, 1)","row(A, 2)"],seealso:["column","matrixFromRows"]},size:{name:"size",category:"Matrix",syntax:["size(x)"],description:"Calculate the size of a matrix.",examples:["size(2.3)",'size("hello world")',"a = [1, 2; 3, 4; 5, 6]","size(a)","size(1:6)"],seealso:["concat","count","det","diag","identity","inv","ones","range","squeeze","subset","trace","transpose","zeros"]},sort:{name:"sort",category:"Matrix",syntax:["sort(x)","sort(x, compare)"],description:'Sort the items in a matrix. Compare can be a string "asc", "desc", "natural", or a custom sort function.',examples:["sort([5, 10, 1])",'sort(["C", "B", "A", "D"])',"sortByLength(a, b) = size(a)[1] - size(b)[1]",'sort(["Langdon", "Tom", "Sara"], sortByLength)','sort(["10", "1", "2"], "natural")'],seealso:["map","filter","forEach"]},squeeze:{name:"squeeze",category:"Matrix",syntax:["squeeze(x)"],description:"Remove inner and outer singleton dimensions from a matrix.",examples:["a = zeros(3,2,1)","size(squeeze(a))","b = zeros(1,1,3)","size(squeeze(b))"],seealso:["concat","det","diag","identity","inv","ones","range","size","subset","trace","transpose","zeros"]},subset:{name:"subset",category:"Matrix",syntax:["value(index)","value(index) = replacement","subset(value, [index])","subset(value, [index], replacement)"],description:"Get or set a subset of the entries of a matrix or characters of a string. Indexes are one-based. There should be one index specification for each dimension of the target. Each specification can be a single index, a list of indices, or a range in colon notation `l:u`. In a range, both the lower bound l and upper bound u are included; and if a bound is omitted it defaults to the most extreme valid value. The cartesian product of the indices specified in each dimension determines the target of the operation.",examples:["d = [1, 2; 3, 4]","e = []","e[1, 1:2] = [5, 6]","e[2, :] = [7, 8]","f = d * e","f[2, 1]","f[:, 1]","f[[1,2], [1,3]] = [9, 10; 11, 12]","f"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","trace","transpose","zeros"]},trace:{name:"trace",category:"Matrix",syntax:["trace(A)"],description:"Calculate the trace of a matrix: the sum of the elements on the main diagonal of a square matrix.",examples:["A = [1, 2, 3; -1, 2, 3; 2, 0, 3]","trace(A)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","transpose","zeros"]},transpose:{name:"transpose",category:"Matrix",syntax:["x'","transpose(x)"],description:"Transpose a matrix",examples:["a = [1, 2, 3; 4, 5, 6]","a'","transpose(a)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","zeros"]},zeros:{name:"zeros",category:"Matrix",syntax:["zeros(m)","zeros(m, n)","zeros(m, n, p, ...)","zeros([m])","zeros([m, n])","zeros([m, n, p, ...])"],description:"Create a matrix containing zeros.",examples:["zeros(3)","zeros(3, 5)","a = [1, 2, 3; 4, 5, 6]","zeros(size(a))"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose"]},fft:{name:"fft",category:"Matrix",syntax:["fft(x)"],description:"Calculate N-dimensional fourier transform",examples:["fft([[1, 0], [1, 0]])"],seealso:["ifft"]},ifft:{name:"ifft",category:"Matrix",syntax:["ifft(x)"],description:"Calculate N-dimensional inverse fourier transform",examples:["ifft([[2, 2], [0, 0]])"],seealso:["fft"]},combinations:{name:"combinations",category:"Probability",syntax:["combinations(n, k)"],description:"Compute the number of combinations of n items taken k at a time",examples:["combinations(7, 5)"],seealso:["combinationsWithRep","permutations","factorial"]},combinationsWithRep:{name:"combinationsWithRep",category:"Probability",syntax:["combinationsWithRep(n, k)"],description:"Compute the number of combinations of n items taken k at a time with replacements.",examples:["combinationsWithRep(7, 5)"],seealso:["combinations","permutations","factorial"]},factorial:{name:"factorial",category:"Probability",syntax:["n!","factorial(n)"],description:"Compute the factorial of a value",examples:["5!","5 * 4 * 3 * 2 * 1","3!"],seealso:["combinations","combinationsWithRep","permutations","gamma"]},gamma:{name:"gamma",category:"Probability",syntax:["gamma(n)"],description:"Compute the gamma function. For small values, the Lanczos approximation is used, and for large values the extended Stirling approximation.",examples:["gamma(4)","3!","gamma(1/2)","sqrt(pi)"],seealso:["factorial"]},kldivergence:{name:"kldivergence",category:"Probability",syntax:["kldivergence(x, y)"],description:"Calculate the Kullback-Leibler (KL) divergence between two distributions.",examples:["kldivergence([0.7,0.5,0.4], [0.2,0.9,0.5])"],seealso:[]},lgamma:{name:"lgamma",category:"Probability",syntax:["lgamma(n)"],description:"Logarithm of the gamma function for real, positive numbers and complex numbers, using Lanczos approximation for numbers and Stirling series for complex numbers.",examples:["lgamma(4)","lgamma(1/2)","lgamma(math.i)","lgamma(complex(1.1, 2))"],seealso:["gamma"]},multinomial:{name:"multinomial",category:"Probability",syntax:["multinomial(A)"],description:"Multinomial Coefficients compute the number of ways of picking a1, a2, ..., ai unordered outcomes from `n` possibilities. multinomial takes one array of integers as an argument. The following condition must be enforced: every ai > 0.",examples:["multinomial([1, 2, 1])"],seealso:["combinations","factorial"]},permutations:{name:"permutations",category:"Probability",syntax:["permutations(n)","permutations(n, k)"],description:"Compute the number of permutations of n items taken k at a time",examples:["permutations(5)","permutations(5, 3)"],seealso:["combinations","combinationsWithRep","factorial"]},pickRandom:{name:"pickRandom",category:"Probability",syntax:["pickRandom(array)","pickRandom(array, number)","pickRandom(array, weights)","pickRandom(array, number, weights)","pickRandom(array, weights, number)"],description:"Pick a random entry from a given array.",examples:["pickRandom(0:10)","pickRandom([1, 3, 1, 6])","pickRandom([1, 3, 1, 6], 2)","pickRandom([1, 3, 1, 6], [2, 3, 2, 1])","pickRandom([1, 3, 1, 6], 2, [2, 3, 2, 1])","pickRandom([1, 3, 1, 6], [2, 3, 2, 1], 2)"],seealso:["random","randomInt"]},random:{name:"random",category:"Probability",syntax:["random()","random(max)","random(min, max)","random(size)","random(size, max)","random(size, min, max)"],description:"Return a random number.",examples:["random()","random(10, 20)","random([2, 3])"],seealso:["pickRandom","randomInt"]},randomInt:{name:"randomInt",category:"Probability",syntax:["randomInt(max)","randomInt(min, max)","randomInt(size)","randomInt(size, max)","randomInt(size, min, max)"],description:"Return a random integer number",examples:["randomInt(10, 20)","randomInt([2, 3], 10)"],seealso:["pickRandom","random"]},compare:{name:"compare",category:"Relational",syntax:["compare(x, y)"],description:"Compare two values. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:["compare(2, 3)","compare(3, 2)","compare(2, 2)","compare(5cm, 40mm)","compare(2, [1, 2, 3])"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compareNatural","compareText"]},compareNatural:{name:"compareNatural",category:"Relational",syntax:["compareNatural(x, y)"],description:"Compare two values of any type in a deterministic, natural way. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:["compareNatural(2, 3)","compareNatural(3, 2)","compareNatural(2, 2)","compareNatural(5cm, 40mm)",'compareNatural("2", "10")',"compareNatural(2 + 3i, 2 + 4i)","compareNatural([1, 2, 4], [1, 2, 3])","compareNatural([1, 5], [1, 2, 3])","compareNatural([1, 2], [1, 2])","compareNatural({a: 2}, {a: 4})"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compare","compareText"]},compareText:{name:"compareText",category:"Relational",syntax:["compareText(x, y)"],description:"Compare two strings lexically. Comparison is case sensitive. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:['compareText("B", "A")','compareText("A", "B")','compareText("A", "A")','compareText("2", "10")','compare("2", "10")',"compare(2, 10)",'compareNatural("2", "10")','compareText("B", ["A", "B", "C"])'],seealso:["compare","compareNatural"]},deepEqual:{name:"deepEqual",category:"Relational",syntax:["deepEqual(x, y)"],description:"Check equality of two matrices element wise. Returns true if the size of both matrices is equal and when and each of the elements are equal.",examples:["deepEqual([1,3,4], [1,3,4])","deepEqual([1,3,4], [1,3])"],seealso:["equal","unequal","smaller","larger","smallerEq","largerEq","compare"]},equal:{name:"equal",category:"Relational",syntax:["x == y","equal(x, y)"],description:"Check equality of two values. Returns true if the values are equal, and false if not.",examples:["2+2 == 3","2+2 == 4","a = 3.2","b = 6-2.8","a == b","50cm == 0.5m"],seealso:["unequal","smaller","larger","smallerEq","largerEq","compare","deepEqual","equalText"]},equalText:{name:"equalText",category:"Relational",syntax:["equalText(x, y)"],description:"Check equality of two strings. Comparison is case sensitive. Returns true if the values are equal, and false if not.",examples:['equalText("Hello", "Hello")','equalText("a", "A")','equal("2e3", "2000")','equalText("2e3", "2000")','equalText("B", ["A", "B", "C"])'],seealso:["compare","compareNatural","compareText","equal"]},larger:{name:"larger",category:"Relational",syntax:["x > y","larger(x, y)"],description:"Check if value x is larger than y. Returns true if x is larger than y, and false if not.",examples:["2 > 3","5 > 2*2","a = 3.3","b = 6-2.8","(a > b)","(b < a)","5 cm > 2 inch"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compare"]},largerEq:{name:"largerEq",category:"Relational",syntax:["x >= y","largerEq(x, y)"],description:"Check if value x is larger or equal to y. Returns true if x is larger or equal to y, and false if not.",examples:["2 >= 1+1","2 > 1+1","a = 3.2","b = 6-2.8","(a >= b)"],seealso:["equal","unequal","smallerEq","smaller","compare"]},smaller:{name:"smaller",category:"Relational",syntax:["x < y","smaller(x, y)"],description:"Check if value x is smaller than value y. Returns true if x is smaller than y, and false if not.",examples:["2 < 3","5 < 2*2","a = 3.3","b = 6-2.8","(a < b)","5 cm < 2 inch"],seealso:["equal","unequal","larger","smallerEq","largerEq","compare"]},smallerEq:{name:"smallerEq",category:"Relational",syntax:["x <= y","smallerEq(x, y)"],description:"Check if value x is smaller or equal to value y. Returns true if x is smaller than y, and false if not.",examples:["2 <= 1+1","2 < 1+1","a = 3.2","b = 6-2.8","(a <= b)"],seealso:["equal","unequal","larger","smaller","largerEq","compare"]},unequal:{name:"unequal",category:"Relational",syntax:["x != y","unequal(x, y)"],description:"Check unequality of two values. Returns true if the values are unequal, and false if they are equal.",examples:["2+2 != 3","2+2 != 4","a = 3.2","b = 6-2.8","a != b","50cm != 0.5m","5 cm != 2 inch"],seealso:["equal","smaller","larger","smallerEq","largerEq","compare","deepEqual"]},setCartesian:{name:"setCartesian",category:"Set",syntax:["setCartesian(set1, set2)"],description:"Create the cartesian product of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays and the values will be sorted in ascending order before the operation.",examples:["setCartesian([1, 2], [3, 4])"],seealso:["setUnion","setIntersect","setDifference","setPowerset"]},setDifference:{name:"setDifference",category:"Set",syntax:["setDifference(set1, set2)"],description:"Create the difference of two (multi)sets: every element of set1, that is not the element of set2. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setDifference([1, 2, 3, 4], [3, 4, 5, 6])","setDifference([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setIntersect","setSymDifference"]},setDistinct:{name:"setDistinct",category:"Set",syntax:["setDistinct(set)"],description:"Collect the distinct elements of a multiset. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setDistinct([1, 1, 1, 2, 2, 3])"],seealso:["setMultiplicity"]},setIntersect:{name:"setIntersect",category:"Set",syntax:["setIntersect(set1, set2)"],description:"Create the intersection of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setIntersect([1, 2, 3, 4], [3, 4, 5, 6])","setIntersect([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setDifference"]},setIsSubset:{name:"setIsSubset",category:"Set",syntax:["setIsSubset(set1, set2)"],description:"Check whether a (multi)set is a subset of another (multi)set: every element of set1 is the element of set2. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setIsSubset([1, 2], [3, 4, 5, 6])","setIsSubset([3, 4], [3, 4, 5, 6])"],seealso:["setUnion","setIntersect","setDifference"]},setMultiplicity:{name:"setMultiplicity",category:"Set",syntax:["setMultiplicity(element, set)"],description:"Count the multiplicity of an element in a multiset. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setMultiplicity(1, [1, 2, 2, 4])","setMultiplicity(2, [1, 2, 2, 4])"],seealso:["setDistinct","setSize"]},setPowerset:{name:"setPowerset",category:"Set",syntax:["setPowerset(set)"],description:"Create the powerset of a (multi)set: the powerset contains very possible subsets of a (multi)set. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setPowerset([1, 2, 3])"],seealso:["setCartesian"]},setSize:{name:"setSize",category:"Set",syntax:["setSize(set)","setSize(set, unique)"],description:'Count the number of elements of a (multi)set. When the second parameter "unique" is true, count only the unique values. A multi-dimension array will be converted to a single-dimension array before the operation.',examples:["setSize([1, 2, 2, 4])","setSize([1, 2, 2, 4], true)"],seealso:["setUnion","setIntersect","setDifference"]},setSymDifference:{name:"setSymDifference",category:"Set",syntax:["setSymDifference(set1, set2)"],description:"Create the symmetric difference of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setSymDifference([1, 2, 3, 4], [3, 4, 5, 6])","setSymDifference([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setIntersect","setDifference"]},setUnion:{name:"setUnion",category:"Set",syntax:["setUnion(set1, set2)"],description:"Create the union of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setUnion([1, 2, 3, 4], [3, 4, 5, 6])","setUnion([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setIntersect","setDifference"]},erf:{name:"erf",category:"Special",syntax:["erf(x)"],description:"Compute the erf function of a value using a rational Chebyshev approximations for different intervals of x",examples:["erf(0.2)","erf(-0.5)","erf(4)"],seealso:[]},cumsum:{name:"cumsum",category:"Statistics",syntax:["cumsum(a, b, c, ...)","cumsum(A)"],description:"Compute the cumulative sum of all values.",examples:["cumsum(2, 3, 4, 1)","cumsum([2, 3, 4, 1])","cumsum([1, 2; 3, 4])","cumsum([1, 2; 3, 4], 1)","cumsum([1, 2; 3, 4], 2)"],seealso:["max","mean","median","min","prod","std","sum","variance"]},mad:{name:"mad",category:"Statistics",syntax:["mad(a, b, c, ...)","mad(A)"],description:"Compute the median absolute deviation of a matrix or a list with values. The median absolute deviation is defined as the median of the absolute deviations from the median.",examples:["mad(10, 20, 30)","mad([1, 2, 3])"],seealso:["mean","median","std","abs"]},max:{name:"max",category:"Statistics",syntax:["max(a, b, c, ...)","max(A)","max(A, dim)"],description:"Compute the maximum value of a list of values.",examples:["max(2, 3, 4, 1)","max([2, 3, 4, 1])","max([2, 5; 4, 3])","max([2, 5; 4, 3], 1)","max([2, 5; 4, 3], 2)","max(2.7, 7.1, -4.5, 2.0, 4.1)","min(2.7, 7.1, -4.5, 2.0, 4.1)"],seealso:["mean","median","min","prod","std","sum","variance"]},mean:{name:"mean",category:"Statistics",syntax:["mean(a, b, c, ...)","mean(A)","mean(A, dim)"],description:"Compute the arithmetic mean of a list of values.",examples:["mean(2, 3, 4, 1)","mean([2, 3, 4, 1])","mean([2, 5; 4, 3])","mean([2, 5; 4, 3], 1)","mean([2, 5; 4, 3], 2)","mean([1.0, 2.7, 3.2, 4.0])"],seealso:["max","median","min","prod","std","sum","variance"]},median:{name:"median",category:"Statistics",syntax:["median(a, b, c, ...)","median(A)"],description:"Compute the median of all values. The values are sorted and the middle value is returned. In case of an even number of values, the average of the two middle values is returned.",examples:["median(5, 2, 7)","median([3, -1, 5, 7])"],seealso:["max","mean","min","prod","std","sum","variance","quantileSeq"]},min:{name:"min",category:"Statistics",syntax:["min(a, b, c, ...)","min(A)","min(A, dim)"],description:"Compute the minimum value of a list of values.",examples:["min(2, 3, 4, 1)","min([2, 3, 4, 1])","min([2, 5; 4, 3])","min([2, 5; 4, 3], 1)","min([2, 5; 4, 3], 2)","min(2.7, 7.1, -4.5, 2.0, 4.1)","max(2.7, 7.1, -4.5, 2.0, 4.1)"],seealso:["max","mean","median","prod","std","sum","variance"]},mode:{name:"mode",category:"Statistics",syntax:["mode(a, b, c, ...)","mode(A)","mode(A, a, b, B, c, ...)"],description:"Computes the mode of all values as an array. In case mode being more than one, multiple values are returned in an array.",examples:["mode(2, 1, 4, 3, 1)","mode([1, 2.7, 3.2, 4, 2.7])","mode(1, 4, 6, 1, 6)"],seealso:["max","mean","min","median","prod","std","sum","variance"]},prod:{name:"prod",category:"Statistics",syntax:["prod(a, b, c, ...)","prod(A)"],description:"Compute the product of all values.",examples:["prod(2, 3, 4)","prod([2, 3, 4])","prod([2, 5; 4, 3])"],seealso:["max","mean","min","median","min","std","sum","variance"]},quantileSeq:{name:"quantileSeq",category:"Statistics",syntax:["quantileSeq(A, prob[, sorted])","quantileSeq(A, [prob1, prob2, ...][, sorted])","quantileSeq(A, N[, sorted])"],description:"Compute the prob order quantile of a matrix or a list with values. The sequence is sorted and the middle value is returned. Supported types of sequence values are: Number, BigNumber, Unit Supported types of probablity are: Number, BigNumber. \n\nIn case of a (multi dimensional) array or matrix, the prob order quantile of all elements will be calculated.",examples:["quantileSeq([3, -1, 5, 7], 0.5)","quantileSeq([3, -1, 5, 7], [1/3, 2/3])","quantileSeq([3, -1, 5, 7], 2)","quantileSeq([-1, 3, 5, 7], 0.5, true)"],seealso:["mean","median","min","max","prod","std","sum","variance"]},std:{name:"std",category:"Statistics",syntax:["std(a, b, c, ...)","std(A)","std(A, normalization)"],description:'Compute the standard deviation of all values, defined as std(A) = sqrt(variance(A)). Optional parameter normalization can be "unbiased" (default), "uncorrected", or "biased".',examples:["std(2, 4, 6)","std([2, 4, 6, 8])",'std([2, 4, 6, 8], "uncorrected")','std([2, 4, 6, 8], "biased")',"std([1, 2, 3; 4, 5, 6])"],seealso:["max","mean","min","median","prod","sum","variance"]},sum:{name:"sum",category:"Statistics",syntax:["sum(a, b, c, ...)","sum(A)"],description:"Compute the sum of all values.",examples:["sum(2, 3, 4, 1)","sum([2, 3, 4, 1])","sum([2, 5; 4, 3])"],seealso:["max","mean","median","min","prod","std","sum","variance"]},variance:{name:"variance",category:"Statistics",syntax:["variance(a, b, c, ...)","variance(A)","variance(A, normalization)"],description:'Compute the variance of all values. Optional parameter normalization can be "unbiased" (default), "uncorrected", or "biased".',examples:["variance(2, 4, 6)","variance([2, 4, 6, 8])",'variance([2, 4, 6, 8], "uncorrected")','variance([2, 4, 6, 8], "biased")',"variance([1, 2, 3; 4, 5, 6])"],seealso:["max","mean","min","median","min","prod","std","sum"]},acos:{name:"acos",category:"Trigonometry",syntax:["acos(x)"],description:"Compute the inverse cosine of a value in radians.",examples:["acos(0.5)","acos(cos(2.3))"],seealso:["cos","atan","asin"]},acosh:{name:"acosh",category:"Trigonometry",syntax:["acosh(x)"],description:"Calculate the hyperbolic arccos of a value, defined as `acosh(x) = ln(sqrt(x^2 - 1) + x)`.",examples:["acosh(1.5)"],seealso:["cosh","asinh","atanh"]},acot:{name:"acot",category:"Trigonometry",syntax:["acot(x)"],description:"Calculate the inverse cotangent of a value.",examples:["acot(0.5)","acot(cot(0.5))","acot(2)"],seealso:["cot","atan"]},acoth:{name:"acoth",category:"Trigonometry",syntax:["acoth(x)"],description:"Calculate the hyperbolic arccotangent of a value, defined as `acoth(x) = (ln((x+1)/x) + ln(x/(x-1))) / 2`.",examples:["acoth(2)","acoth(0.5)"],seealso:["acsch","asech"]},acsc:{name:"acsc",category:"Trigonometry",syntax:["acsc(x)"],description:"Calculate the inverse cotangent of a value.",examples:["acsc(2)","acsc(csc(0.5))","acsc(0.5)"],seealso:["csc","asin","asec"]},acsch:{name:"acsch",category:"Trigonometry",syntax:["acsch(x)"],description:"Calculate the hyperbolic arccosecant of a value, defined as `acsch(x) = ln(1/x + sqrt(1/x^2 + 1))`.",examples:["acsch(0.5)"],seealso:["asech","acoth"]},asec:{name:"asec",category:"Trigonometry",syntax:["asec(x)"],description:"Calculate the inverse secant of a value.",examples:["asec(0.5)","asec(sec(0.5))","asec(2)"],seealso:["acos","acot","acsc"]},asech:{name:"asech",category:"Trigonometry",syntax:["asech(x)"],description:"Calculate the inverse secant of a value.",examples:["asech(0.5)"],seealso:["acsch","acoth"]},asin:{name:"asin",category:"Trigonometry",syntax:["asin(x)"],description:"Compute the inverse sine of a value in radians.",examples:["asin(0.5)","asin(sin(0.5))"],seealso:["sin","acos","atan"]},asinh:{name:"asinh",category:"Trigonometry",syntax:["asinh(x)"],description:"Calculate the hyperbolic arcsine of a value, defined as `asinh(x) = ln(x + sqrt(x^2 + 1))`.",examples:["asinh(0.5)"],seealso:["acosh","atanh"]},atan:{name:"atan",category:"Trigonometry",syntax:["atan(x)"],description:"Compute the inverse tangent of a value in radians.",examples:["atan(0.5)","atan(tan(0.5))"],seealso:["tan","acos","asin"]},atanh:{name:"atanh",category:"Trigonometry",syntax:["atanh(x)"],description:"Calculate the hyperbolic arctangent of a value, defined as `atanh(x) = ln((1 + x)/(1 - x)) / 2`.",examples:["atanh(0.5)"],seealso:["acosh","asinh"]},atan2:{name:"atan2",category:"Trigonometry",syntax:["atan2(y, x)"],description:"Computes the principal value of the arc tangent of y/x in radians.",examples:["atan2(2, 2) / pi","angle = 60 deg in rad","x = cos(angle)","y = sin(angle)","atan2(y, x)"],seealso:["sin","cos","tan"]},cos:{name:"cos",category:"Trigonometry",syntax:["cos(x)"],description:"Compute the cosine of x in radians.",examples:["cos(2)","cos(pi / 4) ^ 2","cos(180 deg)","cos(60 deg)","sin(0.2)^2 + cos(0.2)^2"],seealso:["acos","sin","tan"]},cosh:{name:"cosh",category:"Trigonometry",syntax:["cosh(x)"],description:"Compute the hyperbolic cosine of x in radians.",examples:["cosh(0.5)"],seealso:["sinh","tanh","coth"]},cot:{name:"cot",category:"Trigonometry",syntax:["cot(x)"],description:"Compute the cotangent of x in radians. 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null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:Ou(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Ru,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class Yu extends Ru{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:ho((()=>Tu(r).variable(!1)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:ho((()=>Tu(r).variable(!1)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const i=this.accumulatedGrads[n].variable,s=this.accumulatedUpdates[n].variable;ho((()=>{const e=wu(Mu(i,this.rho),Mu(ku(a),1-this.rho)),t=Mu(Nu(Su(wu(s,this.epsilon)),Su(wu(i,this.epsilon))),a),n=wu(Mu(s,this.rho),Mu(ku(t),1-this.rho));i.assign(e),s.assign(n);const o=wu(Mu(t,-this.learningRate),r);r.assign(o)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(fo(this.accumulatedGrads.map((e=>e.variable))),fo(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}function Bu(e,t,n){const r={shape:e,value:t,dtype:n};return _i.runKernel(an,{},r)}Yu.className="Adadelta",lu(Yu);class zu extends Ru{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:ho((()=>Bu(r.shape,this.initialAccumulatorValue).variable(e)))}}const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const i=this.accumulatedGrads[n].variable;ho((()=>{const e=wu(i,ku(a));i.assign(e);const t=wu(Mu(Nu(a,Su(wu(e,_i.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&fo(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),this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}zu.className="Adagrad",lu(zu);const Pu=Fi({pow_:function(e,t){let n=Ai(e,"base","pow"),r=Ai(t,"exp","pow");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(ar,a)}}),Hu=Fi({sub_:function(e,t){let n=Ai(e,"a","sub"),r=Ai(t,"b","sub");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(Wr,a)}});class ju extends Ru{constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ho((()=>{this.accBeta1=Ou(t).variable(),this.accBeta2=Ou(n).variable()})),null==r&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ho((()=>{const n=Hu(1,this.accBeta1),r=Hu(1,this.accBeta2);t.forEach(((t,a)=>{const i=_i.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:ho((()=>Tu(i).variable(!1)))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:ho((()=>Tu(i).variable(!1)))});const s=Array.isArray(e)?e[a].tensor:e[t];if(null==s)return;const o=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,l=wu(Mu(o,this.beta1),Mu(s,1-this.beta1)),c=wu(Mu(u,this.beta2),Mu(ku(s),1-this.beta2)),p=Nu(l,n),d=Nu(c,r);o.assign(l),u.assign(c);const h=wu(Mu(Nu(p,wu(Su(d),this.epsilon)),-this.learningRate),i);i.assign(h)})),this.accBeta1.assign(Mu(this.accBeta1,this.beta1)),this.accBeta2.assign(Mu(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&fo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&fo(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),ho((()=>{this.accBeta1.assign(Pu(this.beta1,this.iterations_+1)),this.accBeta2.assign(Pu(this.beta2,this.iterations_+1))}));const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}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)}}ju.className="Adam",lu(ju);const Uu=Fi({abs_:function(e){const t=Ai(e,"x","abs");if("complex64"===t.dtype){const e={x:t};return _i.runKernel(St,e)}{const e={x:t};return _i.runKernel(Qe,e)}}}),qu=Fi({maximum_:function(e,t){let n=Ai(e,"a","maximum"),r=Ai(t,"b","maximum");[n,r]=di(n,r),"bool"===n.dtype&&(n=Ls(n,"int32"),r=Ls(r,"int32")),Lo(n.shape,r.shape);const a={a:n,b:r};return _i.runKernel($n,a)}});class Wu extends Ru{constructor(e,t,n,r=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ho((()=>{this.iteration=Ou(0).variable(),this.accBeta1=Ou(t).variable()})),null==r&&(this.epsilon=_i.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);ho((()=>{const n=Hu(1,this.accBeta1),r=Nu(-this.learningRate,wu(Mu(this.iteration,this.decay),1));t.forEach(((t,a)=>{const i=_i.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Tu(i).variable(!1)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:Tu(i).variable(!1)});const s=Array.isArray(e)?e[a].tensor:e[t];if(null==s)return;const o=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,l=wu(Mu(o,this.beta1),Mu(s,1-this.beta1)),c=Mu(u,this.beta2),p=Uu(s),d=qu(c,p);o.assign(l),u.assign(d);const h=wu(Mu(Nu(r,n),Nu(l,wu(d,this.epsilon))),i);i.assign(h)})),this.iteration.assign(wu(this.iteration,1)),this.accBeta1.assign(Mu(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&fo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&fo(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)}}Wu.className="Adamax",lu(Wu);class Vu extends Ru{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=_i.registeredVariables[t];ho((()=>{const e=wu(Mu(this.c,r),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=mo(Ou(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Vu.className="SGD",lu(Vu);class Gu extends Vu{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ou(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:ho((()=>Tu(r).variable(e)))}}const a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[t];null!=i&&ho((()=>{let e;const t=wu(Mu(this.m,a),i);e=this.useNesterov?wu(Mu(this.c,wu(i,Mu(t,this.m))),r):wu(Mu(this.c,t),r),a.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&fo(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),this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Gu.className="Momentum",lu(Gu);class Ku extends Ru{constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=_i.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=_i.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:ho((()=>Tu(r).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:ho((()=>Tu(r).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:ho((()=>Tu(r).variable(a)))});const i=Array.isArray(e)?e[n].tensor:e[t];if(null==i)return;const s=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;ho((()=>{const e=wu(Mu(s,this.decay),Mu(ku(i),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=wu(Mu(t,this.decay),Mu(i,1-this.decay)),u=Nu(Mu(i,this.learningRate),Su(Hu(e,wu(ku(a),this.epsilon)))),l=wu(Mu(o,this.momentum),u);s.assign(e),t.assign(a),o.assign(l);const c=Hu(r,l);r.assign(c)}else{const e=wu(Mu(s,this.decay),Mu(ku(i),1-this.decay)),t=wu(Mu(o,this.momentum),Nu(Mu(i,this.learningRate),Su(wu(e,this.epsilon))));s.assign(e),o.assign(t);const n=Hu(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&fo(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&fo(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&fo(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.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)}}Ku.className="RMSProp",lu(Ku);class Ju{static sgd(e){return new Vu(e)}static momentum(e,t,n=!1){return new Gu(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Ku(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new ju(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Yu(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Wu(e,t,n,r,a)}static adagrad(e,t=.1){return new zu(e,t)}}const Xu=Fi({acos_:function(e){const t={x:Ai(e,"x","acos")};return _i.runKernel(et,t)}}),Zu=Fi({acosh_:function(e){const t={x:Ai(e,"x","acosh")};return _i.runKernel(tt,t)}}),Qu=Fi({addN_:function(e){se(Array.isArray(e),(()=>"The argument passed to tf.addN() must be a list of tensors")),se(e.length>=1,(()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`));const t=e.map(((e,t)=>Ai(e,`tensors${t}`,"addN"))),n=t[0];t.forEach((e=>{if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")})),t.forEach((e=>{if(!de(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")}));const r=t;return _i.runKernel(rt,r)}}),el=Fi({all_:function(e,t=null,n=!1){const r={x:Ai(e,"x","all","bool")},a={axis:t,keepDims:n};return _i.runKernel(at,r,a)}}),tl=Fi({any_:function(e,t=null,n=!1){const r={x:Ai(e,"x","any","bool")},a={axis:t,keepDims:n};return _i.runKernel(it,r,a)}}),nl=Fi({argMax_:function(e,t=0){const n={x:Ai(e,"x","argMax")},r={axis:t};return _i.runKernel(st,n,r)}}),rl=Fi({argMin_:function(e,t=0){const n={x:Ai(e,"x","argMin")},r={axis:t};return _i.runKernel(ot,n,r)}}),al=Fi({asin_:function(e){const t={x:Ai(e,"x","asin")};return _i.runKernel(ut,t)}}),il=Fi({asinh_:function(e){const t={x:Ai(e,"x","asinh")};return _i.runKernel(lt,t)}}),sl=Fi({atan_:function(e){const t={x:Ai(e,"x","atan")};return _i.runKernel(ct,t)}}),ol=Fi({atan2_:function(e,t){let n=Ai(e,"a","atan2"),r=Ai(t,"b","atan2");[n,r]=di(n,r);const a={a:n,b:r};return _i.runKernel(dt,a)}}),ul=Fi({atanh_:function(e){const t={x:Ai(e,"x","atanh")};return _i.runKernel(pt,t)}});function ll(e,t,n,r,a="NHWC",i){return dl(e,[...t,e[3]],n,i,r,null,null,_l(a))}function cl(e,t,n,r,a,i,s="channelsLast"){const[o,u]=ml(t);let l;if("channelsLast"===s)l=[o,u,e[3],e[3]];else{if("channelsFirst"!==s)throw new Error(`Unknown dataFormat ${s}`);l=[o,u,e[1],e[1]]}return dl(e,l,n,r,a,i,!1,s)}function pl(e,t,n,r,a,i,s="NDHWC"){const[o,u,l]=gl(t);let c,p;if("NDHWC"===s)p="channelsLast",c=[o,u,l,e[4],e[4]];else{if("NCDHW"!==s)throw new Error(`Unknown dataFormat ${s}`);p="channelsFirst",c=[o,u,l,e[1],e[1]]}return hl(e,c,n,r,a,!1,p,i)}function dl(e,t,n,r,a,i,s=!1,o="channelsLast"){let[u,l,c,p]=[-1,-1,-1,-1];if("channelsLast"===o)[u,l,c,p]=e;else{if("channelsFirst"!==o)throw new Error(`Unknown dataFormat ${o}`);[u,p,l,c]=e}const[d,h,,f]=t,[m,g]=ml(n),[y,b]=ml(r),v=yl(d,y),x=yl(h,b),{padInfo:_,outHeight:w,outWidth:D}=function(e,t,n,r,a,i,s,o,u){let l,c,p;if("number"==typeof e){l={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};const a=function(e,t,n,r,a){null==r&&(r=fl(e,t,n));const i=e[1];return[bl((e[0]-t+2*r)/n+1,a),bl((i-t+2*r)/n+1,a)]}([t,n],i,r,e,o);c=a[0],p=a[1]}else if("same"===e){c=Math.ceil(t/r),p=Math.ceil(n/a);const e=Math.max(0,(c-1)*r+i-t),o=Math.max(0,(p-1)*a+s-n),u=Math.floor(e/2),d=e-u,h=Math.floor(o/2);l={top:u,bottom:d,left:h,right:o-h,type:"SAME"}}else if("valid"===e)l={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-i+1)/r),p=Math.ceil((n-s+1)/a);else{if("object"!=typeof e)throw Error(`Unknown padding parameter: ${e}`);{const d="channelsLast"===u?e[1][0]:e[2][0],h="channelsLast"===u?e[1][1]:e[2][1],f="channelsLast"===u?e[2][0]:e[3][0],m="channelsLast"===u?e[2][1]:e[3][1];l={top:d,bottom:h,left:f,right:m,type:0===d&&0===h&&0===f&&0===m?"VALID":"EXPLICIT"},c=bl((t-i+d+h)/r+1,o),p=bl((n-s+f+m)/a+1,o)}}return{padInfo:l,outHeight:c,outWidth:p}}(a,l,c,m,g,v,x,i,o),N=s?f*p:f;let M;return"channelsFirst"===o?M=[u,N,w,D]:"channelsLast"===o&&(M=[u,w,D,N]),{batchSize:u,dataFormat:o,inHeight:l,inWidth:c,inChannels:p,outHeight:w,outWidth:D,outChannels:N,padInfo:_,strideHeight:m,strideWidth:g,filterHeight:d,filterWidth:h,effectiveFilterHeight:v,effectiveFilterWidth:x,dilationHeight:y,dilationWidth:b,inShape:e,outShape:M,filterShape:t}}function hl(e,t,n,r,a,i=!1,s="channelsLast",o){let[u,l,c,p,d]=[-1,-1,-1,-1,-1];if("channelsLast"===s)[u,l,c,p,d]=e;else{if("channelsFirst"!==s)throw new Error(`Unknown dataFormat ${s}`);[u,d,l,c,p]=e}const[h,f,m,,g]=t,[y,b,v]=gl(n),[x,_,w]=gl(r),D=yl(h,x),N=yl(f,_),M=yl(m,w),{padInfo:S,outDepth:k,outHeight:T,outWidth:C}=function(e,t,n,r,a,i,s,o,u,l,c){let p,d,h,f;if("number"==typeof e){p={top:e,bottom:e,left:e,right:e,front:e,back:e,type:0===e?"VALID":"NUMBER"};const i=function(e,t,n,r,a,i){null==a&&(a=fl(e,t,r));const s=e[1],o=e[2];return[bl((e[0]-t+2*a)/r+1,i),bl((s-t+2*a)/r+1,i),bl((o-t+2*a)/r+1,i),1]}([t,n,r,1],o,0,a,e,c);d=i[0],h=i[1],f=i[2]}else if("same"===e){d=Math.ceil(t/a),h=Math.ceil(n/i),f=Math.ceil(r/s);const e=(d-1)*a+o-t,c=(h-1)*i+u-n,m=(f-1)*s+l-r,g=Math.floor(e/2),y=e-g,b=Math.floor(c/2),v=c-b,x=Math.floor(m/2);p={top:b,bottom:v,left:x,right:m-x,front:g,back:y,type:"SAME"}}else{if("valid"!==e)throw Error(`Unknown padding parameter: ${e}`);p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),h=Math.ceil((n-u+1)/i),f=Math.ceil((r-l+1)/s)}return{padInfo:p,outDepth:d,outHeight:h,outWidth:f}}(a,l,c,p,y,b,v,D,N,M,o),E=i?g*d:g;let 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u={indices:a,values:i,denseShape:s,defaultValue:o},l=_i.runKernel($r,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}}),Oh=Fi({sparseReshape_:function(e,t,n){const r=Ai(e,"inputIndices","sparseReshape","int32"),a=Ai(t,"inputShape","sparseReshape","int32"),i=Ai(n,"newShape","sparseReshape","int32");if(2!==r.rank)throw new Error(`Input indices should be Tensor2D but received shape\n ${r.shape}`);if(1!==a.rank)throw new Error(`Input shape should be Tensor1D but received shape ${a.shape}`);if(1!==i.rank)throw new Error(`New shape should be Tensor1D but received shape ${i.shape}`);const s={inputIndices:r,inputShape:a,newShape:i},o=_i.runKernel(Or,s);return{outputIndices:o[0],outputShape:o[1]}}}),Rh=Fi({sparseSegmentMean_:function(e,t,n){const r=Ai(e,"data","sparseSegmentMean"),a=Ai(t,"indices","sparseSegmentMean","int32"),i=Ai(n,"segmentIds","sparseSegmentMean","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but 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Got: ${e.scale}`);var t;this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,t=this.mode,zg(Iy,"FanMode",t),this.distribution=null==e.distribution?"normal":e.distribution,function(e){zg(Ly,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){const n=function(e,t="channelsLast"){let n,r;if(ty(t),2===e.length)n=e[0],r=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){const t=cy(e,2);n=e[1]*t,r=e[0]*t}else if("channelsLast"===t){const t=cy(e,0,e.length-2);n=e[e.length-2]*t,r=e[e.length-1]*t}}else{const t=cy(e);n=Math.sqrt(t),r=Math.sqrt(t)}return[n,r]}(e),r=n[0],a=n[1];let i=this.scale;if("fanIn"===this.mode?i/=Math.max(1,r):"fanOut"===this.mode?i/=Math.max(1,a):i/=Math.max(1,(r+a)/2),"normal"===this.distribution){const n=Math.sqrt(i);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Dg(`${this.getClassName()} does not support dType ${t}.`);return Dd(e,0,n,t,this.seed)}{const n=Math.sqrt(3*i);return Rp(e,-n,n,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}Hy.className="VarianceScaling",lu(Hy);class jy extends Hy{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}jy.className="GlorotUniform",lu(jy);class Uy extends Hy{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}Uy.className="GlorotNormal",lu(Uy);class qy extends Hy{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}qy.className="HeNormal",lu(qy);class Wy extends Hy{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}Wy.className="HeUniform",lu(Wy);class Vy extends Hy{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}Vy.className="LeCunNormal",lu(Vy);class Gy extends Hy{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Hy.className}}Gy.className="LeCunNormal",lu(Gy);class Ky extends Fy{constructor(e){if(super(),this.DEFAULT_GAIN=1,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,null!=this.seed)throw new Dg("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return ho((()=>{if(e.length<2)throw new Dg("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const t=Ny(e[0]>e[1]?[e[1],e[0]]:e,0,1,"float32");let n=qh.gramSchmidt(t);return e[0]>e[1]&&(n=Co(n)),Mu(this.gain,n)}))}getConfig(){return{gain:this.gain,seed:this.seed}}}Ky.className="Orthogonal",lu(Ky);const Jy={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Xy(e,t={}){return Og(e,uu.getMap().classNameMap,t,"initializer")}function Zy(e){return Fg(e)}function Qy(e){if("string"==typeof e){const t=e in Jy?Jy[e]:e;if("GlorotNormal"===t)return new Uy;if("GlorotUniform"===t)return new jy;if("HeNormal"===t)return new qy;if("HeUniform"===t)return new Wy;if("LeCunNormal"===t)return new Vy;if("LeCunUniform"===t)return new Gy;{const e={};return e.className=t,e.config={},Xy(e)}}return e instanceof Fy?e:Xy(e)}function eb(e){return Array.isArray(e)&&Array.isArray(e[0])}function tb(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function nb(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new wg(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function rb(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return e[0];throw new wg(`Expected exactly 1 Shape; got ${e.length}`)}return e}function ab(e){let t=0;for(const n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}class ib{constructor(e,t="float32",n="Variable",r=!0,a=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=Wg(),n=null==n?"Variable":n,this.originalName=sy(n),this.name=oy(this.originalName),this.trainable_=r,this.constraint=a,this.val=Td(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function sb(e){return e.map((e=>e.read()))}function ob(e){e.forEach((e=>{e[0].write(e[1])}))}class ub{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class lb{constructor(e,t,n,r,a,i,s){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=s,this.id=Wg(),null!=i&&(this.originalName=sy(i),this.name=oy(this.originalName)),this.rank=t.length}}let cb=0;class pb{constructor(e,t){this.callArgs=t,this.id=cb++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let db=0;class hb extends ou{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=db++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const e=this.getClassName();t=Ag(e)+"_"+Gg(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new _g(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new wg(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Cg(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Cg(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new xg(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new xg(`Layer ${this.name} is not connected, no input to return.`);return Cg(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new xg(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new xg(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Cg(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((e=>e()))}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach((t=>t.trainable=e)),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((e=>e.trainable)):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((e=>!e.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Eg(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=Eg(this.inputSpec);if(e.length!==t.length)throw new wg(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;na.maxNDim)throw new wg(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${i}`);if(null!=a.minNDim&&i=0?e[r]:e[e.length+r];if(null!=i&&-1===[i,null].indexOf(s))throw new wg(`Input ${n} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${i} but got shape ${e}.`)}}if(null!=a.shape)for(let e=0;e{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of Eg(e))t.push(n.shape);this.build(Cg(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let r=this.call(e,t);const a=Eg(r),i=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),i.push(e);if(r=Cg(i),null!=this.activityRegularizer)throw new Dg("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=Eg(e);const t=[];for(const n of e)t.push(n.shape);return Cg(t)}(e),r=this.computeOutputShape(n);let a;const i="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),a=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new lb(i,n,this,Eg(e),t,this.name,r))):new lb(i,r,this,Eg(e),t,this.name),this.addInboundNode(e,a,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new Dg("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new xg(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new xg(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new _g(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return ab(this.weights)}build(e){this.built=!0}getWeights(e=!1){return sb(e?this.trainableWeights:this.weights)}setWeights(e){ho((()=>{const t=this.weights;if(t.length!==e.length)throw new wg(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(0===t.length)return;const n=[],r=sb(t);for(let a=0;aa.apply(l.read()))),null==i&&(i=!0),i?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=Eg(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);t.forEach((e=>{if(null!=e)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,r,a,i,s=null){const o=Eg(e);t=Eg(t),n=Eg(n),r=Eg(r),a=tb(a),i=tb(i);const u=[],l=[],c=[];for(const e of o)u.push(e.sourceLayer),l.push(e.nodeIndex),c.push(e.tensorIndex);new pb({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:i},s);for(let e=0;ee.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function fb(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{const e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{const t=[];for(let n=0;ne.name)),u=[],l=t.names();for(const e of o)-1!==l.indexOf(e)?u.push(t.getValue(e)):u.push(null);null!=r&&(r.maxNumTensors=-1/0,r.minNumTensors=1/0);const c=o.join(",")+"|"+t.names().sort().join(",");let p,d=bb.get(c);if(null==d){const e=function(e,t){se(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],r={};if(1===e.length){const a=wb(e[0],t);n=a.sorted,r=a.recipientMap}else{const a=new Set;for(const i of e){const{sorted:e,recipientMap:s}=wb(i,t);for(const t of e)a.has(t.name)||(n.push(t),a.add(t.name));for(const e in s)null==r[e]&&(r[e]=new Set),s[e].forEach((t=>r[e].add(t)))}}return{sorted:n,recipientCounts:_b(r)}}(s,t);d=e.sorted,p=e.recipientCounts,bb.put(c,d),vb.put(c,p)}p={},a||Object.assign(p,vb.get(c));const h=new yb(t);for(let e=0;er.maxNumTensors&&(r.maxNumTensors=e),e0;){const e=i[i.length-1];if(n.has(e.name)){i.pop();continue}const t=s[s.length-1]===i.length-1;if(0===e.inputs.length||t)i.pop(),r.push(e),n.add(e.name),t&&s.pop();else{s.push(i.length-1);for(const t of e.inputs)null==a[t.name]&&(a[t.name]=new Set),a[t.name].add(e.name),n.has(t.name)||i.push(t)}}return{sorted:r,recipientMap:a}}function Db(e){let t;if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;tSu(Nc(Mu(e,e),t,!0))))}Ge().registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",(()=>100),(function(e){null!=bb&&bb.setMaxEntries(e),null!