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u?W(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var mw=L({dilation2d_:u3}),cp={};_e(cp,{assertAndGetBroadcastShape:()=>ct,getBroadcastDims:()=>ZN,getReductionAxes:()=>Bt});function ZN(e,t){let n=e.length,a=[];for(let r=0;r1&&i===1&&a.unshift(s)}return a}function Bt(e,t){let n=[];for(let a=0;a1)&&n.unshift(s)}return n}function ct(e,t){let n=Math.max(e.length,t.length),a=new Array(n);for(let r=0;r`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${a.rank}.`);let r=n.rank===1?n.size:n.shape[1],s=a.rank===1?a.size:a.shape[0];if(A(r===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${s}.`),n.rank===1&&a.rank===1){let i=W(n,[1,-1]),o=W(a,[-1,1]),l=$e(i,o);return W(l,[])}else if(n.rank===1&&a.rank===2){let i=W(n,[1,-1]),o=W(a,[a.shape[0],a.shape[1]]),l=$e(i,o);return W(l,[l.size])}else if(n.rank===2&&a.rank===1){let i=W(a,[-1,1]),o=$e(n,i);return W(o,[o.size])}else{let i=W(a,[a.shape[0],a.shape[1]]);return $e(n,i)}}var gw=L({dot_:m3});function f3(e,...t){let n=t.map((r,s)=>E(r,`tensors${s}`,"einsum")),a={equation:e};return P.runKernel(Wm,n,a)}var Qs=L({einsum_:f3});function g3(e){let t={x:E(e,"x","elu","float32")};return P.runKernel(Ki,t)}var dp=L({elu_:g3});function b3(e,t){let n=E(e,"x","ensureShape","string_or_numeric");if(!nN(n.shape,t))throw new Error(`EnsureShape: Shape of tensor ${n.shape} is not compatible with expected shape ${t}`);return e}var JN=L({ensureShape_:b3});function y3(e){let t=E(e,"x","erf");A(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=re(t,"float32"));let n={x:t};return P.runKernel(Xi,n)}var af=L({erf_:y3});function bw(e,t){for(let n=0;ne[s]);return[n,r]}function gi(e,t){let n=t.map(a=>1);return QN(e,n,t)}function x3(e,t,n){A(bw(t,n),()=>`${e} supports only inner-most axes for now. 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d=E(e,"x","conv2d","float32"),c=E(t,"filter","conv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),A(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),A(c.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${c.rank}.`),Tn("fused conv2d",a,i);let f=r==="NHWC"?h.shape[3]:h.shape[1];A(c.shape[2]===f,()=>`Error in conv2d: depth of input (${f}) must match input depth for filter ${c.shape[2]}.`),A(mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=ot(m),b=ot(f);A(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let y=ct(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),v=a?W(u,[b,h,d]):W(u,[b,d,h]),I;r!=null&&(I=E(r,"bias","fused matMul"),[I]=At(I,l),ct(y,I.shape));let N;i!=null&&(N=E(i,"prelu weights","fused matMul"));let C=(D,$)=>{let[S,M,B,U]=$,H=Nf(W(D,B.shape),B,s),q,K;if(!n&&!a?(q=$e(H,M,!1,!0),K=$e(S,H,!0,!1)):!n&&a?(q=$e(H,M,!1,!1),K=$e(H,S,!0,!1)):n&&!a?(q=$e(M,H,!1,!0),K=$e(S,H,!1,!1)):(q=$e(M,H,!0,!0),K=$e(H,S,!0,!0)),r!=null){let Z=Tf(U,H);return[q,K,Z]}else return[q,K]},_={a:x,b:v,bias:I,preluActivationWeights:N},F={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?dr((D,$,S)=>{let M=P.runKernel(li,_,F);return S([D,$,M]),{value:W(M,y),gradFunc:C}})(x,v):dr((D,$,S,M)=>{let B=P.runKernel(li,_,F);return M([D,$,B,S]),{value:W(B,y),gradFunc:C}})(x,v,I)}var sW=L({fusedMatMul_:rW});function iW(e){return Sf(e,.54,.46)}var oW=L({hammingWindow_:iW});function lW(e){return Sf(e,.5,.5)}var GT=L({hannWindow_:lW});function uW(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ve(e,s,t)),s+=n;if(a)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),A(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),A(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),A(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length 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t=E(e,"image","RGBToGrayscale"),n=t.rank-1,a=t.shape[n];A(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),A(a===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${a}.`);let r=t.dtype,s=re(t,"float32"),i=je([.2989,.587,.114]),o;switch(t.rank){case 2:o=Qs("ij,j->i",s,i);break;case 3:o=Qs("ijk,k->ij",s,i);break;case 4:o=Qs("ijkl,l->ijk",s,i);break;case 5:o=Qs("ijklm,m->ijkl",s,i);break;case 6:o=Qs("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return o=Gt(o,-1),re(o,r)}var xW=L({rgbToGrayscale_:yW});function vW(e,t,n=0,a=.5){let r=E(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return P.runKernel(pp,s,i)}var wW=L({rotateWithOffset_:vW});function gp(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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g=0;gr&&u.push({score:t[g],boxIndex:g,suppressBeginIndex:0});u.sort(II);let p=s>0?-.5/s:0,d=[],c=[];for(;d.length0;){let g=u.pop(),{score:b,boxIndex:y,suppressBeginIndex:x}=g;if(b=x;--I){let N=EW(e,y,d[I]);if(N>=a){v=!0;break}if(g.score=g.score*_W(a,p,N),g.score<=r)break}g.suppressBeginIndex=d.length,v||(g.score===b?(d.push(y),c.push(g.score)):g.score>r&&SW(u,g,II))}let h=d.length,m=n-h;o&&m>0&&(d.push(...new Array(m).fill(0)),c.push(...new Array(m).fill(0)));let f={selectedIndices:d};return i&&(f.selectedScores=c),l&&(f.validOutputs=h),f}function EW(e,t,n){let a=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),s=Math.min(a[0],a[2]),i=Math.min(a[1],a[3]),o=Math.max(a[0],a[2]),l=Math.max(a[1],a[3]),u=Math.min(r[0],r[2]),p=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),c=Math.max(r[1],r[3]),h=(o-s)*(l-i),m=(d-u)*(c-p);if(h<=0||m<=0)return 0;let f=Math.max(s,u),g=Math.max(i,p),b=Math.min(o,d),y=Math.min(l,c),x=Math.max(b-f,0)*Math.max(y-g,0);return x/(h+m-x)}function _W(e,t,n){let a=Math.exp(t*n*n);return n<=e?a:0}function II(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function AW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppressionAsync"),i=E(t,"scores","nonMaxSuppressionAsync"),o=gp(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],p=l[1],{selectedIndices:d}=jT(u,p,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),je(d,"int32")}var FW=AW;function $W(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gp(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},p={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},d=P.runKernel(Gu,u,p);return{selectedIndices:d[0],selectedScores:d[1]}}var DW=L({nonMaxSuppressionWithScore_:$W});async function RW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gp(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),p=u[0],d=u[1],{selectedIndices:c,selectedScores:h}=KT(p,d,n,a,r,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:je(c,"int32"),selectedScores:je(h)}}var MW=RW;function OW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gp(i,o,n,a,r,null),u=l.maxOutputSize,p=l.iouThreshold,d=l.scoreThreshold,c={boxes:i,scores:o},h={maxOutputSize:u,iouThreshold:p,scoreThreshold:d,padToMaxOutputSize:s},m=P.runKernel(Uu,c,h);return{selectedIndices:m[0],validOutputs:m[1]}}var PW=L({nonMaxSuppressionPadded_:OW});async function LW(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=!1){let 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className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>yn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=X(s,pt(r));s.assign(i);let o=X(z(he(r,rn(X(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Jw=class extends Or{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:O(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:O(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(pt(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(rn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),O(()=>{this.accBeta1.assign(cr(this.beta1,this.iterations_+1)),this.accBeta2.assign(cr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Qw=class extends Or{static get className(){return"Adamax"}constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof nv))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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xn{apply(e,t=1){return O(()=>z(ma(z(e,t)),e))}};_C.className="swish";ne.registerClass(_C);function ys(e){return e.getClassName()}function Ix(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function xs(e){if(e==null){let t={};return t.className="linear",t.config={},Ix(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ix(t)}else return e instanceof xn?e:Ix(e)}function A0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var AC=class extends ne.Serializable{},Fd=class extends AC{constructor(e){super(),A0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Ed(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function Sj(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function Nj(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof AC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function Cj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var $C=class DC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=xs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ys(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Uf=class RC extends $C{constructor(t,n){super(t,n),this.kernel=null,RC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,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:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=O2(this.activation.getClassName());if(s!=null&&this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=Tj(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=Cj(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Gf=class MC extends Uf{constructor(t){super(2,t),MC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gf.className="Conv2D";ne.registerClass(Gf);var Hf=class OC extends Uf{constructor(t){super(3,t),OC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hf.className="Conv3D";ne.registerClass(Hf);var L0=class extends Gf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=rr(o,d,u,this.padding),m=rr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ka(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=rr(t[a],o,s,this.padding),t[r]=rr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L0.className="Conv2DTranspose";ne.registerClass(L0);var z0=class extends Hf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=dw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var PC=class extends Uf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ds(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};PC.className="SeparableConv";var W0=class extends PC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class LC extends Uf{constructor(t){super(1,t),LC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,MG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function Ej(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=As(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends $C{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 O(()=>{e=Te(e);let n=Ej(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function zC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function WC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class BC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Kf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Yx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`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=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=zC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=WC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Kx(n,[1,a]):n):this.cell.stateSize>1?