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face-api
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face-api/dist/tfjs.esm.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:n},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:n}=this.initializeBackend(t);if(!(n?await e:e))return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new yx(this.backendInstance),!0}setupRegisteredKernels(){hx(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){hx(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof jo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o<this.pendingBackendInitId?!1:(this.registry[t]=i,this.pendingBackendInit=null,!0)).catch(i=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,Pi(`Initialization of backend ${t} failed`),Pi(i.stack||i.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=n,{success:!0,asyncInit:!1}}catch(n){return Pi(`Initialization of backend ${t} failed`),Pi(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let n=t[e],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let n=this.state.tensorInfo.get(e),o=n.backend,s=this.readSync(e),i=o.refCount(e);o.disposeData(e,!0),n.backend=t,t.move(e,s,n.shape,n.dtype,i),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let n=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new 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o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=tI(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(tI(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let y=Kd(d,this.backendName);A(y!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=y.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let v=w.map(k=>{if(k.rank!=null)return k;let{dataId:E,shape:$,dtype:D}=k;return this.makeTensorFromDataId(E,$,D)});if(o){let k=this.getTensorsForGradient(d,h,v);n=this.saveTensorsForBackwardMode(k)}return v}}else{let{forwardFunc:d}=t,h=g=>{!o||(n=g.map(y=>this.keep(this.clone(y))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let y=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,y),y}}let{inputs:c,attrs:p}=t,m=tI(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=BC(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(A(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let 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i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),xo(i,a,u,c,l,s)}var $I=N({batchNorm4d_:VH});function GH(r,t,e){let n=C(r,"x","bincount"),o=C(t,"weights","bincount");A(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),A(e>=0,()=>`size must be non-negative, but got 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Ys=N({depthwiseConv2d_:cq});function pq(r){let e={x:C(r,"x","diag")};return _.runKernel(Pp,e)}var mq=N({diag_:pq});function fq(r,t,e,n,o=[1,1],s="NHWC"){let i=C(r,"x","dilation2d"),a=C(t,"filter","dilation2d");A(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),A(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=O(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=_.runKernel(Fl,c,p);return l?O(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var bh=N({dilation2d_:fq});function dq(r,t){let e=C(r,"a","equal","string_or_numeric"),n=C(t,"b","equal","string_or_numeric");[e,n]=jt(e,n),zt(e.shape,n.shape);let o={a:e,b:n};return _.runKernel(fa,o)}var Sr=N({equal_:dq});function hq(r,t,e){let n=C(t,"a","where"),o=C(e,"b","where"),s=C(r,"condition","where","bool"),i=zt(zt(s.shape,n.shape),o.shape),a=ja(s,i),u=ja(n,i),l=ja(o,i),c={condition:a,t:u,e:l};return _.runKernel(_i,c)}var _e=N({where_:hq});function gq(r){let e={x:C(r,"x","zerosLike")};return _.runKernel(Ri,e)}var St=N({zerosLike_:gq});function xq(r,t){let e=C(r,"a","div"),n=C(t,"b","div");[e,n]=jt(e,n);let o=ct(e,n),s=St(o),i=Sr(n,s);return _e(i,s,o)}var wh=N({divNoNan_:xq});function yq(r,t){let e=C(r,"t1","dot"),n=C(t,"t2","dot");A((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(A(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=O(e,[1,-1]),a=O(n,[-1,1]),u=Lt(i,a);return O(u,[])}else if(e.rank===1&&n.rank===2){let i=O(e,[1,-1]),a=O(n,[n.shape[0],n.shape[1]]),u=Lt(i,a);return 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Ch=N({expm1_:Sq});function kq(r,t){let e=C(r,"x","tile","string_or_numeric");A(e.rank===t.length,()=>`Error in transpose: rank of input ${e.rank} must match length of reps ${t}.`);let n={x:e},o={reps:t};return _.runKernel(Yn,n,o)}var kr=N({tile_:kq});function Nq(r,t,e,n="float32"){t==null&&(t=r);let o=It([r,t],n),s=r<=t?r:t;for(let a=0;a<s;++a)o.set(1,a,a);let i=O(o.toTensor(),[r,t]);if(e==null)return i;if(e.length===1)return kr(fr(i,0),[e[0],1,1]);if(e.length===2)return kr(fr(fr(i,0),0),[e[0],e[1],1,1]);if(e.length===3)return kr(fr(fr(fr(i,0),0),0),[e[0],e[1],e[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${e.length}D.