=vb&&vb.setMaxEntries(e)}));class Mb extends ou{getConfig(){return{}}}class Sb extends Mb{constructor(e){super(),this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>{const t=Nb(e,this.axis),n=zl(t,0,this.maxValue);return Mu(e,Nu(n,wu(my(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}Sb.className="MaxNorm",lu(Sb);class kb extends Mb{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>Nu(e,wu(my(),Nb(e,this.axis)))))}getConfig(){return{axis:this.axis}}}kb.className="UnitNorm",lu(kb);class Tb extends Mb{apply(e){return zp(e)}}Tb.className="NonNeg",lu(Tb);class Cb extends Mb{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=e.minValue?e.minValue:this.defaultMinValue,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.rate=null!=e.rate?e.rate:this.defaultRate,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return ho((()=>{const t=Nb(e,this.axis),n=wu(Mu(this.rate,zl(t,this.minValue,this.maxValue)),Mu(1-this.rate,t));return Mu(e,Nu(n,wu(my(),t)))}))}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}}Cb.className="MinMaxNorm",lu(Cb);const Eb={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ab(e){return Fg(e)}function Ib(e,t={}){return Og(e,uu.getMap().classNameMap,t,"constraint")}function Lb(e){return null==e?null:"string"==typeof e?Ib({className:e in Eb?Eb[e]:e,config:{}}):e instanceof Mb?e:Ib(e)}function Fb(e){return new Sb(e)}function $b(e){return new kb(e)}function Ob(){return new Tb}function Rb(e){return new Cb(e)}function Yb(){return new $y}function Bb(){return new Oy}function zb(e){return new Ry(e)}function Pb(e){return new Yy(e)}function Hb(e){return new By(e)}function jb(e){return new zy(e)}function Ub(e){return new Py(e)}function qb(e){return new Hy(e)}function Wb(e){return new jy(e)}function Vb(e){return new Uy(e)}function Gb(e){return new qy(e)}function Kb(e){return new Wy(e)}function Jb(e){return new Vy(e)}function Xb(e){return new Gy(e)}function Zb(e){return new Ky(e)}async function Qb(e){if(null==e)return;const t=[],n=[],r=[];for(const a in e){const i=e[a];if("number"!=typeof i){const e=i;t.push(e.data()),n.push(a),r.push(e)}}if(t.length>0){const a=await Promise.all(t);for(let t=0;twu(this.totals[e],Mu(r,n))));this.totals[e]=a,null!=t&&t.dispose()}}}async onEpochEnd(e,t){if(null!=t)for(const e of this.params.metrics)null!=this.totals[e]&&("number"==typeof this.totals[e]?t[e]=this.totals[e]/this.seen:ho((()=>{const n=Mu(Nu(1,this.seen),this.totals[e]);t[e]=n,this.totals[e].dispose(),mo(t[e])})))}}class iv extends nv{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){null==t&&(t={}),this.epoch.push(e);for(const e in t)null==this.history[e]&&(this.history[e]=[]),this.history[e].push(t[e])}async syncData(){const e=[],t=[],n=[];for(const r in this.history){const a=this.history[r];for(let i=0;i{const s=null!=n?n():Ba();return s-anew sv(e,t)))}class uv{constructor(){}static registerCallbackConstructor(e,t){se(e>=0&&Number.isInteger(e),(()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`)),uv.checkForDuplicate(t),null==uv.constructors[e]&&(uv.constructors[e]=[]),uv.constructors[e].push(t)}static checkForDuplicate(e){for(const t in uv.constructors)uv.constructors[+t].forEach((t=>{if(t===e)throw new wg("Duplicate callback constructor.")}))}static clear(){uv.constructors={}}static createCallbacks(e){const t=[];for(const n in uv.constructors){const r=+n;e>=r&&t.push(...uv.constructors[r])}return t.map((e=>new e))}}function lv(e,t,n,r,a,i,s,o,u){const l=new iv,c=[new av,...uv.createCallbacks(t)];null!=e&&c.push(...e),c.push(l);const p=new rv(c);return p.setParams({epochs:n,initialEpoch:r,samples:a,steps:i,batchSize:s,verbose:t,doValidation:o,metrics:u}),{callbackList:p,history:l}}function cv(e,t={},n=!1){return Og(e,uu.getMap().classNameMap,t,"layer",n)}function pv(e,t){return ho((()=>{"float32"!==e.dtype&&(e=Ls(e,"float32"));const n=Nc(ky(e),t,!0),r=Bu(n.shape,my()),a=Su(qu(n,r));return Nu(e,a)}))}function dv(e,t){return ho((()=>op(ky(Hu(t,e)),-1)))}function hv(e,t){return ho((()=>op(Uu(Hu(t,e)),-1)))}function fv(e,t){return ho((()=>{const n=Hu(e,t),r=zl(Uu(e),my(),Number.MAX_VALUE),a=Uu(Nu(n,r));return Mu(100,op(a,-1))}))}function mv(e,t,n=!1){return ho((()=>{if(n)t=id(t);else{const e=Nc(t,t.shape.length-1,!0);t=Nu(t,e)}return t=zl(t,my(),1-my()),ko(Nc(Mu(Ls(e,"float32"),qc(t)),t.shape.length-1))}))}function gv(e,t,n=!1){return ho((()=>{const r=Ls(Lc(function(e){const t=[cy(e.shape)];return Dl(e,t)}(e)),"int32"),a=(t=zl(t,my(),1-my())).shape;return mv(Dl(ro(r,a[a.length-1]),a),t,n)}))}function yv(e,t){return ho((()=>{let n;return n=zl(t,my(),1-my()),n=qc(Nu(n,Hu(1,n))),op(function(e,t){if(!de(e.shape,t.shape))throw new wg(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return ho((()=>{const n=zp(t),r=ko(Uu(t));return wu(Hu(n,Mu(t,e)),Wc(Tc(r)))}))}(e,n),-1)}))}function bv(e,t){return ho((()=>{const n=pv(e,-1),r=pv(t,-1),a=Mu(n,r);return ko(Nc(a,-1))}))}uv.constructors={};const vv={meanSquaredError:dv,meanAbsoluteError:hv,meanAbsolutePercentageError:fv,meanSquaredLogarithmicError:function(e,t){return ho((()=>{const n=zl(t,my(),Number.MAX_VALUE),r=qc(wu(1,n)),a=zl(e,my(),Number.MAX_VALUE),i=qc(wu(1,a));return op(ky(Hu(r,i)),-1)}))},squaredHinge:function(e,t){return ho((()=>{const n=qu(0,Hu(1,Mu(e,t)));return op(ky(n),-1)}))},hinge:function(e,t){return ho((()=>{const n=qu(0,Hu(1,Mu(e,t)));return op(n,-1)}))},categoricalHinge:function(e,t){return ho((()=>{const n=Nc(Mu(e,t),-1),r=wc(Mu(Hu(1,e),t),-1);return qu(0,wu(1,Hu(r,n)))}))},logcosh:function(e,t){return ho((()=>{const n=Math.log(2),r=Hu(t,e),a=Hu(wu(r,Vc(Mu(-2,r))),n);return op(a,-1)}))},categoricalCrossentropy:mv,sparseCategoricalCrossentropy:gv,binaryCrossentropy:yv,kullbackLeiblerDivergence:function(e,t){return ho((()=>{const n=zl(e,my(),1),r=zl(t,my(),1);return Nc(Mu(e,qc(Nu(n,r))),-1)}))},poisson:function(e,t){return ho((()=>{const n=qc(wu(my(),t));return op(Hu(t,Mu(e,n)),-1)}))},cosineProximity:bv};function xv(e){if("string"==typeof e){if(e in vv)return vv[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. 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It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= 1048576.`)}}function Lv(e){if(null===e)return!0;if("object"==typeof e){if(Object.getPrototypeOf(e)===Object.prototype){const t=Object.keys(e);for(const n of t){if("string"!=typeof n)return!1;if(!Lv(e[n]))return!1}return!0}if(Array.isArray(e)){for(const t of e)if(!Lv(t))return!1;return!0}return!1}{const t=typeof e;return"string"===t||"number"===t||"boolean"===t}}function Fv(e,t,n=console.log){let r="";for(let n=0;n0&&(r=r.slice(0,r.length-1)+" "),r+=e[n],r=r.slice(0,t[n]),r+=" ".repeat(t[n]-r.length);n(r)}function $v(e,t,n){let r,a;try{a=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){a="multiple"}try{r=JSON.stringify(e.outputShape)}catch(e){r="multiple"}Fv([`${e.name} (${e.getClassName()})`,a,r,e.countParams().toString()],t,n)}function Ov(e,t,n,r){let a,i;try{i=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){i="multiple"}try{a=JSON.stringify(e.outputShape)}catch(e){a="multiple"}const s=[];for(const t of e.inboundNodes)if(!(null!=n&&n.length>0&&-1===n.indexOf(t)))for(let e=0;ee.name))}`);Yg(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((e=>e.name))}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const e of this.outputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;this.outputLayers.push(t),this.outputLayersNodeIndices.push(n),this.outputLayersTensorIndices.push(r)}for(const e of this.inputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;kg(0===n,"input layer has >1 nodes"),kg(0===r,"input layer has >1 tensors"),this.inputLayers.push(t),this.inputLayersNodeIndices.push(n),this.inputLayersTensorIndices.push(r)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let t=0;te.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));const t={},n={},r={},a={},i={},s=[],o=(e,t,n,r,a,u)=>{null!=r&&null!=a&&null!=u||(r=e.sourceLayer,a=e.nodeIndex,u=e.tensorIndex);const l=r.inboundNodes[a];if(-1!==n.indexOf(l))throw new _g(`The tensor ${e.name} at layer "${r.name}" is part of a cycle.`);if(-1!==t.indexOf(l))return;this.containerNodes.add(Pv.nodeKey(r,a)),r.id in i||(i[r.id]=Object.keys(i).length),-1===n.indexOf(l)&&n.push(l);const c=l.inboundLayers.length;for(let e=0;e=0;)n.splice(n.indexOf(l),1);s.push(l)},u=[],l=[];for(const e of this.outputs)o(e,u,l);const c=s.slice().reverse();for(const e of c){n[e.id]=e,e.id in t||(t[e.id]=0);let i=t[e.id];const s=null==r[e.outboundLayer.id]?0:r[e.outboundLayer.id];i=Math.max(i,s),r[e.outboundLayer.id]=i,a[e.outboundLayer.id]=e.outboundLayer,t[e.id]=i;for(let r=0;rparseInt(e,10))).sort(Rg);this.layers=[];for(const e of h){const t=d[e];t.sort(((e,t)=>{const n=i[e.id],r=i[t.id];return nr?1:0}));for(const e of t)e instanceof Pv&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=d,h=Object.keys(p).map((e=>parseInt(e,10))).sort(Rg);const f=this.inputs.slice(),m=[];for(const e of h)for(const t of p[e]){const e=t.outboundLayer;if(null!=e){for(const n of t.inputTensors)if(-1===f.indexOf(n))throw new _g(`Graph disconnected: cannot obtain value for tensor ${n} at layer "${e.name}". The following previous layers were accessed without issue: ${m}`);for(const e of t.outputTensors)f.push(e);m.push(e.name)}}this.nodesByDepth=p;const g=this.layers.map((e=>e.name));for(const e of g){const t=g.filter((t=>t===e)).length;if(1!==t)throw new _g(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new pb({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach((t=>{t._trainableWeights.forEach((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new wg("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let r=0;for(const e of this.layers)for(const t of e.weights){if(null!=n[t.originalName])throw new wg(`Duplicate weight name: ${t.originalName}`);n[t.originalName]=t,r++}const a=[];for(const r in e){let i=r;if(null==n[r]){const e=r.split("/");i=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[i])a.push([n[i],e[r]]);else if(t)throw new wg(`Provided weight data has no target variable: ${r}`);delete n[i]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new wg(`${e.length} of ${r} weights are not set: ${e}`)}ob(a)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${zv}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Bv(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ho((()=>{e=Eg(e);const n=new yb;for(let t=0;t{let n;return e=Eg(e),n=null==t?Sg(null,e.length):Eg(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=tb(e);if(t.length!==this.inputLayers.length)throw new wg(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let e=0;eparseInt(e,10))).sort(Rg);if(r.length>1)for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const r=[];for(let t=0;tparseInt(e,10))).sort(Rg);for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,r=e.inputTensors,a=e.outputTensors,i=new Array;for(const e of r)e.id in n&&i.push(n[e.id]);if(i.length===r.length){let r,s,o,u,l={};if(null!=e.callArgs&&(l=e.callArgs),1===i.length){const[e,n]=i[0];null==l.mask&&(l.mask=n),o=Eg(t.call(e,l)),u=Eg(t.computeMask(e,n)),r=[e],s=[n]}else r=i.map((e=>e[0])),s=i.map((e=>e[1])),null==l.mask&&(l.mask=s),o=Eg(t.call(r,l)),u=Eg(t.computeMask(r,s));if(t.activityRegularizer)throw new Dg("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e{const e=[];for(const t of this.layers)for(let n=0;n0){const e=[];for(let n=0;n0&&e.apply(Cg(n),r)}function u(e){const n=e.name,i=cv(e,null!=t.customObjects?t.customObjects:{});i.setFastWeightInitDuringBuild(r),a[n]=i,e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new wg(`Corrupted configuration, expected array for nodeData: ${e}`);s(i,e)}))}const l=t.name,c=t.layers;for(const e of c)u(e);for(;!Bg(i);)for(const e of c){const t=a[e.name];if(t.name in i){const e=i[t.name];delete i[t.name];for(const n of e)o(t,n)}}const p=[],d=[],h=t.inputLayers;for(const e of h){const t=e[0],n=e[1],r=e[2];kg(t in a);const i=a[t].inboundNodes[n].outputTensors;p.push(i[r])}const f=t.outputLayers;for(const e of f){const t=e[0],n=e[1],r=e[2];kg(t in a);const i=a[t].inboundNodes[n].outputTensors;d.push(i[r])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new wg("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ho((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}function Hv(e,t){return function(e,t,n){const r=t.length;if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>null));if(1===r)return Array.isArray(e)&&1===e.length?e:"object"==typeof e&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}if("object"==typeof e&&Object.keys(e).length>0&&"object"==typeof e[Object.keys(e)[0]]){const n=[];return t.forEach((t=>{t in e?n.push(e[t]):n.push(null)})),n}throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}(e,t,"classWeight")}async function jv(e,t,n,r){if(null!=t||null!=r)throw new Error("Support sampleWeight is not implemented yet");if(null!=n){const t=ho((()=>{if(1===e.shape.length)return Fs(e);if(2===e.shape.length){if(e.shape[1]>1)return nl(e,1);if(1===e.shape[1])return Dl(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)})),r=Array.from(await t.data());fo(t);const a=[];return r.forEach((e=>{if(null==n[e])throw new Error(`classWeight must contain all classes in the training data. The class ${e} exists in the data but not in classWeight`);a.push(n[e])})),yd(a,"float32")}return null}function Uv(e,t){return Mu(e,t)}function qv(e,t){let n,r;const a=t;n=a.xs,r=a.ys,se(null!=n&&null!=r,(()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`));const i=Wv("input",e.inputNames,n),s=Wv("output",e.outputNames,r),o=i[0].shape[0];se(i.length===e.inputs.length,(()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${i.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`)),se(s.length===e.outputs.length,(()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${s.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`));for(let t=0;t`Batch size mismatch: input ${e.inputNames[t]} has ${i[t].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`));for(let t=0;t`Batch size mismatch: output ${e.outputNames[t]} has ${s[t].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`));return{xs:i,ys:s}}function Wv(e,t,n){if(n instanceof ti)return[n];if(Array.isArray(n))return se(n.length===t.length,(()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`)),n;{const r=[];for(const a of t){if(null==n[a])throw new wg(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Vv(e){return"function"==typeof e.iterator}function Gv(e){se(e>0&&Number.isInteger(e),(()=>`batchSize is required to be a positive integer, but got ${e}`))}function Kv(e,t,n){return null==e?[null]:Array.isArray(e)?e.map((e=>by(e,t,n-t))):by(e,t,n-t)}function Jv(e,t){return ho((()=>null==e?null:Array.isArray(e)?e.map((e=>Jv(e,t))):Sy(e,"int32"===t.dtype?t:Ls(t,"int32"))))}function Xv(e,t){const n=[];let r=0,a=null;for(;r=e&&(a=e),n.push([r,a]),r=a;return n}function Zv(e){const t=[];e instanceof ti&&(e=[e]);for(let n=0;nn.push(e.id)));else if(null!=t)for(const e in t){const r=t[e];n.push(r.id)}const r=[];if(e instanceof ti)-1===n.indexOf(e.id)&&r.push(e);else if(Array.isArray(e))e.forEach((e=>{-1===n.indexOf(e.id)&&r.push(e)}));else if(null!=e)for(const t in e){const a=e[t];-1===n.indexOf(a.id)&&r.push(a)}r.forEach((e=>{e.isDisposed||e.dispose()}))}function ex(e){return Array.isArray(e)}function tx(e){return!function(e){return e instanceof ti}(e)&&!ex(e)}function nx(e,t,n,r=!0,a=""){if(null==t||0===t.length){if(null!=e){let t=!1;if(ex(e)&&e.length>0)t=!0;else if(tx(e)){for(const n in e)if(e.hasOwnProperty(n)){t=!0;break}}else t=!0;if(t)throw new wg(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(null==e)return t.map((e=>null));let i;if(tx(e)){i=[];for(const n of t){if(null==e[n])throw new wg(`No data provided for "${n}". Need data for each key in: ${t}`);i.push(e[n])}}else if(ex(e)){if(e.length!==t.length)throw new wg(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);i=e}else{if(t.length>1)throw new wg(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. 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For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new wg("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===e.inboundNodes.length){if(null==e.batchInputShape)throw new wg("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=gb({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(t)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==e.inboundNodes.length)throw new wg(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(1!==e.inboundNodes[0].outputTensors.length)throw new wg("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=fb(this.outputs[0])}this.inboundNodes=[],new pb({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Sg(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs[0].shape})}else{const t=e.apply(this.outputs[0]);if(Array.isArray(t))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[t],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return null==this.model&&this.build(),this.model.call(e,t)}build(e){if(rb(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ax({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){null==this.model&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new _g("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new _g("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return null==this.model&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return null==this.model&&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 null==this.model?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new _g("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 _g("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={},r=!1){let a,i={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new wg("Legacy serialization format not supported yet.");a=t}else se(null!=t.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),a=t.layers,delete t.layers,i=t;const s=new e(i);if(!(s instanceof ox))throw new Dg(`Sequential.fromConfig called on non-Sequential input: ${s}`);for(const e of a){const t=cv(e,void 0,r);r&&t.setFastWeightInitDuringBuild(!0),s.add(t)}return s}set stopTraining(e){if(null==this.model)throw new wg("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new wg("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}function ux(e){return new ax(e)}function lx(e){return new ox(e)}function cx(e,t){return null==t&&(t={}),async function(e,t){if(null==t&&(t={}),"string"==typeof e){const n=es(e,t);if(0===n.length)n.push(Gs(e,t));else if(n.length>1)throw new wg(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return async function(e,t,n){if(null==n&&(n={}),null==e.load)throw new wg("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const r=await e.load();let a=r.modelTopology;null!=a.model_config&&(a=a.model_config);const i=null==n.strict||n.strict,s=null!=r.weightData&&null!=r.weightSpecs&&i,o=cv(Yv(a),void 0,s),u=r.trainingConfig;if(null!=u&&o.loadTrainingConfig(u),null!=r.userDefinedMetadata&&o.setUserDefinedMetadata(r.userDefinedMetadata),null!=r.weightData){if(null==r.weightSpecs)throw new wg("LayersModel artifacts contains weight data, but not weight specs. 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ho((()=>pp(6,zp(e))))}}yx.className="relu6",lu(yx);class bx extends hx{apply(e){return e}}bx.className="linear",lu(bx);class vx extends hx{apply(e){return kl(e)}}vx.className="sigmoid",lu(vx);class xx extends hx{apply(e){return function(e){return ho((()=>{const t=wu(.5,Mu(.2,e));return zl(t,0,1)}))}(e)}}xx.className="hardSigmoid",lu(xx);class _x extends hx{apply(e){return Vc(e)}}_x.className="softplus",lu(_x);class wx extends hx{apply(e){return function(e){return ho((()=>Nu(e,wu(Uu(e),1))))}(e)}}wx.className="softsign",lu(wx);class Dx extends hx{apply(e){return Cl(e)}}Dx.className="tanh",lu(Dx);class Nx extends hx{apply(e,t=-1){return id(e,t)}}Nx.className="softmax",lu(Nx);class Mx extends hx{apply(e,t=-1){return Kc(e,t)}}Mx.className="logSoftmax",lu(Mx);class Sx extends hx{apply(e,t=1){return ho((()=>Mu(kl(Mu(e,t)),e)))}}Sx.className="swish",lu(Sx);class kx extends hx{apply(e){return ho((()=>Mu(e,Cl(Vc(e)))))}}function Tx(e){return e.getClassName()}function Cx(e,t={}){return Og(e,uu.getMap().classNameMap,t,"activation")}function Ex(e){if(null==e){return Cx({className:"linear",config:{}})}if("string"==typeof e){const t={};return t.className=e,t.config={},Cx(t)}return e instanceof hx?e:Cx(e)}function Ax(e){if(null!=e&&"object"!=typeof e)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}kx.className="mish",lu(kx);class Ix extends ou{}class Lx extends Ix{constructor(e){super(),Ax(e),this.l1=null==e||null==e.l1?.01:e.l1,this.l2=null==e||null==e.l2?.01:e.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(e){return ho((()=>{let t=up([1]);return this.hasL1&&(t=wu(t,Nc(Mu(this.l1,Uu(e))))),this.hasL2&&(t=wu(t,Nc(Mu(this.l2,ky(e))))),Dl(t,[])}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Lx.className="L1L2",lu(Lx);const Fx={l1l2:"L1L2"};function $x(e){return Fg(e)}function Ox(e,t={}){return Og(e,uu.getMap().classNameMap,t,"regularizer")}function Rx(e){return null==e?null:"string"==typeof e?Ox({className:e in Fx?Fx[e]:e,config:{}}):e instanceof Ix?e:Ox(e)}class Yx extends hb{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=nb(e);let n=zp(e);return null!=this.maxValue&&(n=zl(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Yx.className="ReLU",lu(Yx);class Bx extends hb{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=nb(e);return zc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Bx.className="LeakyReLU",lu(Bx);class zx extends hb{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Qy(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rx(e.alphaRegularizer),this.alphaConstraint=Lb(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new wg(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=rb(e)).slice(1);if(null!=this.sharedAxes)for(const e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let t=1;t(ty(t),"channelsFirst"===t?Co(e,[0,2,3,1]):e)))}function Gx(e,t){return ho((()=>(ty(t),"channelsFirst"===t?Co(e,[0,2,3,4,1]):e)))}function Kx(e,t,n,r=[1,1],a="valid",i,s,o=null){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),3!==e.rank&&4!==e.rank)throw new wg(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new wg(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Vx(e,i);if("causal"===a)throw new Dg("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=qd({x:u,filter:t,strides:r,pad:"same"===a?"same":"valid",dilations:s,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===i&&(u=Co(u,[0,3,1,2])),u}))}jx.className="Softmax",lu(jx);class Jx extends hb{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jx.verifyArgs(t),this.rank=e,Hg(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Dg(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ux(t.kernelSize,e,"kernelSize"),this.strides=Ux(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,ny(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,ty(this.dataFormat),this.activation=Ex(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=Qy(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lb(t.biasConstraint),this.biasRegularizer=Rx(t.biasRegularizer),this.activityRegularizer=Rx(t.activityRegularizer),this.dilationRate=Ux(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new wg(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new wg(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new wg(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(kg("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,3))throw new wg(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Tx(this.activation),useBias:this.useBias,biasInitializer:Zy(this.biasInitializer),biasRegularizer:$x(this.biasRegularizer),activityRegularizer:$x(this.activityRegularizer),biasConstraint:Ab(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Xx extends Jx{constructor(e,t){super(e,t),this.kernel=null,Xx.verifyArgs(t),this.filters=t.filters,Hg(this.filters,"filters"),this.kernelInitializer=Qy(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lb(t.kernelConstraint),this.kernelRegularizer=Rx(t.kernelRegularizer)}build(e){e=rb(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ho((()=>{let t;e=nb(e);const n=null==this.bias?null:this.bias.read(),r=Ug(this.activation.getClassName());if(null!=r&&2===this.rank)t=Kx(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=function(e,t,n,r=1,a="valid",i,s=1){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),3!==e.shape.length)throw new wg(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new wg(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new wg(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if("channelsFirst"===i&&(e=Co(e,[0,2,1])),"causal"===a)throw new Dg("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Wl(e,t,r,"same"===a?"same":"valid","NWC",s);return null!=n&&(o=Cy(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=Kx(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Dg("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,r=[1,1,1],a="valid",i,s){return ho((()=>{if(null==i&&(i="channelsLast"),ty(i),4!==e.rank&&5!==e.rank)throw new wg(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new wg(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=Gx(e,i);if("causal"===a)throw new Dg("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Kl(o,t,r,"same"===a?"same":"valid","NDHWC",s),null!=n&&(o=Cy(o,n)),"channelsFirst"===i&&(o=Co(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=rb(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e 0 but got ${JSON.stringify(e.filters)}`)}}class Zx extends Xx{constructor(e){super(2,e),Zx.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,2))throw new wg(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Zx.className="Conv2D",lu(Zx);class Qx extends Xx{constructor(e){super(3,e),Qx.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new wg(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Qx.className="Conv3D",lu(Qx);class e_ extends Zx{constructor(e){if(super(e),this.inputSpec=[new ub({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new wg(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=rb(e)).length)throw new wg("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ub({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ho((()=>{let t=nb(e);if(4!==t.shape.length)throw new wg(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,i;"channelsFirst"===this.dataFormat?(a=2,i=3):(a=1,i=2);const s=n[a],o=n[i],u=this.kernelSize[0],l=this.kernelSize[1],c=this.strides[0],p=this.strides[1],d=[r,Wx(s,c,u,this.padding),Wx(o,p,l,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Co(t,[0,2,3,1]));let h=Gl(t,this.kernel.read(),d,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(h=Co(h,[0,3,1,2])),null!=this.bias&&(h=Cy(h,this.bias.read(),this.dataFormat)),null!=this.activation&&(h=this.activation.apply(h)),h}))}computeOutputShape(e){const t=(e=rb(e)).slice();let n,r,a;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3):(n=3,r=1,a=2);const i=this.kernelSize[0],s=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=Wx(t[r],o,i,this.padding),t[a]=Wx(t[a],u,s,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}e_.className="Conv2DTranspose",lu(e_);class t_ extends Qx{constructor(e){if(super(e),this.inputSpec=[new ub({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new wg(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(5!==(e=rb(e)).length)throw new wg("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new wg("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ub({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return ho((()=>{let t=nb(e);if(5!==t.shape.length)throw new wg(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,i,s;"channelsFirst"===this.dataFormat?(s=2,a=3,i=4):(s=1,a=2,i=3);const o=n[s],u=n[a],l=n[i],c=this.kernelSize[0],p=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],f=this.strides[1],m=this.strides[2],g=[r,Wx(o,h,c,this.padding),Wx(u,f,p,this.padding),Wx(l,m,d,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=Co(t,[0,2,3,4,1]));let y=Xl(t,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(y=Co(y,[0,4,1,2,3])),null!==this.bias&&(y=Cy(y,this.bias.read(),this.dataFormat)),null!==this.activation&&(y=this.activation.apply(y)),y}))}computeOutputShape(e){const t=(e=rb(e)).slice();let n,r,a,i;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3,i=4):(n=4,r=1,a=2,i=3);const s=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=Wx(t[r],l,s,this.padding),t[a]=Wx(t[a],c,o,this.padding),t[i]=Wx(t[i],p,u,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}t_.className="Conv3DTranspose",lu(t_);class n_ extends Xx{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new wg("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new wg("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new wg(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=Qy(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rx(t.depthwiseRegularizer),this.depthwiseConstraint=Lb(t.depthwiseConstraint),this.pointwiseInitializer=Qy(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rx(t.pointwiseRegularizer),this.pointwiseConstraint=Lb(t.pointwiseConstraint)}build(e){if((e=rb(e)).length{let t;if(e=nb(e),1===this.rank)throw new Dg("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=Co(e,[0,2,3,1])),t=Jp(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=Cy(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=Co(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Zy(this.depthwiseInitializer),e.pointwiseInitializer=Zy(this.pointwiseInitializer),e.depthwiseRegularizer=$x(this.depthwiseRegularizer),e.pointwiseRegularizer=$x(this.pointwiseRegularizer),e.depthwiseConstraint=Ab(this.depthwiseConstraint),e.pointwiseConstraint=Ab(this.pointwiseConstraint),e}}n_.className="SeparableConv";class r_ extends n_{constructor(e){super(2,e)}}r_.className="SeparableConv2D",lu(r_);class a_ extends Xx{constructor(e){super(1,e),a_.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!Pg(e.kernelSize,"number",1,1))throw new wg(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}a_.className="Conv1D",lu(a_);class i_ extends hb{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[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 ho((()=>{if(e=nb(e),"channelsLast"===this.dataFormat){const t=xy(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return xy(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=xy(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return xy(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}i_.className="Cropping2D",lu(i_);class s_ extends hb{constructor(e){var t;super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,zg(Jg,"InterpolationFormat",t)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ho((()=>{let t=nb(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=Co(t,[0,2,3,1]);const e=this.size[0]*n[2],r=this.size[1]*n[3],a="nearest"===this.interpolation?Uh.resizeNearestNeighbor(t,[e,r]):Uh.resizeBilinear(t,[e,r]);return Co(a,[0,3,1,2])}{const e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?Uh.resizeNearestNeighbor(t,[e,r]):Uh.resizeBilinear(t,[e,r])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}}s_.className="UpSampling2D",lu(s_);class o_ extends Jx{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=Qy(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lb(e.depthwiseConstraint),this.depthwiseRegularizer=Rx(e.depthwiseRegularizer)}build(e){if((e=rb(e)).length<4)throw new wg(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new wg(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ho((()=>{let t=function(e,t,n=[1,1],r="valid",a,i){return ho((()=>{null==a&&(a="channelsLast"),ty(a);let s=Vx(e,a);if(4!==e.rank)throw new wg(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new wg(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return s=ac(s,t,n,"same"===r?"same":"valid","NHWC",i),"channelsFirst"===a&&(s=Co(s,[0,3,1,2])),s}))}(e=nb(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=Cy(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=rb(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=qx(t,this.kernelSize[0],this.padding,this.strides[0]),i=qx(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,a,i]:[e[0],a,i,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Zy(this.depthwiseInitializer),e.depthwiseRegularizer=$x(this.depthwiseRegularizer),e.depthwiseConstraint=Ab(this.depthwiseRegularizer),e}}function u_(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new wg("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function l_(e,t,n,r=!1,a,i,s=!1,o=!1){return ho((()=>{const u=t.shape.length;if(u<3)throw new wg(`Input should be at least 3D, but is ${u}D.`);const l=[1,0].concat(hy(2,u));if(t=Co(t,l),null!=i)throw new Dg("The rnn() functoin of the deeplearn.js backend does not support constants yet.");s&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=a&&((a=Ls(Ls(a,"bool"),"float32")).rank===u-1&&(a=Cc(a,-1)),a=Co(a,l)),r&&(t=Hp(t,0),null!=a&&(a=Hp(a,0)));const c=[];let p,d=n;const h=t.shape[0],f=Sd(t);let m,g;null!=a&&(m=Sd(a));for(let t=0;te(n,d)));if(null==a)p=r[0],d=r[1];else{const e=ho((()=>{const e=m[t],n=Hu(bp(e),e),a=wu(Mu(r[0],e),Mu(d[0],n)),i=d.map(((t,a)=>wu(Mu(r[1][a],e),Mu(t,n))));return{output:a,newStates:i}}));p=e.output,d=e.newStates}o&&c.push(p)}return o&&(g=hd(c,1)),[p,g,d]}))}o_.className="DepthwiseConv2D",lu(o_);class c_ extends hb{constructor(e){let t;if(super(e),null==e.cell)throw new wg("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new b_({cells:e.cell}):e.cell,null==t.stateSize)throw new wg("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new ub({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){return null==this.states_?hy(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null)):this.states_}setStates(e){this.states_=e}computeOutputShape(e){eb(e)&&(e=e[0]);let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let r;if(r=this.returnSequences?[e[0],e[1],n]:[e[0],n],this.returnState){const n=[];for(const r of t)n.push([e[0],r]);return[r].concat(n)}return r}computeMask(e,t){return ho((()=>{Array.isArray(t)&&(t=t[0]);const e=this.returnSequences?t:null;if(this.returnState){const t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ne.shape[e.shape.length-1])),a))throw new wg(`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((e=>new ub({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){ho((()=>{if(!this.stateful)throw new xg("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new wg("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(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>up([n,e]))):this.states_=[up([n,this.cell.stateSize])];else if(null==e)fo(this.states_),null!=this.keptStates&&(fo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>up([n,e]))):this.states_[0]=up([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new wg(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);!0===t?this.keptStates.push(this.states_.slice()):fo(this.states_);for(let t=0;tmo(e.clone())))}))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=u_(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let i=[],s=[];if(null!=n){t.initialState=n,i=i.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new ub({shape:e.shape}));s=s.concat(this.stateSpec)}if(null!=r&&(t.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof lb){const n=[e].concat(i),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return ho((()=>{const n=null==t?null:t.mask,r=null==t?null:t.training;let a=null==t?null:t.initialState;e=nb(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));const i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==i)throw new wg(`RNN Layer has ${i} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const s={training:r},o=l_(((e,t)=>{const n=this.cell.call([e].concat(t),s);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],c=o[2];this.stateful&&this.resetStates(c,r);const p=this.returnSequences?l:u;return this.returnState?[p].concat(c):p}))}getInitialState(e){return ho((()=>{let t=up(e.shape);return t=Nc(t,[1,2]),t=yy(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?Dy(t,[1,e]):t)):this.cell.stateSize>1?[Dy(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),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===c_.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const r=cv(t.cell,n);return new e(Object.assign(t,{cell:r}))}}c_.className="RNN",lu(c_);class p_ extends hb{}class d_ extends p_{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,Hg(this.units,"units"),this.activation=Ex(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rx(e.kernelRegularizer),this.recurrentRegularizer=Rx(e.recurrentRegularizer),this.biasRegularizer=Rx(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=rb(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 ho((()=>{if(2!==e.length)throw new wg(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const r=null!=t.training&&t.training;let a;0bp(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0bp(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,s=this.recurrentDropoutMask;a=My(null!=i?Mu(e,i):e,this.kernel.read()),null!=this.bias&&(a=Cy(a,this.bias.read())),null!=s&&(n=Mu(n,s));let o=wu(a,My(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Tx(this.activation),useBias:this.useBias,kernelInitializer:Zy(this.kernelInitializer),recurrentInitializer:Zy(this.recurrentInitializer),biasInitializer:Zy(this.biasInitializer),kernelRegularizer:$x(this.kernelRegularizer),recurrentRegularizer:$x(this.recurrentRegularizer),biasRegularizer:$x(this.biasRegularizer),activityRegularizer:$x(this.activityRegularizer),kernelConstraint:Ab(this.kernelConstraint),recurrentConstraint:Ab(this.recurrentConstraint),biasConstraint:Ab(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}d_.className="SimpleRNNCell",lu(d_);class h_ extends c_{constructor(e){e.cell=new d_(e),super(e)}call(e,t){return ho((()=>{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return new e(t)}}h_.className="SimpleRNN",lu(h_);class f_ extends p_{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new wg("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Hg(this.units,"units"),this.activation=Ex(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ex(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rx(e.kernelRegularizer),this.recurrentRegularizer=Rx(e.recurrentRegularizer),this.biasRegularizer=Rx(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=rb(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ho((()=>{if(2!==e.length)throw new wg(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=null!=t.training&&t.training;let r=e[1];e=e[0],0bp(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0bp(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));const a=this.dropoutMask,i=this.recurrentDropoutMask;let s,o,u;0{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}m_.className="GRU",lu(m_);class g_ extends p_{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,Hg(this.units,"units"),this.activation=Ex(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ex(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Qy(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rx(e.kernelRegularizer),this.recurrentRegularizer=Rx(e.recurrentRegularizer),this.biasRegularizer=Rx(e.biasRegularizer),this.kernelConstraint=Lb(e.kernelConstraint),this.recurrentConstraint=Lb(e.recurrentConstraint),this.biasConstraint=Lb(e.biasConstraint),this.dropout=py([1,dy([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=py([1,dy([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=rb(e))[e.length-1];let r;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;r=new((t=class extends Fy{apply(t,r){const a=e.apply([n]),i=(new Oy).apply([n]),s=e.apply([2*n]);return wy(wy(a,i),s)}}).className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ho((()=>{const n=null!=t.training&&t.training;if(3!==e.length)throw new wg(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1];const a=e[2];e=e[0],0bp(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0bp(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,s=this.recurrentDropoutMask;let o,u,l,c;0{null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}y_.className="LSTM",lu(y_);class b_ extends p_{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ho((()=>{let n=e.slice(1);const r=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();const a=[];let i;for(let s=0;s{iy(`RNNCell_${r}`,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign({},e,t)}static fromConfig(e,t,n={}){const r=[];for(const e of t.cells)r.push(cv(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return sb(e)}setWeights(e){const t=[];for(const n of this.cells){const r=n.weights.length,a=e.splice(r);for(let e=0;enull!=i?i(t(),n):Ey(t(),n),o=()=>Ay(s,t,r);return!a||a<=1?mo(o().clone()):Array(a).fill(void 0).map(o).map((e=>mo(e.clone())))}b_.className="StackedRNNCells",lu(b_);class x_ extends c_{constructor(e){if(e.unroll)throw new Dg("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Dg("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new ub({ndim:5})]}call(e,t){return ho((()=>{if(null!=this.cell.dropoutMask&&(fo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(fo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new wg("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ho((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=up([r[0],...r.slice(2)]);return Array.isArray(t)?Array(t.length).fill(a):[a]}))}resetStates(e,t=!1){ho((()=>{if(!this.stateful)throw new xg("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(null==n[0])throw new wg("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(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>up(a))):this.states_=[up(a)];else if(null==e)fo(this.states_),null!=this.keptStates&&(fo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>up(a))):this.states_[0]=up(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new wg(`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()):fo(this.states_);for(let t=0;tmo(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:i,dilationRate:s}=this.cell,o="channelsFirst"===t,u=e[o?3:2],l=e[o?4:3],c=qx(u,r[0],a,i[0],s[0]),p=qx(l,r[1],a,i[1],s[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}}x_.className="ConvRNN2D";class __ extends g_{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:i,dilationRate:s}=e;super(Object.assign({},e,{units:t})),this.filters=t,Hg(this.filters,"filters"),this.kernelSize=Ux(n,2,"kernelSize"),this.kernelSize.forEach((e=>Hg(e,"kernelSize"))),this.strides=Ux(r||1,2,"strides"),this.strides.forEach((e=>Hg(e,"strides"))),this.padding=a||"valid",ny(this.padding),this.dataFormat=i||"channelsLast",ty(this.dataFormat),this.dilationRate=Ux(s||1,2,"dilationRate"),this.dilationRate.forEach((e=>Hg(e,"dilationRate")))}build(e){var t;e=rb(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new wg(`The channel dimension of the input should be defined. Found ${e[n]}`);const r=e[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const i=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,r=this.filters;e=new((t=class extends Fy{apply(e,t){return _y([n.apply([r]),lp([r]),n.apply([2*r])])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ho((()=>{if(3!==e.length)throw new wg(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,r=e[0],a=e[1],i=e[2];0bp(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const s=this.dropoutMask,o=(e,t,n)=>t&&t[n]?Mu(t[n],e):e;let u=o(r,s,0),l=o(r,s,1),c=o(r,s,2),p=o(r,s,3);0bp(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const d=this.recurrentDropoutMask;let h=o(a,d,0),f=o(a,d,1),m=o(a,d,2),g=o(a,d,3);const[y,b,v,x]=ld(this.kernel.read(),4,3),[_,w,D,N]=this.useBias?ld(this.bias.read(),4):[null,null,null,null];u=this.inputConv(u,y,_,this.padding),l=this.inputConv(l,b,w,this.padding),c=this.inputConv(c,v,D,this.padding),p=this.inputConv(p,x,N,this.padding);const[M,S,k,T]=ld(this.recurrentKernel.read(),4,3);h=this.recurrentConv(h,M),f=this.recurrentConv(f,S),m=this.recurrentConv(m,k),g=this.recurrentConv(g,T);const C=this.recurrentActivation.apply(wu(u,h)),E=this.recurrentActivation.apply(wu(l,f)),A=wu(Mu(E,i),Mu(C,this.activation.apply(wu(c,m)))),I=Mu(this.recurrentActivation.apply(wu(p,g)),this.activation.apply(A));return[I,I,A]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var a=0;for(r=Object.getOwnPropertySymbols(e);a{this.invokeCallHook(e,t);const n=nb(e);if(0Ey(n,this.rate,r,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}D_.className="Dropout",lu(D_);class N_ extends D_{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}N_.className="SpatialDropout1D",lu(N_);class M_ extends hb{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Hg(this.units,"units"),this.activation=Ex(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=Qy(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Qy(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lb(e.kernelConstraint),this.biasConstraint=Lb(e.biasConstraint),this.kernelRegularizer=Rx(e.kernelRegularizer),this.biasRegularizer=Rx(e.biasRegularizer),this.activityRegularizer=Rx(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=rb(e))[e.length-1];null==this.kernel&&(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){const t=(e=rb(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e),r=Ug(this.activation.getClassName());let a;return null!