[Kx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===BC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},jf=class extends $d{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,tn(this.units,"units"),this.activation=xs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jf.className="SimpleRNNCell";ne.registerClass(jf);var H0=class extends Pr{constructor(e){e.cell=new jf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{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 V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};j0.className="GRU";ne.registerClass(j0);var Dd=class extends $d{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,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ra{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};q0.className="LSTM";ne.registerClass(q0);var Kf=class extends $d{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ii(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Zx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):U2(t(),n),o=()=>_d(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _j=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;sHt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Va(l,a[0],r,s[0],i[0]),d=Va(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};VC.className="ConvRNN2D";var Xf=class extends Dd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,tn(this.filters,"filters"),this.kernelSize=Gl(n,2,"kernelSize"),this.kernelSize.forEach(o=>tn(o,"kernelSize")),this.strides=Gl(a||1,2,"strides"),this.strides.forEach(o=>tn(o,"strides")),this.padding=r||"valid",wa(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=Gl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>tn(o,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return u0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_j(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xf.className="ConvLSTM2DCell";ne.registerClass(Xf);var K0=class extends VC{constructor(e){let t=new Xf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Yf=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0U2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Yf.className="Dropout";ne.registerClass(Yf);var X0=class extends Yf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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s==="max"?i=Dt(e,t,n,o):i=xa(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}function UC(e,t,n,a,r,s){return O(()=>{Rt(r),L2(s),wa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ga()),s==null&&(s="max"),e=FC(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Cw(e,t,n,o):i=ew(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var GC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},x1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"max")}};x1.className="MaxPooling2D";ne.registerClass(x1);var v1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"avg")}};v1.className="AveragePooling2D";ne.registerClass(v1);var jC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),wa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},w1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"max")}};w1.className="MaxPooling3D";ne.registerClass(w1);var k1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"avg")}};k1.className="AveragePooling3D";ne.registerClass(k1);var qC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ba(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};E1.className="Bidirectional";ne.registerClass(E1);var _1=class extends We{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=lr(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. 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o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function I5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ti(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new _E(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=v5(this.graph,a),l=I5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>dn(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(ti(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(ti(l))continue;let u=iS(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return ti(l)||s.has(l.name)}if(!(ti(t)||i==null))for(let l of i){if(o(l))continue;let u=iS(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new dS(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>dn(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Zn(v)[0]]),o=a.map(v=>Zn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=hS(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Zn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!ti(v)&&!dn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Tr(d.node.name,a)),r[d.node.name]==null){let h=cS(d.node,r,a,this._resourceManager);c||([c]=Tr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Tr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},_5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},A5="?tfjs-format=file",F5="model.json",z1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new _5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await $N(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=a,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new mS(oS.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=oS.Instance.transformGraph(e.modelInitializer);this.initializer=new mS(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function $5(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[a,r]=e;if(!a)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in a))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in a))throw new Error("Model JSON is missing 'weightsManifest'");let s=jt.getWeightSpecs(a.weightsManifest),i=jt.getModelArtifactsForJSONSync(a,s,r);t=jt.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=jt.fromMemorySync(e);else throw new Error("Unknown model format");let n=new z1(t);return n.load(),n}function R5(e){return e.endsWith("/")||(e=e+"/"),`${e}${F5}${A5}`}var M5="4.21.0",AE={};_e(AE,{CSVDataset:()=>BE,Dataset:()=>xp,FileDataSource:()=>KE,TextLineDataset:()=>WE,URLDataSource:()=>XE,array:()=>r8,csv:()=>b8,func:()=>y8,generator:()=>x8,microphone:()=>w8,version_data:()=>k8,webcam:()=>v8,zip:()=>s8});var O5=Is(Fm()),P5=Is(Fm());function L5(e,t){return wm(e,t)}function wm(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(ru(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=wm(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function z5(e,t=$E){return FE(e,t)}function FE(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(ru(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=FE(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function $E(e){return e===null?null:ru(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function DE(e,t){let n=new Map;wm(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(w.isPromise(r)){let s=await r;n.set(a,s)}}return wm(e,t,n)}function ru(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=QS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function W5(e){return e==null||B5(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ce||w.isTypedArray(e)}function B5(e){return e===null||typeof e!="object"&&typeof e!="function"}function V5(e){return L5(e,U5)}function U5(e){return e instanceof Ce?{value:e.clone(),recurse:!1}:ru(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var RE=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},ME=class OE extends RE{constructor(){super(OE.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,n=new Array(t),a=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new Z5(this,e,t)}columnMajorBatch(e,t=!0,n=$E){return this.rowMajorBatch(e,t).map(a=>z5(a,n))}concatenate(e,t){return new LE(PE([this,e]),t)}take(e){return e<0||e==null?this:new Y5(this,e)}skip(e){return e<0||e==null?this:new X5(this,e)}prefetch(e){return new zE(this,e)}shuffle(e,t){return new a8(this,e,t)}serial(){return new K5(this)}},j5=class extends sn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:V5(e),done:!1}}},q5=class extends sn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},J5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Q5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},e8=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},fS=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},B1=class extends sn{constructor(){super(),this.outputQueue=new ME,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},t8=class extends B1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},LE=class extends sn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var n8=class extends sn{constructor(e,t=rs.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await DE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new VE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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 t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},u8=class UE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new UE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=wi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var AX={kernelName:fu,backendName:"cpu",kernelFunc:_X};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=H1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var $X={kernelName:gu,backendName:"cpu",kernelFunc:FX};function DX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RX={kernelName:Bc,backendName:"cpu",kernelFunc:DX},MX=lt(Ns,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return hr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>vi({inputs:{input:v},backend:n})),g=l.map(v=>iu({inputs:{input:v},backend:n})),b=ou({inputs:f,backend:n,attrs:{axis:s}}),y=ou({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=j1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var WX={kernelName:yu,backendName:"cpu",kernelFunc:ou};function j_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),QX={kernelName:Vi,backendName:"cpu",kernelFunc:JX},eY=lt(Ui,e=>Math.cosh(e)),tY={kernelName:Ui,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},vY={kernelName:ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${ql}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},wY={kernelName:jl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${jl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function kY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=pc(this.gl,r),ha(this.gl,s))this.textureHalfFloatExtension=pc(this.gl,s);else if(G().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),ha(this.gl,a))this.colorBufferHalfFloatExtension=pc(this.gl,a);else if(G().