=r?a=My(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=My(n,this.kernel.read()),null!=this.bias&&(a=Cy(a,this.bias.read())),null!=this.activation&&(a=this.activation.apply(a))),a}))}getConfig(){const e={units:this.units,activation:Tx(this.activation),useBias:this.useBias,kernelInitializer:Zy(this.kernelInitializer),biasInitializer:Zy(this.biasInitializer),kernelRegularizer:$x(this.kernelRegularizer),biasRegularizer:$x(this.biasRegularizer),activityRegularizer:$x(this.activityRegularizer),kernelConstraint:Ab(this.kernelConstraint),biasConstraint:Ab(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}M_.className="Dense",lu(M_);class S_ extends hb{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=rb(e);for(const t of e.slice(1))if(null==t)throw new wg(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],cy(e,1)]}call(e,t){return ho((()=>{this.invokeCallHook(e,t);let n=nb(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t{this.invokeCallHook(e,t);const n=nb(e);return this.activation.apply(n)}))}getConfig(){const e={activation:Tx(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}k_.className="Activation",lu(k_);class T_ extends hb{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ho((()=>function(e,t){return ho((()=>{if(2!==e.shape.length)throw new wg(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);return Dy(yy(e,1),[1,t,1])}))}(e=nb(e),this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}T_.className="RepeatVector",lu(T_);class C_ extends hb{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e{this.invokeCallHook(e,t);const n=nb(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return Dl(n,a)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}C_.className="Reshape",lu(C_);class E_ extends hb{constructor(e){if(super(e),null==e.dims)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.`);const t=hy(1,e.dims.length+1);if(!de(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 ub({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=rb(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return Co(nb(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}E_.className="Permute",lu(E_);class A_ extends hb{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=nb(e);return tl(yp(n,this.maskValue),-1)}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e),r=tl(yp(n,this.maskValue),-1,!0);return Mu(n,Ls(r,n.dtype))}))}}A_.className="Masking",lu(A_);class I_ extends hb{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Eg(e.inputLength))}this.inputDim=e.inputDim,Hg(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Hg(this.outputDim,"outputDim"),this.embeddingsInitializer=Qy(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rx(e.embeddingsRegularizer),this.activityRegularizer=Rx(e.activityRegularizer),this.embeddingsConstraint=Lb(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 ho((()=>this.maskZero?(e=nb(e),yp(e,Tu(e))):null))}computeOutputShape(e){if(e=rb(e),null==this.inputLength)return[...e,this.outputDim];const t=Eg(this.inputLength);if(t.length!==e.length-1)throw new wg(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r{this.invokeCallHook(e,t);let n=nb(e);"int32"!==n.dtype&&(n=gy(n,"int32"));const r=Sy(this.embeddings.read(),Dl(n,[n.size]));return Dl(r,rb(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Zy(this.embeddingsInitializer),embeddingsRegularizer:$x(this.embeddingsRegularizer),activityRegularizer:$x(this.activityRegularizer),embeddingsConstraint:Ab(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}I_.className="Embedding",lu(I_);class L_ extends hb{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Dg}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length1)throw new wg(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;te.length));-1===e.indexOf(null)&&1===Yg(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ho((()=>{if(this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=dy(n);for(let n of e){const e=n.rank;for(let t=0;t1){const a=hy(1,e).concat([0]);t.push(Co(r,a)),n=!0}else t.push(r)}let r=this.mergeFunction(t);const a=r.rank;if(n)if(null==a){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=Dl(Co(Dl(r,[-1,t]),[1,0]),n)}else if(a>1){const e=[a-1].concat(hy(0,a-1));r=Co(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==e[0]?null:e[0].slice(1);for(let n=1;n{if(null==t)return null;if(!Array.isArray(t))throw new wg("`mask` should be an Array");if(!Array.isArray(e))throw new wg("`inputs` should be an Array");if(t.length!==e.length)throw new wg(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:Cc(e,0))))[0];for(let e=1;e{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new wg("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ho((()=>_y(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new wg("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(const e of t.slice(1)){if(null==n[r]||null==e[r]){n[r]=null;break}n[r]+=e[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new wg("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new wg("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new wg(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ho((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const r=[];for(let n=0;n"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Dg("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new wg(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new wg(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>z_(t,e[n].shape.length))):[z_(this.axes,n.shape.length),z_(this.axes,r.shape.length)],this.normalize&&(n=pv(n,t[0]),r=pv(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Dg("batchDot is not implemented for tensors of 4D or higher rank yet");if(se(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),se(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"==typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new Dg("batchDot is not implemented for complex64-type Tensors yet.");const r=e.shape.length,a=t.shape.length;null==n&&(n=[r-1,a-2]);const i=n;return ho((()=>{let n,s;if(r>a){n=r-a;const e=[];for(let t=0;tr){n=a-r;const t=[];for(let e=0;e0){let e;e=r>a?r+a-3:r-1;const t=[];for(let r=e;r"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Dg("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);const a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}P_.className="Dot",lu(P_);class H_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e);return Ay((()=>wu(Ny(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}H_.className="GaussianNoise",lu(H_);class j_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ho((()=>{this.invokeCallHook(e,t);const n=nb(e);return this.rate>0&&this.rate<1?Ay((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return Mu(n,Ny(n.shape,1,e))}),(()=>n),t.training||!1):n}))}}j_.className="GaussianDropout",lu(j_);class U_ extends hb{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||nb(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ho((()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),r=()=>{const t=nb(e),r=-1.7580993408473766;let a=Oc(Rp(n),this.rate);a=gy(a,"float32");const i=((1-this.rate)*(1+this.rate*r**2))**-.5,s=-i*r*this.rate,o=wu(Mu(t,a),Mu(wu(a,-1),r));return wu(Mu(o,i),s)};return Ay(r,(()=>nb(e)),t.training||!1)}return e}))}}function q_(e,t,n,r,a,i=.001){let s;if(2===e.rank)s=Ll(e,t,n,r,a,i);else if(3===e.rank)s=Fl(e,t,n,r,a,i);else{if(4!==e.rank)throw new Dg(`batchNormalization is not implemented for array of rank ${e.rank} yet`);s=$l(e,t,n,r,a,i)}return s}U_.className="AlphaDropout",lu(U_);class W_ extends hb{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Qy(e.betaInitializer||"zeros"),this.gammaInitializer=Qy(e.gammaInitializer||"ones"),this.movingMeanInitializer=Qy(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Qy(e.movingVarianceInitializer||"ones"),this.betaConstraint=Lb(e.betaConstraint),this.gammaConstraint=Lb(e.gammaConstraint),this.betaRegularizer=Rx(e.betaRegularizer),this.gammaRegularizer=Rx(e.gammaRegularizer)}build(e){e=rb(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new wg(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new ub({ndim:e.length,axes:{[t]:n}})];const r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ho((()=>{const n=null!=t.training&&t.training,r=nb(e),a=r.shape,i=a.length,s=hy(0,i),o=this.axis>=0?this.axis:this.axis+i;s.splice(o,1);const u=Sg(1,i);u[o]=a[o];const l=s.slice();l.sort();const c=!de(l,hy(0,i).slice(0,i-1));if(!n)return(()=>{if(c){const e=Dl(this.movingMean.read(),u),t=Dl(this.movingVariance.read(),u),n=this.center?Dl(this.beta.read(),u):null,a=this.scale?Dl(this.gamma.read(),u):null;return q_(r,e,t,n,a,this.epsilon)}return q_(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[p,d,h]=function(e,t,n,r,a=.001){return de(r.slice().sort(),hy(0,e.rank-1))?function(e,t,n,r,a=.001){return ho((()=>{const i=fp(e,r),s=i.mean,o=i.variance;return[q_(e,s,o,n,t,a),s,o]}))}(e,t,n,r,a):function(e,t,n,r,a=.001){return ho((()=>{const i=fp(e,r),s=i.mean,o=i.variance,u=[];for(const t of hy(0,e.rank))-1!==r.indexOf(t)?u.push(1):u.push(e.shape[t]);const l=Dl(s,u),c=Dl(o,u),p=null==t?null:Dl(t,u),d=null==n?null:Dl(n,u);return[q_(e,l,c,d,p,a),s,o]}))}(e,t,n,r,a)}(r,this.gamma.read(),this.beta.read(),s,this.epsilon),f=(e,t,n)=>{ho((()=>{const r=1-n,a=e.read(),i=Mu(Hu(a,t),r);e.write(Hu(a,i))}))};return(()=>{f(this.movingMean,d,this.momentum),f(this.movingVariance,h,this.momentum)})(),p}))}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Zy(this.betaInitializer),gammaInitializer:Zy(this.gammaInitializer),movingMeanInitializer:Zy(this.movingMeanInitializer),movingVarianceInitializer:Zy(this.movingVarianceInitializer),betaRegularizer:$x(this.betaRegularizer),gammaRegularizer:$x(this.gammaRegularizer),betaConstraint:Ab(this.betaConstraint),gammaConstraint:Ab(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}W_.className="BatchNormalization",lu(W_);class V_ extends hb{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Qy(e.betaInitializer||"zeros"),this.gammaInitializer=Qy(e.gammaInitializer||"ones"),this.betaRegularizer=Rx(e.betaRegularizer),this.gammaRegularizer=Rx(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=rb(e)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let e=0;e=t)throw new Error(`Invalid axis: ${e}`);if(this.axis.length!==Yg(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,!0):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,!0):this.beta=null,this.built=!0}call(e,t){const n=nb(e),r=n.shape,a=r.length;return ho((()=>{let{mean:e,variance:t}=fp(n,this.axis,!0);const i=Sg(1,a);for(const e of this.axis)i[e]=r[e];const s=e=>null!=e&&e.shape.length!==a?Dl(e,i):e;let o=this.scale?s(this.gamma.read()):null,u=this.center?s(this.beta.read()):null;const l=[],c=[];for(let e=0;e=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return ho((()=>{return t=nb(e),n=this.padding,r=this.dataFormat,ho((()=>{if(4!==t.rank)throw new wg(`temporalPadding expects input tensor to be 4-D, but received a ${t.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new wg("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r="channelsLast"),"channelsLast"!==r&&"channelsFirst"!==r)throw new wg(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let e;return e="channelsFirst"===r?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],xp(t,e)}));var t,n,r}))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}function K_(e,t,n,r,a,i){return ho((()=>{let s;ty(a),ry(i),ny(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==i&&(i="max"),e=Vx(e,a);const o="same"===r?"same":"valid";return s="max"===i?ap(e,t,n,o):Nl(e,t,n,o),"channelsFirst"===a&&(s=Co(s,[0,3,1,2])),s}))}function J_(e,t,n,r,a,i){return ho((()=>{let s;ty(a),ry(i),ny(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==i&&(i="max"),e=Gx(e,a);const o="same"===r?"same":"valid";return s="max"===i?ip(e,t,n,o):Ml(e,t,n,o),"channelsFirst"===a&&(s=Co(s,[0,4,1,2,3])),s}))}G_.className="ZeroPadding2D",lu(G_);class X_ extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new wg(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if(Hg(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new wg(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,ny(this.padding),this.inputSpec=[new ub({ndim:3})]}computeOutputShape(e){const t=qx((e=rb(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ho((()=>{this.invokeCallHook(e,t),e=yy(nb(e),2);const n=this.poolingFunction(nb(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dd(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Z_ extends X_{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"max")}}Z_.className="MaxPooling1D",lu(Z_);class Q_ extends X_{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"avg")}}Q_.className="AveragePooling1D",lu(Q_);class ew extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new wg(`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];Hg(this.poolSize,"poolSize"),Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),ny(this.padding),this.inputSpec=[new ub({ndim:4})]}computeOutputShape(e){e=rb(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=qx(t,this.poolSize[0],this.padding,this.strides[0]),n=qx(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ho((()=>(this.invokeCallHook(e,t),this.poolingFunction(nb(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class tw extends ew{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"max")}}tw.className="MaxPooling2D",lu(tw);class nw extends ew{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),K_(e,t,n,r,a,"avg")}}nw.className="AveragePooling2D",lu(nw);class rw extends hb{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new wg(`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];Hg(this.poolSize,"poolSize"),Hg(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),ny(this.padding),this.inputSpec=[new ub({ndim:5})]}computeOutputShape(e){e=rb(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=qx(t,this.poolSize[0],this.padding,this.strides[0]),n=qx(n,this.poolSize[1],this.padding,this.strides[1]),r=qx(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return ho((()=>(this.invokeCallHook(e,t),this.poolingFunction(nb(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class aw extends rw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),J_(e,t,n,r,a,"max")}}aw.className="MaxPooling3D",lu(aw);class iw extends rw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return ty(a),ny(r),J_(e,t,n,r,a,"avg")}}iw.className="AveragePooling3D",lu(iw);class sw extends hb{constructor(e){super(e),this.inputSpec=[new ub({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Dg}}class ow extends sw{constructor(e){super(e||{})}call(e,t){return ho((()=>{const t=nb(e);return op(t,1)}))}}ow.className="GlobalAveragePooling1D",lu(ow);class uw extends sw{constructor(e){super(e||{})}call(e,t){return ho((()=>{const t=nb(e);return wc(t,1)}))}}uw.className="GlobalMaxPooling1D",lu(uw);class lw extends hb{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,ty(this.dataFormat),this.inputSpec=[new ub({ndim:4})]}computeOutputShape(e){return"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Dg}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class cw extends lw{call(e,t){return ho((()=>{const t=nb(e);return"channelsLast"===this.dataFormat?op(t,[1,2]):op(t,[2,3])}))}}cw.className="GlobalAveragePooling2D",lu(cw);class pw extends lw{call(e,t){return ho((()=>{const t=nb(e);return"channelsLast"===this.dataFormat?wc(t,[1,2]):wc(t,[2,3])}))}}pw.className="GlobalMaxPooling2D",lu(pw);class dw extends hb{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const r=cv(t.layer,n);delete t.layer;const a={layer:r};return Object.assign(a,t),new e(a)}}class hw extends dw{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=rb(e)).length<3)throw new wg(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=rb(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return ho((()=>l_(((e,n)=>[nb(this.layer.call(e,t)),[]]),e=nb(e),[],!1,null,null,!1,!0)[1]))}}hw.className="TimeDistributed",lu(hw);class fw extends dw{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=cv(n),t.goBackwards=!0!==t.goBackwards;const r={};var a;if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=cv(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,a=this.mergeMode,zg(Qg,"BidirectionalMergeMode",a),e.weights)throw new Dg("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t,n,r,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),this.returnState?(r=a.slice(1),t=a[0]):t=a[0],"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):Cg(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=u_(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);const i=[],s=[];if(null!=n){const e=n.length;if(e%2>0)throw new wg("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,i.push(...n);const r=n.map((e=>new ub({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),s.push(...r)}if(null!=r)throw new Dg("Support for constants in Bidirectional layers is not implemented yet.");const o=i[0]instanceof lb;for(const e of i)if(e instanceof lb!==o)throw new wg("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){const n=[e].concat(i),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return ho((()=>{const n=t.initialState;let r,a,i,s;if(null==n)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{const i=n.slice(0,n.length/2),s=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:s}))}return this.returnState&&(Array.isArray(r)&&(i=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Hp(a,1)),"concat"===this.mergeMode?s=_y([r,a]):"sum"===this.mergeMode?s=wu(r,a):"ave"===this.mergeMode?s=Mu(.5,wu(r,a)):"mul"===this.mergeMode?s=Mu(r,a):null==this.mergeMode&&(s=[r,a]),this.returnState?null==this.mergeMode?s.concat(i):[s].concat(i):s}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){iy(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),iy(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get 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is",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],CN=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}],EN=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],AN=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}],IN=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"t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r=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const a=0===n?0:t.size/n,i=[];ho((()=>{t=Dl(t,[1,n,a]);for(let n=0;n{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);KN(t,e.shape,"TensorList shape mismatch: "),mo(e)})),this.idTensor=Ou(0),this.maxNumElements=r,mo(this.idTensor)}get id(){return this.idTensor.id}copy(){return new eM([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.id)||t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new 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"),mo(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);KN(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());const r=XN(this.elementShape,this.tensors,n);return 0===e.length?Ri([],[0].concat(r)):ho((()=>{const t=e.map((e=>Dl(this.tensors[e],r)));return hd(t,0)}))}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);KN(this.elementShape,t,"TensorList shape mismatch: ");const n=XN(this.elementShape,this.tensors,t);return 0===this.size()?Ri([],[0].concat(n)):ho((()=>{const e=this.tensors.map((e=>Dl(e,n)));return Sl(e,0)}))}}function tM(e,t,n){const[r,a]=oN("fusedOps",e,t,n),i="biasadd"===r,s=!i,o="prelu"===a,u="fusedbatchnorm"===r,l=oN("numArgs",e,t,n);if(i){if(o&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and 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this.tensorMap.size}tensorSize(){return Ou(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),ho((()=>{const e=Sd(t),r=n.length,a=e.length;se(r===a,(()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`));for(let t=0;t{const e=[];for(let r=0;r{switch(e.category){case"arithmetic":return 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a((()=>((e,t,n,r=j)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(oN("x",e,t,n))];case"Acos":return[r.acos(oN("x",e,t,n))];case"Acosh":return[r.acosh(oN("x",e,t,n))];case"Asin":return[r.asin(oN("x",e,t,n))];case"Asinh":return[r.asinh(oN("x",e,t,n))];case"Atan":return[r.atan(oN("x",e,t,n))];case"Atan2":return[r.atan2(oN("x",e,t,n),oN("y",e,t,n))];case"Atanh":return[r.atanh(oN("x",e,t,n))];case"Ceil":return[r.ceil(oN("x",e,t,n))];case"Complex":return[r.complex(oN("real",e,t,n),oN("imag",e,t,n))];case"Cos":return[r.cos(oN("x",e,t,n))];case"Cosh":return[r.cosh(oN("x",e,t,n))];case"Elu":return[r.elu(oN("x",e,t,n))];case"Erf":return[r.erf(oN("x",e,t,n))];case"Exp":return[r.exp(oN("x",e,t,n))];case"Expm1":return[r.expm1(oN("x",e,t,n))];case"Floor":return[r.floor(oN("x",e,t,n))];case"Log":return[r.log(oN("x",e,t,n))];case"Log1p":return[r.log1p(oN("x",e,t,n))];case"Imag":return[r.imag(oN("x",e,t,n))];case"Neg":return[r.neg(oN("x",e,t,n))];case"Reciprocal":return[r.reciprocal(oN("x",e,t,n))];case"Real":return[r.real(oN("x",e,t,n))];case"Relu":return[r.relu(oN("x",e,t,n))];case"Round":return[r.round(oN("x",e,t,n))];case"Selu":return[r.selu(oN("x",e,t,n))];case"Sigmoid":return[r.sigmoid(oN("x",e,t,n))];case"Sin":return[r.sin(oN("x",e,t,n))];case"Sign":return[r.sign(oN("x",e,t,n))];case"Sinh":return[r.sinh(oN("x",e,t,n))];case"Softplus":return[r.softplus(oN("x",e,t,n))];case"Sqrt":return[r.sqrt(oN("x",e,t,n))];case"Square":return[r.square(oN("x",e,t,n))];case"Tanh":return[r.tanh(oN("x",e,t,n))];case"Tan":return[r.tan(oN("x",e,t,n))];case"ClipByValue":return[r.clipByValue(oN("x",e,t,n),oN("clipValueMin",e,t,n),oN("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(oN("x",e,t,n))];case"Rsqrt":return[r.rsqrt(uN(e.inputNames[0],t,n))];case"Prod":return[r.prod(oN("x",e,t,n),oN("axes",e,t,n))];case"LeakyRelu":return[r.leakyRelu(oN("x",e,t,n),oN("alpha",e,t,n))];case"Prelu":return[r.prelu(oN("x",e,t,n),oN("alpha",e,t,n))];case"IsNan":return[r.isNaN(uN(e.inputNames[0],t,n))];default:throw 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t.forEach(((e,t)=>{i.setItem(e,s[t])})),i}(oN("tensor",e,t,n),r,oN("elementShape",e,t,n),oN("numElements",e,t,n));return n.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const r=oN("elementShape",e,t,n),a=oN("elementDType",e,t,n);let i;i="TensorListReserve"===e.op?"numElements":"maxNumElements";const s=oN(i,e,t,n),o=function(e,t,n,r){return new eM([],e,t,r)}(r,a,0,"TensorListReserve"===e.op?-1:s);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{const r=oN("tensorListId",e,t,n),a=oN("indices",e,t,n),i=oN("elementShape",e,t,n),s=oN("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,s,i)]}case"TensorListStack":{const r=oN("tensorListId",e,t,n),a=oN("elementShape",e,t,n),i=oN("elementDType",e,t,n),s=oN("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,i,s)]}case"TensorListFromTensor":{const r=function(e,t,n){const r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: 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a.image.nonMaxSuppressionAsync(r,i,s,o,u)]}case"Where":{const r=a.cast(oN("condition",e,t,n),"bool"),i=[await a.whereAsync(r)];return r.dispose(),i}case"ListDiff":return a.setdiff1dAsync(oN("x",e,t,n),oN("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"evaluation":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"LowerBound":{const a=oN("sortedSequence",e,t,n),i=oN("values",e,t,n);return[r.lowerBound(a,i)]}case"TopKV2":{const a=oN("x",e,t,n),i=oN("k",e,t,n),s=oN("sorted",e,t,n),o=r.topk(a,i,s);return[o.values,o.indices]}case"UpperBound":{const a=oN("sortedSequence",e,t,n),i=oN("values",e,t,n);return[r.upperBound(a,i)]}case"Unique":{const a=oN("x",e,t,n),i=r.unique(a);return[i.values,i.indices]}case"UniqueV2":{const a=oN("x",e,t,n),i=oN("axis",e,t,n),s=r.unique(a,i);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"ResizeBilinear":{const a=oN("images",e,t,n),i=oN("size",e,t,n),s=oN("alignCorners",e,t,n),o=oN("halfPixelCenters",e,t,n);return[r.image.resizeBilinear(a,[i[0],i[1]],s,o)]}case"ResizeNearestNeighbor":{const a=oN("images",e,t,n),i=oN("size",e,t,n),s=oN("alignCorners",e,t,n),o=oN("halfPixelCenters",e,t,n);return[r.image.resizeNearestNeighbor(a,[i[0],i[1]],s,o)]}case"CropAndResize":{const a=oN("image",e,t,n),i=oN("boxes",e,t,n),s=oN("boxInd",e,t,n),o=oN("cropSize",e,t,n),u=oN("method",e,t,n),l=oN("extrapolationValue",e,t,n);return[r.image.cropAndResize(a,i,s,o,u,l)]}case"ImageProjectiveTransformV3":{const a=oN("images",e,t,n),i=oN("transforms",e,t,n),s=oN("outputShape",e,t,n),o=oN("fillValue",e,t,n),u=oN("interpolation",e,t,n),l=oN("fillMode",e,t,n);return[r.image.transform(a,i,u.toLowerCase(),l.toLowerCase(),o,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const a=oN("default",e,t,n);return[uN(e.name,t,n)||a];case"Placeholder":return[uN(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":case"Snapshot":return[hN(oN("x",e,t,n))];case"IdentityN":return oN("x",e,t,n).map((e=>hN(e)));case"Shape":return[r.tensor1d(oN("x",e,t,n).shape,"int32")];case"ShapeN":return oN("x",e,t,n).map((e=>r.tensor1d(e.shape)));case"Size":return[r.scalar(oN("x",e,t,n).size,"int32")];case"Rank":return[r.scalar(oN("x",e,t,n).rank,"int32")];case"NoOp":return[r.scalar(1)];case"Print":const i=oN("x",e,t,n),s=oN("data",e,t,n),o=oN("message",e,t,n),u=oN("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let e=0;e((e,t,n,r=j)=>{switch(e.op){case"Equal":return[r.equal(oN("a",e,t,n),oN("b",e,t,n))];case"NotEqual":return[r.notEqual(oN("a",e,t,n),oN("b",e,t,n))];case"Greater":return[r.greater(oN("a",e,t,n),oN("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(oN("a",e,t,n),oN("b",e,t,n))];case"Less":return[r.less(oN("a",e,t,n),oN("b",e,t,n))];case"LessEqual":return[r.lessEqual(oN("a",e,t,n),oN("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(oN("a",e,t,n),oN("b",e,t,n))];case"LogicalNot":return[r.logicalNot(oN("a",e,t,n))];case"LogicalOr":return[r.logicalOr(oN("a",e,t,n),oN("b",e,t,n))];case"Select":case"SelectV2":return[r.where(oN("condition",e,t,n),oN("a",e,t,n),oN("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(oN("a",e,t,n),oN("b",e,t,n),oN("transposeA",e,t,n),oN("transposeB",e,t,n))];case"Einsum":return[r.einsum(oN("equation",e,t,n),...oN("tensors",e,t,n))];case"Transpose":return[r.transpose(oN("x",e,t,n),oN("perm",e,t,n))];case"_FusedMatMul":const[a,i]=oN("fusedOps",e,t,n),s="biasadd"===a,o="prelu"===i,u=oN("numArgs",e,t,n),l=oN("leakyreluAlpha",e,t,n);if(s){if(o&&2!==u)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&1!==u)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,p]=oN("args",e,t,n);return[r.fused.matMul({a:oN("a",e,t,n),b:oN("b",e,t,n),transposeA:oN("transposeA",e,t,n),transposeB:oN("transposeB",e,t,n),bias:c,activation:i,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(oN("x",e,t,n),oN("axis",e,t,n),oN("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(oN("x",e,t,n),oN("mean",e,t,n),oN("variance",e,t,n),oN("offset",e,t,n),oN("scale",e,t,n),oN("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(oN("x",e,t,n),oN("radius",e,t,n),oN("bias",e,t,n),oN("alpha",e,t,n),oN("beta",e,t,n))];case"Softmax":return[r.softmax(oN("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(oN("x",e,t,n))];case"SparseToDense":return[r.sparseToDense(oN("sparseIndices",e,t,n),oN("outputShape",e,t,n),oN("sparseValues",e,t,n),oN("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Max":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.max(oN("x",e,t,n),a,i)]}case"Mean":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.mean(oN("x",e,t,n),a,i)]}case"Min":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.min(oN("x",e,t,n),a,i)]}case"Sum":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.sum(oN("x",e,t,n),a,i)]}case"All":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.all(oN("x",e,t,n),a,i)]}case"Any":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.any(oN("x",e,t,n),a,i)]}case"ArgMax":{const a=oN("axis",e,t,n);return[r.argMax(oN("x",e,t,n),a)]}case"ArgMin":{const a=oN("axis",e,t,n);return[r.argMin(oN("x",e,t,n),a)]}case"Prod":{const a=oN("axis",e,t,n),i=oN("keepDims",e,t,n);return[r.prod(oN("x",e,t,n),a,i)]}case"Cumprod":{const a=oN("axis",e,t,n),i=oN("exclusive",e,t,n),s=oN("reverse",e,t,n);return[r.cumprod(oN("x",e,t,n),a,i,s)]}case"Cumsum":{const a=oN("axis",e,t,n),i=oN("exclusive",e,t,n),s=oN("reverse",e,t,n);return[r.cumsum(oN("x",e,t,n),a,i,s)]}case"Bincount":const a=oN("x",e,t,n),i=oN("weights",e,t,n),s=oN("size",e,t,n);return[r.bincount(a,i,s)];case"DenseBincount":{const a=oN("x",e,t,n),i=oN("weights",e,t,n),s=oN("size",e,t,n),o=oN("binaryOutput",e,t,n);return[r.denseBincount(a,i,s,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"ConcatV2":case"Concat":{const a=oN("n",e,t,n),i=oN("axis",e,t,n);let s=oN("tensors",e,t,n);return s=s.slice(0,a),[r.concat(s,i)]}case"Gather":{const a=oN("x",e,t,n),i=oN("indices",e,t,n);return[r.gather(a,r.cast(i,"int32"),0)]}case"GatherV2":{const a=oN("axis",e,t,n),i=oN("batchDims",e,t,n),s=oN("x",e,t,n),o=oN("indices",e,t,n);return[r.gather(s,r.cast(o,"int32"),a,i)]}case"Reverse":{const a=oN("dims",e,t,n),i=[];for(let e=0;e{const a=oN("axis",e,t,n),i=oN("tensors",e,t,n),s=i[0].shape,o=r.squeeze(i[0]).shape,u=i.map((e=>{const t=de(e.shape,s);if(!t&&!de(r.squeeze(e).shape,o))throw new Error("the input tensors shape does not match");return t?e:r.reshape(e,s)}));return[r.stack(u,a)]}));case"Unpack":{const a=oN("axis",e,t,n),i=oN("tensor",e,t,n);return r.unstack(i,a)}case"Tile":{const a=oN("reps",e,t,n);return[r.tile(oN("x",e,t,n),a)]}case"Split":case"SplitV":{const a=oN("axis",e,t,n),i=oN("numOrSizeSplits",e,t,n),s=oN("x",e,t,n);return r.split(s,i,a)}case"ScatterNd":{const a=oN("indices",e,t,n),i=oN("values",e,t,n),s=oN("shape",e,t,n);return[r.scatterND(a,i,s)]}case"GatherNd":{const a=oN("x",e,t,n),i=oN("indices",e,t,n);return[r.gatherND(a,i)]}case"SparseToDense":{const a=oN("sparseIndices",e,t,n),i=oN("outputShape",e,t,n),s=oN("sparseValues",e,t,n),o=oN("defaultValue",e,t,n);return[r.sparseToDense(a,s,i,s.dtype===o.dtype?o:r.cast(o,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:a,outputValues:i,emptyRowIndicator:s,reverseIndexMap:o}=r.sparse.sparseFillEmptyRows(oN("indices",e,t,n),oN("values",e,t,n),oN("denseShape",e,t,n),oN("defaultValue",e,t,n));return[a,i,s,o]}case"SparseReshape":{const{outputIndices:a,outputShape:i}=r.sparse.sparseReshape(oN("inputIndices",e,t,n),oN("inputShape",e,t,n),oN("newShape",e,t,n));return[a,i]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(oN("data",e,t,n),oN("indices",e,t,n),oN("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(oN("data",e,t,n),oN("indices",e,t,n),oN("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"FFT":return[r.fft(oN("x",e,t,n))];case"IFFT":return[r.ifft(oN("x",e,t,n))];case"RFFT":return[r.rfft(oN("x",e,t,n))];case"IRFFT":return[r.irfft(oN("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"StringNGrams":{const{nGrams:a,nGramsSplits:i}=r.string.stringNGrams(oN("data",e,t,n),oN("dataSplits",e,t,n),oN("separator",e,t,n),oN("nGramWidths",e,t,n),oN("leftPad",e,t,n),oN("rightPad",e,t,n),oN("padWidth",e,t,n),oN("preserveShortSequences",e,t,n));return[a,i]}case"StringSplit":{const{indices:a,values:i,shape:s}=r.string.stringSplit(oN("input",e,t,n),oN("delimiter",e,t,n),oN("skipEmpty",e,t,n));return[a,i,s]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(oN("input",e,t,n),oN("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return a((()=>((e,t,n,r=j)=>{switch(e.op){case"Cast":return[r.cast(oN("x",e,t,n),oN("dtype",e,t,n))];case"ExpandDims":{const a=oN("axis",e,t,n);return[r.expandDims(oN("x",e,t,n),a)]}case"Squeeze":{const a=oN("axis",e,t,n);return[r.squeeze(oN("x",e,t,n),a)]}case"Reshape":return[r.reshape(oN("x",e,t,n),oN("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(oN("x",e,t,n),oN("padding",e,t,n),oN("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(oN("x",e,t,n),oN("padding",e,t,n),oN("constantValue",e,t,n))];case"SpaceToBatchND":{const a=oN("blockShape",e,t,n),i=oN("paddings",e,t,n);return[r.spaceToBatchND(oN("x",e,t,n),a,i)]}case"BatchToSpaceND":{const a=oN("blockShape",e,t,n),i=oN("crops",e,t,n);return[r.batchToSpaceND(oN("x",e,t,n),a,i)]}case"DepthToSpace":{const a=oN("blockSize",e,t,n),i=oN("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(oN("x",e,t,n),a,i)]}case"BroadcastTo":return[r.broadcastTo(oN("x",e,t,n),oN("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(oN("s0",e,t,n),oN("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const a=oN("keyDType",e,t,n),i=oN("valueDType",e,t,n),s=new rM(a,i);return r.addHashTable(e.name,s),[s.handle]}case"LookupTableImport":case"LookupTableImportV2":{const a=oN("tableHandle",e,t,n,r),i=oN("keys",e,t,n),s=oN("values",e,t,n),o=r.getHashTableById(a.id);return[await o.import(i,s)]}case"LookupTableFind":case"LookupTableFindV2":{const a=oN("tableHandle",e,t,n,r),i=oN("keys",e,t,n),s=oN("defaultValue",e,t,n),o=r.getHashTableById(a.id);return[await o.find(i,s)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=oN("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);case"custom":const i=iN(e.op);if(i&&i.customExecutor)return i.customExecutor(new GN(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return qe(i)?i.then((e=>[].concat(e))):[].concat(i)}class iM{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.iterationId}`)).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function sM(e,t,n,r){const a=new Set,i=[];let s=null,o=null;const u=new Set,l=Object.keys(e).map((e=>pN(e)[0]));let c=[];null!=r&&(c=r.map((e=>pN(e.name)[0])));const p=[...t];for(;p.length>0;){const e=p.pop();(cM(e)||pM(e)||dM(e))&&null==s&&(s=e,o=s.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&-1===l.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),p.push(e))})):i.push(e.name))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:i,dynamicNode:s,syncInputs:o}}const oM=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],uM=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],lM=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function cM(e){return oM.indexOf(e.op)>=0}function pM(e){return uM.indexOf(e.op)>=0}function dM(e){return lM.indexOf(e.op)>=0}class hM{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new hM(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const 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){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=sM(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:i}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}return function(e,t,n){const{usedNodes:r,inputs:a}=n,i=[],s=Object.keys(a).map((e=>pN(e)[0])).map((t=>e.nodes[t])),o=e.initNodes;s.forEach((e=>{r.has(e.name)&&i.push(e)})),e.weights.forEach((e=>{r.has(e.name)&&i.push(e)})),null!=o&&o.forEach((e=>{r.has(e.name)&&i.push(e)}));const u=new Set,l=[];for(;i.length>0;){const e=i.pop();u.add(e.name),t[e.name]||l.push(e),e.children.forEach((e=>{!u.has(e.name)&&r.has(e.name)&&e.inputs.every((e=>u.has(e.name)))&&i.push(e)}))}return l}(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map((e=>this.graph.nodes[pN(e)[0]])),a=t.map((e=>pN(e)[0]));let i=a.map((e=>this.graph.nodes[e]));this.resetIntermediateTensors(),0===i.length&&(i=this._outputs);const s=this.getCompilationKey(r,i);let o=this.compiledMap.get(s);null==o&&(o=this.compile(e,i),this.compiledMap.set(s,o));const u={},l={};return ho((()=>{const n=new iM(this.weightMap,u,l,this.functionExecutorMap),r=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,a]=pN(t),i=[];i[a]=e[t],r[n]=i}));const i=this.getFrozenTensorIds(r),s={};for(let e=0;euN(e,r,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,i,s){"control"!==t.category&&-1===i.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(s[e.id]=(s[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const i=function(e,t,n){return t[cN(e,n.currentContextId)]}(e.name,n,r);null!=i&&i.forEach((e=>{if(e&&!e.kept&&!a.has(e.id)){const n=s[e.id];if(1===n){if(this.keepTensorForDebug){const[n,a]=lN(t.name,r);this.intermediateTensors[n]||(this.intermediateTensors[n]=[]),this.intermediateTensors[n][a]=e}else e.dispose();delete s[e.id]}else null!=n&&s[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.intermediateTensors&&(Object.keys(this.intermediateTensors).forEach((e=>this.intermediateTensors[e].forEach((e=>e.dispose())))),this.disposeTensorsMap())}disposeTensorsMap(){this.tensorsMap&&Object.keys(this.tensorsMap).forEach((e=>{this.tensorsMap[e].forEach((e=>{!e||e.kept||e.isDisposed||this.keepIds.has(e.id)||e.dispose()}))}))}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(const e in this.intermediateTensors)this.intermediateTensors[e].forEach((e=>e.dispose())),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Ge().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){console.warn(e.message)}this.resetIntermediateTensors();const i=new iM(this.weightMap,r,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,i,t,n);const s=t.map((e=>uN(e,this.tensorsMap,i))),o=s.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id));return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),null==this.parent&&i.dispose(this.keepIds),s}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const a=Object.keys(e),i=a.map((e=>this.graph.nodes[pN(e)[0]])),s=n.map((e=>pN(e)[0]));let o=s.map((e=>this.graph.nodes[e]));0===o.length&&(o=this._outputs);const{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=sM(e,o,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),h=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=pN(t),a=[];a[r]=e[t],h[n]=a}));const f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){const e=this.processStack(i,d,t,h,g,m,s,f,u);await Promise.all(e)}null!=c||r||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=o.filter((e=>!cM(e)&&!uN(e.name,h,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${l}]. ${e}`)}return h}processStack(e,t,n,r,a,i,s,o,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&oN("isConstant",e.node,r,n)&&([c]=lN(e.node.name,n)),null==r[e.node.name]){const p=aM(e.node,r,n,this._resourceManager);c||([c]=lN(e.node.name,n));const d=n.currentContext;qe(p)?l.push(p.then((l=>(r[c]=l,n.currentContext=d,this.checkTensorForDisposal(c,e.node,r,n,i,s,o),this.processChildNodes(e.node,t,n,r,a,u),l)))):(r[c]=p,this.checkTensorForDisposal(c,e.node,r,n,i,s,o),this.processChildNodes(e.node,t,n,r,a,u))}else this.processChildNodes(e.node,t,n,r,a,u)}return l}processChildNodes(e,t,n,r,a,i){e.children.forEach((e=>{const[s]=lN(e.name,n);!a[s]&&i.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!uN(e,r,n)))&&(a[s]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!uN(e,r,n)))&&(a[s]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[r]=pN(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));se(t,(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&se(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]?t[this._signature.inputs[n].name]=e[n]:t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=pN(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]?this._signature.outputs[e].name:e),{})}checkOutputs(e){e.forEach((e=>{const[t]=pN(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class fM{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class mM{constructor(e,t={},n=i){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new fM}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return qe(e)?e.then((e=>this.loadSync(e))):this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(n=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new hM(LN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=LN.Instance.transformGraph(e.modelInitializer);this.initializer=new hM(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)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){const n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){const e={};return(n instanceof ti?[n]:n).forEach(((t,n)=>e[this.structuredOutputKeys[n]]=t)),e}return n}normalizeInputs(e){if(!(e instanceof ti||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function gM(e,t={},n=i){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&"string"==typeof e&&(e=function(e){return e.endsWith("/")||(e+="/"),`${e}model.json?tfjs-format=file`}(e));const r=new mM(e,t,n);return await r.load(),r}function yM(e){if(null==e)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");if(!e.load)throw new Error(`modelUrl IO Handler ${e} has no load function`);const t=new mM(e);return t.load(),t}const bM="3.19.0";function vM(e,t,n=new Map,r=new Set){if(null==e)return null;if("function"==typeof Blob&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse){if(NM(e)){const a=Array.isArray(e)?[]:{};r.add(e);for(const i in e){const s=vM(e[i],t,n,r);a[i]=s}return r.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,a.value),a.value}function xM(e,t=wM){return _M(e,t)}function _M(e,t,n=new Set){const r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse){if(NM(r)){const a=Array.isArray(r)?[]:{};n.add(r);for(const i in r){const r=_M(e.map((e=>e[i])),t,n);a[i]=r}return n.delete(r),a}throw new Error(`Can't recurse into non-iterable type: ${r}`)}return a.value}function wM(e){return null===e?null:NM(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function DM(e,t){const n=new Map;vM(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(qe(t)){const r=await t;n.set(e,r)}}return vM(e,t,n)}function NM(e){let t=!1;if(Ge().get("IS_BROWSER"))t=e instanceof TextDecoder;else{const{StringDecoder:r}=n(4977);t=e instanceof r}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof ti)&&!(e instanceof Promise)&&!t)}function MM(e){return e instanceof ti?{value:e.clone(),recurse:!1}:NM(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class SM{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)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 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class kM extends SM{constructor(){super(kM.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=2*this.capacity,t=new Array(e),n=this.length();for(let e=0;e!0===e))}rowMajorBatch(e,t=!0){return new OM(this,e,t)}columnMajorBatch(e,t=!0,n=wM){return this.rowMajorBatch(e,t).map((e=>xM(e,n)))}concatenate(e,t){return new jM(TM([this,e]),t)}take(e){return e<0||null==e?this:new $M(this,e)}skip(e){return e<0||null==e?this:new FM(this,e)}prefetch(e){return new WM(this,e)}shuffle(e,t){return new VM(this,e,t)}serial(){return new LM(this)}}class AM extends EM{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:(t=e,function(e,t){return vM(e,t)}(t,MM)),done:!1};var t}}class IM extends EM{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class LM extends EM{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class FM extends EM{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class OM extends EM{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class RM extends EM{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;fo(e.value)}}}class YM extends EM{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mi(e.value),n=this.transform(e.value),r=mi(n);for(const e of t)fi(e,r)||e.dispose();return{value:n,done:!1}}}class BM extends EM{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class zM extends EM{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=mi(e.value),n=await this.transform(e.value),r=mi(n);for(const e of t)fi(e,r)||e.dispose();return{value:n,done:!1}}}class PM extends EM{constructor(){super(),this.outputQueue=new kM,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class HM extends PM{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=mi(e.value),n=this.transform(e.value),r=mi(n);this.outputQueue.pushAll(n);for(const e of t)fi(e,r)||e.dispose();return!0}}class jM extends EM{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,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var UM;!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(UM||(UM={}));class qM extends EM{constructor(e,t=UM.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;const r=await DM(this.iterators,(function(e){return e instanceof EM?{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}:{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case UM.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case UM.SHORTEST:return{value:null,done:!0};case UM.LONGEST:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class WM extends EM{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new SM(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class VM extends WM{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ep.alea(n||Ba().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}class GM{constructor(){this.size=null}batch(e,t=!0){const n=this;let r;return se(e>0,(()=>`batchSize needs to be positive, but it is\n ${e}`)),r=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),KM((async()=>(await n.iterator()).columnMajorBatch(e,t,ZM)),r)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,KM((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,KM((async()=>(await t.iterator()).filter((t=>ho((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return KM((async()=>(await t.iterator()).map((t=>ho((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return KM((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return KM((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,KM((async()=>{return n=CM((async()=>({value:await t.iterator(),done:!1}))).take(e),new jM(n,r);var n,r}),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const r=this,a=Ep.alea(t||Ba().toString());return KM((async()=>{let t=a.int32();return n&&(t+=a.int32()),(await r.iterator()).shuffle(e,t.toString())}),this.size)}take(e){const t=this;let n;return n=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,KM((async()=>(await t.iterator()).take(e)),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}function KM(e,t=null){return new class extends GM{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function JM(e){return KM((async()=>TM(e)),e.length)}function XM(e){if(!NM(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;nfunction(e,t=UM.FAIL){return new qM(e,t)}(await DM(e,(e=>{if(e instanceof GM)return{value:e.iterator(),recurse:!1};if(NM(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),UM.SHORTEST)),t)}function ZM(e){if(null===e)return null;return null==(t=e[0])||null===(n=t)||"object"!=typeof n&&"function"!=typeof n||Array.isArray(t)||"object"==typeof t&&t instanceof ti||ke(t)?{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ti?hd(e):Ri(e)}(e),recurse:!1}:{value:null,recurse:!0};var t,n}GM.MAX_BUFFER_SIZE=1e4;class QM extends GM{constructor(e){super(),this.input=e}async iterator(){const e=(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)));return e}}const eS='"',tS=Symbol("out"),nS=Symbol("field"),rS=Symbol("quote"),aS=Symbol("quoteafterquote"),iS=Symbol("quoteinquote");class sS extends GM{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 QM(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(se(null==t.delimiter,(()=>"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(){const 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&&se(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);const t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(se(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(const e of Object.keys(this.columnConfigs))if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").");this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value;return this.parseRow(n,!1)}return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){const t=this.parseRow(e),n={},r={};for(let a=0;a14||!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=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!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(!Ge().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");const t=new oS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`)}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,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;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((r=>{const a=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&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){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(ce(t));return n.set(e,n.length-e.length),Ri(n,t)}}class uS extends EM{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=yd([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,r=(1-t)/2,a=n+e,i=t+r;this.cropBox=bd([r,n,i,a],[1,4])}else this.cropBox=bd([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Ge().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new uS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&se("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`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=Bo(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return ho((()=>{const t=Cc(Ls(e,"float32"),0);let n;n=Uh.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const r=n.shape;return Dl(n,r.slice(1))}))}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class lS{}class cS extends EM{split(e){return new pS(this,e)}}class pS extends cS{constructor(e,t){super(),this.upstream=e,this.impl=new dS(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class dS extends PM{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}}class hS extends EM{decodeUTF8(){return new fS(this)}}class fS extends cS{constructor(e){super(),this.upstream=e,this.impl=new mS(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class mS extends PM{constructor(e){if(super(),this.upstream=e,Ge().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:e}=n(1601);this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t,n;return!e.done&&(t=e.value,n=Ge().get("IS_BROWSER")?this.decoder.decode(t,{stream:!0}):this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0)}}class gS extends hS{constructor(e,t={}){super(),this.file=e,this.options=t,se(e instanceof Uint8Array||!!Ge().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise(((e,t)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{const r=new FileReader;r.onload=n=>{let a=r.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(a)},r.onabort=e=>t(new Error("Aborted")),r.onerror=e=>t(new Error(e.type));const a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}));return{value:await e,done:!1}}}function yS(e){return"string"==typeof e&&"file://"===e.slice(0,7)}class bS extends lS{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(yS(this.input)&&Ge().get("IS_NODE")){const e=n(7792);this.input=e.readFileSync(this.input.slice(7))}return new gS(this.input,this.options)}}class vS extends lS{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return yS(this.url)?new bS(this.url,this.fileOptions).iterator():async function(e,t={},n){let r,a;var i;"string"==typeof e?r=e:(r=e.url,a={method:(i=e).method,headers:i.headers,body:i.body,mode:i.mode,credentials:i.credentials,cache:i.cache,redirect:i.redirect,referrer:i.referrer,integrity:i.integrity});const s=await(n||za)(r,a);if(s.ok){const e=new Uint8Array(await s.arrayBuffer());return new gS(e,t)}throw new Error(s.statusText)}(this.url,this.fileOptions)}}function xS(e,t={}){return new sS(new vS(e),t)}function _S(e){const t=CM(e);return KM((async()=>t))}function wS(e){return KM((async()=>{const t=await e();return CM((()=>t.next()))}))}async function DS(e,t){return uS.create(e,t)}async function NS(e){return oS.create(e)}const MS="3.19.0";function SS(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&se("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}const kS=Cd;class TS extends J{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new K(this,lo())}nextDataId(){return TS.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Ge().get("IS_NODE")&&ca("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================"));const r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&Ee(n[0])){const a=n.map((e=>Pa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){this.data.get(e).refCount++}decRef(e){this.data.has(e)&&this.data.get(e).refCount--}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);return"complex64"===t?yf(this.readSync(n.real.dataId),this.readSync(n.imag.dataId)):this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>Ha(e)));return Is(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Is(e.shape,e.dtype,t)}makeOutput(e,t,n){return lo().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;const{complexTensorInfos:n}=this.data.get(e);null!=n&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){const t=Ba();return e(),{kernelMs:Ba()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){SS([e],"where");const t=this.readSync(e.dataId);return kS(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}function CS(e,t,n){return({inputs:r,attrs:a,backend:i})=>{const{x:s}=r;if(SS(s,e),"string"===s.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=i,u=o.data.get(s.dataId).values,l=ce(s.shape),c=n||s.dtype,p=De(c,l);for(let e=0;e{const{x:s}=r;if(SS(s,e),"string"===s.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=i,u=o.data.get(s.dataId).values,l=n||s.dtype,c=t(u,l,a);return o.makeTensorInfo(s.shape,l,c)}}TS.nextDataId=0,Do("cpu",(()=>new TS),1);const AS=CS(Jt,(e=>e>=0?e:Math.exp(e)-1)),IS={kernelName:Jt,backendName:"cpu",kernelFunc:AS};function LS(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const FS={kernelName:fn,backendName:"cpu",kernelFunc:LS};function $S(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:i}=r;SS([a],"leakyRelu");const s=ce(a.shape),o=n.data.get(a.dataId).values,u=we("float32",s);for(let e=0;e{const s=Lo(t,n),o=s.length,u=Oe(s),l=we(i,ce(s)),c=t.length,p=n.length,d=Oe(t),h=Oe(n),f=Ao(t,s),m=Ao(n,s);if(f.length+m.length===0)for(let t=0;ti[e]=0));const s=je(i,c,d),g=n.slice(-p);m.forEach((e=>g[e]=0));const y=je(g,p,h);l[t]=e(r[s],a[y])}return[l,s]}}const YS=RS(((e,t)=>e<0?t*e:e));function BS(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t;SS([r,a],"prelu");const i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,[o,u]=YS(r.shape,a.shape,i,s,"float32");return n.makeTensorInfo(u,"float32",o)}const zS={kernelName:ir,backendName:"cpu",kernelFunc:BS},PS=CS(cr,(e=>Math.max(0,e))),HS={kernelName:cr,backendName:"cpu",kernelFunc:PS},jS=CS(gr,(e=>Math.min(Math.max(0,e),6))),US={kernelName:gr,backendName:"cpu",kernelFunc:jS};function qS(e){return(t,n,r)=>{const a=we(n,t.length);for(let n=0;n1/(1+Math.exp(-e)))),VS=CS(Tr,(e=>1/(1+Math.exp(-e)))),GS={kernelName:Tr,backendName:"cpu",kernelFunc:VS};function KS(e,t,n,r,a){if("linear"===n)return LS({inputs:{x:t},backend:e});if("relu"===n)return PS({inputs:{x:t},backend:e});if("elu"===n)return AS({inputs:{x:t},backend:e});if("relu6"===n)return jS({inputs:{x:t},backend:e});if("prelu"===n)return BS({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return $S({inputs:{x:t},backend:e,attrs:{alpha:a}});if("sigmoid"===n)return VS({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function JS(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",i),imag:n.makeTensorInfo(a.shape,"float32",s)},o}const XS={kernelName:Mt,backendName:"cpu",kernelFunc:JS};function ZS(e,t,n="float32"){if("complex64"===n)return JS({inputs:{real:ZS(e,t,"float32"),imag:ZS(e,t,"float32")},backend:e});const r=ze(ce(t),n);return e.makeTensorInfo(t,n,r)}function QS(e){const{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,i=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,i)}const ek={kernelName:ur,backendName:"cpu",kernelFunc:QS};function tk(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:i}=r;if("complex64"===i){if("complex64"===a.dtype)return LS({inputs:{x:a},backend:n});const e=ZS(n,a.shape,a.dtype),t=tk({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),r=JS({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),r}if("complex64"===a.dtype){const e=QS({inputs:{input:a},backend:n}),t=tk({inputs:{x:e},backend:n,attrs:{dtype:i}});return n.disposeIntermediateTensorInfo(e),t}if(!Se(a.dtype,i)){const e=LS({inputs:{x:a},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:i}}if("int32"===i){const e=n.data.get(a.dataId).values,t=Int32Array.from(e);return n.makeTensorInfo(a.shape,"int32",t)}if("bool"===i){const e=n.data.get(a.dataId).values,t=Ya([0],a.dtype),[r,i]=RS(((e,t)=>e!==t?1:0))(a.shape,[],e,t,"bool");return n.makeTensorInfo(i,"bool",r)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${i}`)}const nk={kernelName:wt,backendName:"cpu",kernelFunc:tk};function rk(e,t,n,r){return null==n?({inputs:n,backend:a})=>{const{a:i,b:s}=n,o=a;SS([i,s],e);const u=o.data.get(i.dataId).values,l=o.data.get(s.dataId).values,c="string"===i.dtype?Gf(u):u,p="string"===i.dtype?Gf(l):l,d=r||i.dtype,[h,f]=t(i.shape,s.shape,c,p,d);return o.makeTensorInfo(f,d,h)}:({inputs:e,backend:a})=>{const{a:i,b:s}=e,o=a;if("complex64"===i.dtype||"complex64"===s.dtype){const e=tk({inputs:{x:i},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),r=t.complexTensorInfos.real,a=t.complexTensorInfos.imag,u=o.data.get(r.dataId).values,l=o.data.get(a.dataId).values,c=tk({inputs:{x:s},backend:o,attrs:{dtype:"complex64"}}),p=o.data.get(c.dataId),d=p.complexTensorInfos.real,h=p.complexTensorInfos.imag,f=o.data.get(d.dataId).values,m=o.data.get(h.dataId).values,[g,y,b]=n(i.shape,s.shape,u,l,f,m),v=o.makeTensorInfo(b,"float32",g),x=o.makeTensorInfo(b,"float32",y),_=JS({inputs:{real:v,imag:x},backend:o});return o.disposeIntermediateTensorInfo(e),o.disposeIntermediateTensorInfo(c),o.disposeIntermediateTensorInfo(v),o.disposeIntermediateTensorInfo(x),_}{const e=o.data.get(i.dataId).values,n=o.data.get(s.dataId).values,a=r||i.dtype,[u,l]=t(i.shape,s.shape,e,n,a);return o.makeTensorInfo(l,a,u)}}}function ak(e){return(t,n,r,a,i,s)=>{const o=Lo(t,n),u=ce(o),l=o.length,c=Oe(o),p=we("float32",u),d=we("float32",u),h=Ao(t,o),f=Ao(n,o),m=yf(r,a),g=yf(i,s),y=t.length,b=Oe(t),v=n.length,x=Oe(n);if(h.length+f.length===0)for(let t=0;tr[e]=0));const a=je(r,y,b),i=n.slice(-v);f.forEach((e=>i[e]=0));const s=je(i,v,x),o=e(m[2*a],m[2*a+1],g[2*s],g[2*s+1]);p[t]=o.real,d[t]=o.imag}return[p,d,o]}}const ik=RS(((e,t)=>e+t)),sk=ak(((e,t,n,r)=>({real:e+n,imag:t+r}))),ok=rk(nt,ik,sk),uk={kernelName:nt,backendName:"cpu",kernelFunc:ok};function lk(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:i}=r,s=ce(a.shape),o=ve(i,s),u=ce(o);se(s===u,(()=>`The new shape (${o}) has ${u} elements and the old shape (${a.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`)),n.incRef(a.dataId);const l=n.data.get(a.dataId);if(null!=l.complexTensorInfos){const e=l.complexTensorInfos.real,t=l.complexTensorInfos.imag;e.shape=o,t.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}const ck={kernelName:pr,backendName:"cpu",kernelFunc:lk};function pk(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:i}=t,{transposeA:s,transposeB:o}=r;SS([a,i],"matMul");const u=a.shape.length,l=i.shape.length,c=s?a.shape[u-2]:a.shape[u-1],p=o?i.shape[l-1]:i.shape[l-2],d=s?a.shape[u-1]:a.shape[u-2],h=o?i.shape[l-2]:i.shape[l-1],f=a.shape.slice(0,-2),m=i.shape.slice(0,-2),g=ce(f),y=ce(m),b=Lo(a.shape.slice(0,-2),i.shape.slice(0,-2)).concat([d,h]);se(c===p,(()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${a.shape} and ${i.shape} and transposeA=${s} and transposeB=${o} must match.`));const v=o?[y,h,p]:[y,p,h],x=lk({inputs:{x:a},backend:n,attrs:{shape:s?[g,c,d]:[g,d,c]}}),_=lk({inputs:{x:i},backend:n,attrs:{shape:v}}),w=s?x.shape[1]:x.shape[2],D=s?x.shape[2]:x.shape[1],N=o?_.shape[1]:_.shape[2],M=Math.max(g,y),S=n.data.get(x.dataId).values,k=n.data.get(_.dataId).values,T=Oe(x.shape),C=Oe(_.shape),[E,A,I]=s?[T[0],1,T[1]]:[T[0],T[1],1],[L,F,$]=o?[1,C[1],C[0]]:[C[1],1,C[0]],O=D*N,R=Is([M,D,N],x.dtype),Y=R.values,B=n.blockSize;for(let e=0;e{const{x:t}=e.inputs,n=e.backend;SS(t,"abs");let r=new Float32Array(ce(t.shape));return r=fk(n.data.get(t.dataId).values),n.makeOutput(r,t.shape,t.dtype)}},gk=CS(et,(e=>Math.acos(e))),yk={kernelName:et,backendName:"cpu",kernelFunc:gk},bk=CS(tt,(e=>Math.acosh(e))),vk={kernelName:tt,backendName:"cpu",kernelFunc:bk},xk={kernelName:rt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,r=t;SS(t,"addN");const a=r.map((e=>n.data.get(e.dataId).values)),i=Is(r[0].shape,r[0].dtype),s=i.values;for(let e=0;en&&(n=a,r=e)}d[e]=r}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c,"int32",d)}},kk={kernelName:ot,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i}=r;SS(a,"argMin");let s=xe(i,a.shape);const o=vc(s,a.shape.length);let u=a;const l=[];null!=o&&(u=wk({inputs:{x:a},backend:n,attrs:{perm:o}}),l.push(u),s=_c(s.length,u.shape.length)),s=[s[0]],bc("argMin",s,u.shape.length);const[c,p]=gc(u.shape,s),d=ze(ce(c),"int32"),h=ce(p),f=n.data.get(u.dataId).values;for(let e=0;en.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c,"int32",d)}},Tk=CS(ut,(e=>Math.asin(e))),Ck={kernelName:ut,backendName:"cpu",kernelFunc:Tk},Ek=CS(lt,(e=>Math.asinh(e))),Ak={kernelName:lt,backendName:"cpu",kernelFunc:Ek},Ik=CS(ct,(e=>Math.atan(e))),Lk={kernelName:ct,backendName:"cpu",kernelFunc:Ik},Fk=RS(((e,t)=>Math.atan2(e,t))),$k=rk(dt,Fk),Ok={kernelName:dt,backendName:"cpu",kernelFunc:$k},Rk=CS(pt,(e=>Math.atanh(e))),Yk={kernelName:pt,backendName:"cpu",kernelFunc:Rk};function Bk(e,t,n,r,a,i){const s=a.strideHeight,o=a.strideWidth,u=a.dilationHeight,l=a.dilationWidth,c=a.effectiveFilterHeight,p=a.effectiveFilterWidth,d=a.padInfo.top,h=a.padInfo.left,f="max"===i?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Is(a.outShape,n),g=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],b=a.outShape[2]*a.outShape[3],v=a.outShape[3];for(let t=0;ty?y=s:"avg"===i&&(b+=s,x++)}if(isNaN(y))break}g[D+n*v+t]="avg"===i?b/x:y}}}return m}function zk(e,t,n,r,a=!1,i=!1){const s=Is(r.outShape,"int32"),o=r.strideHeight,u=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,h=r.padInfo.top,f=r.padInfo.left,m=Is(t,n,e);for(let e=0;ex&&(x=l,_=a?i?((e*r.inHeight+n)*r.inWidth+o)*r.inChannels+t:(n*r.inWidth+o)*r.inChannels+t:s*d+u)}}s.set(_,e,n,o,t)}}return s}function Pk(e,t,n,r,a,i){const s=a.strideDepth,o=a.strideHeight,u=a.strideWidth,l=a.dilationDepth,c=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterDepth,h=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,g=a.padInfo.top,y=a.padInfo.left,b="max"===i?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=Is(a.outShape,n),x=v.values,_=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],w=a.outShape[2]*a.outShape[3]*a.outShape[4],D=a.outShape[3]*a.outShape[4],N=a.outShape[4];for(let t=0;tw?w=a:"avg"===i&&(D+=a,M++),isNaN(w))break}if(isNaN(w))break}if(isNaN(w))break}x[g+t]="avg"===i?D/M:w}}}}return v}const Hk={kernelName:ht,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;SS(a,"avgPool");const{filterSize:i,strides:s,pad:o,dimRoundingMode:u}=r;se(xl(s,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);let c;if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))c=LS({inputs:{x:a},backend:n});else{const e=n.data.get(a.dataId).values,t=Oe(a.shape),r=Bk(e,a.shape,a.dtype,t,l,"avg");c=n.makeTensorInfo(l.outShape,a.dtype,r.values)}return c}},jk={kernelName:mt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dimRoundingMode:u,dataFormat:l}=r;SS(a,"avgPool3d");const c=pl(a.shape,i,s,1,o,u,l),p=Pk(n.data.get(a.dataId).values,a.shape,a.dtype,Oe(a.shape),c,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}},Uk={kernelName:gt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,{filterSize:s,strides:o,pad:u,dimRoundingMode:l}=r;SS([a,i],"avgPool3DGrad");const c=pl(i.shape,s,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,_=c.effectiveFilterHeight,w=c.effectiveFilterWidth,D=x-1-c.padInfo.front,N=w-1-c.padInfo.left,M=_-1-c.padInfo.top,S=Is(i.shape,"float32"),k=1/(f*m*g),T=n.bufferSync(a);for(let e=0;e=c.outDepth||Math.floor(r)!==r))for(let n=0;n<_;n+=b){const a=(s+n)/d;if(!(a<0||a>=c.outHeight||Math.floor(a)!==a))for(let n=0;n=c.outWidth||Math.floor(i)!==i||(u+=T.get(e,r,a,i,t))}}}S.set(u*k,e,n,r,a,t)}return 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n=0;n=a||(s[r]+=i>0?t[n]:1)}return s}function Zk(e,t,n,r=!1){const a=e.shape[0],i=e.shape[1],s=Is([a,n],t.dtype);for(let o=0;o=n||(r?s.set(1,o,i):t.size>0?s.set(s.get(o,i)+t.get(o,a),o,i):s.set(s.get(o,i)+1,o,i))}return s}const Qk={kernelName:vt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s}=r,o=Xk(n.data.get(a.dataId).values,n.data.get(i.dataId).values,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,o)}},eT={kernelName:_t,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,i=n.data.get(r.dataId).values,s=n.data.get(a.dataId).values,o=Lo(Array.from(i),Array.from(s));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},tT=qS((e=>Math.ceil(e))),nT=ES(Dt,tT),rT={kernelName:Dt,backendName:"cpu",kernelFunc:nT},aT=CS(Nt,((e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e{const{x:t}=e.inputs,n=e.backend,r=new 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e=0;e=d.inHeight)continue;const i=e*w[0],s=t+n*N;for(let e=0;e=d.inWidth)continue;const a=s+r*M;let o=i+e*w[1];for(let e=0;e=l.inDepth)continue;const i=e*M[0],s=t+n*N[1];for(let e=0;e=l.inHeight)continue;const a=i+e*M[1],o=s+r*N[2];for(let e=0;e=l.inWidth)continue;const i=a+e*M[2],s=o+t*l.inChannels;let u=i;for(let e=0;eMath.cos(e))),xT={kernelName:Ft,backendName:"cpu",kernelFunc:vT},_T=CS($t,(e=>Math.cosh(e))),wT={kernelName:$t,backendName:"cpu",kernelFunc:_T},DT={kernelName:Yt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:i,boxInd:s}=t,{cropSize:o,method:u,extrapolationValue:l}=r,[c,p,d,h]=a.shape,f=i.shape[0],[m,g]=o,y=Is([f,m,g,h],"float32"),b=n.data.get(i.dataId).values,v=n.data.get(s.dataId).values,x=n.data.get(a.dataId).values,_=Oe(a.shape),w=Oe(y.shape);for(let e=0;e=c)continue;const o=m>1?(a-n)*(p-1)/(m-1):0,f=g>1?(i-r)*(d-1)/(g-1):0;for(let t=0;t1?n*(p-1)+t*o:.5*(n+a)*(p-1);if(c<0||c>p-1)for(let 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d}},jT={kernelName:Xt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{dy:r,y:a}=t;SS([r,a],"eluGrad");const i=new Float32Array(ce(a.shape)),s=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let e=0;e=1?o[e]:o[e]*(t+1)}return n.makeTensorInfo(a.shape,"float32",i)}},UT=RS(((e,t)=>e===t?1:0)),qT=rk(Qt,UT,null,"bool"),WT={kernelName:Qt,backendName:"cpu",kernelFunc:qT},VT=pf,GT=df,KT=hf,JT=ff,XT=mf,ZT=gf,QT=CS(Zt,(e=>{const t=Math.sign(e),n=Math.abs(e),r=1/(1+VT*n);return t*(1-((((ZT*r+XT)*r+JT)*r+KT)*r+GT)*r*Math.exp(-n*n))})),eC={kernelName:Zt,backendName:"cpu",kernelFunc:QT},tC=qS((e=>Math.exp(e))),nC=ES(en,tC,"float32"),rC={kernelName:en,backendName:"cpu",kernelFunc:nC};function aC(e){const{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:i}=r,s=a.shape.length,o=a.shape.slice();let u=i;return i<0&&(se(-(s+1)<=i,(()=>`Axis must be in the interval [${-(s+1)}, ${s}]`)),u=s+i+1),o.splice(u,0,1),lk({inputs:{x:a},backend:n,attrs:{shape:o}})}const 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e=0;e=0&&sMath.floor(e))),NC=ES(on,DC),MC={kernelName:on,backendName:"cpu",kernelFunc:NC},SC=RS(((e,t)=>Math.floor(e/t))),kC=rk(un,SC,null,"int32"),TC={kernelName:un,backendName:"cpu",kernelFunc:kC},CC={kernelName:ua,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r;let m=dT({inputs:{x:a,filter:i},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d}});if(s){const e=m;if("NCHW"===c&&1===s.shape.length&&1!==s.shape[0]){const e=lk({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}});m=ok({inputs:{a:m,b:e},backend:n}),n.disposeIntermediateTensorInfo(e)}else m=ok({inputs:{a:m,b:s},backend:n});n.disposeIntermediateTensorInfo(e)}if(h){const e=m;if("NCHW"===c&&"prelu"===h&&1===o.shape.length&&1!==o.shape[0]){const 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2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n",{getBroadcastDims:wL}=g;function DL(e,t,n){const r=[];if(e.forEach((e=>{const t=ce(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform?r.push(`uniform float ${e.name}${t>1?`[${t}]`:""};`):(r.push(`uniform sampler2D ${e.name};`),r.push(`uniform int offset${e.name};`)),n.enableShapeUniforms){const{uniformShape:t}=LL(n.packedInputs,e.shapeInfo.logicalShape,e.shapeInfo.texShape);switch(t.length){case 1:r.push(`uniform int ${e.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${e.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${e.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${e.name}Shape;`)}r.push(`uniform ivec2 ${e.name}TexShape;`)}})),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;")}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach((e=>{r.push(`uniform ${e.type} ${e.name}${e.arrayIndex?`[${e.arrayIndex}]`:""};`)}));const a=r.join("\n"),i=e.map((e=>function(e,t,n=!1,r){let a="";a+=n?ML(e,r):NL(e,r);const i=e.shapeInfo.logicalShape,s=t.logicalShape;return i.length<=s.length&&(a+=n?function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",i=e.shapeInfo.logicalShape.length,s=t.logicalShape.length,o=wL(e.shapeInfo.logicalShape,t.logicalShape),u=IL(s),l=s-i;let c;const p=["x","y","z","w","u","v"];c=0===i?"":s<2&&o.length>=1?"coords = 0;":o.map((e=>`coords.${p[e+l]} = 0;`)).join("\n");let d="";d=s<2&&i>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${p[t+l]}`)).join(", ");let h="return outputValue;";const f=1===ce(e.shapeInfo.logicalShape),m=1===ce(t.logicalShape);if(1!==i||f||m){if(f&&!m)h=1===s?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(o.length){const e=i-2,t=i-1;o.indexOf(e)>-1&&o.indexOf(t)>-1?h="return vec4(outputValue.x);":o.indexOf(e)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(t)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}}else h="\n return vec4(outputValue.xy, outputValue.xy);\n ";return`\n vec4 ${a}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${r}(${d});\n ${h}\n }\n `}(e,t):function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",i=t.texShape,s=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&null==e.shapeInfo.flatOffset&&de(s,i))return`\n float ${a}() {\n return sampleTexture(${n}, resultUV);\n }\n `;const l=IL(u),c=wL(e.shapeInfo.logicalShape,t.logicalShape),p=u-o;let d;const h=["x","y","z","w","u","v"];d=0===o?"":u<2&&c.length>=1?"coords = 0;":c.map((e=>`coords.${h[e+p]} = 0;`)).join("\n");let f="";return f=u<2&&o>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${h[t+p]}`)).join(", "),`\n float ${a}() {\n ${l} coords = getOutputCoords();\n ${d}\n return get${r}(${f});\n }\n `}(e,t)),a}(e,t,n.packedInputs,n.enableShapeUniforms))).join("\n"),s=t.texShape,o=yL(),u=function(e){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${e.texture2D}(textureSampler, uv).r;\n }\n `}(o);let l,c,p=function(e){return`${e.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${e.varyingFs} vec2 resultUV;\n ${e.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${e.defineSpecialNaN}\n ${e.defineSpecialInf}\n ${e.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${SL}\n ${kL}\n ${TL}\n `}(o);return t.isPacked?(l=function(e,t,n){switch(e.length){case 0:return"\n int getOutputCoords() {\n return 0;\n }\n ";case 1:return function(e,t,n){const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===r[0]?n?"\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n ":`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${r[1]}.0);\n }\n `:1===r[1]?n?"\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n ":`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${r[0]}.0);\n }\n `:n?"\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n ":`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);\n }\n `}(0,t,n);case 2:return function(e,t,n){const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(de(e,t))return n?"\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n }\n ":`\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));\n }\n `;const a=Math.ceil(e[1]/2);return n?"\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec2(r, c);\n }\n ":`\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n\n int index = resTexRC.x * ${r[1]} + resTexRC.y;\n int r = 2 * (index / ${a});\n int c = imod(index, ${a}) * 2;\n\n return ivec2(r, c);\n }\n `}(e,t,n);case 3:return function(e,t,n){if(n)return"\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),i=a*Math.ceil(e[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${r[0]}, ${r[1]}));\n int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n int b = index / ${i};\n index -= b * ${i};\n\n int r = 2 * (index / ${a});\n int c = imod(index, ${a}) * 2;\n\n return ivec3(b, r, c);\n }\n `}(e,t,n);default:return function(e,t,n){if(n)return"\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),i=a*Math.ceil(e[e.length-2]/2);let s=i,o="",u="b, r, c";for(let t=2;t1&&!de(t,n)&&r.lengthe[t])).join(", ")}function OL(e,t,n){const r={},a={},i={},s=[];let o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),1===Ge().getNumber("WEBGL_VERSION")&&(c=e.getUniformLocation(n,"INFINITY",!1));const d=!1;for(let s=0;s{s[r]=e.getUniformLocation(n,t.name,d)})),{uniformLocations:r,customUniformLocations:s,infLoc:c,nanLoc:p,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function RL(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach(((e,n)=>{const r=e.logicalShape,a=t[n],i=a.shape;if(!de(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(e.isUniform&&a.isUniform)return;const s=e.texShape,o=a.isUniform?null:a.texData.texShape;if(!de(s,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${s} and ${o} must match`)}))}function YL(e){return Ge().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}class BL{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=zI.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?vL(["r","c","d"],e):bL(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${t.output} = result;\n }\n `}}class zL{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=zI.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?vL(["r","c","d"],e):bL(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}}class PL{constructor(e){this.variableNames=["A"],this.outTexUsage=PI.DOWNLOAD;const t=yL();this.outputShape=e,this.userCode=`\n ${_L}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}}class HL{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=PI.DOWNLOAD;const t=yL();this.outputShape=e,this.userCode=`\n ${_L}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}}class jL{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`\n ${this.enableShapeUniforms?"\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n":xL(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n vec4 values = ${n.texture2D}(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n ${n.output} = vec4(${r}, 0., 0., 0.);\n }\n `}}class UL{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=yL();this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);let r="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let t=0;t<=1;t++)for(let a=0;a<=1;a++){const i=2*t+a;r+=`\n localCoords = coords;\n if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {\n localCoords[2] += ${a};\n if (localCoords[1] + ${t} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n localCoords[1] += ${t};\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n values = ${n.texture2D}(A, uv);\n\n if (offset == 0) {\n result[${i}] = values[0];\n } else if (offset == 1) {\n result[${i}] = values[1];\n } else if (offset == 2) {\n result[${i}] = values[2];\n } else {\n result[${i}] = values[3];\n }\n }\n }\n `}this.userCode=`\n ${this.enableShapeUniforms?"\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n":xL(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n vec4 result = vec4(0.);\n int flatIndex, r, c, offset;\n ivec3 localCoords;\n vec2 uv;\n vec4 values;\n\n ${r}\n\n ${n.output} = ${a};\n }\n `}}function qL(e,t,n,r,a,i){!function(e,t){const n=Ge().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const s=function(e){return aL(e,(()=>e.createTexture()),"Unable to create WebGLTexture.")}(e),o=e.TEXTURE_2D;return VI(e,(()=>e.bindTexture(o,s))),VI(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),VI(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),VI(e,(()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST))),VI(e,(()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST))),1===Ge().getNumber("WEBGL_VERSION")?VI(e,(()=>e.texImage2D(o,0,r,t,n,0,a,i,null))):VI(e,(()=>e.texStorage2D(o,1,r,t,n))),VI(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),{texture:s,texShape:[n,t]}}function WL(e){return e.internalFormatFloat}function VL(e){return e.internalFormatHalfFloat}function GL(e){return e.downloadTextureFormat}function KL(e){return e.internalFormatPackedFloat}function JL(e){return e.internalFormatPackedHalfFloat}class XL{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=Ge().getNumber("WEBGL_VERSION");null!=e?(this.gl=e,function(e,t){RI[e]=t}(t,e)):this.gl=BI(t);let n="WEBGL_color_buffer_float";const r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),1===Ge().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=KI(this.gl,e),dL(this.gl,t))this.textureHalfFloatExtension=KI(this.gl,t);else if(Ge().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),dL(this.gl,r))this.colorBufferHalfFloatExtension=KI(this.gl,r);else if(Ge().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",dL(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!dL(this.gl,r))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(r)}this.vertexBuffer=function(e){return function(e,t){const n=aL(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return VI(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),VI(e,(()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}(this.gl),this.indexBuffer=function(e){return function(e,t){const n=aL(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return VI(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n))),VI(e,(()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Uint16Array([0,1,2,2,1,3]))}(this.gl),this.framebuffer=function(e){return aL(e,(()=>e.createFramebuffer()),"Unable to create WebGLFramebuffer.")}(this.gl),this.textureConfig=WI(this.gl,this.textureHalfFloatExtension)}get debug(){return Ge().getBool("DEBUG")}dispose(){if(this.disposed)return;null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;VI(e,(()=>e.finish())),VI(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),VI(e,(()=>e.deleteFramebuffer(this.framebuffer))),VI(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),VI(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),VI(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=jI(t,n);return qL(e,a,i,WL(r),r.textureFormatFloat,e.FLOAT)}(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=jI(t,n);return qL(e,a,i,VL(r),r.textureFormatFloat,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=jI(t,n);return qL(e,a,i,GL(r),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){VI(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),n.data instanceof Uint8Array?2===Ge().getNumber("WEBGL_VERSION")?VI(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data))):VI(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data))):2===Ge().getNumber("WEBGL_VERSION")?VI(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n))):VI(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n))),VI(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),function(e,t,n,r,a,i){let s,o,u;VI(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),a instanceof Uint8Array?(s=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(s=new Float32Array(n*r*4),o=e.FLOAT,u=i.internalFormatPackedFloat),s.set(a),2===Ge().getNumber("WEBGL_VERSION")?VI(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,o,s))):VI(e,(()=>e.texImage2D(e.TEXTURE_2D,0,u,n,r,0,e.RGBA,o,s))),VI(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=qI(t,n);return qL(e,a,i,JL(r),e.RGBA,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,i]=qI(t,n);return qL(e,a,i,KL(r),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(nL(this.gl,this.framebuffer),this.outputTexture=null),VI(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n,r){const[a,i]=jI(t,n),s=new Uint8Array(t*n*4);return VI(e,(()=>e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s))),new Float32Array(s.buffer)}(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,a,i){return function(e,t,n,r,a,i,s,o){const u=e,l=new Float32Array(function(e,t){const[n,r]=qI(e,t);return n*r*4}(i,s));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}(this.gl,e,0,0,0,a,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=function(e,t,n,r){const a=e.createBuffer();VI(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a)));const i=16*t*n;return VI(e,(()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ))),VI(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0))),VI(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null))),a}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Ge().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(a,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=a}else Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n){const r=new Float32Array(t*n*4);return VI(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r))),r}(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=function(e){const t=yL();return function(e,t){const n=aL(e,(()=>e.createShader(e.VERTEX_SHADER)),"Unable to create vertex WebGLShader.");if(VI(e,(()=>e.shaderSource(n,t))),VI(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`)}(t));const n=function(e){return aL(e,(()=>e.createProgram()),"Unable to create WebGLProgram.")}(t);return VI(t,(()=>t.attachShader(n,this.vertexShader))),VI(t,(()=>t.attachShader(n,e))),function(e,t){if(VI(e,(()=>e.linkProgram(t))),!Ge().get("ENGINE_COMPILE_ONLY")&&!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,n),this.debug&&ZI(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=function(e,t,n){return VI(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),QI(e,t,"clipSpacePos",n,3,20,0)&&QI(e,t,"uv",n,2,20,12)}(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&VI(this.gl,(()=>this.gl.deleteProgram(e)))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&ZI(this.gl,this.program),VI(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return aL(e,(()=>e.getUniformLocation(t,n)),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),VI(this.gl,(()=>this.gl.getAttribLocation(e,t)))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),eL(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,a]=qI(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&ZI(this.gl,this.program),rL(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),VI(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),VI(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=KI(this.gl,2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await be((()=>this.disposed||this.isQueryAvailable(e,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=function(e){let t=0;for(;te.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||be((()=>(this.pollItems(),0===this.itemsToPoll.length)))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),tL(this.gl,e,this.framebuffer),this.debug&&rL(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(tL(this.gl,this.outputTexture,this.framebuffer),this.debug&&rL(this.gl)):nL(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const r=this.gl;tL(r,e,this.framebuffer),this.debug&&rL(r),this.outputTexture=e,VI(r,(()=>r.viewport(0,0,t,n))),VI(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),VI(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}}const{addImpl:ZL,bincountImpl:QL,bincountReduceImpl:eF,ceilImpl:tF,concatImpl:nF,equalImpl:rF,expImpl:aF,expm1Impl:iF,floorImpl:sF,gatherNdImpl:oF,gatherV2Impl:uF,greaterImpl:lF,greaterEqualImpl:cF,lessImpl:pF,lessEqualImpl:dF,linSpaceImpl:hF,logImpl:fF,maxImpl:mF,maximumImpl:gF,minimumImpl:yF,multiplyImpl:bF,negImpl:vF,notEqualImpl:xF,prodImpl:_F,rangeImpl:wF,rsqrtImpl:DF,scatterImpl:NF,sigmoidImpl:MF,simpleAbsImpl:SF,sliceImpl:kF,sparseFillEmptyRowsImpl:TF,sparseReshapeImpl:CF,sparseSegmentReductionImpl:EF,sqrtImpl:AF,stridedSliceImpl:IF,stringNGramsImpl:LF,stringSplitImpl:FF,stringToHashBucketFastImpl:$F,subImpl:OF,tileImpl:RF,topKImpl:YF,transposeImpl:BF,uniqueImpl:zF}=q;function PF(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function HF(e,t){return 1===t?[e]:PF(e,t)}class jF{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=YL(this.outputShape.length),0===this.rank)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{const e=HF("rc",this.rank),t=IL(this.rank),n=this.getOutOfBoundsCondition(e),r=this.getSetup(e),a=this.getOutput(e);this.userCode=`\n void main() {\n ${t} rc = getOutputCoords();\n\n if(${n}) {\n setOutput(vec4(0));\n } else {\n ${r}\n\n setOutput(vec4(${a}));\n }\n }\n `}}getSourceCoordsArr(e){const t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let a=`${0===n?"r":"rp1"}, ${0===r?"c":"cp1"}`;for(let t=2;t ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${r};\n `}getOutput(e){const t=this.getSourceCoordsArr(e);return 1===this.rank?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}}class UF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);let n="";for(let e=0;e<4;e++){let t="thisRC = rc;";e%2==1&&(t+="thisRC.z += 1;"),e>1&&(t+="thisRC.y += 1;"),n+=`\n ${t}\n ${e>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${e}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${e>0?"}":""}\n `}var r,a;this.userCode=`\n ${r=t,a=this.enableShapeUniforms,`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${a?function(e,t,n="index"){const r=e.map(((e,t)=>t)),a=function(e,t){const n=e.length,r=e.map((e=>`${t}[${e}]`)),a=new Array(n-1);a[n-2]=r[n-1];for(let e=n-3;e>=0;--e)a[e]=`(${a[e+1]} * ${r[e+1]})`;return a}(r,t);return a.map(((t,r)=>`int ${e[r]} = ${n} / ${a[r]}; ${r===a.length-1?`int ${e[r+1]} = ${n} - ${e[r]} * ${a[r]}`:`index -= ${e[r]} * ${a[r]}`};`)).join("")}(["r","c","d"],"inputShape"):bL(["r","c","d"],r)}\n return ivec3(r, c, d);\n }\n `}\n ${this.enableShapeUniforms?"\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n":xL(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};\n int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}}class qF{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const r=VF(t,n),a=GF(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);const i=WF(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();const e=this.freeTextures[a].shift();return this.usedTextures[a].push(e),e}let s;return r===HI.PACKED_2X2_FLOAT32?s=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===HI.PACKED_2X2_FLOAT16?s=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===HI.UNPACKED_FLOAT32?s=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===HI.UNPACKED_FLOAT16?s=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===HI.PACKED_4X1_UNSIGNED_BYTE&&(s=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(s),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),s}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;const a=VF(n,r),i=GF(t,a,r);i in this.freeTextures||(this.freeTextures[i]=[]);const s=WF(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=Ge().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=s):(this.freeTextures[i].push(e),this.numFreeTextures++,this._numBytesFree+=s),this.numUsedTextures--;const u=this.usedTextures[i],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function WF(e,t,n,r,a){const i=function(e,t){switch(e){case HI.PACKED_2X2_FLOAT32:return KL(t);case HI.PACKED_2X2_FLOAT16:return JL(t);case HI.UNPACKED_FLOAT32:return WL(t);case HI.UNPACKED_FLOAT16:return VL(t);case HI.PACKED_4X1_UNSIGNED_BYTE:return GL(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);let s;if(a){const[t,n]=qI(e[0],e[1]);s=t*n}else{const[t,n]=jI(e[0],e[1]);s=t*n}const o=function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,i);return s*o}function VF(e,t){if(e===PI.UPLOAD)return HI.PACKED_2X2_FLOAT32;if(e===PI.RENDER||null==e)return function(e){return Ge().