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",ha(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ha(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable 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t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),WA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(de(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yh(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?mA(this.gl,e,t):fA(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),de(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(),gA(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=vp(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Yh(this.gl,this.program),cc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Zh(a,e,this.framebuffer),this.debug&&cc(a),this.outputTexture=e,de(a,()=>a.viewport(0,0,t,n)),de(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),de(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oQ(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:ZA(e,t)}function QQ(e,t){if(e===1)return"rc";let n="";for(let a=0;a ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n}; bool rEdge = rp1 >= ${a}; `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= 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vec4(0.); ivec3 thisRC; int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; ${n} setOutput(result); } `}};function tee(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${t?f9(["r","c","d"],"inputShape"):Qo(["r","c","d"],e)} return ivec3(r, c, d); } `}var nee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=CS(t,n),r=ES(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=TS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=CS(n,a),s=ES(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=TS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function aee(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function TS(e,t,n,a,r){let s=ree(t,a),i;if(r){let[l,u]=vp(e[0],e[1]);i=l*u}else{let[l,u]=Md(e[0],e[1]);i=l*u}let o=aee(n,s);return i*o}function ree(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return hk(t);case hn.PACKED_2X2_FLOAT16:return mk(t);case hn.UNPACKED_FLOAT32:return pk(t);case hn.UNPACKED_FLOAT16:return ck(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return dk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function see(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?hn.PACKED_2X2_FLOAT32:hn.UNPACKED_FLOAT32:e?hn.PACKED_2X2_FLOAT16:hn.UNPACKED_FLOAT16}function CS(e,t){if(e===da.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return see(t);if(e===da.DOWNLOAD||e===da.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ES(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Ma="if (isnan(x)) return x;",iee="return x;",_S="return abs(x);",oee="return (x >= 0.0) ? x : (exp(x) - 1.0);",lee=Ma+` return (x < 0.0) ? 0.0 : x; `,uee=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); `,es="return x;",pee="return 1.0 / (1.0 + exp(-1.0 * x));",cee="return x;",dee=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,hee=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,mee=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,fee="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}},gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ht(t),r=QQ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${a} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } `}},bee=gr.whereImpl,yee=1e-7,xee=1e-4,Tx={};function vee(e){return e in Tx||(Tx[e]={}),Tx[e]}var wee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),kee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*kee/1024/1024}var gk=class QA extends Pc{nextDataId(){return QA.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof em)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new em(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new em(a),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new nee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new $m(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=dc(n),p=new NS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:da.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ss(o,es):c=new ir(o,es);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=wee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new gee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new eee(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new JA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new nQ(o):l=new tQ(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=eQ(t,p,d),h=this.getAndSaveBinary(c,()=>J9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||Q9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yee:xee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=xA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new iQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=See(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. 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NAN : result.a; `,Tp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${ht(r)} coords = getOutputCoords(); `,r===1)this.enableShapeUniforms?s+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=In("coords",r);this.enableShapeUniforms?s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = (${i[r-1]} + 1) >= outShape[${r} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; bool nextColOutOfBounds = (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; result.y = nextColOutOfBounds ? 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setOutput(result); } `}};function tm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return aa({inputs:{x:a[0]},backend:n});if(a.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=tm({inputs:a.slice(0,o),backend:n}),u=tm({inputs:a.slice(o),backend:n});return tm({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ga(o,l)),s=a.map(o=>o.shape),i=G().getBool("WEBGL_PACK")?new ate(a[0].shape,s):new nte(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var rte={kernelName:_i,backendName:"webgl",kernelFunc:tm};function ste(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"all",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var ite={kernelName:pu,backendName:"webgl",kernelFunc:ste};function ote(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[c,h]=T.computeOutAndReduceShapes(d.shape,u),m=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=nl(f,f.dtype,"any",n),b;if(i){let y=T.expandShapeToKeepDim(c,l);b=ce({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=ce({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),b}var lte={kernelName:cu,backendName:"webgl",kernelFunc:ote},ute=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${a}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${a}; i++) { int inIdx = ${o}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},pte=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ht(o),u=In("coords",o),p,d;if(s===1){d=o+1;let C=ht(d);p=` ${C} sourceLocR = ${C}(${u.join()}, 0); ++${u[o-1]}; ${C} sourceLocG = ${C}(${u.join()}, 0); ++${u[o-2]}; ${C} sourceLocA = ${C}(${u.join()}, 0); --${u[o-1]}; ${C} sourceLocB = ${C}(${u.join()}, 0); --${u[o-2]};`}else d=o,p=` ${l} sourceLocR = coords; ++${u[o-1]}; ${l} sourceLocG = coords; ++${u[o-2]}; ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),b=In("sourceLocB",d-1).concat("inIdx.b"),y=In("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":` inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${y.join()})));`,I=`vec4( getAChannel(${f.join()}), hasNextCol ? getAChannel(${g.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,N=a?"":` float getBestIndicesAChannel(${m.join()}) { return getChannel(getBestIndicesA(${c.join()}), vec2(${c.slice(-2).join()})); }`;this.userCode=` float getAChannel(${m.join()}) { return getChannel(getA(${c.join()}), vec2(${c.slice(-2).join()})); } ${N} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; ${p} ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, sourceLocB${h}, sourceLocA${h}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${I}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${v} vec4 candidate = ${I}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, replace.z ? candidate.z : bestValue.z, replace.w ? candidate.w : bestValue.w); bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); srcIdx++; } setOutput(bestIndex); } `}};function oF(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new ute(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=oF(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function lF(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new pte(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=lF(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function uF(e,t,n,a){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(p),c=ce({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=oF(e,c,a);s.push(h);let m=ce({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return lF(e,t,a)}function cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=uF(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var dte={kernelName:du,backendName:"webgl",kernelFunc:cte};function hte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=uF(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var mte={kernelName:hu,backendName:"webgl",kernelFunc:hte},fte=Ma+` if (abs(x) > 1.) { return NAN; } return asin(x); `,gte=Ze({opSnippet:fte}),bte={kernelName:Ai,backendName:"webgl",kernelFunc:gte},yte=Ma+"return log(x + sqrt(x * x + 1.0));",xte=Ze({opSnippet:yte}),vte={kernelName:Fi,backendName:"webgl",kernelFunc:xte},wte=Ma+` return atan(x); `,kte=Ze({opSnippet:wte}),Ite={kernelName:$i,backendName:"webgl",kernelFunc:kte},Ste=bk+` return atan(a, b); `,Nte=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+tl+` return result; `,Tte=fn({opSnippet:Ste,packedOpSnippet:Nte}),Cte={kernelName:Ri,backendName:"webgl",kernelFunc:Tte},Ete=Ma+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_te=Ze({opSnippet:Ete}),Ate={kernelName:Di,backendName:"webgl",kernelFunc:_te},Rc=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(m||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${p}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC += ${u}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${C} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / max(count, 1.0)");let v=Math.floor(s/4)*4,I=s%4,N=` if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${y}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); const float initializationValue = ${b}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${p}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${v}; wC += 4) { int xC = xCCorner + wC * ${u}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), getValue(batch, xR, xC + 3 * ${u}, d) ); ${N} } int xC = xCCorner + ${v}; if (${I===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${N} } else if (${I===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${N} } else if (${I===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${N} } } setOutput(${x}); } `}},xk=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${b}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${d}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xD, xR, xC, ch); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${F} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${a?r?`(((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} * ${m} + wR * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let v="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let N=Math.floor(s/4)*4,C=s%4,_=` if (${y}) { avgValue += dot(values, ones); } else { minMaxValue = ${v}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${b}); const float initializationValue = ${x}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${c}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${N}; wC += 4) { int xC = xCCorner + wC * ${d}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), getValue(batch, xD, xR, xC + 2 * ${d}, ch), getValue(batch, xD, xR, xC + 3 * ${d}, ch) ); ${_} } int xC = xCCorner + ${N}; if (${C===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${_} } else if (${C===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), initializationValue, initializationValue ); ${_} } else if (${C===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), getValue(batch, xD, xR, xC + 2 * ${d}, ch), initializationValue ); ${_} } } } setOutput(${I}); } `}};function Fte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;wp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var $te={kernelName:Mi,backendName:"webgl",kernelFunc:Fte};function Dte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new xk(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Rte={kernelName:mu,backendName:"webgl",kernelFunc:Dte},Mte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${d}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${o}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${l}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},Ote=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${p}; wD += ${o}) { float dyD = float(dyDCorner + wD) / ${r}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${d}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${u}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function Pte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new Ote(c);return n.runWebGLProgram(h,[r],i.dtype)}var Lte={kernelName:Wc,backendName:"webgl",kernelFunc:Pte};function zte(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;wp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=new Mte(p);return n.runWebGLProgram(d,[r],i.dtype)}var Wte={kernelName:zc,backendName:"webgl",kernelFunc:zte};function Bte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Cm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Vte={kernelName:Oi,backendName:"webgl",kernelFunc:Bte},Ute=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${o}; float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},Gte=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(T.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${s})); setOutput((x - mean) * inv + offset); } `}},Hte=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Gte(a.shape,r.shape,s.shape,p,d,l):new Ute(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},jte={kernelName:eo,backendName:"webgl",kernelFunc:Hte},qte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Kte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${kv[i]} = start[${i}] + coords.${kv[i]};`);a=` ${t} sourceLoc; 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So the safe solution is // to ensure underflow-safety in all cases. setOutput( mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } `}};function PS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function wne(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new vne(a.shape),i=[PS(a,r.complexTensorInfos.real),PS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var kne={kernelName:Vc,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( getT${m}(${jh(i,l,f)}), vec2(${jh(u,l,f)})); }`}let c=o.length,h=o[o.length-1];d+=` return getChannel( getT${c}(${jh(i,l,h)}), vec2(${jh(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${d} } void main() { ${r} coords = getOutputCoords(); 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${n} }`:r?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:x=` float activation(float x) { ${n} } `,v="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${y}]; ivec2 xRCCorner = ivec2(coords[${g}], coords[${b}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${p}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${f}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${m===1}) { if (${f}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), getX(batch, xR, xC, ${h} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC), getX(batch, ${h} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${I} ${v} setOutput(result); } `}},Ene=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${a}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${p}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), getX(batch, xF, xR, xC, ${h} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2), getW(wF, wR, wC, ${h} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},hF=class{constructor(e,t=!1,n=null,a=!1,r=!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=vn(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=0 && xR < inDims[0]) { `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(d+=` xC = xCCorner + ${g*o}; `,i===1){if(g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } `,o===1&&g>0?d+=` xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy); `:d+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${g} = vec4(previous.zw, xTexelC${g}.xy); } else { xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy); } `):d+=` if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } xC${g} = xTexelC${g}; `,g+1= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } `,o>1?d+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy); } else { xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy); } `:d+=` xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy); `):b===1?d+=` xC${g+1} = xTexelC${g}; `:d+=` xCOffset = xC + ${b}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } xC${g+1} = xTexelC${g+1}; `}}else g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw); `,g+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy); `)):(d+=` if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.); } xTexelC${g+1}Ready = 1; } xC${g} = vec4( xTexelC${g}.xy, xTexelC${g+1}.xy); `,g+1= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${i}] && d1 >= 0) { ch = imod(pos, inChannels); if (${r}) { innerDims = vec2(d1, ch); result[${u*2+p}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${u*2+p}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${l} ${a.output} = result; } `}};function Em(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function mF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,b=[];if(s!=null){let y=Em(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Em(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>iF)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let y=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,y,n.inChannels],dtype:e.dtype},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert($c(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let N=Cm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(N.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=aa({inputs:{x:N},backend:a}),g.shape=n.outShape,b.push(N)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Cm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function fF({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=Em(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=Em(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let N=new _ne(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=a.runWebGLProgram(N,[e],"float32",C),F=ce({inputs:{x:_},backend:a,attrs:{shape:b}});v.push(_),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Dc(o,!0):null,B=new sF(m?F.shape:I.shape,m?I.shape:F.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,x,D,M,$,S),U=m?[F,I]:[I,F];if(r&&U.push(r),$&&U.push(s),S){let K=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),q=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return q}function Ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=mF({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new hF(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=fF({x:r,filter:s,convInfo:c,backend:n});else{let f=new dF(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ce({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Fne={kernelName:zi,backendName:"webgl",kernelFunc:Ane},$ne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } ${s?`float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } } } setOutput(dotProd); } `}},Dne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${p}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${s}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},Rne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; float dotProd = 0.0; for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { int xF = wF + yF * ${t} - ${r}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${n} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${a} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},Mne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${u}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${r}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${a} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function One(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new $ne(c);return n.runWebGLProgram(h,[r,s],"float32")}var Pne={kernelName:Om,backendName:"webgl",kernelFunc:One},Lne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${a}, ${r}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; vec4 result = vec4(0.); for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { int wCPerm = ${n} - 1 - wC; float dyC = float(dyCCorner + wC) / strides[1]; bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0) && (fract(dyC) == 0.0); int idyC = int(dyC); float dyC2 = float(dyCCorner + wC + 1) / strides[1]; bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0) && (fract(dyC2) == 0.0); int idyC2 = int(dyC2); if (idyCVal && idyCVal2) { for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec4 dySample2 = (idyC / 2 == idyC2 / 2) ? dySample : getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? dySample.xy : dySample.zw; result.xy += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); dyValue = mod(float(idyC2), 2.) == 0. ? dySample2.xy : dySample2.zw; result.zw += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } else if (idyCVal) { for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? dySample.xy : dySample.zw; result.xy += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } else if (idyCVal2) { for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC2), 2.) == 0. ? dySample.xy : dySample.zw; result.zw += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } } } setOutput(result); } `}};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=T.convertConv2DDataFormat(u),c=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&d==="channelsLast"){let h=[[c.strideHeight,c.strideWidth]],m=new Lne(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Dne(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Wne={kernelName:Wi,backendName:"webgl",kernelFunc:zne};function Bne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Ene(u);return n.runWebGLProgram(p,[r,s],"float32")}var Vne={kernelName:Bi,backendName:"webgl",kernelFunc:Bne};function Une(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Rne(u);return n.runWebGLProgram(p,[r,s],"float32")}var Gne={kernelName:xu,backendName:"webgl",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Mne(u);return n.runWebGLProgram(p,[r,s],"float32")}var jne={kernelName:vu,backendName:"webgl",kernelFunc:Hne},qne=Cp+` return cos(x); `,Kne=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${tl} return result; `,Xne=Ze({opSnippet:qne,packedOpSnippet:Kne}),Yne={kernelName:Vi,backendName:"webgl",kernelFunc:Xne},Zne=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,Jne=Ze({opSnippet:Zne}),Qne={kernelName:Ui,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,v]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${y}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${s}) { return; } float height_scale = ${g}; float width_scale = ${x}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${v}; if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${c} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},tae=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new eae(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},nae={kernelName:ku,backendName:"webgl",kernelFunc:tae},Mc;(function(e){e.Prod="*",e.Sum="+"})(Mc||(Mc={}));var LS=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Mc.Prod?"1.0":"0.0",i=n?s:`getX(${zS(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ht(r)} coords = getOutputCoords(); int end = ${WS(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; ${WS(r,"coords",this.op)} = idx; val ${this.op}= getX(${zS(r,"coords",this.op)}); } setOutput(val); } `}};function zS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function WS(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function gF(e,t,n,a,r,s){let i=t.shape.length,o=T.