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?HI.PACKED_2X2_FLOAT32:HI.UNPACKED_FLOAT32:e?HI.PACKED_2X2_FLOAT16:HI.UNPACKED_FLOAT16}(t);if(e===PI.DOWNLOAD||e===PI.PIXELS)return HI.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function GF(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class KF{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}const JF="return abs(x);",XF="return x;";class ZF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}class QF{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);const t=e.length,n=HF("rc",t),r=IL(t),a=function(e,t){if(1===e)return"rc";let n="";for(let r=0;rt.push(e)))}const t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:i,complexTensorInfos:s,isPacked:o}=t;if(null!=a){let t;t=o?new ZF(r,XF):new KF(r,XF);const n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:i}],i),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if(Ge().getBool("DEBUG")&&!Ge().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===Ge().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u,l,c=null;if("complex64"!==i&&Ge().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);const t=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(t.texture.texture,...UI(r))}if(this.pendingRead.set(e,[]),"complex64"!==i&&await this.gpgpu.createAndWaitForFence(),"complex64"===i){const e=await Promise.all([this.read(s.real.dataId),this.read(s.imag.dataId)]);l=yf(e[0],e[1])}else if(null==c)l=this.getValuesFromTexture(e);else{const e=ce(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}if(null!=u&&this.disposeIntermediateTensorInfo(u),null!=c){const e=this.gpgpu.gl;VI(e,(()=>e.deleteBuffer(c)))}const p=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach((e=>e(p))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&lo().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){const n=this.texData.get(e),{values:r,shape:a,slice:i,dtype:s,isPacked:o,texture:u}=n;if("complex64"===s)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=i){let n;n=o?new ZF(a,XF):new KF(a,XF);const r=this.runWebGLProgram(n,[{dataId:e,shape:a,dtype:s}],s),i=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),i}if(null==u)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const l=this.decode(e,t.customTexShape),c=lo().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return Object.assign({tensorRef:c},p.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>Ha(e)));return Is(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Is(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t0}time(e){const t=this.activeTimers,n=[];let r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();const a=le(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),i=le(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);const s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(a);s.kernelMs=re(e),s.getExtraProfileInfo=()=>e.map(((e,t)=>({name:i[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else s.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,s})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Ba(),endMs:null}}endTimer(e){return Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Ba(),e)}async getQueryTime(e){if(Ge().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:a,isPacked:i,slice:s}=this.texData.get(e),o=s&&s.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,i)));const l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=n$){return Ge().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&ce(e.shape)0&&Ee(n[0])){const a=n.map((e=>Pa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return lo().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new QF(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new jF(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[iL(e.shape),...sL(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[iL(t),...sL(t)],i=new UF(a,n),s=[n],o=this.runWebGLProgram(i,[r],e.dtype,s,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:r,shape:a,dtype:i}=n;null!=t&&se(ce(a)<=t[0]*t[1]*4,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."));const s=oL(a);let o;o=r?new zL(s):new BL(s);const u=[null!=t?t:UI(s)];return{dtype:i,shape:a,dataId:this.runWebGLProgram(o,[{shape:s,dtype:i,dataId:e}],i,u,!0,t).dataId}}runWebGLProgram(e,t,n,r,a=!1,i){const s=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(s.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===zI.DENSE){const t=null!=i?i:UI(e.outputShape);o.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(o.usage=e.outTexUsage),0===ce(s.shape))return o.values=we(s.dtype,0),s;const u=[],l=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&ce(t.shape)<=Ge().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),u.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!lL(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),u.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(s.dataId);const c={shape:s.shape,texData:o,isUniform:!1},p=function(e,t,n){let r="";t.concat(n).forEach((t=>{const a=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const i=t.texData.texShape,{useSqueezeShape:s,uniformShape:o,keptDims:u}=LL(e.packedInputs,t.shape,i);let l="",c="",p="";if(1===o.length&&e.packedInputs){const e=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];l=`${e[0]>1}_${e[1]>1}`}else if(2!==o.length||e.packedInputs){if(o.length>2&&!e.packedInputs){const e=Oe(o);p=`${e[0]===i[1]}_${e[e.length-1]===i[1]}`}}else c=`${o[0]>1}_${o[1]>1}`;const d=t.shape.length,h=2===o.length&&de(t.shape,i),f=1===ce(t.shape),m=Ao(t.shape,n.shape),g=!e.packedInputs&&d===n.shape.length&&de(i,n.texData.texShape),y=e.packedInputs||o.length>2?"":`${i[0]>1}_${i[1]>1}`;r+=`${d}_${g}_${s?u:""}_${o.length}_${f}_${m}_${h}_${l}_${c}_${p}_${y}_${a}`}else{const e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${a}`}}));const a=e.userCode;let i=e.constructor.name;return i+="_"+r+"_"+a+`${Ge().getNumber("WEBGL_VERSION")}`,i}(e,l,c),d=this.getAndSaveBinary(p,(()=>function(e,t,n,r){const a=n.map(((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),i=a.map((e=>e.shapeInfo)),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},o=DL(a,s,t),u=function(e,t){const n=aL(e,(()=>e.createShader(e.FRAGMENT_SHADER)),"Unable to create fragment WebGLShader.");if(VI(e,(()=>e.shaderSource(n,t))),VI(e,(()=>e.compileShader(n))),Ge().get("ENGINE_COMPILE_ONLY"))return n;if(!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw XI(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}(e.gl,o),l=e.createProgram(u);return Ge().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:s,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:s},OL(e,t,l))}(this.gpgpu,e,l,c))),h=null!=this.activeTimers;let f;h&&(f=this.startTimer()),Ge().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,a){t.program.enableShapeUniforms||(RL(t.inShapeInfos,n),RL([t.outShapeInfo],[r]));const i=r.texData.texture,s=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(i.texture,s[0],s[1]):e.setOutputMatrixTexture(i.texture,s[0],s[1]),e.setProgram(t.webGLProgram),1===Ge().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach(((n,r)=>{const a=t.program.variableNames[r],i=t.uniformLocations[a],s=t.uniformLocations[`offset${a}`],o=t.inShapesLocations[`${a}Shape`],u=t.inTexShapesLocations[`${a}TexShape`];if(o){const{uniformShape:r}=LL(t.program.packedInputs,n.shape,n.texData.texShape);switch(r.length){case 1:e.gl.uniform1iv(o,new Int32Array(r));break;case 2:e.gl.uniform2iv(o,new Int32Array(r));break;case 3:e.gl.uniform3iv(o,new Int32Array(r));break;case 4:e.gl.uniform4iv(o,new Int32Array(r))}}if(u&&e.gl.uniform2i(u,n.texData.texShape[0],n.texData.texShape[1]),null!=i)if(n.isUniform)if(ce(n.shape)<2)e.gl.uniform1f(i,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(i,t)}else null!=n.texData.slice&&null!=s&&e.gl.uniform1i(s,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture.texture,i,r)}));const o=t.outShapeLocation;if(o)switch(r.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(r.shape))}if(t.outShapeStridesLocation){const n=Oe(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach(((n,r)=>{const i=t.customUniformLocations[r],s=a[r];if("float"===n.type)e.gl.uniform1fv(i,s);else if("vec2"===n.type)e.gl.uniform2fv(i,s);else if("vec3"===n.type)e.gl.uniform3fv(i,s);else if("vec4"===n.type)e.gl.uniform4fv(i,s);else if("int"===n.type)e.gl.uniform1iv(i,s);else if("ivec2"===n.type)e.gl.uniform2iv(i,s);else if("ivec3"===n.type)e.gl.uniform3iv(i,s);else{if("ivec4"!==n.type)throw Error(`uniform type ${n.type} is not supported yet.`);e.gl.uniform4iv(i,s)}})),e.executeProgram()}(this.gpgpu,d,l,c,r),u.forEach((e=>this.disposeIntermediateTensorInfo(e))),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));const m=Ge().get("WEBGL_FLUSH_THRESHOLD");if(m>0){const e=Ba();e-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!Ge().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&!1===a){const e=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),e}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Ge().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})),this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=ho((()=>{if(!Ge().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=Ge().getBool("DEBUG");Ge().set("DEBUG",!1);const t=this.abs(Ou(1e-8)).dataSync()[0];if(Ge().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:i,usage:s,isPacked:o}=t;if(null!=i)return;const u=null!=this.activeTimers;let l;u&&(l=Ba());let c=t.texShape;if(null==c&&(c=function(e,t=!1){let n=Ge().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,e=e.map(((t,n)=>n>=e.length-2?te(e[n]):e[n])),1===e.length&&(e=[2,e[0]])),2!==e.length){const t=_e(e);e=t.newShape}let r=ce(e);if(e.length<=1&&r<=n)return[1,r];if(2===e.length&&e[0]<=n&&e[1]<=n)return e;if(3===e.length&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(3===e.length&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const t=iL(e);let n=2,a=2;return e.length&&([n,a]=sL(e)),r=t*(n/2)*(a/2),me(r).map((e=>2*e))}return me(r)}(n,o),t.texShape=c),null!=a){const e=oL(n);let i,s=c[1],p=c[0];const d=a instanceof Uint8Array||a instanceof Uint8ClampedArray;!o&&d||([s,p]=qI(c[0],c[1])),i=o?new UL(e,d):new jL(e,d);const h=d?[p,s]:c,f=this.makeTensorInfo(h,r),m=this.texData.get(f.dataId);m.usage=d?PI.PIXELS:PI.UPLOAD,m.texShape=h,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(f.dataId),s,p,a);const g=[[p,s]],y=!0,b=this.runWebGLProgram(i,[f],r,g,y),v=this.texData.get(b.dataId);t.texShape=v.texShape,t.isPacked=v.isPacked,t.usage=v.usage,Ge().get("ENGINE_COMPILE_ONLY")?this.disposeData(b.dataId):(t.texture=v.texture,t.values=null,this.texData.delete(b.dataId)),this.disposeIntermediateTensorInfo(f),u&&(this.uploadWaitMs+=Ba()-l)}else{const e=this.acquireTexture(c,s,r,o);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*Te(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(const[,t]of Object.entries(this.binaryCache)){const n=new Promise((e=>{try{this.checkCompletion_(t),e(!0)}catch(e){throw e}}));e.push(n)}return Promise.all(e)}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Xh(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)){if(console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS))throw XI(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.");throw new Error("Failed to link vertex and fragment shaders.")}return!0}getUniformLocations(){for(const[,e]of Object.entries(this.binaryCache)){const{uniformLocations:t,customUniformLocations:n,infLoc:r,nanLoc:a,inShapesLocations:i,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=OL(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=a,e.inShapesLocations=i,e.inTexShapesLocations=s,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}}r$.nextDataId=0,Si()&&Do("webgl",(()=>new r$),2);class a${constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=Lo(t,n),this.enableShapeUniforms=YL(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}}class i${constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Lo(t,n);const a=this.outputShape.length;this.enableShapeUniforms=YL(a);let i="";if(r)if(0===a||1===ce(this.outputShape))i="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else if(i=`\n ${IL(a)} coords = getOutputCoords();\n `,1===a)this.enableShapeUniforms?i+="\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n ":i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{const e=HF("coords",a);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= outShape[${a} - 2];\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= outShape[${a} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}}function s$(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const o$={kernelName:fn,backendName:"webgl",kernelFunc:s$};function u$(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,i=n.makeTensorInfo(r.shape,"complex64"),s=n.texData.get(i.dataId),o=s$({inputs:{x:r},backend:n}),u=s$({inputs:{x:a},backend:n});return s.complexTensorInfos={real:o,imag:u},i}const l$={kernelName:Mt,backendName:"webgl",kernelFunc:u$},c$="return (a < 0.) ? b * a : a;",p$="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + 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e=o.texData.get(s.dataId),t=n(e.values,u);return o.makeTensorInfo(s.shape,u,t)}let l;return l=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new ZF(s.shape,t):new KF(s.shape,e),o.runWebGLProgram(l,[s],u)}}function y$({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:i}){return({inputs:s,backend:o})=>{const{a:u,b:l}=s,c=o;if(r&&"complex64"===u.dtype){const t=c.texData.get(u.dataId),n=c.texData.get(l.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:u.shape},i={dataId:r.dataId,dtype:r.dtype,shape:l.shape},s=new a$(e,u.shape,l.shape);return c.runWebGLProgram(s,[a,i],ci(n.dtype,r.dtype))})),i=u$({inputs:{real:r,imag:a},backend:c});return c.disposeIntermediateTensorInfo(r),c.disposeIntermediateTensorInfo(a),i}const p=i||ci(u.dtype,l.dtype);if(("string"===u.dtype||"string"===l.dtype||c.shouldExecuteOnCPU([u,l]))&&null!=a){const e=c.texData.get(u.dataId).values,t=c.texData.get(l.dataId).values,n="string"===u.dtype?Gf(e):e,r="string"===u.dtype?Gf(t):t,[i,s]=a(u.shape,l.shape,n,r,p),o=c.makeTensorInfo(s,p);return c.texData.get(o.dataId).values=i,o}let d;return d=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new i$(t,u.shape,l.shape,n):new a$(e,u.shape,l.shape),c.runWebGLProgram(d,[u,l],p)}}function b$(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n";if("elu"===e)return t?"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g 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v${constructor(e,t,n,r=!1,a=!1,i=!1,s=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=YL(this.outputShape.length);const l=r?e[1]:e[2],c=Math.ceil(l/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";s&&(m=o?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${s}\n }`:u?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${s}\n }`:`vec4 activation(vec4 x) {\n ${s}\n }`,g="result = activation(result);");const y=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",v="rc.x";e[0]`The new shape (${u}) has ${l} elements and the old shape (${a.shape}) has ${o} elements. 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return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${s}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${s};\n if (${1===o}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${2===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${3===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}}class k${constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:i}=e;this.outputShape=[r,i];let s="0.0",o="";"prod"===t?s="1.0":"min"===t?(s="1.0 / 1e-20",o="min"):"max"===t&&(s="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");const l=4*Math.floor(n/4),c=n%4;let p=`\n if (${"sum"===t}) {\n sumValue += dot(values, ones);\n } else if (${"prod"===t}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${o}(values, minMaxValue);\n if (${"min"===t} || ${"max"===t}) {\n minMaxValue = ${o}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d="vec4";"all"===t?(s="1.0",p="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n 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inIdx = inOffset + ${l};\n if (${1===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${2===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${3===c}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${p}\n }\n setOutput(${u});\n }\n `}}function T$(e,t,n,r){const a=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],r=tf(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let i=e;for(let s=0;s6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let t=0;t6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=IL(this.rank),a=PF("rc",this.rank),i=new Array(this.rank);for(let e=0;e`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));const x=n?[y,p,h]:[y,h,p],_=r?[b,f,d]:[b,d,f],w=N$({inputs:{x:e},backend:a,attrs:{shape:x}}),D=N$({inputs:{x:t},backend:a,attrs:{shape:_}}),N=[w,D],M=Math.max(y,b),S=n?w.shape[1]:w.shape[2],k=null!=i,T=null!=s,C="leakyrelu"===u,E=null!=u?b$(u,!0):null;let A;if((1===h||1===f)&&S>1e3&&!1===(k||T||C||null!=E)){let e=w,t=D;n&&(e=F$({inputs:{x:w},backend:a,attrs:{perm:[0,2,1]}}),N.push(e)),r&&(t=F$({inputs:{x:D},backend:a,attrs:{perm:[0,2,1]}}),N.push(t));const i=1===f;let s=e;1!==f&&(s=N$({inputs:{x:e},backend:a,attrs:{shape:[M,S,1]}}),N.push(s));const o=1===f?2:1;let u=t;i&&(u=N$({inputs:{x:t},backend:a,attrs:{shape:[M,1,S]}}),N.push(u));const l=w$({inputs:{a:s,b:u},backend:a});A=I$({inputs:{x:l},backend:a,attrs:{axis:o,keepDims:!0}}),N.push(l)}else{const u=ci(e.dtype,t.dtype),l=new v$(x,_,[M,h,f],n,r,k,E,T,C),c=[w,D];if(null!=i&&c.push(i),T&&c.push(s),C){const e=a.makeTensorInfo([],"float32",Ra(o,"float32"));c.push(e),N.push(e)}A=a.runWebGLProgram(l,c,u)}const I=N$({inputs:{x:A},backend:a,attrs:{shape:v}});N.push(A);for(const e of N)a.disposeIntermediateTensorInfo(e);return I}const R$={kernelName:oa,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:i,bias:s,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=r;return O$({a,b:i,transposeA:u,transposeB:l,backend:n,bias:s,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}},Y$="return abs(x);",B$={kernelName:Qe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=SF(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let a;return a=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new ZF(r.shape,Y$):new KF(r.shape,Y$),n.runWebGLProgram(a,[r],r.dtype)}},z$=g$({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n"}),P$={kernelName:et,backendName:"webgl",kernelFunc:z$},H$=g$({opSnippet:"if (isnan(x)) return x;\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),j$={kernelName:tt,backendName:"webgl",kernelFunc:H$},U$="return a + b;",q$=y$({opSnippet:U$,packedOpSnippet:U$,supportsComplex:!0,cpuKernelImpl:ZL}),W$={kernelName:nt,backendName:"webgl",kernelFunc:q$};class V${constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n float result = ${r};\n setOutput(result);\n }\n `}}class G${constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n vec4 result = ${r};\n setOutput(result);\n }\n `}}const K$={kernelName:rt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,a=n;if(1===a.length)return s$({inputs:{x:a[0]},backend:r});if(a.length>Ge().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:r}),i=e({inputs:a.slice(t),backend:r});return e({inputs:[n,i],backend:r})}const i=a.map((e=>e.dtype)).reduce(((e,t)=>ci(e,t))),s=a.map((e=>e.shape)),o=Ge().getBool("WEBGL_PACK")?new G$(a[0].shape,s):new V$(a[0].shape,s);return r.runWebGLProgram(o,a,i)}},J$={kernelName:at,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=xe(i,a.shape);let l=u;const c=vc(l,o);let p=a;null!=c&&(p=F$({inputs:{x:a},backend:n,attrs:{perm:c}}),l=_c(l.length,o)),bc("all",l,o);const[d,h]=gc(p.shape,l),f=N$({inputs:{x:p},backend:n,attrs:{shape:[-1,ce(h)]}}),m=T$(f,f.dtype,"all",n);let g;return g=N$(s?{inputs:{x:m},backend:n,attrs:{shape:yc(d,u)}}:{inputs:{x:m},backend:n,attrs:{shape:d}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(p),g}},X$={kernelName:it,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=xe(i,a.shape);let l=u;const c=vc(l,o);let p=a;null!=c&&(p=F$({inputs:{x:a},backend:n,attrs:{perm:c}}),l=_c(l.length,o)),bc("any",l,o);const[d,h]=gc(p.shape,l),f=N$({inputs:{x:p},backend:n,attrs:{shape:[-1,ce(h)]}}),m=T$(f,f.dtype,"any",n);let g;return g=N$(s?{inputs:{x:m},backend:n,attrs:{shape:yc(d,u)}}:{inputs:{x:m},backend:n,attrs:{shape:d}}),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(p),g}};class Z${constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:a,outSize:i}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,i];const s="max"===t?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${r};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${r}; i++) {\n int inIdx = ${o};\n float candidate = getA(batch, inIdx);\n if (candidate ${s} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}}class Q${constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,se(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const a=e[e.length-1],i=Math.ceil(a/t);this.outputShape=e.slice(0,-1),i>1&&this.outputShape.push(i),r||this.variableNames.push("bestIndicesA");const s=this.outputShape,o=s.length,u=IL(o),l=HF("coords",o);let c,p;if(1===i){p=o+1;const e=IL(p);c=`\n ${e} sourceLocR = ${e}(${l.join()}, 0);\n ++${l[o-1]};\n ${e} sourceLocG = ${e}(${l.join()}, 0);\n ++${l[o-2]};\n ${e} sourceLocA = ${e}(${l.join()}, 0);\n --${l[o-1]};\n ${e} sourceLocB = ${e}(${l.join()}, 0);\n --${l[o-2]};`}else p=o,c=`\n ${u} sourceLocR = coords;\n ++${l[o-1]};\n ${u} sourceLocG = coords;\n ++${l[o-2]};\n ${u} sourceLocA = coords;\n --${l[o-1]};\n ${u} sourceLocB = coords;\n --${l[o-2]};`;const d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map((e=>"int "+e)),m=HF("sourceLocR",p-1).concat("inIdx.r"),g=HF("sourceLocG",p-1).concat("inIdx.g"),y=HF("sourceLocB",p-1).concat("inIdx.b"),b=HF("sourceLocA",p-1).concat("inIdx.a"),v="max"===n?"greaterThan":"lessThan",x=r?"":`\n inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${y.join()}),\n getBestIndicesAChannel(${b.join()})));`,_=`vec4(\n getAChannel(${m.join()}),\n hasNextCol ? getAChannel(${g.join()}) : 0.,\n hasNextRow ? getAChannel(${y.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,w=r?"":`\n float getBestIndicesAChannel(${f.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${f.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${w}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${l[o-1]} < ${s[o-1]-1};\n bool hasNextRow = ${l[o-2]} < ${s[o-2]-1};\n ${c}\n ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n sourceLocB${h}, sourceLocA${h}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${x}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}}function eO(e,t,n,r=null){let a=t.shape[0],i=t.shape[1];null!=r&&(a=r.shape[0],i=r.shape[1]);const s=tf(i),o={windowSize:s,inSize:i,batchSize:a,outSize:Math.ceil(i/s)},u=new Z$(o,n,null==r),l=[t];null!=r&&l.push(r);const 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rO={kernelName:st,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i}=r;let s=xe(i,a.shape);const o=vc(s,a.shape.length);let u=a;const l=[];null!=o&&(u=F$({inputs:{x:a},backend:n,attrs:{perm:o}}),l.push(u),s=_c(s.length,u.shape.length)),bc("argMax",[s[0]],u.shape.length);const c=nO(n,u,s[0],"max");return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},aO={kernelName:ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i}=r;let s=xe(i,a.shape);const o=vc(s,a.shape.length);let u=a;const l=[];null!=o&&(u=F$({inputs:{x:a},backend:n,attrs:{perm:o}}),l.push(u),s=_c(s.length,u.shape.length)),bc("argMin",[s[0]],u.shape.length);const c=nO(n,u,s[0],"min");return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},iO=g$({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n"}),sO={kernelName:ut,backendName:"webgl",kernelFunc:iO},oO=g$({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),uO={kernelName:lt,backendName:"webgl",kernelFunc:oO},lO=g$({opSnippet:"if (isnan(x)) return x;\n return atan(x);\n"}),cO={kernelName:ct,backendName:"webgl",kernelFunc:lO},pO=y$({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",packedOpSnippet:"\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),dO={kernelName:dt,backendName:"webgl",kernelFunc:pO},hO=g$({opSnippet:"if (isnan(x)) return x;\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),fO={kernelName:pt,backendName:"webgl",kernelFunc:hO};class mO{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,s=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec2 strides = ivec2(${s}, ${o});\n const ivec2 pads = ivec2(${d}, ${h});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${p};\n wC += ${l}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?m:g:`wR * ${p} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / count");const v=4*Math.floor(i/4),x=i%4,_=`\n if (${f}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${o});\n const ivec2 pads = ivec2(${d}, ${h});\n const float initializationValue = ${y};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${y});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${v}; wC += 4) {\n int xC = xCCorner + wC * ${l};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n getValue(batch, xR, xC + 3 * ${l}, d)\n );\n\n ${_}\n }\n\n int xC = xCCorner + ${v};\n if (${1===x}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${2===x}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${3===x}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n initializationValue\n );\n\n ${_}\n }\n }\n setOutput(${b});\n }\n `}}class gO{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const i=e.filterWidth,s=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let v="0.0";if(b||(v="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec3 strides =\n ivec3(${s}, ${o}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${h};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f};\n wC += ${p}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n wR * ${f} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(x="avgValue / count");const _=4*Math.floor(i/4),w=i%4,D=`\n if (${b}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${s}, ${o}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n const float initializationValue = ${v};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${v});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${h};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${_}; wC += 4) {\n int xC = xCCorner + wC * ${p};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${_};\n if (${1===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${2===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${3===w}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n setOutput(${x});\n }\n }\n `}}const yO={kernelName:ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;mL(a,"avgPool");const{filterSize:i,strides:s,pad:o,dimRoundingMode:u}=r;se(xl(s,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))return s$({inputs:{x:a},backend:n});const c=new mO(l,"avg",!1);return n.runWebGLProgram(c,[a],"float32")}},bO={kernelName:mt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dimRoundingMode:u,dataFormat:l}=r,c=pl(a.shape,i,s,[1,1,1],o,u,l),p=new gO(c,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}};class vO{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n const ivec2 pads = ivec2(${l}, ${c});\n const float avgMultiplier = float(${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${o};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${s}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}}class xO{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n const ivec3 pads = ivec3(${h}, ${f}, ${m});\n const float avgMultiplier = float(${g});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${c};\n wD += ${o}) {\n float dyD = float(dyDCorner + wD) / ${a}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${p};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${l}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const _O={kernelName:gt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=r,p=pl(s.shape,o,u,[1,1,1],l,c),d=new xO(p);return n.runWebGLProgram(d,[a],s.dtype)}},wO={kernelName:ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i;mL([a,i],"avgPoolGrad");const{filterSize:o,strides:u,pad:l}=r,c=cl(s.shape,o,u,1,l),p=new vO(c);return n.runWebGLProgram(p,[a],s.dtype)}},DO={kernelName:yt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:i}=t,{transposeA:s,transposeB:o}=r;return O$({a,b:i,transposeA:s,transposeB:o,backend:n})}};class NO{constructor(e,t,n,r,a,i){this.outputShape=[],this.variableNames=["x","mean","variance"],Lo(e,t),Lo(e,n);let s="0.0";null!=r&&(Lo(e,r),this.variableNames.push("offset"),s="getOffsetAtOutCoords()");let o="1.0";null!=a&&(Lo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${s};\n float scale = ${o};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}}class MO{constructor(e,t,n,r,a,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Lo(e,t),Lo(e,n);let s="vec4(0.0)";null!=r&&(Lo(e,r),this.variableNames.push("offset"),s="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=a&&(Lo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${s};\n vec4 scale = ${o};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}}const SO={kernelName:ln,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,mean:a,variance:i,offset:s,scale:o}=e;se(a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),se(null==s||a.shape.length===s.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),se(null==o||a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:u}=n;null==u&&(u=.001);const l=[r,a,i];let c=null;null!=s&&(c=s.shape,l.push(s));let p=null;null!=o&&(p=o.shape,l.push(o));const d=Ge().getBool("WEBGL_PACK_NORMALIZATION")?new MO(r.shape,a.shape,i.shape,c,p,u):new NO(r.shape,a.shape,i.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)}};class kO{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=IL(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const n=function(e){if(1===e)return"sourceLoc";if(e<=6)return TO.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let r;const a=e.map(((e,t)=>`sourceLoc.${TO[t]} = start[${t}] + coords.${TO[t]};`));r=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${a.join("\n")}\n `,this.userCode=`\n void main() {\n ${r}\n setOutput(getSource(${n}));\n }\n `}}const TO=["x","y","z","w","u","v"];class CO{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const t=IL(this.rank),n=HF("coords",this.rank),r=HF("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,i=`getChannel(getSource(${r.join()}), ${a})`,s=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.y = ${i};\n --${r[this.rank-1]};\n }\n `,o=1===this.rank?"":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n ++${r[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.w = ${i};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${s}\n ${o}\n setOutput(result);\n }\n `}}function EO(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:i,size:s}=r,[o,u]=au(a,i,s);if(Uo(a,o,u),0===ce(u))return n.makeTensorInfo(u,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=kF(e.values,o,u,a.shape,a.dtype);return n.makeTensorInfo(u,a.dtype,t)}const{isPacked:l}=n.texData.get(a.dataId),c=nu(a.shape,o,u);if(l||!c){const e=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CO(u):new kO(u),t=[o];return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){const a=r.texData.get(e.dataId),i=r.makeTensorInfo(n,e.dtype),s=r.texData.get(i.dataId);Object.assign(s,a),s.refCount=1,s.shape=n,s.dtype=e.dtype;let o=ru(t,Oe(e.shape));a.slice&&(o+=a.slice.flatOffset),s.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};const u=r.dataRefCount.get(s.slice.origDataId)||1;return r.dataRefCount.set(s.slice.origDataId,u+1),i}(a,o,u,n)}const AO={kernelName:Nr,backendName:"webgl",kernelFunc:EO},IO={kernelName:bt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:i,crops:s}=r;se(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const o=i.reduce(((e,t)=>e*t)),u=rf(a.shape,i,o),l=af(u.length,i.length),c=sf(a.shape,i,o),p=of(s,i.length),d=uf(c,s,i.length),h=[],f=N$({inputs:{x:a},backend:n,attrs:{shape:u}}),m=F$({inputs:{x:f},backend:n,attrs:{perm:l}}),g=N$({inputs:{x:m},backend:n,attrs:{shape:c}}),y=EO({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}},LO={kernelName:vt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s}=r,o=n.readSync(a.dataId),u=n.readSync(i.dataId),l=QL(o,u,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,l)}},FO={kernelName:_t,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,i=n.readSync(r.dataId),s=n.readSync(a.dataId),o=Lo(Array.from(i),Array.from(s));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},$O=y$({opSnippet:"return float(a != b);",cpuKernelImpl:xF,dtype:"bool"}),OO={kernelName:Kn,backendName:"webgl",kernelFunc:$O};function RO(e){const{inputs:t,backend:n}=e,{input:r}=t;return s$({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}const YO={kernelName:ur,backendName:"webgl",kernelFunc:RO},BO={kernelName:wt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r,attrs:a}=t,{x:i}=n,{dtype:s}=a;if("complex64"===s){if("complex64"===i.dtype)return s$({inputs:{x:i},backend:r});const t=up(i.shape),n=e({inputs:{x:i},backend:r,attrs:{dtype:"float32"}}),a=u$({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===i.dtype){const t=RO({inputs:{input:i},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(t),n}if(!Se(i.dtype,s)){const e=s$({inputs:{x:i},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:s}}if("int32"===s)return function(e,t){const n=new KF(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(i,r);if("bool"===s){const e=r.makeTensorInfo([],"bool",we("bool",1)),t=$O({inputs:{a:i,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${s}`)}},zO="return ceil(x);",PO=g$({opSnippet:zO,packedOpSnippet:zO,cpuKernelImpl:tF}),HO={kernelName:Dt,backendName:"webgl",kernelFunc:PO};class jO{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n "}}class UO{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n "}}const qO={kernelName:Nt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:i,clipValueMax:s}=r;let o;o=Ge().getBool("WEBGL_PACK_CLIP")?new UO(a.shape):new jO(a.shape);const u=[[i],[s]];return n.runWebGLProgram(o,[a],a.dtype,u)}};class WO{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n "}}function VO(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const GO={kernelName:St,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),i=new WO(r.shape),s=[VO(r,a.complexTensorInfos.real),VO(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(i,s,s[0].dtype)}};class KO{constructor(e){this.outputShape=[],this.outputShape=Qh(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));const t=new Array(e.length-1);t[0]=e[0][1];for(let n=1;n`T${t}`));const o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n= ${o[e-1]}) {\n return getChannel(\n getT${e}(${XO(s,u,t)}),\n vec2(${XO(l,u,t)}));\n }`}const d=o.length,h=o[o.length-1];p+=`\n return getChannel(\n getT${d}(${XO(s,u,h)}),\n vec2(${XO(l,u,h)}));`,this.userCode=`\n float getValue(${s.map((e=>"int "+e))}) {\n ${p}\n }\n\n void main() {\n ${a} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[r-1]} = ${i[r-1]} + 1;\n if (${i[r-1]} < ${n[r-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[r-2]} = ${i[r-2]} + 1;\n if (${i[r-2]} < ${n[r-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[r-1]} = ${i[r-1]} - 1;\n if (${i[r-2]} < ${n[r-2]} &&\n ${i[r-1]} < ${n[r-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}}function XO(e,t,n){const r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function ZO(e){const{inputs:t,backend:n}=e,{input:r}=t;return s$({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const QO={kernelName:gn,backendName:"webgl",kernelFunc:ZO};function eR(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map((e=>RO({inputs:{input:e},backend:n}))),a=e.map((e=>ZO({inputs:{input:e},backend:n}))),i=eR(r,t,n),s=eR(a,t,n),o=u$({inputs:{real:i,imag:s},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),a.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(s),o}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){const a=e.map((e=>{const r=ce(e.shape.slice(t));return N$({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})})),i=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),s=Qh(a.map((e=>e.shape)),1),o=1===a[0].shape[0],u=nF(i,s,r,o),l=Qh(e.map((e=>e.shape)),t),c=n.makeTensorInfo(l,r,u);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}const i=Ge().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>i){const r=[];for(let a=0;a1){const a=new JO(e.map((e=>e.shape)),t);return n.runWebGLProgram(a,e,r)}const{tensors2D:s,outShape:o}=function(e,t,n){const r=Qh(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>N$({inputs:{x:e},attrs:{shape:[-1,ce(e.shape.slice(t))]},backend:n}))),outShape:r}}(e,t,n),u=new KO(s.map((e=>e.shape))),l=n.runWebGLProgram(u,s,r);s.forEach((e=>n.disposeIntermediateTensorInfo(e)));const c=N$({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function tR(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,i=xe(a,t[0].shape)[0],s=Qh(t.map((e=>e.shape)),i);if(0===ce(s))return n.makeTensorInfo(s,t[0].dtype,[]);const o=t.filter((e=>ce(e.shape)>0));return 1===o.length?s$({inputs:{x:o[0]},backend:n}):(Zh(o.map((e=>e.shape)),i),eR(o,i,n))}const nR={kernelName:kt,backendName:"webgl",kernelFunc:tR};class rR{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,s=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let v="",x="";n&&(v=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,x="result = activation(result);");const _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${v}\n\n const ivec2 strides = ivec2(${o}, ${u});\n const ivec2 pads = ivec2(${i}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${b}];\n\n ivec2 xRCCorner =\n ivec2(coords[${g}], coords[${y}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${h}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${m}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${1===f}) {\n\n if (${m}) {\n dotProd +=\n getX(batch, xR, xC, ${h}) *\n getW(wR, wC, ${h}, d2);\n } else {\n dotProd +=\n getX(batch, ${h}, xR, xC) *\n getW(wR, wC, ${h}, d2);\n }\n\n } else if (${2===f}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${h}, d2),\n getW(wR, wC, ${h} + 1, d2)\n );\n\n if (${m}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${h}),\n getX(batch, xR, xC, ${h} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${h}, xR, xC),\n getX(batch, ${h} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${3===f}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${h}, d2),\n getW(wR, wC, ${h} + 1, d2),\n getW(wR, wC, ${h} + 2, d2)\n );\n\n if (${m}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${h}),\n getX(batch, xR, xC, ${h} + 1),\n getX(batch, xR, xC, ${h} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${h}, xR, xC),\n getX(batch, ${h} + 1, xR, xC),\n getX(batch, ${h} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${x}\n setOutput(result);\n }\n `}}class aR{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${a}, ${i}, ${s});\n const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${c}; wF++) {\n int xF = xFCorner + wF * ${o};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${h}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${1===f}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${h}) *\n getW(wF, wR, wC, ${h}, d2);\n } else if (${2===f}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${h}),\n getX(batch, xF, xR, xC, ${h} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${h}, d2),\n getW(wF, wR, wC, ${h} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${3===f}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${h}),\n getX(batch, xF, xR, xC, ${h} + 1),\n getX(batch, xF, xR, xC, ${h} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${h}, d2),\n getW(wF, wR, wC, ${h} + 1, d2),\n getW(wF, wR, wC, ${h} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class iR{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=YL(this.outputShape.length);const{dataFormat:n}=t,r=yL(),a="channelsLast"===n,i=a?1:2,s=a?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let u="";for(let e=0;e<=1;e++)for(let t=0;t<=1;t++)u+=`\n blockIndex = rc.z + ${t};\n pos = rc.y + ${e};\n\n ${o}\n offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n if(d0 < inputShape[${i}] && d0 >= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${s}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${a}) {\n innerDims = vec2(d1, ch);\n result[${2*e+t}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${2*e+t}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${r.output} = result;\n }\n `}}function sR(e,t){const n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function oR({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:s=0,activation:o=null}){const u=e.shape,l=r.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h="channelsLast"===n.dataFormat;let f;const m=[];if(null!=i){const e=sR(i.shape,h);null!=e&&(i=N$({inputs:{x:i},backend:r,attrs:{shape:e}}),m.push(i))}if(null!=a){const e=sR(a.shape,h);null!=e&&(a=N$({inputs:{x:a},backend:r,attrs:{shape:e}}),m.push(a))}if((1!==p&&1!==d||!(c>1e3))&&l.isPacked&&h&&null!=l.texture&&u[2]%2!=0&&de(l.shape.slice(-3),u.slice(-3))){const c=u[0]*u[1]*(u[2]+1),p={dataId:e.dataId,shape:[1,c,n.inChannels],dtype:e.dtype},d=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,se(lL(l.shape,p.shape),(()=>`packed reshape ${l.shape} to ${p.shape} isn't free`));const h=N$({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});m.push(h);const g=O$({a:p,b:h,backend:r,transposeA:!1,transposeB:!1,bias:a,activation:o,preluActivationWeights:i,leakyreluAlpha:s}),y=r.texData.get(g.dataId);se(y.isPacked,(()=>"batchMatMul result is expected to be packed")),l.shape=d,y.shape=n.outShape,f=s$({inputs:{x:g},backend:r}),f.shape=n.outShape,m.push(g)}else{const u=n.outHeight*n.outWidth,l=N$({inputs:{x:e},backend:r,attrs:{shape:h?[n.batchSize,u,n.inChannels]:[n.batchSize,n.inChannels,u]}}),c=N$({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),p=O$({a:h?l:c,b:h?c:l,transposeA:!h,transposeB:!1,backend:r,bias:a,activation:o,preluActivationWeights:i,leakyreluAlpha:s});f=N$({inputs:{x:p},backend:r,attrs:{shape:n.outShape}}),m.push(l),m.push(c),m.push(p)}for(const e of m)r.disposeIntermediateTensorInfo(e);return f}function uR({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:s=0,activation:o=null}){const{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f="channelsLast"===h,m=u*l*c,g=d*p,y=[n.batchSize,m,g],b=[];if(null!=i){const e=sR(i.shape,f);null!=e&&(i=N$({inputs:{x:i},backend:r,attrs:{shape:e}}),b.push(i))}if(null!=a){const e=sR(a.shape,f);null!=e&&(a=N$({inputs:{x:a},backend:r,attrs:{shape:e}}),b.push(a))}const v=N$({inputs:{x:t},backend:r,attrs:{shape:[1,m,ce(t.shape)/m]}});b.push(v);const x=new iR(y,n),_=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],w=r.runWebGLProgram(x,[e],"float32",_),D=N$({inputs:{x:w},backend:r,attrs:{shape:y}});b.push(w),b.push(D);const N=null!=a,M=null!=i,S="leakyrelu"===o,k=o?b$(o,!0):null,T=new v$(f?D.shape:v.shape,f?v.shape:D.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],!0,!1,N,k,M,S),C=f?