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new LS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new LS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=T.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function aae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Prod,r,n,s,i,o)}var rae={kernelName:wu,backendName:"webgl",kernelFunc:aae};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return gF(Mc.Sum,r,n,s,i,o)}var iae={kernelName:Gi,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=KA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=uQ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var lae={kernelName:Uc,backendName:"webgl",kernelFunc:oae},uae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int h = ${this.getHeightCoordString()}; int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; int in_h = h / ${t}; int offset_h = imod(h, ${t}); int in_w = w / ${t}; int offset_w = imod(w, ${t}); int offset_d = (offset_h * ${t} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function pae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new uae(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var cae={kernelName:Iu,backendName:"webgl",kernelFunc:pae},bF=class{constructor(e,t=!1,n=null,a=!1,r=!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=vn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:l=` float activation(float x) { ${n} } `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${l} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${o}; int q = d2 - d1 * ${o}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. for (int wR = 0; wR < ${s}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${i}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${p} ${u} setOutput(result); } `}},yF=class{constructor(e,t=!1,n=null,a=!1,r=!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=vn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(d+1)/2;g++){let b=g*2;if(c+=` xC = xCCorner + ${b*l}; `,o===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } `,l===1&&b>0?c+=` xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); `:c+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${b} = vec4(previous.zw, xTexelC${b}.xy); } else { xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); } `):c+=` if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xC${b} = xTexelC${b}; `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } `,l>1?c+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); } else { xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); } `:c+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); `):y===1?c+=` xC${b+1} = xTexelC${b}; `:c+=` xCOffset = xC + ${y}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b+1} = xTexelC${b+1}; `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); `)):(c+=` if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.); } xTexelC${b+1}Ready = 1; } xC${b} = vec4( xTexelC${b}.xy, xTexelC${b+1}.xy); `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new yF(d):c=new bF(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var hae={kernelName:Hi,backendName:"webgl",kernelFunc:dae},mae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${s} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},fae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${o}; dm++) { int d2 = d1 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new mae(d);return n.runWebGLProgram(c,[r,s],"float32")}var bae={kernelName:Pm,backendName:"webgl",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new fae(d);return n.runWebGLProgram(c,[r,s],"float32")}var xae={kernelName:Lm,backendName:"webgl",kernelFunc:yae},vae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function wae(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new vae(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ce({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var kae={kernelName:Gc,backendName:"webgl",kernelFunc:wae},Iae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${o}; w++) { int wIn = wBeg + w * ${u}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function Sae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new Iae(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=ce({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var Nae={kernelName:ji,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=ng({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Cae={kernelName:Wm,backendName:"webgl",kernelFunc:Tae},Eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",_ae=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,Aae=Ze({opSnippet:Eae,packedOpSnippet:_ae}),Fae={kernelName:Ki,backendName:"webgl",kernelFunc:Aae},$ae="return (b >= 0.0) ? a : a * (b + 1.0);",Dae=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,Rae=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tp(Dae,a.shape,r.shape):new Si($ae,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Mae={kernelName:Su,backendName:"webgl",kernelFunc:Rae},Oae=` return vec4(equal(a, b)); `,Pae="return float(a == b);",Lae=fn({opSnippet:Pae,packedOpSnippet:Oae,dtype:"bool",cpuKernelImpl:mQ}),zae={kernelName:Nu,backendName:"webgl",kernelFunc:Lae},Wae=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${T.ERF_P}; float a1 = ${T.ERF_A1}; float a2 = ${T.ERF_A2}; float a3 = ${T.ERF_A3}; float a4 = ${T.ERF_A4}; float a5 = ${T.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,Bae=Ze({opSnippet:Wae}),Vae={kernelName:Xi,backendName:"webgl",kernelFunc:Bae},Uae=Cp+` return exp(x); `,Gae=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,xF=Ze({opSnippet:Uae,packedOpSnippet:Gae,cpuKernelImpl:fQ,dtype:"float32"}),Hae={kernelName:Yi,backendName:"webgl",kernelFunc:xF};function Sv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var jae={kernelName:Tu,backendName:"webgl",kernelFunc:Sv},BS="return exp(x) - 1.0;",qae=Ze({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:gQ}),Kae={kernelName:Zi,backendName:"webgl",kernelFunc:qae},VS=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${a}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${a}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function vF(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ce({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new VS("real",l,t),p=new VS("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Os({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ce({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Xae(e){let{inputs:t,backend:n}=e,{input:a}=t;return vF(a,!1,n)}var Yae={kernelName:Bm,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function Ld(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Zae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Jae={kernelName:Hc,backendName:"webgl",kernelFunc:Ld},Qae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${t} - x - 1; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},ere={kernelName:Cu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Qae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},US="return floor(x);",tre=Ze({opSnippet:US,packedOpSnippet:US,cpuKernelImpl:bQ}),nre={kernelName:Ji,backendName:"webgl",kernelFunc:tre},are=` float s = sign(a) * sign(b); 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getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${s}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${s}; j <= ${s}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${o}; setOutput(result); } `}},gse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new fse(r.shape,s,i,o,l):new mse(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},bse={kernelName:uo,backendName:"webgl",kernelFunc:gse},yse=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${a}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${a}) * float(${r}) * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},xse=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new yse(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},vse={kernelName:Pu,backendName:"webgl",kernelFunc:xse};function wse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function kF(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let y=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Rc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Ese={kernelName:ho,backendName:"webgl",kernelFunc:Cse};function _se(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=T.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new xk(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Ase={kernelName:Lu,backendName:"webgl",kernelFunc:_se},Fse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${r}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},$se=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${d}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${o}; wD += ${r}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${s}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${u}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${h} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${u} + wR * ${u} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function Dse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=T.computePool3DInfo(i.shape,o,l,d,u,p),h=new xk(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new $se(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Rse={kernelName:qc,backendName:"webgl",kernelFunc:Dse};function Mse(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;wp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new Rc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Fse(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var Ose={kernelName:jc,backendName:"webgl",kernelFunc:Mse};function Pse(e,t,n,a){let r=new Rc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Rc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Lse={kernelName:Kc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Pse(a,o,p,l);return[d,c]}};function zse(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=nl(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Wse={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,p=T.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=ht(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${a}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${r} coords = outC - start; setOutput(getX(${o})); } `}},Kse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ht(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; } else if (source >= end) { source = (end - 1) * 2 - source + ${d}; } source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${p}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${p}); } `}else{let h=` ${r} source = rc; ${r} lt = ${r}(lessThan(source, start)); ${r} gte = ${r}(greaterThanEqual(source, end)); ${r} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${d}) + gte * ((end - 1) * 2 - source + ${d}); source -= start; `;c=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${p}); ${o[a-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${p}); } rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${p}); ${o[a-1]} += 1; if(${u}) { ${h} result[3] = getChannel(getX(${l.join()}), ${p}); } } `}this.userCode=` const ${r} start = ${r}(${s}); const ${r} end = ${r}(${i}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${c} setOutput(result); } `}},Xse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kse(a.shape,r,s):new qse(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Yse={kernelName:bo,backendName:"webgl",kernelFunc:Xse},Zse=`if (b == 0.0) return NAN; return mod(a, b);`,Jse=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+tl+` return result; `,Qse=fn({opSnippet:Zse,packedOpSnippet:Jse}),eie={kernelName:yo,backendName:"webgl",kernelFunc:Qse},tie=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${t-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${t-1})); } `}},nie=` if (a == b) { return 1.0; }; return a / b;`,aie=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,IF=fn({opSnippet:nie,packedOpSnippet:aie,checkOutOfBounds:!0}),rie={kernelName:qi,backendName:"webgl",kernelFunc:IF},GS="return a - b;",SF=fn({opSnippet:GS,packedOpSnippet:GS,supportsComplex:!0,cpuKernelImpl:XQ}),sie={kernelName:Uo,backendName:"webgl",kernelFunc:SF};function NF(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=kF({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=ce({inputs:{x:o},backend:n,attrs:{shape:l}}),p=SF({inputs:{a:r,b:u},backend:n}),d=xF({inputs:{x:p},backend:n}),c=ng({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=IF({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var iie={kernelName:Bo,backendName:"webgl",kernelFunc:NF};function oie(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:NF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new tie(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var lie={kernelName:zu,backendName:"webgl",kernelFunc:oie},uie=Ma+` return -x; `,pie=` vec4 result = -x; bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `;function cie(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=AQ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,pie):r=new ir(a.shape,uie),n.runWebGLProgram(r,[a],a.dtype)}var die={kernelName:Wu,backendName:"webgl",kernelFunc:cie},hie=gr.nonMaxSuppressionV3Impl;function mie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=hie(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var fie={kernelName:Vu,backendName:"webgl",kernelFunc:mie},gie=gr.nonMaxSuppressionV4Impl;function bie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=gie(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var yie={kernelName:Uu,backendName:"webgl",kernelFunc:bie},xie=gr.nonMaxSuppressionV5Impl;function vie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:b}=xie(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var wie={kernelName:Gu,backendName:"webgl",kernelFunc:vie},kie=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${a}), float(${n}), float(index == coords.y))); } `}},Iie=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new kie(u,i,o,l),d=ce({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=ce({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Sie={kernelName:vo,backendName:"webgl",kernelFunc:Iie};function _m(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=_m({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Nie={kernelName:up,backendName:"webgl",kernelFunc:_m};function TF(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Pd({inputs:{input:a},backend:n}),s=TF({inputs:{x:r},backend:n}),i=ag({inputs:{input:a},backend:n}),o=_m({inputs:{x:i},backend:n}),l=Os({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Tie={kernelName:Hu,backendName:"webgl",kernelFunc:TF};function Cie(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Sv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Sv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=cF({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Eie={kernelName:ju,backendName:"webgl",kernelFunc:Cie},_ie=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=ht(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${r} start = ${r}(${s}); ${r} end = ${r}(${i}); void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${o})); } } `}},Aie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ht(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=In("rc",a),l=In("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1; if(${u}) { `,a===1?"":`} rc = outputLoc; ${o[a-2]} += 1; if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Ld({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Aie(r.shape,s,i):new _ie(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Fie={kernelName:wo,backendName:"webgl",kernelFunc:CF},$ie=` if(a < 0.0 && floor(b) < b){ return NAN; } if (b == 0.0) { return 1.0; } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `,Die=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+tl+` return result; `,Rie=fn({opSnippet:$ie,packedOpSnippet:Die}),Mie={kernelName:ko,backendName:"webgl",kernelFunc:Rie};function Oie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=T.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=T.getInnerMostAxes(p.length,o),l.push(c)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=$Q(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=T.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Xm(r.dtype),x=nl(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=T.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Pie={kernelName:So,backendName:"webgl",kernelFunc:Oie};function Lie(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.readSync(b.dataId)),u=r.map(b=>b.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=DQ(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var zie={kernelName:Gm,backendName:"webgl",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=RQ(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Bie={kernelName:Hm,backendName:"webgl",kernelFunc:Wie};function Vie(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=MQ(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Uie={kernelName:jm,backendName:"webgl",kernelFunc:Vie},EF=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=OQ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Gie={kernelName:Xc,backendName:"webgl",kernelFunc:EF},Hie="return 1.0 / x;",jie=Ze({opSnippet:Hie}),qie={kernelName:No,backendName:"webgl",kernelFunc:jie},Kie=Ma+` return (x < 0.0) ? 0.0 : x; `,Xie=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Yie=Ze({opSnippet:Kie,packedOpSnippet:Xie}),Zie={kernelName:To,backendName:"webgl",kernelFunc:Yie},Jie=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Qie=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,eoe=Ze({opSnippet:Jie,packedOpSnippet:Qie}),toe={kernelName:_o,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}},aoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, ${u[1]/p[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function roe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aoe(r.shape,l,u,s,i):new noe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var soe={kernelName:Eo,backendName:"webgl",kernelFunc:roe},ioe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${p}); const float invHeightScale = float(${d}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function ooe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new ioe(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var loe={kernelName:Xu,backendName:"webgl",kernelFunc:ooe},uoe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},poe=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, ${u[1]/p[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${c}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function coe(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new poe(r.shape,l,u,s,i):new uoe(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var doe={kernelName:Co,backendName:"webgl",kernelFunc:coe},hoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${p}); const float invHeightScale = float(${d}); const float invWidthScale = float(${c}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${s}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float sourceFracRow = float(${o[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${o[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${a}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : 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Sle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=T.fromUint8ToStringArray(s),o=GQ(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var Nle={kernelName:ed,backendName:"webgl",kernelFunc:Sle};function Tle({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new ir(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Cle={kernelName:Cs,backendName:"webgl",kernelFunc:Tle},Ele=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ht(n.length),s=ht(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); void main() { ${s} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function _le(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(y,x,v),_=Ep({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});I=ce({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Pe(r.shape,r.dtype,C),F=HQ(h,_,v,y);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let C=new Ele(y,v,h);I=n.runWebGLProgram(C,[r],r.dtype)}let N=ce({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),N}var Ale={kernelName:sp,backendName:"webgl",kernelFunc:_le};function Fle(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=jQ(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var $le={kernelName:td,backendName:"webgl",kernelFunc:Fle};function Dle(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=qQ(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Rle={kernelName:nd,backendName:"webgl",kernelFunc:Dle};function Mle(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=KQ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var Ole={kernelName:ad,backendName:"webgl",kernelFunc:Mle},Ple="return tan(x);",Lle=Ze({opSnippet:Ple}),zle={kernelName:Go,backendName:"webgl",kernelFunc:Lle},Wle=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,Ble=Ze({opSnippet:Wle}),Vle={kernelName:Ho,backendName:"webgl",kernelFunc:Ble};function Ule(e){let{inputs:t,backend:n,attrs:a}=e,{tensor:r,indices:s,updates:i}=t,{}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(i,s,r.shape),c=[d/u,u];if(d===0)return n.makeTensorInfo(r.shape,s.dtype);let h=ce({inputs:{x:s},backend:n,attrs:{shape:[l,o]}}),m=ce({inputs:{x:i},backend:n,attrs:{shape:[l,u]}}),f=ce({inputs:{x:r},backend:n,attrs:{shape:c}}),g=new vk(l,o,h.shape.length,m.shape.length,p,c,!1,!0),b=n.runWebGLProgram(g,[m,h,f],f.dtype),y=ce({inputs:{x:b},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),y}var Gle={kernelName:Zu,backendName:"webgl",kernelFunc:Ule},Hle=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>w.