[D,v]:[v,D];if(a&&C.push(a),M&&C.push(i),S){const e=r.makeTensorInfo([],"float32",Ra(s,"float32"));C.push(e),b.push(e)}const E=r.runWebGLProgram(T,C,"float32"),A=N$({inputs:{x:E},backend:r,attrs:{shape:n.outShape}});b.push(E);for(const e of b)r.disposeIntermediateTensorInfo(e);return A}const lR={kernelName:Tt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=r,p=_l(u),d=dl(a.shape,i.shape,s,l,o,c,!1,p);let h;if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type)if(Ge().getBool("WEBGL_CONV_IM2COL"))h=uR({x:a,filter:i,convInfo:d,backend:n});else{const e=new rR(d);h=n.runWebGLProgram(e,[a,i],"float32")}else h=oR({x:a,filter:i,convInfo:d,backend:n});const f=N$({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}};class cR{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,i="channelsLast"===e.dataFormat;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${i}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class pR{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i="channelsLast"===e.dataFormat,s=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=i?1:2,l=i?2:3,c=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${s}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${c}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class dR{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,i=e.padInfo.top,s=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${a};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${r} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class hR{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,i=e.strideHeight,s=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${a}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${r}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${r} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const fR={kernelName:Ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=r,p=_l(u),d=dl(a.shape,c,s,1,o,l,!1,p),h=new cR(d);return n.runWebGLProgram(h,[a,i],"float32")}},mR={kernelName:Et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{inputShape:s,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=r,p=_l(l),d=dl(s,i.shape,o,1,u,c,!1,p),h=new pR(d);return n.runWebGLProgram(h,[a,i],"float32")}},gR={kernelName:At,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dilations:u}=r,l=hl(a.shape,i.shape,s,u,o),c=new aR(l);return n.runWebGLProgram(c,[a,i],"float32")}},yR={kernelName:It,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,pad:o,filterShape:u}=r,l=hl(a.shape,u,s,1,o),c=new dR(l);return n.runWebGLProgram(c,[a,i],"float32")}},bR={kernelName:Lt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{pad:s,strides:o,inputShape:u}=r,l=hl(u,i.shape,o,1,s),c=new hR(l);return n.runWebGLProgram(c,[a,i],"float32")}},vR=g$({opSnippet:"if (isnan(x)) return x;\n return cos(x);\n"}),xR={kernelName:Ft,backendName:"webgl",kernelFunc:vR},_R=g$({opSnippet:"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n"}),wR={kernelName:$t,backendName:"webgl",kernelFunc:_R};class DR{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[i,s,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];const d="bilinear"===r?1:0,[h,f]=[s-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(s-1)/(c-1),"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[""+(o-1)/(p-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n const float height_ratio = float(${m});\n const float width_ratio = float(${b});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${g};\n float width_scale = ${v};\n\n float in_y = ${y};\n if( in_y < 0.0 || in_y > ${h} ) {\n setOutput(float(${a}));\n return;\n }\n float in_x = ${x};\n if( in_x < 0.0 || in_x > ${f} ) {\n setOutput(float(${a}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}}const NR={kernelName:Yt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:i,boxInd:s}=t,{cropSize:o,method:u,extrapolationValue:l}=r,c=new DR(a.shape,i.shape,o,u,l);return n.runWebGLProgram(c,[a,i,s],"float32")}};var MR;!function(e){e.Prod="*",e.Sum="+"}(MR||(MR={}));class SR{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const a=this.outputShape.length,i=this.op===MR.Prod?"1.0":"0.0",s=n?i:`getX(${kR(a,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1];let u="",l="";n?(u=r?"end != "+(o-1):"end != 0",l=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${o}`:"end >= pow2",l=r?"end + pow2":"end - pow2"),this.userCode=`\n void main() {\n ${IL(a)} coords = getOutputCoords();\n int end = ${TR(a,"coords",this.op)};\n float val = ${s};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${l};\n ${TR(a,"coords",this.op)} = idx;\n val ${this.op}= getX(${kR(a,"coords",this.op)});\n }\n setOutput(val);\n }\n `}}function kR(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function TR(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function CR(e,t,n,r,a,i){const s=t.shape.length,o=vc([r],s);let u=t;null!=o&&(u=F$({inputs:{x:t},backend:n,attrs:{perm:o}}));const l=_c(1,s)[0];if(l!==s-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);const c=u.shape[l];let p=s$({inputs:{x:u},backend:n});for(let t=0;t<=Math.ceil(Math.log2(c))-1;t++){const r=new SR(e,u.shape,!1,i),a=[[t]],s=p;p=n.runWebGLProgram(r,[p],p.dtype,a),n.disposeIntermediateTensorInfo(s)}if(a){const t=new SR(e,u.shape,a,i),r=p;p=n.runWebGLProgram(t,[p],p.dtype),n.disposeIntermediateTensorInfo(r)}if(null!=o){const e=F$({inputs:{x:p},backend:n,attrs:{perm:xc(o)}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),e}return p}const ER={kernelName:Ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,exclusive:s,reverse:o}=r;return CR(MR.Prod,a,n,i,s,o)}},AR={kernelName:Rt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,exclusive:s,reverse:o}=r;return CR(MR.Sum,a,n,i,s,o)}},IR={kernelName:Bt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:i}=t,{size:s,binaryOutput:o}=r;if(1===a.shape.length){const e=n.readSync(a.dataId),t=n.readSync(i.dataId),r=QL(e,t,i.dtype,i.shape,s);return n.makeTensorInfo([s],i.dtype,r)}if(2===a.shape.length){const e=n.bufferSync(a),t=n.bufferSync(i),r=eF(e,t,s,o);return n.makeTensorInfo(r.shape,i.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};class LR{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const FR={kernelName:zt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:i,dataFormat:s}=r,o=a.shape[0],u=("NHWC"===s?a.shape[1]:a.shape[2])*i,l=("NHWC"===s?a.shape[2]:a.shape[3])*i,c=("NHWC"===s?a.shape[3]:a.shape[1])/(i*i),p=new LR("NHWC"===s?[o,u,l,c]:[o,c,u,l],i,s);return n.runWebGLProgram(p,[a],a.dtype)}};class $R{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=YL(this.outputShape.length);const i=e.filterHeight,s=e.filterWidth,o=e.outChannels/e.inChannels;let u="",l="";n&&(u=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,l="result = activation(result);");const c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${o};\n int q = d2 - d1 * ${o};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${s}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${c}\n ${l}\n setOutput(result);\n }\n `}}class OR{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=YL(this.outputShape.length);const i=e.outChannels/e.inChannels,s=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c;let d="\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e=0 && xR < inDims[0]) {\n ";for(let e=0;e<(p+1)/2;e++){const t=2*e;if(d+=`\n xC = xCCorner + ${t*u};\n `,1===o){if(t= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n `,d+=1===u&&t>0?`\n xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n `:`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n } else {\n xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xC${t} = xTexelC${t};\n `,t+1= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n `,u>1&&(d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n xTexelC${t}Ready = 1;\n }\n `),d+=`\n xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);\n `):d+=1===e?`\n xC${t+1} = xTexelC${t};\n `:`\n xCOffset = xC + ${e};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t+1} = xTexelC${t+1};\n `}}else t= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n `,t+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(\n xTexelC${t}.xy, xTexelC${t+1}.xy);\n `,t+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`));const p=dl(a.shape,i.shape,s,c,o,l,!0);let d;d=Ge().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?new OR(p):new $R(p);const h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[a,i],"float32",h)}};class YR{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class BR{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,i=t-1-e.padInfo.top,s=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${o}; dm++) {\n int d2 = d1 * ${o} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const zR={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:i}=t,{strides:s,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=r,p=dl(a.shape,c,s,o,u,l,!0),d=new YR(p);return n.runWebGLProgram(d,[a,i],"float32")}},PR={kernelName:jt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:i}=t,{strides:s,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=r,p=dl(c,i.shape,s,o,u,l,!0),d=new BR(p);return n.runWebGLProgram(d,[a,i],"float32")}};class HR{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n "}}const jR={kernelName:Ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],i=ce(r.shape),s=N$({inputs:{x:r},backend:n,attrs:{shape:[i]}}),o=new HR(i),u=n.runWebGLProgram(o,[s],s.dtype),l=N$({inputs:{x:u},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(u),l}};class UR{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:i,filterHeight:s,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=r;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${i});\n const ivec2 pads = ivec2(${c}, ${p});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${s}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${o}; w++) {\n int wIn = wBeg + w * ${l};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}}const qR={kernelName:qt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i}=t,{strides:s,pad:o,dilations:u}=r,l=ll(a.shape,i.shape,s,o,"NHWC",u);let c;const p=new UR(l);c=n.runWebGLProgram(p,[a,i],"float32");const d=N$({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}},WR={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,i=t,{allDims:s,summedDims:o,idDims:u}=Sf(a,i.length);Tf(s.length,u,i);const{path:l,steps:c}=Cf(o,u),p=c.length;let d=null,h=s.length;const f=[];for(let e=0;e=0&&(d=I$({inputs:{x:d},backend:n,attrs:{axis:l[e]-(s.length-h),keepDims:!1}}),f.push(d)),h--)}for(const e of f)e!==d&&n.disposeIntermediateTensorInfo(e);return d}},VR=g$({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n"}),GR={kernelName:Jt,backendName:"webgl",kernelFunc:VR},KR={kernelName:Xt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:r,y:a}=t,i=Ge().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new i$("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",r.shape,a.shape):new a$("return (b >= 1.0) ? a : a * (b + 1.0);",r.shape,a.shape);return n.runWebGLProgram(i,[r,a],r.dtype)}},JR=y$({opSnippet:"return float(a == b);",packedOpSnippet:"\n return vec4(equal(a, b));\n",dtype:"bool",cpuKernelImpl:rF}),XR={kernelName:Qt,backendName:"webgl",kernelFunc:JR},ZR=g$({opSnippet:`\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = ${pf};\n float a1 = ${df};\n float a2 = ${hf};\n float a3 = ${ff};\n float a4 = ${mf};\n float a5 = ${gf};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),QR={kernelName:Zt,backendName:"webgl",kernelFunc:ZR},eY=g$({opSnippet:"if (isnan(x)) return x;\n return exp(x);\n",packedOpSnippet:"\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",cpuKernelImpl:aF,dtype:"float32"}),tY={kernelName:en,backendName:"webgl",kernelFunc:eY};function nY(e){const{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:i}=t,s=i.shape.length,o=i.shape.slice();let u=a;return a<0&&(se(-(s+1)<=a,(()=>`Axis must be in the interval [${-(s+1)}, ${s}]`)),u=s+a+1),o.splice(u,0,1),N$({inputs:{x:i},backend:r,attrs:{shape:o}})}const rY={kernelName:tn,backendName:"webgl",kernelFunc:nY},aY="return exp(x) - 1.0;",iY=g$({opSnippet:aY,packedOpSnippet:aY,cpuKernelImpl:iF}),sY={kernelName:nn,backendName:"webgl",kernelFunc:iY};class oY{constructor(e,t,n){this.variableNames=["real","imag"];const r=t[1];this.outputShape=t;const a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${r}.0`:"1.0";let s;if("real"===e)s="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);s="return real * expI + imag * expR;"}this.userCode=`\n const float exponentMultiplier = ${a};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${s}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${r});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${r}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}}function uY(e,t,n){const r=n.texData.get(e.dataId),a=ce(e.shape),i=e.shape[e.shape.length-1],s=N$({inputs:{x:e},backend:n,attrs:{shape:[a/i,i]}}),o=s.shape,u=new oY("real",o,t),l=new oY("imag",o,t),c=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:o},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:o}],p=n.runWebGLProgram(u,c,"float32"),d=n.runWebGLProgram(l,c,"float32"),h=u$({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);const f=N$({inputs:{x:h},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(h),f}const lY={kernelName:rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return uY(r,!1,n)}};class cY{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n "}}function pY(e){const{backend:t,attrs:n}=e,{shape:r,value:a}=n;let{dtype:i}=n;if(i=i||Le(a),"string"===i){const e=De(i,ce(r));return e.fill(a),t.makeTensorInfo(r,i,e)}{const e=new cY(r,a),n=[[a]];return t.runWebGLProgram(e,[],i,n)}}const dY={kernelName:an,backendName:"webgl",kernelFunc:pY};class hY{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const fY={kernelName:sn,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,a=new hY(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},mY="return floor(x);",gY=g$({opSnippet:mY,packedOpSnippet:mY,cpuKernelImpl:sF}),yY={kernelName:on,backendName:"webgl",kernelFunc:gY},bY=y$({opSnippet:"\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",packedOpSnippet:"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n",dtype:"int32"}),vY={kernelName:un,backendName:"webgl",kernelFunc:bY};class xY{constructor(e){this.variableNames=["A"];const t=yL(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}}class _Y{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=yL(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${r}.0, ${n}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}}const wY={kernelName:ia,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:a}=t;const{numChannels:i}=r,s="undefined"!=typeof HTMLVideoElement&&a instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&a instanceof HTMLImageElement,[u,l]=s?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[l,u],p=[l,u,i];(o||s)&&(null==DY&&(DY=document.createElement("canvas").getContext("2d")),DY.canvas.width=u,DY.canvas.height=l,DY.drawImage(a,0,0,u,l),a=DY.canvas);const d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=PI.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);const h=Ge().getBool("WEBGL_PACK")?new _Y(p):new xY(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}};let DY;const NY={kernelName:ua,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=_l(c),g=dl(a.shape,i.shape,u,p,l,d,!1,m);let y;const b=[];if(1!==g.filterHeight||1!==g.filterWidth||1!==g.dilationHeight||1!==g.dilationWidth||1!==g.strideHeight||1!==g.strideWidth||"SAME"!==g.padInfo.type&&"VALID"!==g.padInfo.type)if(Ge().getBool("WEBGL_CONV_IM2COL"))y=uR({x:a,filter:i,convInfo:g,backend:n,bias:s,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{const e=null!=s,t=null!=o,r="leakyrelu"===h,u=h?b$(h,!1):null,l=new rR(g,e,u,t,r),p=[a,i],d=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=N$({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return b.push(t),t}return e};if(e&&p.push(d(s,c)),t&&p.push(d(o,c)),r){const e=n.makeTensorInfo([],"float32",Ra(f,"float32"));p.push(e),b.push(e)}y=n.runWebGLProgram(l,p,"float32")}else y=oR({x:a,filter:i,convInfo:g,backend:n,bias:s,activation:h,preluActivationWeights:o,leakyreluAlpha:f});const v=N$({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach((e=>n.disposeIntermediateTensorInfo(e))),v}},MY={kernelName:la,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:i,bias:s,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=[];let m=c;null==m&&(m=[1,1]),se(xl(u,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`));const g=dl(a.shape,i.shape,u,m,l,p,!0),y=Ge().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?b$(d,y):null,v=[a,i],x=null!=s,_=null!=o,w="leakyrelu"===d;if(x&&v.push(s),_&&v.push(o),w){const e=n.makeTensorInfo([],"float32",Ra(h,"float32"));v.push(e),f.push(e)}let D;D=y?new OR(g,x,b,_,w):new $R(g,x,b,_,w);const N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],M=n.runWebGLProgram(D,v,"float32",N);return f.forEach((e=>n.disposeIntermediateTensorInfo(e))),M}};class SY{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;const a=IL(t.length),i=IL(n.length),s=this.sliceDim>1?"strides[j]":"strides",o=IL(r.length),u=r.length>1?"paramsShape[j]":"paramsShape";this.userCode=`\n ${a} strides = ${a}(${this.strides});\n ${o} paramsShape = ${o}(${this.paramsShape});\n void main() {\n ${i} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n for (int j = 0; j < ${this.sliceDim}; j++) {\n int index = round(getIndices(coords[0], j));\n out_of_bounds = out_of_bounds || index < 0;\n out_of_bounds = out_of_bounds || index >= ${u};\n flattenIndex += index * ${s};\n }\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}}const kY={kernelName:pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,i=a.shape,s=i[i.length-1],o=ce(r.shape),[u,l,c,p]=zo(r,a),d=N$({inputs:{x:a},backend:n,attrs:{shape:[l,s]}}),h=N$({inputs:{x:r},backend:n,attrs:{shape:[ce(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){const e=n.readSync(a.dataId),t=n.bufferSync(r),i=oF(e,t,r.dtype,l,s,c,p,r.shape,o);return n.makeTensorInfo(u,r.dtype,i.values)}const f=new SY(s,p,[l,c],r.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=N$({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}};class TY{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=IL(this.rank),r=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let t=0;t= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${r}));\n }\n `}}function CY(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,indices:i}=t,{axis:s,batchDims:o}=r,u=xe(s,a.shape)[0];if(Ge().get("DEBUG")){const e=n.readSync(i.dataId),t=a.shape[u];for(let n=0;n=0,(()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`))}}const l=Vf(a,i,u,o),c=ce(i.shape),p=[],d=N$({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=N$({inputs:{x:i},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);const f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([a,i])||"string"===a.dtype){const e=n.bufferSync(h),t=n.bufferSync(d),r=uF(t,e,f);return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(l.outputShape,r.dtype,r.values)}const m=new TY(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);const y=N$({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}const EY={kernelName:cn,backendName:"webgl",kernelFunc:CY},AY=y$({opSnippet:"return float(a > b);",packedOpSnippet:"\n return vec4(greaterThan(a, b));\n",cpuKernelImpl:lF,dtype:"bool"}),IY={kernelName:dn,backendName:"webgl",kernelFunc:AY},LY=y$({opSnippet:"return float(a >= b);",packedOpSnippet:"\n return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:cF}),FY={kernelName:hn,backendName:"webgl",kernelFunc:LY},$Y={kernelName:mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return uY(r,!0,n)}},OY=g$({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),RY={kernelName:yn,backendName:"webgl",kernelFunc:OY},YY=g$({opSnippet:"return float(isinf(x));",dtype:"bool"}),BY={kernelName:bn,backendName:"webgl",kernelFunc:YY},zY=g$({opSnippet:"return float(isnan(x));",dtype:"bool"}),PY={kernelName:vn,backendName:"webgl",kernelFunc:zY},HY=y$({opSnippet:"return float(a < b);",packedOpSnippet:"\n return vec4(lessThan(a, b));\n",cpuKernelImpl:pF,dtype:"bool"}),jY={kernelName:_n,backendName:"webgl",kernelFunc:HY},UY=y$({opSnippet:"return float(a <= b);",packedOpSnippet:"\n return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:dF,dtype:"bool"}),qY={kernelName:wn,backendName:"webgl",kernelFunc:UY},WY={kernelName:Dn,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,num:i}=n,s=hF(r,a,i);return t.makeTensorInfo([s.length],"float32",s)}},VY=g$({opSnippet:"if (isnan(x)) return x;\n return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n",cpuKernelImpl:fF}),GY={kernelName:Nn,backendName:"webgl",kernelFunc:VY},KY=g$({opSnippet:"if (isnan(x)) return x;\n return log(1.0 + x);\n"}),JY={kernelName:Mn,backendName:"webgl",kernelFunc:KY},XY=y$({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),ZY={kernelName:Sn,backendName:"webgl",kernelFunc:XY},QY=g$({opSnippet:"return float(!(x >= 1.0));"}),eB={kernelName:kn,backendName:"webgl",kernelFunc:QY},tB=y$({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n",dtype:"bool"}),nB={kernelName:Tn,backendName:"webgl",kernelFunc:tB};class rB{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];const i=t,s=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${s}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${o};\n setOutput(val);\n }\n `}}class aB{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const i=t,s=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${s}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${o};\n setOutput(result);\n }\n `}}const iB={kernelName:In,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:i,bias:s,alpha:o,beta:u}=r,l=Ge().getBool("WEBGL_PACK_NORMALIZATION")?new aB(a.shape,i,s,o,u):new rB(a.shape,i,s,o,u);return n.runWebGLProgram(l,[a],a.dtype)}};class sB{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${r}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${r})\n * float(${a})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${a});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}}const oB={kernelName:Ln,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a,y:i,dy:s}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=r,p=new sB(a.shape,o,u,l,c);return n.runWebGLProgram(p,[a,i,s],a.dtype)}};function uB(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:i,keepDims:s}=r,o=a.shape.length,u=xe(i,a.shape);let l=u;const c=vc(l,o),p=null!=c,d=n.shouldExecuteOnCPU([a]);let h=a;if(p){if(d){const e=n.texData.get(h.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:gF}),pB={kernelName:$n,backendName:"webgl",kernelFunc:cB},dB={kernelName:On,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;mL(a,"maxPool");const{filterSize:i,strides:s,pad:o,dimRoundingMode:u}=r;se(xl(s,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '1'`));const l=cl(a.shape,i,s,1,o,u);if(1===l.filterWidth&&1===l.filterHeight&&de(l.inShape,l.outShape))return s$({inputs:{x:a},backend:n});const c=new mO(l,"max",!1);return n.runWebGLProgram(c,[a],a.dtype)}},hB={kernelName:Yn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:i,strides:s,pad:o,dataFormat:u,dimRoundingMode:l}=r,c=pl(a.shape,i,s,[1,1,1],o,l,u),p=new gO(c,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}};class fB{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,i=e.effectiveFilterWidth,s=a-1-e.padInfo.top,o=i-1-e.padInfo.left,u=a*i-1;this.userCode=`\n const ivec2 pads = ivec2(${s}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${a};\n wR += ${r}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}}class mB{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,s=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${o};\n wD += ${a}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC += ${s}) {\n float dyC = float(dyCCorner + wC) / ${r}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${h} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${l} +\n wR * ${l} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const gB={kernelName:Bn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i}=t,s=i,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=r,p=pl(s.shape,o,u,[1,1,1],l,c),d=new gO(p,"max",!0),h=n.runWebGLProgram(d,[s],s.dtype),f=new mB(p),m=n.runWebGLProgram(f,[a,h],s.dtype);return n.disposeIntermediateTensorInfo(h),m}},yB={kernelName:Rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:i,output:s}=t,o=i;mL([i,s],"maxPoolGrad");const{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r,d=cl(o.shape,u,l,1,c,p),h=new mO(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new fB(d),g=n.runWebGLProgram(m,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}},bB={kernelName:zn,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:a,strides:i,pad:s,includeBatchInIndex:o}=t,u=n;se(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));const l=[1,1];se(xl(i,l),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`));const c=cl(r.shape,a,i,l,s),[p,d]=function(e,t,n,r){let a=new mO(n,"max",!1);const i=r.runWebGLProgram(a,[e],"float32");return a=new mO(n,"max",!0,!0,t),[i,r.runWebGLProgram(a,[e],"float32")]}(r,o,c,u);return[p,d]}},vB={kernelName:Pn,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:a,axis:i}=t,s=n,o=r.shape.length,u=xe(i,r.shape);let l=u;const c=vc(l,o),p=null!=c,d=s.shouldExecuteOnCPU([r]),h=[];let f=r;if(p){if(d){const e=s.texData.get(f.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:yF}),wB={kernelName:jn,backendName:"webgl",kernelFunc:_B};class DB{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=IL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u="reflect"===n?0:1;this.userCode=1!==r?`\n ${a} start = ${a}(${i});\n ${a} end = ${a}(${s});\n\n void main() {\n ${a} outC = getOutputCoords();\n for (int i = 0; i < ${r}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${u};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n }\n }\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n `:`\n int start = ${i};\n int end = ${s};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `}}class NB{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=IL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=HF("rc",r),u=HF("source",r),l=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p="reflect"===n?0:1;let d="";if(1===r){const e=`\n ${a} source = rc;\n if (source < start) {\n source = start * 2 - source - ${p};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${p};\n }\n source -= start;\n `;d=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n `}else{const e=`\n ${a} source = rc;\n ${a} lt = ${a}(lessThan(source, start));\n ${a} gte = ${a}(greaterThanEqual(source, end));\n ${a} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${p}) +\n gte * ((end - 1) * 2 - source + ${p});\n source -= start;\n `;d=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {\n ${e}\n result[2] = getChannel(getX(${u.join()}), ${c});\n ${o[r-1]} += 1;\n if(${l}) {\n ${e}\n result[3] = getChannel(getX(${u.join()}), ${c});\n }\n }\n `}this.userCode=`\n const ${a} start = ${a}(${i});\n const ${a} end = ${a}(${s});\n\n void main() {\n ${a} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${d}\n setOutput(result);\n }\n `}}const MB={kernelName:Un,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r}=e,{paddings:a,mode:i}=n,s=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NB(r.shape,a,i):new DB(r.shape,a,i);return t.runWebGLProgram(s,[r],r.dtype)}},SB=y$({opSnippet:"if (b == 0.0) return NAN;\n return mod(a, b);",packedOpSnippet:"\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),kB={kernelName:qn,backendName:"webgl",kernelFunc:SB};class TB{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${t-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${t-1}));\n }\n `}}const CB=y$({opSnippet:"\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n",checkOutOfBounds:!0}),EB={kernelName:Gt,backendName:"webgl",kernelFunc:CB},AB="return a - b;",IB=y$({opSnippet:AB,packedOpSnippet:AB,supportsComplex:!0,cpuKernelImpl:OF}),LB={kernelName:Wr,backendName:"webgl",kernelFunc:IB};function FB(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:i}=r,s=xe([i],a.shape),o=uB({inputs:{x:a},backend:n,attrs:{reductionIndices:s,keepDims:!1}}),u=yc(o.shape,s),l=N$({inputs:{x:o},backend:n,attrs:{shape:u}}),c=IB({inputs:{a,b:l},backend:n}),p=eY({inputs:{x:c},backend:n}),d=I$({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:!1}}),h=N$({inputs:{x:d},backend:n,attrs:{shape:u}}),f=CB({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}const $B={kernelName:Fr,backendName:"webgl",kernelFunc:FB},OB={kernelName:Wn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:i,seed:s,normalized:o}=r,u=o?a:FB({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new TB(l,c,i),d=[[s]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}},RB={kernelName:Gn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){const e=n.texData.get(r.dataId),[t,a]=vF(e.values,r.shape,r.dtype);return n.makeTensorInfo(a,r.dtype,t)}let a;return a=Ge().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new ZF(r.shape,"\n vec4 result = -x;\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"):new KF(r.shape,"if (isnan(x)) return x;\n return -x;\n"),n.runWebGLProgram(a,[r],r.dtype)}},YB=uh,BB={kernelName:Jn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u}=r,l=n.readSync(a.dataId),c=n.readSync(i.dataId),{selectedIndices:p}=YB(l,c,s,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}},zB=lh,PB={kernelName:Xn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=r,c=n.readSync(a.dataId),p=n.readSync(i.dataId),{selectedIndices:d,validOutputs:h}=zB(c,p,s,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}},HB=ch,jB={kernelName:Zn,backendName:"webgl",kernelFunc:function(e){ca("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:i}=t,{maxOutputSize:s,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=r,c=n.readSync(a.dataId),p=n.readSync(i.dataId),d=s,h=o,f=u,m=l,{selectedIndices:g,selectedScores:y}=HB(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class UB{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${r}), float(${n}),\n float(index == coords.y)));\n }\n `}}const qB={kernelName:er,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:i,onValue:s,offValue:o}=r,u=ce(a.shape),l=new UB(u,i,s,o),c=N$({inputs:{x:a},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],a.dtype);n.disposeIntermediateTensorInfo(c);const d=N$({inputs:{x:p},backend:n,attrs:{shape:[...a.shape,i]}});return n.disposeIntermediateTensorInfo(p),d}};function WB(e){const{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){const e=RO({inputs:{input:r},backend:n}),t=WB({inputs:{x:e},backend:n}),a=ZO({inputs:{input:r},backend:n}),i=WB({inputs:{x:a},backend:n}),s=u$({inputs:{real:t,imag:i},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),s}return pY({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const VB={kernelName:ra,backendName:"webgl",kernelFunc:WB},GB={kernelName:Qn,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===a.dtype){const t=RO({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),i=ZO({inputs:{input:a},backend:r}),s=WB({inputs:{x:i},backend:r}),o=u$({inputs:{real:n,imag:s},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(s),o}return pY({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}},KB={kernelName:tr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return nY({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const i=t[0].shape,s=t[0].dtype;t.forEach((e=>{oe(i,e.shape,"All tensors passed to stack must have matching shapes"),se(s===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],u=tR({inputs:t.map((e=>{const t=nY({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),u}};class JB{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=IL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n ${a} start = ${a}(${i});\n ${a} end = ${a}(${s});\n\n void main() {\n ${a} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n }\n `:`\n int start = ${i};\n int end = ${s};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `}}class XB{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=IL(r),i=t.map((e=>e[0])).join(","),s=t.map(((t,n)=>t[0]+e[n])).join(","),o=HF("rc",r),u=HF("source",r),l=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;\n if(${l}) {\n `,1===r?"":`}\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":` ${o[r-1]} += 1;\n if(${l}) {`],d=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let h="";for(let e=0,t=1===r?2:4;e{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:i,constantValue:s}=r;if(0===ce(a.shape)){const e=i.map(((e,t)=>e[0]+a.shape[t]+e[1]));return pY({backend:n,attrs:{shape:e,value:s,dtype:a.dtype}})}const o=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XB(a.shape,i,s):new JB(a.shape,i,s),u=[[s]];return n.runWebGLProgram(o,[a],a.dtype,u)},QB={kernelName:nr,backendName:"webgl",kernelFunc:ZB},ez=y$({opSnippet:"\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),tz={kernelName:ar,backendName:"webgl",kernelFunc:ez},nz={kernelName:sr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:i,keepDims:s}=r,o=a.shape.length,u=[],l=xe(i,a.shape);let c=l;const p=vc(c,o);let d,h=a;if(null!=p&&(h=F$({inputs:{x:a},backend:n,attrs:{perm:p}}),c=_c(c.length,o),u.push(h)),bc("prod",c,o),n.shouldExecuteOnCPU([h])){const e=n.texData.get(h.dataId).values,{outVals:t,outShape:r,outDtype:a}=_F(h.shape,h.dtype,e,c);d=n.makeTensorInfo(r,a,t)}else{const[e,t]=gc(h.shape,c),r=ce(t),i=N$({inputs:{x:h},backend:n,attrs:{shape:[-1,r]}}),s=T$(i,pi(a.dtype),"prod",n);d=N$({inputs:{x:s},backend:n,attrs:{shape:e}}),u.push(i),u.push(s)}if(s){u.push(d);const e=yc(d.shape,l);d=N$({inputs:{x:d},backend:n,attrs:{shape:e}})}return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}},rz=e=>{const{backend:t,attrs:n}=e,{start:r,stop:a,step:i,dtype:s}=n,o=wF(r,a,i,s);return t.makeTensorInfo([o.length],s,o)},az={kernelName:or,backendName:"webgl",kernelFunc:rz},iz=g$({opSnippet:"return 1.0 / x;"}),sz={kernelName:lr,backendName:"webgl",kernelFunc:iz},oz=g$({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),uz={kernelName:cr,backendName:"webgl",kernelFunc:oz},lz=g$({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),cz={kernelName:gr,backendName:"webgl",kernelFunc:lz};class pz{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${s}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}}class dz{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${s}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}}const hz={kernelName:fr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:i,halfPixelCenters:s,size:o}=r,[u,l]=o,c=Ge().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dz(a.shape,u,l,i,s):new pz(a.shape,u,l,i,s);return n.runWebGLProgram(c,[a],"float32")}};class fz{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,i,s]=e,o=[n&&i>1?r-1:r,n&&s>1?a-1:a],u=[n&&i>1?i-1:i,n&&s>1?s-1:s],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=2*Math.ceil(p)+2,f=2*Math.ceil(d)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${h});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${s}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const mz={kernelName:mr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:i}=t,{alignCorners:s}=r,o=new fz(i.shape,a.shape,s);return n.runWebGLProgram(o,[i],i.dtype)}};class gz{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let d;d=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${s}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}}class yz{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,s,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?s-1:s,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let d;d=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${s}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}}const bz={kernelName:dr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:i,halfPixelCenters:s,size:o}=r,[u,l]=o,c=Ge().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yz(a.shape,u,l,i,s):new gz(a.shape,u,l,i,s);return n.runWebGLProgram(c,[a],a.dtype)}};class vz{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,i,s]=e,o=[n&&i>1?r-1:r,n&&s>1?a-1:a],u=[n&&i>1?i-1:i,n&&s>1?s-1:s],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=2*Math.ceil(p)+2,f=2*Math.ceil(d)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${h});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${s}) {\n continue;\n }\n\n float sourceFracRow =\n float(${o[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${o[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${r}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${a}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const xz={kernelName:hr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:i}=t,{alignCorners:s}=r,o=new vz(i.shape,a.shape,s);return n.runWebGLProgram(o,[i],i.dtype)}};class _z{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `);const r=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r))).join(","),a=IL(n);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${r}));\n }\n `}}class wz{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const r=HF("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,s=IL(n);function o(n){const r=e.map(((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(a,n)));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${a}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:`\n void main() {\n ${s} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${function(e){return o(e)}(r.slice())};\n if(${a}){\n result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(r.slice())};\n }\n if(${i}) {\n result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n if(${a}) {\n result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n }\n }\n setOutput(result);\n }\n `}}const Dz={kernelName:yr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:i}=r,s=a.shape.length,o=xe(i,a.shape);if(0===s)return s$({inputs:{x:a},backend:n});const u=Ge().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wz(a.shape,o):new _z(a.shape,o);return n.runWebGLProgram(u,[a],a.dtype)}};class Nz{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const n=e[1],r=e[2];this.outputShape=e;let a="";a="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n vec3 fill = vec3(${t.join(",")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${a}\n if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const Mz={kernelName:sa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,{radians:a,fillValue:i,center:s}=t,o=n,u=new Nz(r.shape,i),[l,c]=nf(s,r.shape[1],r.shape[2]),p=[[l,c,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(u,[r],r.dtype,p)}},Sz=g$({opSnippet:"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n"}),kz={kernelName:br,backendName:"webgl",kernelFunc:Sz},Tz=g$({opSnippet:"return inversesqrt(x);",cpuKernelImpl:DF}),Cz={kernelName:vr,backendName:"webgl",kernelFunc:Tz};class Ez{constructor(e,t,n,r,a,i,s=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;const o=IL(a.length),u=IL(i.length);let l="";1===n?l="i":2===n&&(l="i, j");const c=`getIndices(${l})`;let p="";1===r?p="i":2===r&&(p="i, coords[1]");const d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`\n ${o} strides = ${o}(${a});\n\n void main() {\n ${u} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${c});\n flattenedIndex += index * ${h};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `}}const Az={kernelName:xr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:i}=t,{shape:s}=r,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=jo(0,a,s),d=[p/l,l];if(0===p)return n.makeTensorInfo(s,a.dtype);const h=N$({inputs:{x:a},backend:n,attrs:{shape:[u,o]}}),f=N$({inputs:{x:i},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new Ez(u,o,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),b=N$({inputs:{x:y},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class Iz{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];const a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=2===Ge().getNumber("WEBGL_VERSION")?"while (left < right) {":a,s="left"===r?"<":"<=";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${s} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}}const Lz={kernelName:_r,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sortedSequence:a,values:i}=t,{side:s}=r,o=new Iz(a.shape[0],a.shape[1],i.shape[1],s),u=[[a.shape[1]]];return n.runWebGLProgram(o,[a,i],"int32",u)}};class Fz{constructor(e,t,n){let r,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",r="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[];for(let r=0;r= 1.0) {\n setOutput(getA(${a}));\n } else {\n setOutput(getB(${a}));\n }\n }\n `}}const $z={kernelName:wr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:r,t:a,e:i}=t,s=new Fz(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(s,[r,a,i],ci(a.dtype,i.dtype))}},Oz=g$({opSnippet:`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${lf};\n float scale = ${cf};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),Rz={kernelName:Dr,backendName:"webgl",kernelFunc:Oz},Yz=g$({opSnippet:"if (isnan(x)) return x;\n return 1.0 / (1.0 + exp(-1.0 * x));\n",packedOpSnippet:"\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",cpuKernelImpl:MF}),Bz={kernelName:Tr,backendName:"webgl",kernelFunc:Yz},zz=g$({opSnippet:"\n if (isnan(x)) { return 0.0; }\n return sign(x);\n"}),Pz={kernelName:kr,backendName:"webgl",kernelFunc:zz},Hz=g$({opSnippet:"if (isnan(x)) return x;\n return sin(x);\n"}),jz={kernelName:Mr,backendName:"webgl",kernelFunc:Hz},Uz=g$({opSnippet:"\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n"}),qz={kernelName:Sr,backendName:"webgl",kernelFunc:Uz},Wz=g$({opSnippet:"\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n"}),Vz={kernelName:Cr,backendName:"webgl",kernelFunc:Wz},Gz={kernelName:Ir,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:i,paddings:s}=r;se(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const o=i.reduce(((e,t)=>e*t)),u=[[0,0]];u.push(...s);for(let e=1+i.length;en.disposeIntermediateTensorInfo(e))),g}},Kz={kernelName:$r,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:i,defaultValue:s}=t;if(1!==i.shape.length)throw new Error(`Dense shape must be a vector, saw:\n ${i.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n ${a.shape}`);if(0!==s.shape.length)throw new Error(`Default value must be a scalar, saw:\n ${s.shape}`);const o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=n.readSync(i.dataId),c=n.readSync(s.dataId)[0],[p,d,h,f,m]=TF(o,r.shape,r.dtype,u,a.dtype,l,c);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],a.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map((e=>Number(e))))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}},Jz={kernelName:Or,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:i}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(1!==i.shape.length)throw new Error(`Target shape should be a vector but received shape ${i.shape}`);const s=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),u=Array.from(n.readSync(i.dataId)),[l,c,p]=CF(o,r.shape,r.dtype,s,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([p.length],i.dtype,new Int32Array(p))]}},Xz={kernelName:Rr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:i}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n ${a.shape}`);if(1!==i.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${i.shape}`);const s=n.readSync(r.dataId),o=n.readSync(a.dataId),u=n.readSync(i.dataId),[l,c]=EF(s,r.shape,r.dtype,o,u,!0);return n.makeTensorInfo(c,r.dtype,l)}},Zz={kernelName:Yr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:i}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but 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n.disposeIntermediateTensorInfo(f),m}},eP={kernelName:Lr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:i,axis:s}=r,o=xe(s,a.shape)[0],u=If(a,i,o),l=a.shape.length,c=new Array(l).fill(0),p=a.shape.slice();return u.map((e=>{const t=[...p];t[o]=e;const r=EO({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,r}))}},tP="return sqrt(x);",nP=g$({opSnippet:tP,packedOpSnippet:tP,cpuKernelImpl:AF}),rP={kernelName:Er,backendName:"webgl",kernelFunc:nP},aP=g$({opSnippet:"return x * x;"}),iP={kernelName:Pr,backendName:"webgl",kernelFunc:aP},sP="return (a - b) * (a - b);",oP=y$({opSnippet:sP,packedOpSnippet:sP}),uP={kernelName:zr,backendName:"webgl",kernelFunc:oP},lP={kernelName:aa,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:r}=e,a=`if (isnan(x)) return x;\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,i=new KF(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}};class cP{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const r=n.length,a=IL(n.length),i=IL(n.length);let s="";if(1===r)s="coords * strides + begin";else{let e=0;s=n.map(((t,r)=>(e++,1===n.length?