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=YQ(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Hle(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var qle={kernelName:Ts,backendName:"webgl",kernelFunc:_F},Kle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},Xle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Xs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function qS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=ZQ(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,Ld({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=w.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Xs(n,h);let g=qS(s),b=qS(p),y=null,x=()=>y===null?[f,f]:[f,y],v=(F,D,$)=>{let S=x(),M=new Kle($),B=[[p],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],U=y;y=n.runWebGLProgram(M,S,"int32",B),Xs(n,U)};for(let F=1;F=1;$/=2)v(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Xle([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),Xs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)v(U,H,y.shape)}let I=y;y=Ep({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),Xs(n,I);let N=wF({inputs:{x:f,indices:y},backend:n,attrs:{axis:1,batchDims:1}});Xs(n,f);let C=u.slice(0,-1);C.push(s),I=y,y=ce({inputs:{x:y},attrs:{shape:C},backend:n}),Xs(n,I);let _=N;return N=ce({inputs:{x:N},attrs:{shape:C},backend:n}),Xs(n,_),[N,y]}var Zle={kernelName:ip,backendName:"webgl",kernelFunc:Yle},Jle=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${r}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${r}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function Qle(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],b=new Jle(d,c,i,o,l,g);return n.runWebGLProgram(b,[r,s],"float32")}var eue={kernelName:op,backendName:"webgl",kernelFunc:Qle};function tue(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;wp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=JQ(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var nue={kernelName:rd,backendName:"webgl",kernelFunc:tue};function aue(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var rue={kernelName:lp,backendName:"webgl",kernelFunc:aue},sue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` sumValue += dot(values, segFilter); `,c="";r%n>0&&(c=` if (inIdx < 0 || inIdx >= ${r}) { return initializationValue; } `);let h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { return -1.0; } `),this.userCode=` const float initializationValue = ${o}; float getValue(int batch, int inIdx) { ${c} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${h} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${s})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${s}))); float sumValue = 0.0; for (int i = 0; i < ${u}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${d} } int inIdx = inOffset + ${u}; if (${p===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${d} } else if (${p===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${d} } else if (${p===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${d} } setOutput(${l}); } `}};function iue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=T.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=T.getInnerMostAxes(1,o)[0]);let c=T.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Xm(r.dtype),g=(v,I,N,C,_)=>{let F=v.shape[0],D=v.shape[1],$=T.segment_util.segOpComputeOptimalWindowSize(D,_),S={windowSize:$,inSize:D,batchSize:F,numSegments:_},M=new 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oue={kernelName:sd,backendName:"webgl",kernelFunc:iue},lue=[Hee,qee,Yee,Qee,tte,rte,ite,lte,dte,mte,bte,vte,Ite,Cte,Ate,$te,Rte,Lte,Wte,Vte,jte,Qte,tne,sne,one,hne,fne,xne,Eee,kne,Cne,Fne,Pne,Wne,Vne,Gne,jne,Yne,Qne,nae,rae,iae,lae,cae,hae,bae,xae,kae,Nae,Cae,Fae,Mae,zae,Vae,Hae,jae,Kae,Yae,Jae,ere,nre,ire,ure,dre,mre,bre,vre,Sre,Ere,Cee,Are,Nne,Dre,Ore,zre,Aee,Ure,qre,Xre,Qre,nse,ise,use,hse,bse,vse,kse,Tse,Ese,Ase,Rse,Ose,Lse,Wse,Vse,jse,Yse,eie,lie,Dee,die,fie,yie,wie,une,Sie,Tie,Eie,Fie,Mie,$ee,Pie,zie,Bie,Uie,Gie,pne,rie,qie,Zie,toe,Mee,soe,loe,doe,foe,xoe,woe,Soe,Coe,Aoe,Doe,Ooe,zoe,Uoe,joe,Yoe,Qoe,Zte,iie,nle,rle,ile,lle,ple,dle,mle,gle,yle,wle,Ile,Nle,Cle,Ale,$le,Rle,Ole,sie,Vee,zle,Vle,Gle,qle,Zle,eue,Uee,nue,rue,oue,Nie];for(let e of lue)id(e);var Qe;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Qe||(Qe={}));var Oc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Oc||(Oc={}));var AF;function uue(e){AF=e.wasm.cwrap(li,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function pue(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Oc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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spe={kernelName:fu,backendName:"wasm",kernelFunc:rpe},BF;function ipe(e){BF=e.wasm.cwrap(gu,null,["number","number","boolean","number","number","number"])}function ope(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,weights:s}=n,{size:i}=a,o=s.shape.reduce((d,c)=>d*c,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return BF(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var lpe={kernelName:gu,backendName:"wasm",setupFunc:ipe,kernelFunc:ope},upe=!0,ppe=Ut(bu,upe);function cpe(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.typedArrayFromHeap(a),i=n.typedArrayFromHeap(r),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var dpe={kernelName:Bc,backendName:"wasm",kernelFunc:cpe};function Ls(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var hpe={kernelName:Pi,backendName:"wasm",kernelFunc:Ls},mpe=Xe(Li),VF;function fpe(e){VF=e.wasm.cwrap(Ns,null,["number","number","number","number"])}function gpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return VF(o,s,i,u),l}var bpe={kernelName:Ns,backendName:"wasm",setupFunc:fpe,kernelFunc:gpe};function UF(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);T.assertParamsConsistent(r,a);let s=T.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>w.sizeFromShape(h.shape)>0);if(i.length===1)return rg({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return 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Jpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,I=h.dilationWidth,N=h.strideHeight,C=h.strideWidth,_=h.inChannels,F=h.outChannels,D=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Got ${r.dtype} and ${s.dtype}`);let u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=n.makeOutput(u.outShape,r.dtype);return n$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var sce={kernelName:ji,backendName:"wasm",setupFunc:ace,kernelFunc:rce},a$;function ice(e){a$=e.wasm.cwrap(ql,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return r$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var cce={kernelName:jl,backendName:"wasm",setupFunc:uce,kernelFunc:pce},dce=Xe(Ki),s$;function hce(e){s$=e.wasm.cwrap(Su,null,["number","number","number"])}function mce(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return s$(i(r),i(a),i(s)),s}var fce={kernelName:Su,backendName:"wasm",setupFunc:hce,kernelFunc:mce},gce=!1,bce=Ut(Nu,gce,"bool"),yce=Xe(Xi),xce=Xe(Yi,"float32");function Tv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),zn({inputs:{x:r},backend:a,attrs:{shape:o}})}var vce={kernelName:Tu,backendName:"wasm",kernelFunc:Tv},wce=Xe(Zi,"float32");function i$(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var kce={kernelName:Hc,backendName:"wasm",kernelFunc:i$},o$;function Ice(e){o$=e.wasm.cwrap(Cu,null,["number","number","number","number","number","number"])}function Sce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return o$(s,o,l,u,p,i),r}var Nce={kernelName:Cu,backendName:"wasm",kernelFunc:Sce,setupFunc:Ice},Tce=Xe(Ji),Cce=!1,Ece=Ut(Qi,Cce),l$;function _ce(e){l$=e.wasm.cwrap(eo,null,["number","number","number","number","number","number","number"])}function Ace(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return l$(p,d,c,h,m,r,g),f}var Fce={kernelName:eo,backendName:"wasm",setupFunc:_ce,kernelFunc:Ace},u$;function $ce(e){u$=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return u$(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Rce={kernelName:ui,backendName:"wasm",setupFunc:$ce,kernelFunc:Dce},p$;function Mce(e){p$=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Oce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Oc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return p$(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Pce={kernelName:pi,backendName:"wasm",setupFunc:Mce,kernelFunc:Oce},c$;function Lce(e){c$=e.wasm.cwrap(_u,null,["number","number","number","number","number","number","array","number"])}function zce(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=r0.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return c$(c,Qe[a.dtype],h,i,d,o,m,f),u}var Wce={kernelName:_u,backendName:"wasm",setupFunc:Lce,kernelFunc:zce},d$;function Bce(e){d$=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Vce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=zn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=zn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return d$(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Uce={kernelName:Eu,backendName:"wasm",setupFunc:Bce,kernelFunc:Vce},Gce=!1,Hce=Ut(Au,Gce,"bool"),jce=!1,qce=Ut(to,jce,"bool"),Kce=Xe(ao,"bool"),Xce=Xe(ro,"bool"),Yce=Xe(so,"bool"),h$;function Zce(e){h$=e.wasm.cwrap(io,null,["number","number","number","number"])}function Jce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;h$(r,Qe[t.dtype],n,i)}return s}var Qce={kernelName:io,backendName:"wasm",setupFunc:Zce,kernelFunc:Jce},ede=!1,tde=Ut(Fu,ede,"bool"),nde=!1,ade=Ut($u,nde,"bool"),m$;function rde(e){m$=e.wasm.cwrap(Du,null,["number","number","number","number"])}function sde(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return m$(n.dataIdMap.get(o.dataId).id,a,r,i),o}var ide={kernelName:Du,backendName:"wasm",setupFunc:rde,kernelFunc:sde},ode=Xe(oo),lde=Xe(lo),ude=!1,pde=Ut(Ru,ude,"bool"),cde=Xe(Mu),dde=!1,hde=Ut(Ou,dde,"bool"),mde=!1,fde=Ut(mN,mde,"bool"),f$;function gde(e){f$=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return f$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var yde={kernelName:uo,backendName:"wasm",setupFunc:gde,kernelFunc:bde},g$;function xde(e){g$=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function vde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return g$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var wde={kernelName:Pu,backendName:"wasm",setupFunc:xde,kernelFunc:vde},b$;function kde(e){b$=e.wasm.cwrap(po,null,["number","number","number","number"])}function Ide(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ps(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;T.assertAxesAreInnerMostDims("max",p,h);let[m,f]=T.