`coords * strides[${r}] + begin[${r}]`:`coords[${e-1}] * strides[${r}] + begin[${r}]`))).join(",")}this.userCode=`\n ${a} begin = ${a}(${e});\n ${a} strides = ${a}(${t});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}}const pP={kernelName:Hr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:i,end:s,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:b,end:v,strides:x}=iu(a.shape,i,s,o,u,l,c,p,d);let _;if(m)_=N$({inputs:{x:a},backend:n,attrs:{shape:f}});else if(g||y){se(a.shape.length>=1,(()=>`Input must have rank at least 1, got: ${a.shape.length}`));const e=Wo(b,v,x),t=EO({inputs:{x:a},backend:n,attrs:{begin:b,size:e}});_=N$({inputs:{x:t},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(t)}else if(n.shouldExecuteOnCPU([a])){const e=n.readSync(a.dataId),t=Is(a.shape,a.dtype,e),r=IF(h,t,x,b);_=n.makeTensorInfo(f,a.dtype,r.values)}else{const e=new cP(b,x,h);_=n.runWebGLProgram(e,[a],a.dtype)}const w=N$({inputs:{x:_},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(_),w}},dP={kernelName:jr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:a,nGramWidths:i,leftPad:s,rightPad:o,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=LF(d,h,a,i,s,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}},hP={kernelName:Ur,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:a}=r,{input:i,delimiter:s}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(1!==i.shape.length)throw new Error(`Input must be a vector, got shape: ${i.shape}`);if(0!==s.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${s.shape}`);const o=n.readSync(i.dataId),u=n.readSync(s.dataId)[0],[l,c,p]=FF(o,u,a),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}},fP={kernelName:qr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:a}=r,{input:i}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");const s=n.readSync(i.dataId),o=$F(s,a);return n.makeTensorInfo(i.shape,"int32",o)}},mP=g$({opSnippet:"return tan(x);"}),gP={kernelName:Vr,backendName:"webgl",kernelFunc:mP},yP=g$({opSnippet:"\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),bP={kernelName:Gr,backendName:"webgl",kernelFunc:yP};class vP{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let r=0;r5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let t=0;t5){const e=n.readSync(a.dataId),t="string"===a.dtype?e.map((e=>Ha(e))):e,r=Is(a.shape,a.dtype,t),s=RF(r,i);return n.makeTensorInfo(s.shape,s.dtype,s.values)}const s=new vP(a.shape,i);return n.runWebGLProgram(s,[a],a.dtype)}const _P={kernelName:Kr,backendName:"webgl",kernelFunc:xP};class wP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n "}}class DP{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode="\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n "}}function NP(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function MP(e){let t=1;for(;tu){const e=n.readSync(a.dataId),[t,r]=YF(e,l,a.dtype,i,s);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(r.shape,r.dtype,r.values)]}if(0===i)return l[l.length-1]=0,[n.makeTensorInfo(l,a.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(1===c)return[a,pY({attrs:{shape:l,dtype:"int32",value:0},backend:n})];const p=n.texData.get(a.dataId),d=null!==p&&p.isPacked,h=d?n.unpackTensor(a):a,f=ce(l)/c,m=N$({inputs:{x:h},attrs:{shape:[f,c]},backend:n});d&&NP(n,h);const g=MP(i),y=MP(c);let b=null;const v=()=>null===b?[m,m]:[m,b],x=(e,t,r)=>{const a=v(),i=new wP(r),s=[[c],[null===b?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],o=b;b=n.runWebGLProgram(i,a,"int32",s),NP(n,o)};for(let e=1;e=1;n/=2)x(t,n,[f,y])}for(let e=y;e>g;e/=2){const t=v(),r=new DP([f,e/2]),a=[[c],[null===b?1:0],[g]],i=b;b=n.runWebGLProgram(r,t,"int32",a),NP(n,i);const s=g/2,o=2*s;for(let e=s;e>=1;e/=2)x(o,e,b.shape)}let _=b;b=EO({inputs:{x:b},backend:n,attrs:{begin:0,size:[f,i]}}),NP(n,_);let w=CY({inputs:{x:m,indices:b},backend:n,attrs:{axis:1,batchDims:1}});NP(n,m);const D=l.slice(0,-1);D.push(i),_=b,b=N$({inputs:{x:b},attrs:{shape:D},backend:n}),NP(n,_);const N=w;return w=N$({inputs:{x:w},attrs:{shape:D},backend:n}),NP(n,N),[w,b]}};class kP{constructor(e,t,n,r,a,i){this.variableNames=["Image","Transforms"],this.outputShape=i;const s="nearest"===n?1:2;let o;switch(r){case"constant":default:o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${o} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${o} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${o} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${a});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${a});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${t}));\n float mapY = mapCoord(inY, float(${e}));\n\n if (${s} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}}const TP={kernelName:Xr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:i}=t,{interpolation:s,fillMode:o,fillValue:u,outputShape:l}=r,[c,p,d,h]=a.shape,[f,m]=null!=l?l:[p,d],g=new kP(p,d,s,o,u,[c,f,m,h]);return n.runWebGLProgram(g,[a,i],"float32")}},CP={kernelName:Qr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:i}=t;mL(i,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const s=r.readSync(i.dataId),{outputValues:o,outputShape:u,indices:l}=zF(s,a,i.shape,i.dtype);return[r.makeTensorInfo(u,i.dtype,o),r.makeTensorInfo([l.length],"int32",l)]}},EP={kernelName:ea,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{value:a}=t;let{axis:i}=r;i<0&&(i+=a.shape.length);const s=a,o=s.shape.length,u=a.shape[i],l=new Array(o-1);let c=0;for(let e=0;en.disposeIntermediateTensorInfo(e))),f}};class AP{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,r=e.batchSize,a=e.inSize,i=e.numSegments,s=i*Math.ceil(a/n);this.outputShape=[r,s];const o=4*Math.floor(n/4),u=n%4,l="\n sumValue += dot(values, segFilter);\n ";let c="";a%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return initializationValue;\n }\n `);let p="";a%n>0&&(p=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${p}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${o}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 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Matrix A ("+u+") must match Matrix B ("+h+")");var m,g=u[0],y=u[1],b=n,v=0,x=a;"string"==typeof l&&l===f&&(m=l,b=t.find(n,[m,m]),v=t.convert(0,m),x=t.find(a,[m,m]));var _,w,D,N,M=i&&c?[]:void 0,S=[],k=[],T=M?[]:void 0,C=M?[]:void 0,E=[],A=[];for(w=0;w{var{typed:t,equalScalar:n}=e;return function(e,r,a,i){var s=e._values,o=e._index,u=e._ptr,l=e._size,c=e._datatype;if(!s)throw new Error("Cannot perform operation on Pattern Sparse Matrix and Scalar value");var p,d=l[0],h=l[1],f=n,m=0,g=a;"string"==typeof c&&(p=c,f=t.find(n,[p,p]),m=t.convert(0,p),r=t.convert(r,p),g=t.find(a,[p,p]));for(var y=[],b=[],v=[],x=0;x{var{typed:t,DenseMatrix:n}=e;return function(e,r,a,i){var s=e._values,o=e._index,u=e._ptr,l=e._size,c=e._datatype;if(!s)throw new Error("Cannot perform operation on Pattern Sparse Matrix and Scalar value");var p,d=l[0],h=l[1],f=a;"string"==typeof c&&(p=c,r=t.convert(r,p),f=t.find(a,[p,p]));for(var m=[],g=[],y=[],b=0;b{var{typed:t}=e;return function(e,r,a){var i,s=e._data,o=e._size,u=e._datatype,l=r._data,c=r._size,p=r._datatype,d=[];if(o.length!==c.length)throw new RW(o.length,c.length);for(var h=0;h0?n(f,0,d,d[0],s,l):[];return e.createDenseMatrix({data:m,size:d,datatype:i})};function n(e,t,r,a,i,s){var o=[];if(t===r.length-1)for(var u=0;u{var{typed:t}=e;return function(e,r,a,i){var s,o=e._data,u=e._size,l=e._datatype,c=a;"string"==typeof l&&(s=l,r=t.convert(r,s),c=t.find(a,[s,s]));var p=u.length>0?n(c,0,u,u[0],o,r,i):[];return e.createDenseMatrix({data:p,size:DH(u),datatype:s})};function n(e,t,r,a,i,s,o){var u=[];if(t===r.length-1)for(var l=0;l{var{typed:t,matrix:n}=e,r=VX({typed:t}),a=GX({typed:t});return function(e){var i,s=e.elop,o=e.SD||e.DS;s?(i={"DenseMatrix, DenseMatrix":(e,t)=>r(e,t,s),"Array, Array":(e,t)=>r(n(e),n(t),s).valueOf(),"Array, DenseMatrix":(e,t)=>r(n(e),t,s),"DenseMatrix, Array":(e,t)=>r(e,n(t),s)},e.SS&&(i["SparseMatrix, SparseMatrix"]=(t,n)=>e.SS(t,n,s,!1)),e.DS&&(i["DenseMatrix, SparseMatrix"]=(t,n)=>e.DS(t,n,s,!1),i["Array, SparseMatrix"]=(t,r)=>e.DS(n(t),r,s,!1)),o&&(i["SparseMatrix, DenseMatrix"]=(e,t)=>o(t,e,s,!0),i["SparseMatrix, Array"]=(e,t)=>o(n(t),e,s,!0))):(i={"DenseMatrix, DenseMatrix":t.referToSelf((e=>(t,n)=>r(t,n,e))),"Array, Array":t.referToSelf((e=>(t,a)=>r(n(t),n(a),e).valueOf())),"Array, DenseMatrix":t.referToSelf((e=>(t,a)=>r(n(t),a,e))),"DenseMatrix, Array":t.referToSelf((e=>(t,a)=>r(t,n(a),e)))},e.SS&&(i["SparseMatrix, SparseMatrix"]=t.referToSelf((t=>(n,r)=>e.SS(n,r,t,!1)))),e.DS&&(i["DenseMatrix, SparseMatrix"]=t.referToSelf((t=>(n,r)=>e.DS(n,r,t,!1))),i["Array, SparseMatrix"]=t.referToSelf((t=>(r,a)=>e.DS(n(r),a,t,!1)))),o&&(i["SparseMatrix, DenseMatrix"]=t.referToSelf((e=>(t,n)=>o(n,t,e,!0))),i["SparseMatrix, Array"]=t.referToSelf((e=>(t,r)=>o(n(r),t,e,!0)))));var u=e.scalar||"any";(e.Ds||e.Ss)&&(s?(i["DenseMatrix,"+u]=(e,t)=>a(e,t,s,!1),i[u+", DenseMatrix"]=(e,t)=>a(t,e,s,!0),i["Array,"+u]=(e,t)=>a(n(e),t,s,!1).valueOf(),i[u+", Array"]=(e,t)=>a(n(t),e,s,!0).valueOf()):(i["DenseMatrix,"+u]=t.referToSelf((e=>(t,n)=>a(t,n,e,!1))),i[u+", DenseMatrix"]=t.referToSelf((e=>(t,n)=>a(n,t,e,!0))),i["Array,"+u]=t.referToSelf((e=>(t,r)=>a(n(t),r,e,!1).valueOf())),i[u+", Array"]=t.referToSelf((e=>(t,r)=>a(n(r),t,e,!0).valueOf()))));var l=void 0!==e.sS?e.sS:e.Ss;return s?(e.Ss&&(i["SparseMatrix,"+u]=(t,n)=>e.Ss(t,n,s,!1)),l&&(i[u+", SparseMatrix"]=(e,t)=>l(t,e,s,!0))):(e.Ss&&(i["SparseMatrix,"+u]=t.referToSelf((t=>(n,r)=>e.Ss(n,r,t,!1)))),l&&(i[u+", SparseMatrix"]=t.referToSelf((e=>(t,n)=>l(n,t,e,!0))))),s&&s.signatures&&MH(i,s.signatures),i}})),JX=_U("mod",["typed","matrix","equalScalar","DenseMatrix"],(e=>{var{typed:t,matrix:n,equalScalar:r,DenseMatrix:a}=e,i=HX({typed:t,equalScalar:r}),s=jX({typed:t}),o=UX({typed:t,equalScalar:r}),u=qX({typed:t,equalScalar:r}),l=WX({typed:t,DenseMatrix:a});return t("mod",{"number, number":JV,"BigNumber, BigNumber":function(e,t){if(t.isNeg())throw new Error("Cannot calculate mod for a negative divisor");return t.isZero()?e:e.mod(t)},"Fraction, Fraction":function(e,t){if(t.compare(0)<0)throw new Error("Cannot calculate mod for a negative divisor");return e.compare(0)>=0?e.mod(t):e.mod(t).add(t).mod(t)}},KX({typed:t,matrix:n})({SS:o,DS:s,SD:i,Ss:u,sS:l}))})),XX=_U("matAlgo01xDSid",["typed"],(e=>{var{typed:t}=e;return function(e,n,r,a){var i=e._data,s=e._size,o=e._datatype,u=n._values,l=n._index,c=n._ptr,p=n._size,d=n._datatype;if(s.length!==p.length)throw new RW(s.length,p.length);if(s[0]!==p[0]||s[1]!==p[1])throw new RangeError("Dimension mismatch. 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n=Math.max(BW(e).length,BW(t).length);e=GW(e),t=GW(t);var i=BW(e),s=BW(t);if(1!==i.length||1!==s.length||3!==i[0]||3!==s[0])throw new RangeError("Vectors with length 3 expected (Size A = ["+i.join(", ")+"], B = ["+s.join(", ")+"])");var o=[r(a(e[1],t[2]),a(e[2],t[1])),r(a(e[2],t[0]),a(e[0],t[2])),r(a(e[0],t[1]),a(e[1],t[0]))];return n>1?[o]:o}})),D1=_U("det",["typed","matrix","subtract","multiply","divideScalar","isZero","unaryMinus"],(e=>{var{typed:t,matrix:n,subtract:r,multiply:a,divideScalar:i,isZero:s,unaryMinus:o}=e;return t("det",{any:function(e){return DH(e)},"Array | Matrix":function(e){var t;switch((t=UP(e)?e.size():Array.isArray(e)?(e=n(e)).size():[]).length){case 0:return DH(e);case 1:if(1===t[0])return DH(e.valueOf()[0]);throw new RangeError("Matrix must be square (size: "+IW(t)+")");case 2:var u=t[0];if(u===t[1])return function(e,t,n){if(1===t)return DH(e[0][0]);if(2===t)return r(a(e[0][0],e[1][1]),a(e[1][0],e[0][1]));for(var u=!1,l=new 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r({data:DH(this._data),size:DH(this._size),datatype:this._datatype})},r.prototype.toJSON=function(){return{mathjs:"ImmutableDenseMatrix",data:this._data,size:this._size,datatype:this._datatype}},r.fromJSON=function(e){return new r(e)},r.prototype.swapRows=function(){throw new Error("Cannot invoke swapRows on an Immutable Matrix instance")},r.prototype.min=function(){if(null===this._min){var e=null;this.forEach((function(n){(null===e||t(n,e))&&(e=n)})),this._min=null!==e?e:void 0}return this._min},r.prototype.max=function(){if(null===this._max){var e=null;this.forEach((function(n){(null===e||t(e,n))&&(e=n)})),this._max=null!==e?e:void 0}return this._max},r}),{isClass:!0}),F1=_U("Index",["ImmutableDenseMatrix"],(e=>{var{ImmutableDenseMatrix:t}=e;function n(e){if(!(this instanceof n))throw new SyntaxError("Constructor must be called with the new operator");this._dimensions=[],this._isScalar=!0;for(var 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t=0;t1e-12)return!1;return!0},w.prototype.equalBase=function(e){for(var t=0;t1e-12)return!1;return!0},w.prototype.equals=function(e){return this.equalBase(e)&&f(this.value,e.value)},w.prototype.multiply=function(e){for(var t=this.clone(),n=PP(e)?e:new w(e),r=0;r1e-12&&(AH(P,o)?r.push({unit:P[o].unit,prefix:P[o].prefix,power:n.dimensions[s]||0}):i=!0)}r.length1e-12){if(!AH(z.si,r))throw new Error("Cannot express custom unit "+r+" in SI units");t.push({unit:z.si[r].unit,prefix:z.si[r].prefix,power:e.dimensions[n]||0})}}return e.units=t,e.fixPrefix=!0,e.skipAutomaticSimplification=!0,e},w.prototype.formatUnits=function(){for(var e="",t="",n=0,r=0,a=0;a0?(n++,e+=" "+this.units[a].prefix.name+this.units[a].unit.name,Math.abs(this.units[a].power-1)>1e-15&&(e+="^"+this.units[a].power)):this.units[a].power<0&&r++;if(r>0)for(var i=0;i0?(t+=" "+this.units[i].prefix.name+this.units[i].unit.name,Math.abs(this.units[i].power+1)>1e-15&&(t+="^"+-this.units[i].power)):(t+=" 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y.args[0];if(b)return new g(o.op,o.fn,[y])}if(yH(o)&&o.isBinary()){var k=S(o.args[0],t),T=S(o.args[1],t);if("+"===o.op){if(cH(k)&&a(k.value))return T;if(cH(T)&&a(T.value))return k;yH(T)&&T.isUnary()&&"-"===T.op&&(T=T.args[0],o=new g("-","subtract",[k,T]))}if("-"===o.op)return yH(T)&&T.isUnary()&&"-"===T.op?S(new g("+","add",[k,T.args[0]]),t):cH(k)&&a(k.value)?S(new g("-","unaryMinus",[T])):cH(T)&&a(T.value)?k:new g(o.op,o.fn,[k,T]);if("*"===o.op){if(cH(k)){if(a(k.value))return v;if(r(k.value,1))return T}if(cH(T)){if(a(T.value))return v;if(r(T.value,1))return k;if(M(o,n))return new g(o.op,o.fn,[T,k],o.implicit)}return new g(o.op,o.fn,[k,T],o.implicit)}if("/"===o.op)return cH(k)&&a(k.value)?v:cH(T)&&r(T.value,1)?k:new g(o.op,o.fn,[k,T]);if("^"===o.op&&cH(T)){if(a(T.value))return x;if(r(T.value,1))return k}if("and"===o.op){if(cH(k)){if(!k.value)return w;if(D(T))return T}if(cH(T)){if(!T.value)return w;if(D(k))return k}}if("or"===o.op){if(cH(k)){if(k.value)return _;if(D(T))return 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Approximately equal to 2.71828",examples:["e","e ^ 2","exp(2)","log(e)"],seealso:["exp"]},aee={name:"pi",category:"Constants",syntax:["pi"],description:"The number pi is a mathematical constant that is the ratio of a circle's circumference to its diameter, and is approximately equal to 3.14159",examples:["pi","sin(pi/2)"],seealso:["tau"]},iee={bignumber:{name:"bignumber",category:"Construction",syntax:["bignumber(x)"],description:"Create a big number from a number or string.",examples:["0.1 + 0.2","bignumber(0.1) + bignumber(0.2)",'bignumber("7.2")','bignumber("7.2e500")',"bignumber([0.1, 0.2, 0.3])"],seealso:["boolean","complex","fraction","index","matrix","string","unit"]},boolean:{name:"boolean",category:"Construction",syntax:["x","boolean(x)"],description:"Convert a string or number into a boolean.",examples:["boolean(0)","boolean(1)","boolean(3)",'boolean("true")','boolean("false")',"boolean([1, 0, 1, 1])"],seealso:["bignumber","complex","index","matrix","number","string","unit"]},complex:{name:"complex",category:"Construction",syntax:["complex()","complex(re, im)","complex(string)"],description:"Create a complex number.",examples:["complex()","complex(2, 3)",'complex("7 - 2i")'],seealso:["bignumber","boolean","index","matrix","number","string","unit"]},createUnit:{name:"createUnit",category:"Construction",syntax:["createUnit(definitions)","createUnit(name, definition)"],description:"Create a user-defined unit and register it with the Unit type.",examples:['createUnit("foo")','createUnit("knot", {definition: "0.514444444 m/s", aliases: ["knots", "kt", "kts"]})','createUnit("mph", "1 mile/hour")'],seealso:["unit","splitUnit"]},fraction:{name:"fraction",category:"Construction",syntax:["fraction(num)","fraction(matrix)","fraction(num,den)","fraction({n: num, d: den})"],description:"Create a fraction from a number or from integer numerator and denominator.",examples:["fraction(0.125)","fraction(1, 3) + fraction(2, 5)","fraction({n: 333, d: 53})","fraction([sqrt(9), sqrt(10), sqrt(11)])"],seealso:["bignumber","boolean","complex","index","matrix","string","unit"]},index:{name:"index",category:"Construction",syntax:["[start]","[start:end]","[start:step:end]","[start1, start 2, ...]","[start1:end1, start2:end2, ...]","[start1:step1:end1, start2:step2:end2, ...]"],description:"Create an index to get or replace a subset of a matrix",examples:["[]","[1, 2, 3]","A = [1, 2, 3; 4, 5, 6]","A[1, :]","A[1, 2] = 50","A[0:2, 0:2] = ones(2, 2)"],seealso:["bignumber","boolean","complex","matrix,","number","range","string","unit"]},matrix:{name:"matrix",category:"Construction",syntax:["[]","[a1, b1, ...; a2, b2, ...]","matrix()",'matrix("dense")',"matrix([...])"],description:"Create a matrix.",examples:["[]","[1, 2, 3]","[1, 2, 3; 4, 5, 6]","matrix()","matrix([3, 4])",'matrix([3, 4; 5, 6], "sparse")','matrix([3, 4; 5, 6], "sparse", "number")'],seealso:["bignumber","boolean","complex","index","number","string","unit","sparse"]},number:{name:"number",category:"Construction",syntax:["x","number(x)","number(unit, valuelessUnit)"],description:"Create a number or convert a string or boolean into a number.",examples:["2","2e3","4.05","number(2)",'number("7.2")',"number(true)","number([true, false, true, true])",'number(unit("52cm"), "m")'],seealso:["bignumber","boolean","complex","fraction","index","matrix","string","unit"]},sparse:{name:"sparse",category:"Construction",syntax:["sparse()","sparse([a1, b1, ...; a1, b2, ...])",'sparse([a1, b1, ...; a1, b2, ...], "number")'],description:"Create a sparse matrix.",examples:["sparse()","sparse([3, 4; 5, 6])",'sparse([3, 0; 5, 0], "number")'],seealso:["bignumber","boolean","complex","index","number","string","unit","matrix"]},splitUnit:{name:"splitUnit",category:"Construction",syntax:["splitUnit(unit: Unit, parts: Unit[])"],description:"Split a unit in an array of units whose sum is equal to the original unit.",examples:['splitUnit(1 m, ["feet", "inch"])'],seealso:["unit","createUnit"]},string:{name:"string",category:"Construction",syntax:['"text"',"string(x)"],description:"Create a string or convert a value to a string",examples:['"Hello World!"',"string(4.2)","string(3 + 2i)"],seealso:["bignumber","boolean","complex","index","matrix","number","unit"]},unit:{name:"unit",category:"Construction",syntax:["value unit","unit(value, unit)","unit(string)"],description:"Create a unit.",examples:["5.5 mm","3 inch",'unit(7.1, "kilogram")','unit("23 deg")'],seealso:["bignumber","boolean","complex","index","matrix","number","string"]},e:ree,E:ree,false:{name:"false",category:"Constants",syntax:["false"],description:"Boolean value false",examples:["false"],seealso:["true"]},i:{name:"i",category:"Constants",syntax:["i"],description:"Imaginary unit, defined as i*i=-1. A complex number is described as a + b*i, where a is the real part, and b is the imaginary part.",examples:["i","i * i","sqrt(-1)"],seealso:[]},Infinity:{name:"Infinity",category:"Constants",syntax:["Infinity"],description:"Infinity, a number which is larger than the maximum number that can be handled by a floating point number.",examples:["Infinity","1 / 0"],seealso:[]},LN2:{name:"LN2",category:"Constants",syntax:["LN2"],description:"Returns the natural logarithm of 2, approximately equal to 0.693",examples:["LN2","log(2)"],seealso:[]},LN10:{name:"LN10",category:"Constants",syntax:["LN10"],description:"Returns the natural logarithm of 10, approximately equal to 2.302",examples:["LN10","log(10)"],seealso:[]},LOG2E:{name:"LOG2E",category:"Constants",syntax:["LOG2E"],description:"Returns the base-2 logarithm of E, approximately equal to 1.442",examples:["LOG2E","log(e, 2)"],seealso:[]},LOG10E:{name:"LOG10E",category:"Constants",syntax:["LOG10E"],description:"Returns the base-10 logarithm of E, approximately equal to 0.434",examples:["LOG10E","log(e, 10)"],seealso:[]},NaN:{name:"NaN",category:"Constants",syntax:["NaN"],description:"Not a number",examples:["NaN","0 / 0"],seealso:[]},null:{name:"null",category:"Constants",syntax:["null"],description:"Value null",examples:["null"],seealso:["true","false"]},pi:aee,PI:aee,phi:{name:"phi",category:"Constants",syntax:["phi"],description:"Phi is the golden ratio. Two quantities are in the golden ratio if their ratio is the same as the ratio of their sum to the larger of the two quantities. Phi is defined as `(1 + sqrt(5)) / 2` and is approximately 1.618034...",examples:["phi"],seealso:[]},SQRT1_2:{name:"SQRT1_2",category:"Constants",syntax:["SQRT1_2"],description:"Returns the square root of 1/2, approximately equal to 0.707",examples:["SQRT1_2","sqrt(1/2)"],seealso:[]},SQRT2:{name:"SQRT2",category:"Constants",syntax:["SQRT2"],description:"Returns the square root of 2, approximately equal to 1.414",examples:["SQRT2","sqrt(2)"],seealso:[]},tau:{name:"tau",category:"Constants",syntax:["tau"],description:"Tau is the ratio constant of a circle's circumference to radius, equal to 2 * pi, approximately 6.2832.",examples:["tau","2 * pi"],seealso:["pi"]},true:{name:"true",category:"Constants",syntax:["true"],description:"Boolean value true",examples:["true"],seealso:["false"]},version:{name:"version",category:"Constants",syntax:["version"],description:"A string with the version number of math.js",examples:["version"],seealso:[]},speedOfLight:{description:"Speed of light in vacuum",examples:["speedOfLight"]},gravitationConstant:{description:"Newtonian constant of gravitation",examples:["gravitationConstant"]},planckConstant:{description:"Planck constant",examples:["planckConstant"]},reducedPlanckConstant:{description:"Reduced Planck constant",examples:["reducedPlanckConstant"]},magneticConstant:{description:"Magnetic constant (vacuum permeability)",examples:["magneticConstant"]},electricConstant:{description:"Electric constant (vacuum permeability)",examples:["electricConstant"]},vacuumImpedance:{description:"Characteristic impedance of vacuum",examples:["vacuumImpedance"]},coulomb:{description:"Coulomb's constant",examples:["coulomb"]},elementaryCharge:{description:"Elementary charge",examples:["elementaryCharge"]},bohrMagneton:{description:"Borh magneton",examples:["bohrMagneton"]},conductanceQuantum:{description:"Conductance quantum",examples:["conductanceQuantum"]},inverseConductanceQuantum:{description:"Inverse conductance quantum",examples:["inverseConductanceQuantum"]},magneticFluxQuantum:{description:"Magnetic flux quantum",examples:["magneticFluxQuantum"]},nuclearMagneton:{description:"Nuclear magneton",examples:["nuclearMagneton"]},klitzing:{description:"Von Klitzing constant",examples:["klitzing"]},bohrRadius:{description:"Borh radius",examples:["bohrRadius"]},classicalElectronRadius:{description:"Classical electron radius",examples:["classicalElectronRadius"]},electronMass:{description:"Electron mass",examples:["electronMass"]},fermiCoupling:{description:"Fermi coupling constant",examples:["fermiCoupling"]},fineStructure:{description:"Fine-structure constant",examples:["fineStructure"]},hartreeEnergy:{description:"Hartree energy",examples:["hartreeEnergy"]},protonMass:{description:"Proton mass",examples:["protonMass"]},deuteronMass:{description:"Deuteron Mass",examples:["deuteronMass"]},neutronMass:{description:"Neutron mass",examples:["neutronMass"]},quantumOfCirculation:{description:"Quantum of circulation",examples:["quantumOfCirculation"]},rydberg:{description:"Rydberg constant",examples:["rydberg"]},thomsonCrossSection:{description:"Thomson cross section",examples:["thomsonCrossSection"]},weakMixingAngle:{description:"Weak mixing angle",examples:["weakMixingAngle"]},efimovFactor:{description:"Efimov factor",examples:["efimovFactor"]},atomicMass:{description:"Atomic mass constant",examples:["atomicMass"]},avogadro:{description:"Avogadro's number",examples:["avogadro"]},boltzmann:{description:"Boltzmann constant",examples:["boltzmann"]},faraday:{description:"Faraday constant",examples:["faraday"]},firstRadiation:{description:"First radiation constant",examples:["firstRadiation"]},loschmidt:{description:"Loschmidt constant at T=273.15 K and p=101.325 kPa",examples:["loschmidt"]},gasConstant:{description:"Gas constant",examples:["gasConstant"]},molarPlanckConstant:{description:"Molar Planck constant",examples:["molarPlanckConstant"]},molarVolume:{description:"Molar volume of an ideal gas at T=273.15 K and p=101.325 kPa",examples:["molarVolume"]},sackurTetrode:{description:"Sackur-Tetrode constant at T=1 K and p=101.325 kPa",examples:["sackurTetrode"]},secondRadiation:{description:"Second radiation constant",examples:["secondRadiation"]},stefanBoltzmann:{description:"Stefan-Boltzmann constant",examples:["stefanBoltzmann"]},wienDisplacement:{description:"Wien displacement law constant",examples:["wienDisplacement"]},molarMass:{description:"Molar mass constant",examples:["molarMass"]},molarMassC12:{description:"Molar mass constant of carbon-12",examples:["molarMassC12"]},gravity:{description:"Standard acceleration of gravity (standard acceleration of free-fall on Earth)",examples:["gravity"]},planckLength:{description:"Planck length",examples:["planckLength"]},planckMass:{description:"Planck mass",examples:["planckMass"]},planckTime:{description:"Planck time",examples:["planckTime"]},planckCharge:{description:"Planck charge",examples:["planckCharge"]},planckTemperature:{description:"Planck temperature",examples:["planckTemperature"]},derivative:{name:"derivative",category:"Algebra",syntax:["derivative(expr, variable)","derivative(expr, variable, {simplify: boolean})"],description:"Takes the derivative of an expression expressed in parser Nodes. The derivative will be taken over the supplied variable in the second parameter. If there are multiple variables in the expression, it will return a partial derivative.",examples:['derivative("2x^3", "x")','derivative("2x^3", "x", {simplify: false})','derivative("2x^2 + 3x + 4", "x")','derivative("sin(2x)", "x")','f = parse("x^2 + x")','x = parse("x")',"df = derivative(f, x)","df.evaluate({x: 3})"],seealso:["simplify","parse","evaluate"]},lsolve:{name:"lsolve",category:"Algebra",syntax:["x=lsolve(L, b)"],description:"Finds one solution of the linear system L * x = b where L is an [n x n] lower triangular matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lsolve(a, b)"],seealso:["lsolveAll","lup","lusolve","usolve","matrix","sparse"]},lsolveAll:{name:"lsolveAll",category:"Algebra",syntax:["x=lsolveAll(L, b)"],description:"Finds all solutions of the linear system L * x = b where L is an [n x n] lower triangular matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lsolve(a, b)"],seealso:["lsolve","lup","lusolve","usolve","matrix","sparse"]},lup:{name:"lup",category:"Algebra",syntax:["lup(m)"],description:"Calculate the Matrix LU decomposition with partial pivoting. Matrix A is decomposed in three matrices (L, U, P) where P * A = L * U",examples:["lup([[2, 1], [1, 4]])","lup(matrix([[2, 1], [1, 4]]))","lup(sparse([[2, 1], [1, 4]]))"],seealso:["lusolve","lsolve","usolve","matrix","sparse","slu","qr"]},lusolve:{name:"lusolve",category:"Algebra",syntax:["x=lusolve(A, b)","x=lusolve(lu, b)"],description:"Solves the linear system A * x = b where A is an [n x n] matrix and b is a [n] column vector.",examples:["a = [-2, 3; 2, 1]","b = [11, 9]","x = lusolve(a, b)"],seealso:["lup","slu","lsolve","usolve","matrix","sparse"]},leafCount:{name:"leafCount",category:"Algebra",syntax:["leafCount(expr)"],description:"Computes the number of leaves in the parse tree of the given expression",examples:['leafCount("e^(i*pi)-1")','leafCount(parse("{a: 22/7, b: 10^(1/2)}"))'],seealso:["simplify"]},resolve:{name:"resolve",category:"Algebra",syntax:["resolve(node, scope)"],description:"Recursively substitute variables in an expression tree.",examples:['resolve(parse("1 + x"), { x: 7 })','resolve(parse("size(text)"), { text: "Hello World" })','resolve(parse("x + y"), { x: parse("3z") })','resolve(parse("3x"), { x: parse("y+z"), z: parse("w^y") })'],seealso:["simplify","evaluate"],mayThrow:["ReferenceError"]},simplify:{name:"simplify",category:"Algebra",syntax:["simplify(expr)","simplify(expr, rules)"],description:"Simplify an expression tree.",examples:['simplify("3 + 2 / 4")','simplify("2x + x")','f = parse("x * (x + 2 + x)")',"simplified = simplify(f)","simplified.evaluate({x: 2})"],seealso:["simplifyCore","derivative","evaluate","parse","rationalize","resolve"]},simplifyConstant:{name:"simplifyConstant",category:"Algebra",syntax:["simplifyConstant(expr)","simplifyConstant(expr, options)"],description:"Replace constant subexpressions of node with their values.",examples:['simplifyConatant("(3-3)*x")','simplifyConstant(parse("z-cos(tau/8)"))'],seealso:["simplify","simplifyCore","evaluate"]},simplifyCore:{name:"simplifyCore",category:"Algebra",syntax:["simplifyCore(node)"],description:"Perform simple one-pass simplifications on an expression tree.",examples:['simplifyCore(parse("0*x"))','simplifyCore(parse("(x+0)*2"))'],seealso:["simplify","simplifyConstant","evaluate"]},symbolicEqual:{name:"symbolicEqual",category:"Algebra",syntax:["symbolicEqual(expr1, expr2)","symbolicEqual(expr1, expr2, options)"],description:"Returns true if the difference of the expressions simplifies to 0",examples:['symbolicEqual("x*y","y*x")','symbolicEqual("abs(x^2)", "x^2")','symbolicEqual("abs(x)", "x", {context: {abs: {trivial: true}}})'],seealso:["simplify","evaluate"]},rationalize:{name:"rationalize",category:"Algebra",syntax:["rationalize(expr)","rationalize(expr, scope)","rationalize(expr, scope, detailed)"],description:"Transform a rationalizable expression in a rational fraction. If rational fraction is one variable polynomial then converts the numerator and denominator in canonical form, with decreasing exponents, returning the coefficients of numerator.",examples:['rationalize("2x/y - y/(x+1)")','rationalize("2x/y - y/(x+1)", true)'],seealso:["simplify"]},slu:{name:"slu",category:"Algebra",syntax:["slu(A, order, threshold)"],description:"Calculate the Matrix LU decomposition with full pivoting. Matrix A is decomposed in two matrices (L, U) and two permutation vectors (pinv, q) where P * A * Q = L * U",examples:["slu(sparse([4.5, 0, 3.2, 0; 3.1, 2.9, 0, 0.9; 0, 1.7, 3, 0; 3.5, 0.4, 0, 1]), 1, 0.001)"],seealso:["lusolve","lsolve","usolve","matrix","sparse","lup","qr"]},usolve:{name:"usolve",category:"Algebra",syntax:["x=usolve(U, b)"],description:"Finds one solution of the linear system U * x = b where U is an [n x n] upper triangular matrix and b is a [n] column vector.",examples:["x=usolve(sparse([1, 1, 1, 1; 0, 1, 1, 1; 0, 0, 1, 1; 0, 0, 0, 1]), [1; 2; 3; 4])"],seealso:["usolveAll","lup","lusolve","lsolve","matrix","sparse"]},usolveAll:{name:"usolveAll",category:"Algebra",syntax:["x=usolve(U, b)"],description:"Finds all solutions of the linear system U * x = b where U is an [n x n] upper triangular matrix and b is a [n] column vector.",examples:["x=usolve(sparse([1, 1, 1, 1; 0, 1, 1, 1; 0, 0, 1, 1; 0, 0, 0, 1]), [1; 2; 3; 4])"],seealso:["usolve","lup","lusolve","lsolve","matrix","sparse"]},qr:{name:"qr",category:"Algebra",syntax:["qr(A)"],description:"Calculates the Matrix QR decomposition. Matrix `A` is decomposed in two matrices (`Q`, `R`) where `Q` is an orthogonal matrix and `R` is an upper triangular matrix.",examples:["qr([[1, -1, 4], [1, 4, -2], [1, 4, 2], [1, -1, 0]])"],seealso:["lup","slu","matrix"]},abs:{name:"abs",category:"Arithmetic",syntax:["abs(x)"],description:"Compute the absolute value.",examples:["abs(3.5)","abs(-4.2)"],seealso:["sign"]},add:{name:"add",category:"Operators",syntax:["x + y","add(x, y)"],description:"Add two values.",examples:["a = 2.1 + 3.6","a - 3.6","3 + 2i","3 cm + 2 inch",'"2.3" + "4"'],seealso:["subtract"]},cbrt:{name:"cbrt",category:"Arithmetic",syntax:["cbrt(x)","cbrt(x, allRoots)"],description:"Compute the cubic root value. If x = y * y * y, then y is the cubic root of x. When `x` is a number or complex number, an optional second argument `allRoots` can be provided to return all three cubic roots. If not provided, the principal root is returned",examples:["cbrt(64)","cube(4)","cbrt(-8)","cbrt(2 + 3i)","cbrt(8i)","cbrt(8i, true)","cbrt(27 m^3)"],seealso:["square","sqrt","cube","multiply"]},ceil:{name:"ceil",category:"Arithmetic",syntax:["ceil(x)"],description:"Round a value towards plus infinity. If x is complex, both real and imaginary part are rounded towards plus infinity.",examples:["ceil(3.2)","ceil(3.8)","ceil(-4.2)"],seealso:["floor","fix","round"]},cube:{name:"cube",category:"Arithmetic",syntax:["cube(x)"],description:"Compute the cube of a value. The cube of x is x * x * x.",examples:["cube(2)","2^3","2 * 2 * 2"],seealso:["multiply","square","pow"]},divide:{name:"divide",category:"Operators",syntax:["x / y","divide(x, y)"],description:"Divide two values.",examples:["a = 2 / 3","a * 3","4.5 / 2","3 + 4 / 2","(3 + 4) / 2","18 km / 4.5"],seealso:["multiply"]},dotDivide:{name:"dotDivide",category:"Operators",syntax:["x ./ y","dotDivide(x, y)"],description:"Divide two values element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","b = [2, 1, 1; 3, 2, 5]","a ./ b"],seealso:["multiply","dotMultiply","divide"]},dotMultiply:{name:"dotMultiply",category:"Operators",syntax:["x .* y","dotMultiply(x, y)"],description:"Multiply two values element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","b = [2, 1, 1; 3, 2, 5]","a .* b"],seealso:["multiply","divide","dotDivide"]},dotPow:{name:"dotPow",category:"Operators",syntax:["x .^ y","dotPow(x, y)"],description:"Calculates the power of x to y element wise.",examples:["a = [1, 2, 3; 4, 5, 6]","a .^ 2"],seealso:["pow"]},exp:{name:"exp",category:"Arithmetic",syntax:["exp(x)"],description:"Calculate the exponent of a value.",examples:["exp(1.3)","e ^ 1.3","log(exp(1.3))","x = 2.4","(exp(i*x) == cos(x) + i*sin(x)) # Euler's formula"],seealso:["expm","expm1","pow","log"]},expm:{name:"expm",category:"Arithmetic",syntax:["exp(x)"],description:"Compute the matrix exponential, expm(A) = e^A. The matrix must be square. Not to be confused with exp(a), which performs element-wise exponentiation.",examples:["expm([[0,2],[0,0]])"],seealso:["exp"]},expm1:{name:"expm1",category:"Arithmetic",syntax:["expm1(x)"],description:"Calculate the value of subtracting 1 from the exponential value.",examples:["expm1(2)","pow(e, 2) - 1","log(expm1(2) + 1)"],seealso:["exp","pow","log"]},fix:{name:"fix",category:"Arithmetic",syntax:["fix(x)"],description:"Round a value towards zero. If x is complex, both real and imaginary part are rounded towards zero.",examples:["fix(3.2)","fix(3.8)","fix(-4.2)","fix(-4.8)"],seealso:["ceil","floor","round"]},floor:{name:"floor",category:"Arithmetic",syntax:["floor(x)"],description:"Round a value towards minus infinity.If x is complex, both real and imaginary part are rounded towards minus infinity.",examples:["floor(3.2)","floor(3.8)","floor(-4.2)"],seealso:["ceil","fix","round"]},gcd:{name:"gcd",category:"Arithmetic",syntax:["gcd(a, b)","gcd(a, b, c, ...)"],description:"Compute the greatest common divisor.",examples:["gcd(8, 12)","gcd(-4, 6)","gcd(25, 15, -10)"],seealso:["lcm","xgcd"]},hypot:{name:"hypot",category:"Arithmetic",syntax:["hypot(a, b, c, ...)","hypot([a, b, c, ...])"],description:"Calculate the hypotenusa of a list with values. ",examples:["hypot(3, 4)","sqrt(3^2 + 4^2)","hypot(-2)","hypot([3, 4, 5])"],seealso:["abs","norm"]},lcm:{name:"lcm",category:"Arithmetic",syntax:["lcm(x, y)"],description:"Compute the least common multiple.",examples:["lcm(4, 6)","lcm(6, 21)","lcm(6, 21, 5)"],seealso:["gcd"]},log:{name:"log",category:"Arithmetic",syntax:["log(x)","log(x, base)"],description:"Compute the logarithm of a value. If no base is provided, the natural logarithm of x is calculated. If base if provided, the logarithm is calculated for the specified base. log(x, base) is defined as log(x) / log(base).",examples:["log(3.5)","a = log(2.4)","exp(a)","10 ^ 4","log(10000, 10)","log(10000) / log(10)","b = log(1024, 2)","2 ^ b"],seealso:["exp","log1p","log2","log10"]},log2:{name:"log2",category:"Arithmetic",syntax:["log2(x)"],description:"Calculate the 2-base of a value. This is the same as calculating `log(x, 2)`.",examples:["log2(0.03125)","log2(16)","log2(16) / log2(2)","pow(2, 4)"],seealso:["exp","log1p","log","log10"]},log1p:{name:"log1p",category:"Arithmetic",syntax:["log1p(x)","log1p(x, base)"],description:"Calculate the logarithm of a `value+1`",examples:["log1p(2.5)","exp(log1p(1.4))","pow(10, 4)","log1p(9999, 10)","log1p(9999) / log(10)"],seealso:["exp","log","log2","log10"]},log10:{name:"log10",category:"Arithmetic",syntax:["log10(x)"],description:"Compute the 10-base logarithm of a value.",examples:["log10(0.00001)","log10(10000)","10 ^ 4","log(10000) / log(10)","log(10000, 10)"],seealso:["exp","log"]},mod:{name:"mod",category:"Operators",syntax:["x % y","x mod y","mod(x, y)"],description:"Calculates the modulus, the remainder of an integer division.",examples:["7 % 3","11 % 2","10 mod 4","isOdd(x) = x % 2","isOdd(2)","isOdd(3)"],seealso:["divide"]},multiply:{name:"multiply",category:"Operators",syntax:["x * y","multiply(x, y)"],description:"multiply two values.",examples:["a = 2.1 * 3.4","a / 3.4","2 * 3 + 4","2 * (3 + 4)","3 * 2.1 km"],seealso:["divide"]},norm:{name:"norm",category:"Arithmetic",syntax:["norm(x)","norm(x, p)"],description:"Calculate the norm of a number, vector or matrix.",examples:["abs(-3.5)","norm(-3.5)","norm(3 - 4i)","norm([1, 2, -3], Infinity)","norm([1, 2, -3], -Infinity)","norm([3, 4], 2)","norm([[1, 2], [3, 4]], 1)",'norm([[1, 2], [3, 4]], "inf")','norm([[1, 2], [3, 4]], "fro")']},nthRoot:{name:"nthRoot",category:"Arithmetic",syntax:["nthRoot(a)","nthRoot(a, root)"],description:'Calculate the nth root of a value. The principal nth root of a positive real number A, is the positive real solution of the equation "x^root = A".',examples:["4 ^ 3","nthRoot(64, 3)","nthRoot(9, 2)","sqrt(9)"],seealso:["nthRoots","pow","sqrt"]},nthRoots:{name:"nthRoots",category:"Arithmetic",syntax:["nthRoots(A)","nthRoots(A, root)"],description:'Calculate the nth roots of a value. An nth root of a positive real number A, is a positive real solution of the equation "x^root = A". This function returns an array of complex values.',examples:["nthRoots(1)","nthRoots(1, 3)"],seealso:["sqrt","pow","nthRoot"]},pow:{name:"pow",category:"Operators",syntax:["x ^ y","pow(x, y)"],description:"Calculates the power of x to y, x^y.",examples:["2^3","2*2*2","1 + e ^ (pi * i)","math.pow([[1, 2], [4, 3]], 2)","math.pow([[1, 2], [4, 3]], -1)"],seealso:["multiply","nthRoot","nthRoots","sqrt"]},round:{name:"round",category:"Arithmetic",syntax:["round(x)","round(x, n)"],description:"round a value towards the nearest integer.If x is complex, both real and imaginary part are rounded towards the nearest integer. When n is specified, the value is rounded to n decimals.",examples:["round(3.2)","round(3.8)","round(-4.2)","round(-4.8)","round(pi, 3)","round(123.45678, 2)"],seealso:["ceil","floor","fix"]},sign:{name:"sign",category:"Arithmetic",syntax:["sign(x)"],description:"Compute the sign of a value. The sign of a value x is 1 when x>1, -1 when x<0, and 0 when x=0.",examples:["sign(3.5)","sign(-4.2)","sign(0)"],seealso:["abs"]},sqrt:{name:"sqrt",category:"Arithmetic",syntax:["sqrt(x)"],description:"Compute the square root value. If x = y * y, then y is the square root of x.",examples:["sqrt(25)","5 * 5","sqrt(-1)"],seealso:["square","sqrtm","multiply","nthRoot","nthRoots","pow"]},sqrtm:{name:"sqrtm",category:"Arithmetic",syntax:["sqrtm(x)"],description:"Calculate the principal square root of a square matrix. The principal square root matrix `X` of another matrix `A` is such that `X * X = A`.",examples:["sqrtm([[1, 2], [3, 4]])"],seealso:["sqrt","abs","square","multiply"]},square:{name:"square",category:"Arithmetic",syntax:["square(x)"],description:"Compute the square of a value. The square of x is x * x.",examples:["square(3)","sqrt(9)","3^2","3 * 3"],seealso:["multiply","pow","sqrt","cube"]},subtract:{name:"subtract",category:"Operators",syntax:["x - y","subtract(x, y)"],description:"subtract two values.",examples:["a = 5.3 - 2","a + 2","2/3 - 1/6","2 * 3 - 3","2.1 km - 500m"],seealso:["add"]},unaryMinus:{name:"unaryMinus",category:"Operators",syntax:["-x","unaryMinus(x)"],description:"Inverse the sign of a value. Converts booleans and strings to numbers.",examples:["-4.5","-(-5.6)",'-"22"'],seealso:["add","subtract","unaryPlus"]},unaryPlus:{name:"unaryPlus",category:"Operators",syntax:["+x","unaryPlus(x)"],description:"Converts booleans and strings to numbers.",examples:["+true",'+"2"'],seealso:["add","subtract","unaryMinus"]},xgcd:{name:"xgcd",category:"Arithmetic",syntax:["xgcd(a, b)"],description:"Calculate the extended greatest common divisor for two values. The result is an array [d, x, y] with 3 entries, where d is the greatest common divisor, and d = x * a + y * b.",examples:["xgcd(8, 12)","gcd(8, 12)","xgcd(36163, 21199)"],seealso:["gcd","lcm"]},invmod:{name:"invmod",category:"Arithmetic",syntax:["invmod(a, b)"],description:"Calculate the (modular) multiplicative inverse of a modulo b. Solution to the equation ax ≣ 1 (mod b)",examples:["invmod(8, 12)=NaN","invmod(7, 13)=2","math.invmod(15151, 15122)=10429"],seealso:["gcd","xgcd"]},bitAnd:{name:"bitAnd",category:"Bitwise",syntax:["x & y","bitAnd(x, y)"],description:"Bitwise AND operation. Performs the logical AND operation on each pair of the corresponding bits of the two given values by multiplying them. If both bits in the compared position are 1, the bit in the resulting binary representation is 1, otherwise, the result is 0",examples:["5 & 3","bitAnd(53, 131)","[1, 12, 31] & 42"],seealso:["bitNot","bitOr","bitXor","leftShift","rightArithShift","rightLogShift"]},bitNot:{name:"bitNot",category:"Bitwise",syntax:["~x","bitNot(x)"],description:"Bitwise NOT operation. Performs a logical negation on each bit of the given value. Bits that are 0 become 1, and those that are 1 become 0.",examples:["~1","~2","bitNot([2, -3, 4])"],seealso:["bitAnd","bitOr","bitXor","leftShift","rightArithShift","rightLogShift"]},bitOr:{name:"bitOr",category:"Bitwise",syntax:["x | y","bitOr(x, y)"],description:"Bitwise OR operation. Performs the logical inclusive OR operation on each pair of corresponding bits of the two given values. The result in each position is 1 if the first bit is 1 or the second bit is 1 or both bits are 1, otherwise, the result is 0.",examples:["5 | 3","bitOr([1, 2, 3], 4)"],seealso:["bitAnd","bitNot","bitXor","leftShift","rightArithShift","rightLogShift"]},bitXor:{name:"bitXor",category:"Bitwise",syntax:["bitXor(x, y)"],description:"Bitwise XOR operation, exclusive OR. Performs the logical exclusive OR operation on each pair of corresponding bits of the two given values. The result in each position is 1 if only the first bit is 1 or only the second bit is 1, but will be 0 if both are 0 or both are 1.",examples:["bitOr(1, 2)","bitXor([2, 3, 4], 4)"],seealso:["bitAnd","bitNot","bitOr","leftShift","rightArithShift","rightLogShift"]},leftShift:{name:"leftShift",category:"Bitwise",syntax:["x << y","leftShift(x, y)"],description:"Bitwise left logical shift of a value x by y number of bits.",examples:["4 << 1","8 >> 1"],seealso:["bitAnd","bitNot","bitOr","bitXor","rightArithShift","rightLogShift"]},rightArithShift:{name:"rightArithShift",category:"Bitwise",syntax:["x >> y","rightArithShift(x, y)"],description:"Bitwise right arithmetic shift of a value x by y number of bits.",examples:["8 >> 1","4 << 1","-12 >> 2"],seealso:["bitAnd","bitNot","bitOr","bitXor","leftShift","rightLogShift"]},rightLogShift:{name:"rightLogShift",category:"Bitwise",syntax:["x >>> y","rightLogShift(x, y)"],description:"Bitwise right logical shift of a value x by y number of bits.",examples:["8 >>> 1","4 << 1","-12 >>> 2"],seealso:["bitAnd","bitNot","bitOr","bitXor","leftShift","rightArithShift"]},bellNumbers:{name:"bellNumbers",category:"Combinatorics",syntax:["bellNumbers(n)"],description:"The Bell Numbers count the number of partitions of a set. A partition is a pairwise disjoint subset of S whose union is S. `bellNumbers` only takes integer arguments. The following condition must be enforced: n >= 0.",examples:["bellNumbers(3)","bellNumbers(8)"],seealso:["stirlingS2"]},catalan:{name:"catalan",category:"Combinatorics",syntax:["catalan(n)"],description:"The Catalan Numbers enumerate combinatorial structures of many different types. catalan only takes integer arguments. The following condition must be enforced: n >= 0.",examples:["catalan(3)","catalan(8)"],seealso:["bellNumbers"]},composition:{name:"composition",category:"Combinatorics",syntax:["composition(n, k)"],description:"The composition counts of n into k parts. composition only takes integer arguments. The following condition must be enforced: k <= n.",examples:["composition(5, 3)"],seealso:["combinations"]},stirlingS2:{name:"stirlingS2",category:"Combinatorics",syntax:["stirlingS2(n, k)"],description:"he Stirling numbers of the second kind, counts the number of ways to partition a set of n labelled objects into k nonempty unlabelled subsets. `stirlingS2` only takes integer arguments. The following condition must be enforced: k <= n. If n = k or k = 1, then s(n,k) = 1.",examples:["stirlingS2(5, 3)"],seealso:["bellNumbers"]},config:{name:"config",category:"Core",syntax:["config()","config(options)"],description:"Get configuration or change configuration.",examples:["config()","1/3 + 1/4",'config({number: "Fraction"})',"1/3 + 1/4"],seealso:[]},import:{name:"import",category:"Core",syntax:["import(functions)","import(functions, options)"],description:"Import functions or constants from an object.",examples:["import({myFn: f(x)=x^2, myConstant: 32 })","myFn(2)","myConstant"],seealso:[]},typed:{name:"typed",category:"Core",syntax:["typed(signatures)","typed(name, signatures)"],description:"Create a typed function.",examples:['double = typed({ "number": f(x)=x+x })',"double(2)",'double("hello")'],seealso:[]},arg:{name:"arg",category:"Complex",syntax:["arg(x)"],description:"Compute the argument of a complex value. If x = a+bi, the argument is computed as atan2(b, a).",examples:["arg(2 + 2i)","atan2(3, 2)","arg(2 + 3i)"],seealso:["re","im","conj","abs"]},conj:{name:"conj",category:"Complex",syntax:["conj(x)"],description:"Compute the complex conjugate of a complex value. If x = a+bi, the complex conjugate is a-bi.",examples:["conj(2 + 3i)","conj(2 - 3i)","conj(-5.2i)"],seealso:["re","im","abs","arg"]},re:{name:"re",category:"Complex",syntax:["re(x)"],description:"Get the real part of a complex number.",examples:["re(2 + 3i)","im(2 + 3i)","re(-5.2i)","re(2.4)"],seealso:["im","conj","abs","arg"]},im:{name:"im",category:"Complex",syntax:["im(x)"],description:"Get the imaginary part of a complex number.",examples:["im(2 + 3i)","re(2 + 3i)","im(-5.2i)","im(2.4)"],seealso:["re","conj","abs","arg"]},evaluate:{name:"evaluate",category:"Expression",syntax:["evaluate(expression)","evaluate([expr1, expr2, expr3, ...])"],description:"Evaluate an expression or an array with expressions.",examples:['evaluate("2 + 3")','evaluate("sqrt(" + 4 + ")")'],seealso:[]},help:{name:"help",category:"Expression",syntax:["help(object)","help(string)"],description:"Display documentation on a function or data type.",examples:["help(sqrt)",'help("complex")'],seealso:[]},distance:{name:"distance",category:"Geometry",syntax:["distance([x1, y1], [x2, y2])","distance([[x1, y1], [x2, y2]])"],description:"Calculates the Euclidean distance between two points.",examples:["distance([0,0], [4,4])","distance([[0,0], [4,4]])"],seealso:[]},intersect:{name:"intersect",category:"Geometry",syntax:["intersect(expr1, expr2, expr3, expr4)","intersect(expr1, expr2, expr3)"],description:"Computes the intersection point of lines and/or planes.",examples:["intersect([0, 0], [10, 10], [10, 0], [0, 10])","intersect([1, 0, 1], [4, -2, 2], [1, 1, 1, 6])"],seealso:[]},and:{name:"and",category:"Logical",syntax:["x and y","and(x, y)"],description:"Logical and. Test whether two values are both defined with a nonzero/nonempty value.",examples:["true and false","true and true","2 and 4"],seealso:["not","or","xor"]},not:{name:"not",category:"Logical",syntax:["not x","not(x)"],description:"Logical not. Flips the boolean value of given argument.",examples:["not true","not false","not 2","not 0"],seealso:["and","or","xor"]},or:{name:"or",category:"Logical",syntax:["x or y","or(x, y)"],description:"Logical or. Test if at least one value is defined with a nonzero/nonempty value.",examples:["true or false","false or false","0 or 4"],seealso:["not","and","xor"]},xor:{name:"xor",category:"Logical",syntax:["x xor y","xor(x, y)"],description:"Logical exclusive or, xor. Test whether one and only one value is defined with a nonzero/nonempty value.",examples:["true xor false","false xor false","true xor true","0 xor 4"],seealso:["not","and","or"]},concat:{name:"concat",category:"Matrix",syntax:["concat(A, B, C, ...)","concat(A, B, C, ..., dim)"],description:"Concatenate matrices. By default, the matrices are concatenated by the last dimension. The dimension on which to concatenate can be provided as last argument.",examples:["A = [1, 2; 5, 6]","B = [3, 4; 7, 8]","concat(A, B)","concat(A, B, 1)","concat(A, B, 2)"],seealso:["det","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},count:{name:"count",category:"Matrix",syntax:["count(x)"],description:"Count the number of elements of a matrix, array or string.",examples:["a = [1, 2; 3, 4; 5, 6]","count(a)","size(a)",'count("hello world")'],seealso:["size"]},cross:{name:"cross",category:"Matrix",syntax:["cross(A, B)"],description:"Calculate the cross product for two vectors in three dimensional space.",examples:["cross([1, 1, 0], [0, 1, 1])","cross([3, -3, 1], [4, 9, 2])","cross([2, 3, 4], [5, 6, 7])"],seealso:["multiply","dot"]},column:{name:"column",category:"Matrix",syntax:["column(x, index)"],description:"Return a column from a matrix or array.",examples:["A = [[1, 2], [3, 4]]","column(A, 1)","column(A, 2)"],seealso:["row","matrixFromColumns"]},ctranspose:{name:"ctranspose",category:"Matrix",syntax:["x'","ctranspose(x)"],description:"Complex Conjugate and Transpose a matrix",examples:["a = [1, 2, 3; 4, 5, 6]","a'","ctranspose(a)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","zeros"]},det:{name:"det",category:"Matrix",syntax:["det(x)"],description:"Calculate the determinant of a matrix",examples:["det([1, 2; 3, 4])","det([-2, 2, 3; -1, 1, 3; 2, 0, -1])"],seealso:["concat","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},diag:{name:"diag",category:"Matrix",syntax:["diag(x)","diag(x, k)"],description:"Create a diagonal matrix or retrieve the diagonal of a matrix. When x is a vector, a matrix with the vector values on the diagonal will be returned. When x is a matrix, a vector with the diagonal values of the matrix is returned. When k is provided, the k-th diagonal will be filled in or retrieved, if k is positive, the values are placed on the super diagonal. When k is negative, the values are placed on the sub diagonal.",examples:["diag(1:3)","diag(1:3, 1)","a = [1, 2, 3; 4, 5, 6; 7, 8, 9]","diag(a)"],seealso:["concat","det","identity","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},diff:{name:"diff",category:"Matrix",syntax:["diff(arr)","diff(arr, dim)"],description:["Create a new matrix or array with the difference of the passed matrix or array.","Dim parameter is optional and used to indicant the dimension of the array/matrix to apply the difference","If no dimension parameter is passed it is assumed as dimension 0","Dimension is zero-based in javascript and one-based in the parser","Arrays must be 'rectangular' meaning arrays like [1, 2]","If something is passed as a matrix it will be returned as a matrix but other than that all matrices are converted to arrays"],examples:["diff([1, 2, 4, 7, 0])","diff([1, 2, 4, 7, 0], 0)","diff(matrix([1, 2, 4, 7, 0]))","diff([[1, 2], [3, 4]])","diff([[1, 2], [3, 4]], 0)","diff([[1, 2], [3, 4]], 1)","diff([[1, 2], [3, 4]], bignumber(1))","diff(matrix([[1, 2], [3, 4]]), 1)","diff([[1, 2], matrix([3, 4])], 1)"],seealso:["subtract","partitionSelect"]},dot:{name:"dot",category:"Matrix",syntax:["dot(A, B)","A * B"],description:"Calculate the dot product of two vectors. The dot product of A = [a1, a2, a3, ..., an] and B = [b1, b2, b3, ..., bn] is defined as dot(A, B) = a1 * b1 + a2 * b2 + a3 * b3 + ... + an * bn",examples:["dot([2, 4, 1], [2, 2, 3])","[2, 4, 1] * [2, 2, 3]"],seealso:["multiply","cross"]},getMatrixDataType:{name:"getMatrixDataType",category:"Matrix",syntax:["getMatrixDataType(x)"],description:'Find the data type of all elements in a matrix or array, for example "number" if all items are a number and "Complex" if all values are complex numbers. If a matrix contains more than one data type, it will return "mixed".',examples:["getMatrixDataType([1, 2, 3])","getMatrixDataType([[5 cm], [2 inch]])",'getMatrixDataType([1, "text"])',"getMatrixDataType([1, bignumber(4)])"],seealso:["matrix","sparse","typeOf"]},identity:{name:"identity",category:"Matrix",syntax:["identity(n)","identity(m, n)","identity([m, n])"],description:"Returns the identity matrix with size m-by-n. The matrix has ones on the diagonal and zeros elsewhere.",examples:["identity(3)","identity(3, 5)","a = [1, 2, 3; 4, 5, 6]","identity(size(a))"],seealso:["concat","det","diag","inv","ones","range","size","squeeze","subset","trace","transpose","zeros"]},filter:{name:"filter",category:"Matrix",syntax:["filter(x, test)"],description:"Filter items in a matrix.",examples:["isPositive(x) = x > 0","filter([6, -2, -1, 4, 3], isPositive)","filter([6, -2, 0, 1, 0], x != 0)"],seealso:["sort","map","forEach"]},flatten:{name:"flatten",category:"Matrix",syntax:["flatten(x)"],description:"Flatten a multi dimensional matrix into a single dimensional matrix.",examples:["a = [1, 2, 3; 4, 5, 6]","size(a)","b = flatten(a)","size(b)"],seealso:["concat","resize","size","squeeze"]},forEach:{name:"forEach",category:"Matrix",syntax:["forEach(x, callback)"],description:"Iterates over all elements of a matrix/array, and executes the given callback function.",examples:["numberOfPets = {}","addPet(n) = numberOfPets[n] = (numberOfPets[n] ? numberOfPets[n]:0 ) + 1;",'forEach(["Dog","Cat","Cat"], addPet)',"numberOfPets"],seealso:["map","sort","filter"]},inv:{name:"inv",category:"Matrix",syntax:["inv(x)"],description:"Calculate the inverse of a matrix",examples:["inv([1, 2; 3, 4])","inv(4)","1 / 4"],seealso:["concat","det","diag","identity","ones","range","size","squeeze","subset","trace","transpose","zeros"]},pinv:{name:"pinv",category:"Matrix",syntax:["pinv(x)"],description:"Calculate the Moore–Penrose inverse of a matrix",examples:["pinv([1, 2; 3, 4])","pinv([[1, 0], [0, 1], [0, 1]])","pinv(4)"],seealso:["inv"]},eigs:{name:"eigs",category:"Matrix",syntax:["eigs(x)"],description:"Calculate the eigenvalues and eigenvectors of a real symmetric matrix",examples:["eigs([[5, 2.3], [2.3, 1]])"],seealso:["inv"]},kron:{name:"kron",category:"Matrix",syntax:["kron(x, y)"],description:"Calculates the kronecker product of 2 matrices or vectors.",examples:["kron([[1, 0], [0, 1]], [[1, 2], [3, 4]])","kron([1,1], [2,3,4])"],seealso:["multiply","dot","cross"]},matrixFromFunction:{name:"matrixFromFunction",category:"Matrix",syntax:["math.matrixFromFunction(size, fn)","math.matrixFromFunction(size, fn, format)","math.matrixFromFunction(size, fn, format, datatype)","math.matrixFromFunction(size, format, fn)","math.matrixFromFunction(size, format, datatype, fn)"],description:"Create a matrix by evaluating a generating function at each index.",examples:["f(I) = I[1] - I[2]","matrixFromFunction([3,3], f)","g(I) = I[1] - I[2] == 1 ? 4 : 0",'matrixFromFunction([100, 100], "sparse", g)',"matrixFromFunction([5], random)"],seealso:["matrix","matrixFromRows","matrixFromColumns","zeros"]},matrixFromRows:{name:"matrixFromRows",category:"Matrix",syntax:["math.matrixFromRows(...arr)","math.matrixFromRows(row1, row2)","math.matrixFromRows(row1, row2, row3)"],description:"Create a dense matrix from vectors as individual rows.",examples:["matrixFromRows([1, 2, 3], [[4],[5],[6]])"],seealso:["matrix","matrixFromColumns","matrixFromFunction","zeros"]},matrixFromColumns:{name:"matrixFromColumns",category:"Matrix",syntax:["math.matrixFromColumns(...arr)","math.matrixFromColumns(row1, row2)","math.matrixFromColumns(row1, row2, row3)"],description:"Create a dense matrix from vectors as individual columns.",examples:["matrixFromColumns([1, 2, 3], [[4],[5],[6]])"],seealso:["matrix","matrixFromRows","matrixFromFunction","zeros"]},map:{name:"map",category:"Matrix",syntax:["map(x, callback)"],description:"Create a new matrix or array with the results of the callback function executed on each entry of the matrix/array.",examples:["map([1, 2, 3], square)"],seealso:["filter","forEach"]},ones:{name:"ones",category:"Matrix",syntax:["ones(m)","ones(m, n)","ones(m, n, p, ...)","ones([m])","ones([m, n])","ones([m, n, p, ...])"],description:"Create a matrix containing ones.",examples:["ones(3)","ones(3, 5)","ones([2,3]) * 4.5","a = [1, 2, 3; 4, 5, 6]","ones(size(a))"],seealso:["concat","det","diag","identity","inv","range","size","squeeze","subset","trace","transpose","zeros"]},partitionSelect:{name:"partitionSelect",category:"Matrix",syntax:["partitionSelect(x, k)","partitionSelect(x, k, compare)"],description:"Partition-based selection of an array or 1D matrix. Will find the kth smallest value, and mutates the input array. Uses Quickselect.",examples:["partitionSelect([5, 10, 1], 2)",'partitionSelect(["C", "B", "A", "D"], 1)'],seealso:["sort"]},range:{name:"range",category:"Type",syntax:["start:end","start:step:end","range(start, end)","range(start, end, step)","range(string)"],description:"Create a range. Lower bound of the range is included, upper bound is excluded.",examples:["1:5","3:-1:-3","range(3, 7)","range(0, 12, 2)",'range("4:10")',"a = [1, 2, 3, 4; 5, 6, 7, 8]","a[1:2, 1:2]"],seealso:["concat","det","diag","identity","inv","ones","size","squeeze","subset","trace","transpose","zeros"]},resize:{name:"resize",category:"Matrix",syntax:["resize(x, size)","resize(x, size, defaultValue)"],description:"Resize a matrix.",examples:["resize([1,2,3,4,5], [3])","resize([1,2,3], [5])","resize([1,2,3], [5], -1)","resize(2, [2, 3])",'resize("hello", [8], "!")'],seealso:["size","subset","squeeze","reshape"]},reshape:{name:"reshape",category:"Matrix",syntax:["reshape(x, sizes)"],description:"Reshape a multi dimensional array to fit the specified dimensions.",examples:["reshape([1, 2, 3, 4, 5, 6], [2, 3])","reshape([[1, 2], [3, 4]], [1, 4])","reshape([[1, 2], [3, 4]], [4])"],seealso:["size","squeeze","resize"]},rotate:{name:"rotate",category:"Matrix",syntax:["rotate(w, theta)","rotate(w, theta, v)"],description:"Returns a 2-D rotation matrix (2x2) for a given angle (in radians). Returns a 2-D rotation matrix (3x3) of a given angle (in radians) around given axis.",examples:["rotate([1, 0], math.pi / 2)",'rotate(matrix([1, 0]), unit("35deg"))','rotate([1, 0, 0], unit("90deg"), [0, 0, 1])','rotate(matrix([1, 0, 0]), unit("90deg"), matrix([0, 0, 1]))'],seealso:["matrix","rotationMatrix"]},rotationMatrix:{name:"rotationMatrix",category:"Matrix",syntax:["rotationMatrix(theta)","rotationMatrix(theta, v)","rotationMatrix(theta, v, format)"],description:"Returns a 2-D rotation matrix (2x2) for a given angle (in radians). Returns a 2-D rotation matrix (3x3) of a given angle (in radians) around given axis.",examples:["rotationMatrix(pi / 2)",'rotationMatrix(unit("45deg"), [0, 0, 1])','rotationMatrix(1, matrix([0, 0, 1]), "sparse")'],seealso:["cos","sin"]},row:{name:"row",category:"Matrix",syntax:["row(x, index)"],description:"Return a row from a matrix or array.",examples:["A = [[1, 2], [3, 4]]","row(A, 1)","row(A, 2)"],seealso:["column","matrixFromRows"]},size:{name:"size",category:"Matrix",syntax:["size(x)"],description:"Calculate the size of a matrix.",examples:["size(2.3)",'size("hello world")',"a = [1, 2; 3, 4; 5, 6]","size(a)","size(1:6)"],seealso:["concat","count","det","diag","identity","inv","ones","range","squeeze","subset","trace","transpose","zeros"]},sort:{name:"sort",category:"Matrix",syntax:["sort(x)","sort(x, compare)"],description:'Sort the items in a matrix. Compare can be a string "asc", "desc", "natural", or a custom sort function.',examples:["sort([5, 10, 1])",'sort(["C", "B", "A", "D"])',"sortByLength(a, b) = size(a)[1] - size(b)[1]",'sort(["Langdon", "Tom", "Sara"], sortByLength)','sort(["10", "1", "2"], "natural")'],seealso:["map","filter","forEach"]},squeeze:{name:"squeeze",category:"Matrix",syntax:["squeeze(x)"],description:"Remove inner and outer singleton dimensions from a matrix.",examples:["a = zeros(3,2,1)","size(squeeze(a))","b = zeros(1,1,3)","size(squeeze(b))"],seealso:["concat","det","diag","identity","inv","ones","range","size","subset","trace","transpose","zeros"]},subset:{name:"subset",category:"Matrix",syntax:["value(index)","value(index) = replacement","subset(value, [index])","subset(value, [index], replacement)"],description:"Get or set a subset of the entries of a matrix or characters of a string. Indexes are one-based. There should be one index specification for each dimension of the target. Each specification can be a single index, a list of indices, or a range in colon notation `l:u`. In a range, both the lower bound l and upper bound u are included; and if a bound is omitted it defaults to the most extreme valid value. The cartesian product of the indices specified in each dimension determines the target of the operation.",examples:["d = [1, 2; 3, 4]","e = []","e[1, 1:2] = [5, 6]","e[2, :] = [7, 8]","f = d * e","f[2, 1]","f[:, 1]","f[[1,2], [1,3]] = [9, 10; 11, 12]","f"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","trace","transpose","zeros"]},trace:{name:"trace",category:"Matrix",syntax:["trace(A)"],description:"Calculate the trace of a matrix: the sum of the elements on the main diagonal of a square matrix.",examples:["A = [1, 2, 3; -1, 2, 3; 2, 0, 3]","trace(A)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","transpose","zeros"]},transpose:{name:"transpose",category:"Matrix",syntax:["x'","transpose(x)"],description:"Transpose a matrix",examples:["a = [1, 2, 3; 4, 5, 6]","a'","transpose(a)"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","zeros"]},zeros:{name:"zeros",category:"Matrix",syntax:["zeros(m)","zeros(m, n)","zeros(m, n, p, ...)","zeros([m])","zeros([m, n])","zeros([m, n, p, ...])"],description:"Create a matrix containing zeros.",examples:["zeros(3)","zeros(3, 5)","a = [1, 2, 3; 4, 5, 6]","zeros(size(a))"],seealso:["concat","det","diag","identity","inv","ones","range","size","squeeze","subset","trace","transpose"]},fft:{name:"fft",category:"Matrix",syntax:["fft(x)"],description:"Calculate N-dimensional fourier transform",examples:["fft([[1, 0], [1, 0]])"],seealso:["ifft"]},ifft:{name:"ifft",category:"Matrix",syntax:["ifft(x)"],description:"Calculate N-dimensional inverse fourier transform",examples:["ifft([[2, 2], [0, 0]])"],seealso:["fft"]},combinations:{name:"combinations",category:"Probability",syntax:["combinations(n, k)"],description:"Compute the number of combinations of n items taken k at a time",examples:["combinations(7, 5)"],seealso:["combinationsWithRep","permutations","factorial"]},combinationsWithRep:{name:"combinationsWithRep",category:"Probability",syntax:["combinationsWithRep(n, k)"],description:"Compute the number of combinations of n items taken k at a time with replacements.",examples:["combinationsWithRep(7, 5)"],seealso:["combinations","permutations","factorial"]},factorial:{name:"factorial",category:"Probability",syntax:["n!","factorial(n)"],description:"Compute the factorial of a value",examples:["5!","5 * 4 * 3 * 2 * 1","3!"],seealso:["combinations","combinationsWithRep","permutations","gamma"]},gamma:{name:"gamma",category:"Probability",syntax:["gamma(n)"],description:"Compute the gamma function. For small values, the Lanczos approximation is used, and for large values the extended Stirling approximation.",examples:["gamma(4)","3!","gamma(1/2)","sqrt(pi)"],seealso:["factorial"]},kldivergence:{name:"kldivergence",category:"Probability",syntax:["kldivergence(x, y)"],description:"Calculate the Kullback-Leibler (KL) divergence between two distributions.",examples:["kldivergence([0.7,0.5,0.4], [0.2,0.9,0.5])"],seealso:[]},lgamma:{name:"lgamma",category:"Probability",syntax:["lgamma(n)"],description:"Logarithm of the gamma function for real, positive numbers and complex numbers, using Lanczos approximation for numbers and Stirling series for complex numbers.",examples:["lgamma(4)","lgamma(1/2)","lgamma(math.i)","lgamma(complex(1.1, 2))"],seealso:["gamma"]},multinomial:{name:"multinomial",category:"Probability",syntax:["multinomial(A)"],description:"Multinomial Coefficients compute the number of ways of picking a1, a2, ..., ai unordered outcomes from `n` possibilities. multinomial takes one array of integers as an argument. The following condition must be enforced: every ai > 0.",examples:["multinomial([1, 2, 1])"],seealso:["combinations","factorial"]},permutations:{name:"permutations",category:"Probability",syntax:["permutations(n)","permutations(n, k)"],description:"Compute the number of permutations of n items taken k at a time",examples:["permutations(5)","permutations(5, 3)"],seealso:["combinations","combinationsWithRep","factorial"]},pickRandom:{name:"pickRandom",category:"Probability",syntax:["pickRandom(array)","pickRandom(array, number)","pickRandom(array, weights)","pickRandom(array, number, weights)","pickRandom(array, weights, number)"],description:"Pick a random entry from a given array.",examples:["pickRandom(0:10)","pickRandom([1, 3, 1, 6])","pickRandom([1, 3, 1, 6], 2)","pickRandom([1, 3, 1, 6], [2, 3, 2, 1])","pickRandom([1, 3, 1, 6], 2, [2, 3, 2, 1])","pickRandom([1, 3, 1, 6], [2, 3, 2, 1], 2)"],seealso:["random","randomInt"]},random:{name:"random",category:"Probability",syntax:["random()","random(max)","random(min, max)","random(size)","random(size, max)","random(size, min, max)"],description:"Return a random number.",examples:["random()","random(10, 20)","random([2, 3])"],seealso:["pickRandom","randomInt"]},randomInt:{name:"randomInt",category:"Probability",syntax:["randomInt(max)","randomInt(min, max)","randomInt(size)","randomInt(size, max)","randomInt(size, min, max)"],description:"Return a random integer number",examples:["randomInt(10, 20)","randomInt([2, 3], 10)"],seealso:["pickRandom","random"]},compare:{name:"compare",category:"Relational",syntax:["compare(x, y)"],description:"Compare two values. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:["compare(2, 3)","compare(3, 2)","compare(2, 2)","compare(5cm, 40mm)","compare(2, [1, 2, 3])"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compareNatural","compareText"]},compareNatural:{name:"compareNatural",category:"Relational",syntax:["compareNatural(x, y)"],description:"Compare two values of any type in a deterministic, natural way. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:["compareNatural(2, 3)","compareNatural(3, 2)","compareNatural(2, 2)","compareNatural(5cm, 40mm)",'compareNatural("2", "10")',"compareNatural(2 + 3i, 2 + 4i)","compareNatural([1, 2, 4], [1, 2, 3])","compareNatural([1, 5], [1, 2, 3])","compareNatural([1, 2], [1, 2])","compareNatural({a: 2}, {a: 4})"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compare","compareText"]},compareText:{name:"compareText",category:"Relational",syntax:["compareText(x, y)"],description:"Compare two strings lexically. Comparison is case sensitive. Returns 1 when x > y, -1 when x < y, and 0 when x == y.",examples:['compareText("B", "A")','compareText("A", "B")','compareText("A", "A")','compareText("2", "10")','compare("2", "10")',"compare(2, 10)",'compareNatural("2", "10")','compareText("B", ["A", "B", "C"])'],seealso:["compare","compareNatural"]},deepEqual:{name:"deepEqual",category:"Relational",syntax:["deepEqual(x, y)"],description:"Check equality of two matrices element wise. Returns true if the size of both matrices is equal and when and each of the elements are equal.",examples:["deepEqual([1,3,4], [1,3,4])","deepEqual([1,3,4], [1,3])"],seealso:["equal","unequal","smaller","larger","smallerEq","largerEq","compare"]},equal:{name:"equal",category:"Relational",syntax:["x == y","equal(x, y)"],description:"Check equality of two values. Returns true if the values are equal, and false if not.",examples:["2+2 == 3","2+2 == 4","a = 3.2","b = 6-2.8","a == b","50cm == 0.5m"],seealso:["unequal","smaller","larger","smallerEq","largerEq","compare","deepEqual","equalText"]},equalText:{name:"equalText",category:"Relational",syntax:["equalText(x, y)"],description:"Check equality of two strings. Comparison is case sensitive. Returns true if the values are equal, and false if not.",examples:['equalText("Hello", "Hello")','equalText("a", "A")','equal("2e3", "2000")','equalText("2e3", "2000")','equalText("B", ["A", "B", "C"])'],seealso:["compare","compareNatural","compareText","equal"]},larger:{name:"larger",category:"Relational",syntax:["x > y","larger(x, y)"],description:"Check if value x is larger than y. Returns true if x is larger than y, and false if not.",examples:["2 > 3","5 > 2*2","a = 3.3","b = 6-2.8","(a > b)","(b < a)","5 cm > 2 inch"],seealso:["equal","unequal","smaller","smallerEq","largerEq","compare"]},largerEq:{name:"largerEq",category:"Relational",syntax:["x >= y","largerEq(x, y)"],description:"Check if value x is larger or equal to y. Returns true if x is larger or equal to y, and false if not.",examples:["2 >= 1+1","2 > 1+1","a = 3.2","b = 6-2.8","(a >= b)"],seealso:["equal","unequal","smallerEq","smaller","compare"]},smaller:{name:"smaller",category:"Relational",syntax:["x < y","smaller(x, y)"],description:"Check if value x is smaller than value y. Returns true if x is smaller than y, and false if not.",examples:["2 < 3","5 < 2*2","a = 3.3","b = 6-2.8","(a < b)","5 cm < 2 inch"],seealso:["equal","unequal","larger","smallerEq","largerEq","compare"]},smallerEq:{name:"smallerEq",category:"Relational",syntax:["x <= y","smallerEq(x, y)"],description:"Check if value x is smaller or equal to value y. Returns true if x is smaller than y, and false if not.",examples:["2 <= 1+1","2 < 1+1","a = 3.2","b = 6-2.8","(a <= b)"],seealso:["equal","unequal","larger","smaller","largerEq","compare"]},unequal:{name:"unequal",category:"Relational",syntax:["x != y","unequal(x, y)"],description:"Check unequality of two values. Returns true if the values are unequal, and false if they are equal.",examples:["2+2 != 3","2+2 != 4","a = 3.2","b = 6-2.8","a != b","50cm != 0.5m","5 cm != 2 inch"],seealso:["equal","smaller","larger","smallerEq","largerEq","compare","deepEqual"]},setCartesian:{name:"setCartesian",category:"Set",syntax:["setCartesian(set1, set2)"],description:"Create the cartesian product of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays and the values will be sorted in ascending order before the operation.",examples:["setCartesian([1, 2], [3, 4])"],seealso:["setUnion","setIntersect","setDifference","setPowerset"]},setDifference:{name:"setDifference",category:"Set",syntax:["setDifference(set1, set2)"],description:"Create the difference of two (multi)sets: every element of set1, that is not the element of set2. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setDifference([1, 2, 3, 4], [3, 4, 5, 6])","setDifference([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setIntersect","setSymDifference"]},setDistinct:{name:"setDistinct",category:"Set",syntax:["setDistinct(set)"],description:"Collect the distinct elements of a multiset. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setDistinct([1, 1, 1, 2, 2, 3])"],seealso:["setMultiplicity"]},setIntersect:{name:"setIntersect",category:"Set",syntax:["setIntersect(set1, set2)"],description:"Create the intersection of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setIntersect([1, 2, 3, 4], [3, 4, 5, 6])","setIntersect([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setDifference"]},setIsSubset:{name:"setIsSubset",category:"Set",syntax:["setIsSubset(set1, set2)"],description:"Check whether a (multi)set is a subset of another (multi)set: every element of set1 is the element of set2. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setIsSubset([1, 2], [3, 4, 5, 6])","setIsSubset([3, 4], [3, 4, 5, 6])"],seealso:["setUnion","setIntersect","setDifference"]},setMultiplicity:{name:"setMultiplicity",category:"Set",syntax:["setMultiplicity(element, set)"],description:"Count the multiplicity of an element in a multiset. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setMultiplicity(1, [1, 2, 2, 4])","setMultiplicity(2, [1, 2, 2, 4])"],seealso:["setDistinct","setSize"]},setPowerset:{name:"setPowerset",category:"Set",syntax:["setPowerset(set)"],description:"Create the powerset of a (multi)set: the powerset contains very possible subsets of a (multi)set. A multi-dimension array will be converted to a single-dimension array before the operation.",examples:["setPowerset([1, 2, 3])"],seealso:["setCartesian"]},setSize:{name:"setSize",category:"Set",syntax:["setSize(set)","setSize(set, unique)"],description:'Count the number of elements of a (multi)set. When the second parameter "unique" is true, count only the unique values. A multi-dimension array will be converted to a single-dimension array before the operation.',examples:["setSize([1, 2, 2, 4])","setSize([1, 2, 2, 4], true)"],seealso:["setUnion","setIntersect","setDifference"]},setSymDifference:{name:"setSymDifference",category:"Set",syntax:["setSymDifference(set1, set2)"],description:"Create the symmetric difference of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setSymDifference([1, 2, 3, 4], [3, 4, 5, 6])","setSymDifference([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setUnion","setIntersect","setDifference"]},setUnion:{name:"setUnion",category:"Set",syntax:["setUnion(set1, set2)"],description:"Create the union of two (multi)sets. Multi-dimension arrays will be converted to single-dimension arrays before the operation.",examples:["setUnion([1, 2, 3, 4], [3, 4, 5, 6])","setUnion([[1, 2], [3, 4]], [[3, 4], [5, 6]])"],seealso:["setIntersect","setDifference"]},erf:{name:"erf",category:"Special",syntax:["erf(x)"],description:"Compute the erf function of a value using a rational Chebyshev approximations for different intervals of x",examples:["erf(0.2)","erf(-0.5)","erf(4)"],seealso:[]},cumsum:{name:"cumsum",category:"Statistics",syntax:["cumsum(a, b, c, ...)","cumsum(A)"],description:"Compute the cumulative sum of all values.",examples:["cumsum(2, 3, 4, 1)","cumsum([2, 3, 4, 1])","cumsum([1, 2; 3, 4])","cumsum([1, 2; 3, 4], 1)","cumsum([1, 2; 3, 4], 2)"],seealso:["max","mean","median","min","prod","std","sum","variance"]},mad:{name:"mad",category:"Statistics",syntax:["mad(a, b, c, ...)","mad(A)"],description:"Compute the median absolute deviation of a matrix or a list with values. The median absolute deviation is defined as the median of the absolute deviations from the median.",examples:["mad(10, 20, 30)","mad([1, 2, 3])"],seealso:["mean","median","std","abs"]},max:{name:"max",category:"Statistics",syntax:["max(a, b, c, ...)","max(A)","max(A, dim)"],description:"Compute the maximum value of a list of values.",examples:["max(2, 3, 4, 1)","max([2, 3, 4, 1])","max([2, 5; 4, 3])","max([2, 5; 4, 3], 1)","max([2, 5; 4, 3], 2)","max(2.7, 7.1, -4.5, 2.0, 4.1)","min(2.7, 7.1, -4.5, 2.0, 4.1)"],seealso:["mean","median","min","prod","std","sum","variance"]},mean:{name:"mean",category:"Statistics",syntax:["mean(a, b, c, ...)","mean(A)","mean(A, dim)"],description:"Compute the arithmetic mean of a list of values.",examples:["mean(2, 3, 4, 1)","mean([2, 3, 4, 1])","mean([2, 5; 4, 3])","mean([2, 5; 4, 3], 1)","mean([2, 5; 4, 3], 2)","mean([1.0, 2.7, 3.2, 4.0])"],seealso:["max","median","min","prod","std","sum","variance"]},median:{name:"median",category:"Statistics",syntax:["median(a, b, c, ...)","median(A)"],description:"Compute the median of all values. The values are sorted and the middle value is returned. In case of an even number of values, the average of the two middle values is returned.",examples:["median(5, 2, 7)","median([3, -1, 5, 7])"],seealso:["max","mean","min","prod","std","sum","variance","quantileSeq"]},min:{name:"min",category:"Statistics",syntax:["min(a, b, c, ...)","min(A)","min(A, dim)"],description:"Compute the minimum value of a list of values.",examples:["min(2, 3, 4, 1)","min([2, 3, 4, 1])","min([2, 5; 4, 3])","min([2, 5; 4, 3], 1)","min([2, 5; 4, 3], 2)","min(2.7, 7.1, -4.5, 2.0, 4.1)","max(2.7, 7.1, -4.5, 2.0, 4.1)"],seealso:["max","mean","median","prod","std","sum","variance"]},mode:{name:"mode",category:"Statistics",syntax:["mode(a, b, c, ...)","mode(A)","mode(A, a, b, B, c, ...)"],description:"Computes the mode of all values as an array. In case mode being more than one, multiple values are returned in an array.",examples:["mode(2, 1, 4, 3, 1)","mode([1, 2.7, 3.2, 4, 2.7])","mode(1, 4, 6, 1, 6)"],seealso:["max","mean","min","median","prod","std","sum","variance"]},prod:{name:"prod",category:"Statistics",syntax:["prod(a, b, c, ...)","prod(A)"],description:"Compute the product of all values.",examples:["prod(2, 3, 4)","prod([2, 3, 4])","prod([2, 5; 4, 3])"],seealso:["max","mean","min","median","min","std","sum","variance"]},quantileSeq:{name:"quantileSeq",category:"Statistics",syntax:["quantileSeq(A, prob[, sorted])","quantileSeq(A, [prob1, prob2, ...][, sorted])","quantileSeq(A, N[, sorted])"],description:"Compute the prob order quantile of a matrix or a list with values. The sequence is sorted and the middle value is returned. Supported types of sequence values are: Number, BigNumber, Unit Supported types of probablity are: Number, BigNumber. \n\nIn case of a (multi dimensional) array or matrix, the prob order quantile of all elements will be calculated.",examples:["quantileSeq([3, -1, 5, 7], 0.5)","quantileSeq([3, -1, 5, 7], [1/3, 2/3])","quantileSeq([3, -1, 5, 7], 2)","quantileSeq([-1, 3, 5, 7], 0.5, true)"],seealso:["mean","median","min","max","prod","std","sum","variance"]},std:{name:"std",category:"Statistics",syntax:["std(a, b, c, ...)","std(A)","std(A, normalization)"],description:'Compute the standard deviation of all values, defined as std(A) = sqrt(variance(A)). Optional parameter normalization can be "unbiased" (default), "uncorrected", or "biased".',examples:["std(2, 4, 6)","std([2, 4, 6, 8])",'std([2, 4, 6, 8], "uncorrected")','std([2, 4, 6, 8], "biased")',"std([1, 2, 3; 4, 5, 6])"],seealso:["max","mean","min","median","prod","sum","variance"]},sum:{name:"sum",category:"Statistics",syntax:["sum(a, b, c, ...)","sum(A)"],description:"Compute the sum of all values.",examples:["sum(2, 3, 4, 1)","sum([2, 3, 4, 1])","sum([2, 5; 4, 3])"],seealso:["max","mean","median","min","prod","std","sum","variance"]},variance:{name:"variance",category:"Statistics",syntax:["variance(a, b, c, ...)","variance(A)","variance(A, normalization)"],description:'Compute the variance of all values. 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r=e.args.filter((function(e){return void 0===t[e]})),a=1===r.length?r[0]:new c("*","multiply",r),o=n.concat(y(a,t));return new c("*","multiply",o)}return new c("+","add",e.args.map((function(n){return new c("*","multiply",e.args.map((function(e){return e===n?y(e,t):e.clone()})))})))}if("/"===e.op&&e.isBinary()){var u=e.args[0],p=e.args[1];return void 0!==t[p]?new c("/","divide",[y(u,t),p]):void 0!==t[u]?new c("*","multiply",[new c("-","unaryMinus",[u]),new c("/","divide",[y(p,t),new c("^","pow",[p.clone(),b(2)])])]):new c("/","divide",[new c("-","subtract",[new c("*","multiply",[y(u,t),p.clone()]),new c("*","multiply",[u.clone(),y(p,t)])]),new c("^","pow",[p.clone(),b(2)])])}if("^"===e.op&&e.isBinary()){var d=e.args[0],h=e.args[1];if(void 0!==t[d])return cH(d)&&(s(d.value)||i(d.value,1))?b(0):new c("*","multiply",[e,new c("*","multiply",[new l("log",[d.clone()]),y(h.clone(),t)])]);if(void 0!==t[h]){if(cH(h)){if(s(h.value))return b(0);if(i(h.value,1))return y(d,t)}var f=new 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Joe{constructor(e){this.pInst=e,this.turtle={},this.home()}home(){this.turtle.x=this.pInst.width/2,this.turtle.y=this.pInst.height/2,this.turtle.angle=0,this.turtle.pen=!0,this.turtle.pencolor="black",this.turtle.penwidth=1}pencolor(e){this.turtle.pencolor=e}pensize(e){this.turtle.penwidth=e}pendown(){this.turtle.pen=!0}penup(){this.turtle.pen=!1}setposition(e,t){this.turtle.x=e,this.turtle.y=t}position(){return[this.turtle.x,this.turtle.y]}left(e){this.turtle.angle-=e}right(e){this.turtle.angle+=e}setheading(e){this.turtle.angle=e}heading(){return this.turtle.angle}forward(e){const t=this._angleMode;this.pInst.angleMode(this.pInst.DEGREES);const 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Zoe=fte,Qoe=Nte,eue=bte,tue=pte,nue=wte;const rue=p5;var aue=rue.prototype.createVector,iue=function(){function e(t){if(this.isComplex=!1,"number"==typeof t)this.tensor=Ou(t);else if(t instanceof Array)this.tensor=Ri(t);else if(t instanceof rue.Vector){var n=t.array();this.tensor=Ri(n)}else if(t instanceof e)this.tensor=t.tensor;else{if(!(t instanceof ti))throw new Error("Tensors must be created from Numbers, Arrays, or p5.Vectors.");this.tensor=t}this.shape=this.tensor.shape}return e.prototype.toString=function(){return this.tensor.toString()},e.prototype.print=function(){this.tensor.print()},e.prototype.array=function(){return function(e,t,n,r){return new(n||(n=Promise))((function(a,i){function s(e){try{u(r.next(e))}catch(e){i(e)}}function o(e){try{u(r.throw(e))}catch(e){i(e)}}function u(e){var t;e.done?a(e.value):(t=e.value,t instanceof n?t:new n((function(e){e(t)}))).then(s,o)}u((r=r.apply(e,t||[])).next())}))}(this,void 0,void 0,(function(){var e,t;return function(e,t){var n,r,a,i,s={label:0,sent:function(){if(1&a[0])throw a[1];return a[1]},trys:[],ops:[]};return i={next:o(0),throw:o(1),return:o(2)},"function"==typeof Symbol&&(i[Symbol.iterator]=function(){return this}),i;function o(i){return function(o){return function(i){if(n)throw new TypeError("Generator is already executing.");for(;s;)try{if(n=1,r&&(a=2&i[0]?r.return:i[0]?r.throw||((a=r.return)&&a.call(r),0):r.next)&&!(a=a.call(r,i[1])).done)return a;switch(r=0,a&&(i=[2&i[0],a.value]),i[0]){case 0:case 1:a=i;break;case 4:return s.label++,{value:i[1],done:!1};case 5:s.label++,r=i[1],i=[0];continue;case 7:i=s.ops.pop(),s.trys.pop();continue;default:if(!((a=(a=s.trys).length>0&&a[a.length-1])||6!==i[0]&&2!==i[0])){s=0;continue}if(3===i[0]&&(!a||i[1]>a[0]&&i[1]100)throw new Error("Percentile must be in the range [0, 100].");r.push(n*(e/100))})),r}if(t<0||t>100)throw new Error("Percentile must be in the range [0, 100].");return n*(t/100)},ple=function(e,t){var n=lle(e);if(t instanceof Array){var r=[];return t.forEach((function(e){if(e<0||e>1)throw new Error("Quantile must be in the range [0, 1].");r.push(n*e)})),r}if(t<0||t>1)throw new Error("Quantile must be in the range [0, 1].");return n*t},dle=function(e,t,n){var r=e.tensor.mean(t,n);return new iue(r)},hle=function(e,t){if(t instanceof iue){var n=e.mult(t),r=n.sum(),a=t.sum(),i=r.div(a);return n.dispose(),r.dispose(),a.dispose(),i}return dle(e)},fle=function(e){var t=ho((function(){var t=dle(e),n=e.sub(t).sq().sum(),r=e.shape.reduce((function(e,t){return e*t}));return n.div(r).sqrt().tensor}));return new iue(t)},mle=function(e){var t=fle(e),n=t.sq();return t.dispose(),n};const gle=function(e){var t=e.pg,n=e.originX,r=e.originY,a=e.width,i=e.height,s=e.annotationsPalette;t.push(),t.fill(s.backgroundColor),t.strokeWeight(1),t.stroke(s.backgroundColor),t.rect(n,r,a,-i),t.pop()};var yle=n(7484),ble=function(e,t,n){void 0===n&&(n=50);for(var 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f=xle(e),m=ble(t.min,t.max,u+1),g=-5*c,y=0;y100?n.round(v,0):n.round(v,1)),n.text(b,g,-f[y])}n.pop()}(this.props)},e.prototype.background=function(e){this.props.annotationsPalette.backgroundColor=e||this.props.annotationsPalette.backgroundColor,this.layers.unshift({props:this.props,operation:gle})},e.prototype.margin=function(e){this.props.annotationsPalette.marginColor=e||this.props.annotationsPalette.marginColor,this.layers.unshift({props:this.props,operation:Cle})},e.prototype.bar=function(e){this.props=Vle(Vle({},this.props),e),this.props.numBins=this.props.numBins||20,this.layers.push({props:this.props,operation:Yle})},e.prototype.point=function(e){this.props=Vle(Vle({},this.props),e),this.layers.push({props:this.props,operation:Ble})},e.prototype.line=function(e){this.props=Vle(Vle({},this.props),e),this.layers.push({props:this.props,operation:zle})},e}();p5.prototype.registerMethod("init",(function(){this._plots=[]})),p5.prototype.createPlot=function(e){var t=new Gle(this,e);return this._plots.push(t),t},p5.prototype.registerMethod("post",(function(){this._plots.forEach((function(e){e.clear()}))})),p5.prototype.registerMethod("remove",(function(){this._plots.forEach((function(e){return e.props.pg.remove()})),this._plots=void 0}));var Kle=n(178);function Jle(e,...t){if("function"==typeof e)throw new Error("You must supply the data as the first argument to tidy()");let n=e;for(const e of t)e&&(n=e(n));return n}function Xle(e){return t=>t.filter(e)}function Zle(e){return t=>t.map(e)}function Qle(e){return null==e?[]:Array.isArray(e)?e:[e]}function ece(e,t){return et?1:e>=t?0:NaN}function tce(e){return t=>{const n=Qle(e).map((e=>"function"==typeof e?1===e.length?nce(e):e:nce(e)));return t.slice().sort(((e,t)=>{for(const r of n){const n=r(e,t);if(n)return n}return 0}))}}function nce(e){const t="function"==typeof e?e:t=>t[e];return function(e,n){return ace(t(e),t(n),!1)}}function rce(e){const t="function"==typeof e?e:t=>t[e];return function(e,n){return ace(t(e),t(n),!0)}}function ace(e,t,n){let r=n?t:e,a=n?e:t;if(ice(r)&&ice(a)){const e=(r!=r?0:null===r?1:2)-(a!=a?0:null===a?1:2);return n?-e:e}return ice(r)?n?-1:1:ice(a)?n?1:-1:ece(r,a)}function ice(e){return null==e||e!=e}function sce(e,t){return n=>{t=null!=t?t:{};const r={},a=Object.keys(e);for(const t of a)r[t]=e[t](n);if(t.rest&&n.length){const e=Object.keys(n[0]);for(const i of e)a.includes(i)||(r[i]=t.rest(i)(n))}return[r]}}function oce(e,t,n,r){if(!e.length)return[];const a={};let i;if(null==r)i=Object.keys(e[0]);else{i=[];for(const t of Qle(r))"function"==typeof t?i.push(...t(e)):i.push(t)}for(const r of i)n&&!n(e.map((e=>e[r])))||(a[r]=t(r)(e));return[a]}function uce(e){return t=>oce(t,e)}function lce(e,t){return n=>oce(n,t,e)}function cce(e,t){return n=>oce(n,t,void 0,e)}function pce(e){return t=>{const n=t.map((e=>({...e})));let r=0;for(const t of n){for(const a in e){const i=e[a],s="function"==typeof i?i(t,r,n):i;t[a]=s}++r}return n}}class dce extends Map{constructor(e,t=fce){if(super(),Object.defineProperties(this,{_intern:{value:new Map},_key:{value:t}}),null!=e)for(const[t,n]of e)this.set(t,n)}get(e){return super.get(hce(this,e))}has(e){return super.has(hce(this,e))}set(e,t){return super.set(function({_intern:e,_key:t},n){const r=t(n);return e.has(r)?e.get(r):(e.set(r,n),n)}(this,e),t)}delete(e){return super.delete(function({_intern:e,_key:t},n){const r=t(n);return e.has(r)&&(n=e.get(n),e.delete(r)),n}(this,e))}}function hce({_intern:e,_key:t},n){const r=t(n);return e.has(r)?e.get(r):n}function fce(e){return null!==e&&"object"==typeof e?e.valueOf():e}function mce(e){return e}function gce(e,t){if(null==e||"object"!=typeof e||Array.isArray(e))return e;const n=Object.fromEntries(t.filter((e=>"function"!=typeof e[0])));return Object.assign(n,e)}function yce(e,t,n,r,a,i=0){for(const[s,o]of e.entries()){const e=[...n,s];o instanceof Map?yce(o,r(t,e,i),e,r,a,i+1):a(t,e,o,i)}return t}function bce(e,t,n=(e=>e[e.length-1])){const r=new Map;return yce(e,r,[],(function(e,t){const r=new Map;return e.set(n(t),r),r}),(function(e,r,a){e.set(n(r),t(a,r))})),r}Set;const vce=e=>e;function xce(e,t,n){"function"==typeof t?t=[t]:2!==arguments.length||null==t||Array.isArray(t)||(n=t);const r=r=>{const a=_ce(wce(r,e),t,null==n?void 0:n.addGroupKeys);if(null==n?void 0:n.export)switch(n.export){case"grouped":return a;case"levels":return Mce(a,n);case"entries-obj":case"entriesObject":return Mce(a,{...n,export:"levels",levels:["entries-object"]});default:return Mce(a,{...n,export:"levels",levels:[n.export]})}return Dce(a,null==n?void 0:n.addGroupKeys)};return r}function _ce(e,t,n){let r=e;if(!(null==t?void 0:t.length))return r;for(const e of t)e&&(r=bce(r,((t,r)=>{let a=e(t,{groupKeys:r});return!1!==n&&(a=a.map((e=>gce(e,r)))),a})));return r}function wce(e,t){const n=function(e,...t){return function(e,t,n,r){return function e(a,i){if(i>=r.length)return n(a);const s=new dce,o=r[i++];let u=-1;for(const e of a){const 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