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;b$(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=T.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var Sde={kernelName:po,backendName:"wasm",setupFunc:kde,kernelFunc:Ide},Nde=!1,Tde=Ut(co,Nde),y$;function Cde(e){y$=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ede(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new $p({...i,...s})}},Bd=class{constructor(t,n={}){this.box=new ln(t),this.options=new lg(n)}draw(t){let n=Gn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new dl([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nge(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof Tt?a.score:xr(a)?a.detection.score:void 0,s=a instanceof Tt?a.box:xr(a)?a.detection.box:new ln(a),i=r?`${rl(r)}`:void 0;new Bd(s,{label:i}).draw(e)})}function Vd(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function zk(e){return new Promise((t,n)=>{if(e instanceof tt.getEnv().Canvas||Vd(e)){t(null);return}function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function 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vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Wr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ka(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof tt.getEnv().Canvas?a:Ud(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return yr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Nk({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ce){let o=ka(i)?i:Gt(i);return o=Ak(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qn.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Xo.fromPixels(Vk(i,t,n));throw new 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=wge(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Wr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return _n(e,t),{params:s,paramMappings:t}}function Oa(e,t,n){return O(()=>{let a=$t(e,t.filters,n,"same");return a=X(a,t.batch_norm_offset),an(a,0,6)})}var kge=.0010000000474974513;function Ige(e,t,n){return O(()=>{let a=As(e,t.filters,n,"same");return a=_s(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,kge),an(a,0,6)})}function Sge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function CD(e,t){return O(()=>{let n,a=Oa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=Sge(o);a=Ige(a,s.depthwise_conv,l),a=Oa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function Nge(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),p=Math.max(a[n][0],a[n][2]),d=Math.max(a[n][1],a[n][3]),c=(i-r)*(o-s),h=(p-l)*(d-u);if(c<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,p),b=Math.min(o,d),y=Math.max(g-m,0)*Math.max(b-f,0);return y/(c+h-y)}function ED(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>r).sort((p,d)=>d.score-p.score),l=p=>p<=a?1:0,u=[];return o.forEach(p=>{if(u.length>=i)return;let d=p.score;for(let c=u.length-1;c>=0;--c){let h=Nge(e,p.boxIndex,u[c]);if(h!==0&&(p.score*=l(h),p.score<=r))break}d===p.score&&u.push(p.boxIndex)}),u}function Tge(e){let t=dt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function Cge(e,t){let{sizes:n,centers:a}=Tge(e),r=dt(De(t,[1,0])),s=he(z(mn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(mn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Ft([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function _D(e,t,n){return O(()=>{let a=e.shape[0],r=Cge(W(On(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function xl(e,t){return O(()=>{let n=e.shape[0],a=W(fl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(fl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function AD(e,t,n){return O(()=>{let a=Oa(e,n.conv_0,[1,1]),r=Oa(a,n.conv_1,[2,2]),s=Oa(r,n.conv_2,[1,1]),i=Oa(s,n.conv_3,[2,2]),o=Oa(i,n.conv_4,[1,1]),l=Oa(o,n.conv_5,[2,2]),u=Oa(l,n.conv_6,[1,1]),p=Oa(u,n.conv_7,[2,2]),d=xl(t,n.box_predictor_0),c=xl(e,n.box_predictor_1),h=xl(r,n.box_predictor_2),m=xl(i,n.box_predictor_3),f=xl(l,n.box_predictor_4),g=xl(p,n.box_predictor_5),b=et([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),y=et([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var Ia=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ws=class extends pn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=CD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=AD(s.out,s.conv11,n.prediction_layer);return _D(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TD(t)}extractParams(t){return ND(t)}};function FD(e){let t=new Ws;return t.extractWeights(e),t}function Ege(e){return FD(e)}var Jk=class extends Ws{};var $D=.4,DD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],MD=[117.001,114.697,97.404],OD="tiny_yolov2_model",PD="tiny_yolov2_separable_conv_model";var Cg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Cg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Cg(t.x)&&Cg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ds(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function _ge(e,t){let n=Mp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function LD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=_ge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Age(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function zD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Age(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Eg=class Eg extends pn{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return zD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Eg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return LD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Eg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Eg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$D,classes:["face"],...t?{anchors:RD,meanRgb:MD}:{anchors:DD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?PD:OD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Fge(e,t=!0){let n=new vl(t);return n.extractWeights(e),n}var Zd=class extends Ja{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Sa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function wl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>gl(l)?r(l):l.detection),i=a||(t instanceof Ce?await Rp(t,s):await Dp(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Gp(e,t,n,a,r){return wl([e],t,async s=>n(s[0]),a,r)}var WD=.4,BD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VD=[117.001,114.697,97.404];var kl=class extends Up{constructor(){let t={withSeparableConvs:!0,iouThreshold:WD,classes:["face"],anchors:BD,meanRgb:VD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Ws,tinyFaceDetector:new kl,tinyYolov2:new vl,faceLandmark68Net:new bl,faceLandmark68TinyNet:new Xd,faceRecognitionNet:new yl,faceExpressionNet:new qd,ageGenderNet:new Kd},UD=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),$ge=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Dge=(e,t)=>nt.tinyYolov2.locateFaces(e,t),GD=e=>nt.faceLandmark68Net.detectLandmarks(e),Rge=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Mge=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Oge=e=>nt.faceExpressionNet.predictExpressions(e),Pge=e=>nt.ageGenderNet.predictAgeAndGender(e),HD=e=>nt.ssdMobilenetv1.load(e),Lge=e=>nt.tinyFaceDetector.load(e),zge=e=>nt.tinyYolov2.load(e),Wge=e=>nt.faceLandmark68Net.load(e),Bge=e=>nt.faceLandmark68TinyNet.load(e),Vge=e=>nt.faceRecognitionNet.load(e),Uge=e=>nt.faceExpressionNet.load(e),Gge=e=>nt.ageGenderNet.load(e),Hge=HD,jge=UD,qge=GD;var _g=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Il=class extends _g{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>bg(a,n[r]))}withAgeAndGender(){return new Nl(this,this.input)}},Sl=class extends _g{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Tl(this,this.input)}},Bs=class extends Il{withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Vs=class extends Sl{withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Ag=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Nl=class extends Ag{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Ng(Tg(a,i,o),s)})}withFaceExpressions(){return new Il(this,this.input)}},Tl=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Gp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ng(Tg(t,a,r),n)}withFaceExpressions(){return new Sl(this,this.input)}},Us=class extends Nl{withFaceExpressions(){return new Bs(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Gs=class extends Tl{withFaceExpressions(){return new Vs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Jd=class extends Sa{constructor(n,a){super();this.parentTask=n;this.input=a}},Hr=class extends Jd{async run(){let t=await this.parentTask;return(await wl(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Sg(t[r],a))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}},jr=class extends Jd{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}};var Qd=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},eh=class extends Qd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Rp(this.input,n):await Dp(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Wp(i,r[o]))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},th=class extends Qd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Rp(this.input,[n]):await Dp(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Wp(t,r)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var nh=class extends Sa{constructor(n,a=new Ia){super();this.input=n;this.options=a}},Hp=class extends nh{async run(){let{input:t,options:n}=this,a;if(n instanceof Zd)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ia)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ja)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>pl({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Il(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Nl(this.runAndExtendWithFaceDetections(),this.input)}},ah=class extends nh{async run(){let t=await new Hp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?pl({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Tl(this.runAndExtendWithFaceDetection(),this.input)}};function Kge(e,t=new Ia){return new ah(e,t)}function Fg(e,t=new Ia){return new Hp(e,t)}async function jD(e,t){return Fg(e,new Ia(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Xge(e,t={}){return Fg(e,new Ja(t)).withFaceLandmarks().withFaceDescriptors()}var Yge=jD;function eI(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var tI=class e{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof zs)return i;if(i instanceof Float32Array)return new zs(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new zs(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>eI(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Ap(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>zs.fromJSON(a));return new e(n,t.distanceThreshold)}};function Zge(e){let t=new kl;return t.extractWeights(e),t}function qD(e,t){let{width:n,height:a}=new Un(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>qD(r,{width:n,height:a}));if(gl(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Wp(pl(e,r),s)}return xr(e)?pl(e,e.detection.forSize(n,a)):e instanceof sa||e instanceof Tt?e.forSize(n,a):e}var Jge=hD;return $R(Qge);})();