`)}var gm=N({eye_:Nq});function Js(r,t,e){let n={shape:r,value:t,dtype:e};return _.runKernel(Ol,{},n)}function Tq(r){let e={x:C(r,"x","floor","float32")};return _.runKernel(ps,e)}var Qs=N({floor_:Tq});function _q(r,t,e=0,n=0){let 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g=0;g<t.length;g++)t[g]>o&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(NE);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length<e&&l.length>0;){let g=l.pop(),{score:y,boxIndex:b,suppressBeginIndex:w}=g;if(y<o)break;let v=!1;for(let k=p.length-1;k>=w;--k){let E=y6(r,b,p[k]);if(E>=n){v=!0;break}if(g.score=g.score*b6(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,v||(g.score===y?(p.push(b),m.push(g.score)):g.score>o&&kE(l,g,NE))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function y6(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),y=Math.min(a,p),b=Math.min(u,m),w=Math.max(y-h,0)*Math.max(b-g,0);return w/(f+d-w)}function b6(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function NE(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function w6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),a=Io(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],c=u[1],{selectedIndices:p}=Yx(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Re(p,"int32")}var TE=w6;function v6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=Io(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=_.runKernel(Ea,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var _E=N({nonMaxSuppressionWithScore_:v6});async function C6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=Io(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Jx(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Re(m,"int32"),selectedScores:Re(f)}}var EE=C6;function I6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=Io(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=_.runKernel(_a,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var AE=N({nonMaxSuppressionPadded_:I6});async function S6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=Io(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=Zx(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Re(d,"int32"),validOutputs:pt(h,"int32")}}var $E=S6;function k6(r,t,e=!1,n=!1){let o=C(r,"images","resizeBilinear");A(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),A(t.length===2,()=>`Error in resizeBilinear: new 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n=C(r,"losses","computeWeightedLoss"),o=null;t!=null&&(o=C(t,"weights","computeWeightedLoss"));let s=o==null?n:M(n,o);if(e===Qe.NONE)return s;if(e===Qe.SUM)return mt(s);if(e===Qe.MEAN){if(o==null)return xe(s);{let i=n.size/o.size,a=ct(mt(s),mt(o));return i>1?ct(a,pt(i)):a}}if(e===Qe.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ct(mt(s),pt(n.size));{let i=M(o,lr(n.shape)),a=Z(mt(vo(i,pt(0))),"float32");return ct(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Br=N({computeWeightedLoss_:R6});function F6(r,t,e,n=Qe.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=C(e,"weights","absoluteDifference")),Me(o.shape,s.shape,"Error in absoluteDifference: ");let a=Ne(lt(o,s));return Br(a,i,n)}var LE=N({absoluteDifference_:F6});function O6(r,t,e,n,o=Qe.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),a=null;n!=null&&(a=C(n,"weights","cosineDistance")),Me(s.shape,i.shape,"Error in cosineDistance: ");let u=pt(1),l=lt(u,mt(M(s,i),e,!0));return Br(l,a,o)}var zE=N({cosineDistance_:O6});function M6(r,t,e,n=Qe.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),Me(o.shape,s.shape,"Error in hingeLoss: ");let a=pt(1);o=lt(M(pt(2),o),a);let u=Tr(lt(a,M(o,s)));return Br(u,i,n)}var BE=N({hingeLoss_:M6});function P6(r,t,e,n=1,o=Qe.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),Me(s.shape,i.shape,"Error in huberLoss: ");let u=pt(n),l=Ne(lt(i,s)),c=ti(l,u),p=lt(l,c),m=J(M(pt(.5),Ut(c)),M(u,p));return Br(m,a,o)}var VE=N({huberLoss_:P6});function L6(r,t,e,n=1e-7,o=Qe.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),Me(s.shape,i.shape,"Error in logLoss: ");let u=pt(1),l=pt(n),c=Zt(M(s,wr(J(i,l)))),p=M(lt(u,s),wr(J(lt(u,i),l))),m=lt(c,p);return Br(m,a,o)}var GE=N({logLoss_:L6});function z6(r,t,e,n=Qe.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),Me(o.shape,s.shape,"Error in meanSquaredError: ");let a=dc(o,s);return Br(a,i,n)}var WE=N({meanSquaredError_:z6});function B6(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");Me(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Tr(n),s=M(n,e),i=Za(Ze(Zt(Ne(n))));return J(lt(o,s),i)}function V6(r,t,e,n=0,o=Qe.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","sigmoidCrossEntropy")),Me(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=J(M(s,lt(c,l)),M(p,l))}let u=B6(s,i);return Br(u,a,o)}var UE=N({sigmoidCrossEntropy_:V6});function G6(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};ru.className="Adamax";Nn(ru);var Ui=class extends Vr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=_.registeredVariables[n];B(()=>{let a=J(M(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Pe(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does 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Bt{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=de(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ue(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Jt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<t.length;++o)n[o]=t[o];this.inputSpec=[new ve({ndim:t.length,axes:n})],this.built=!0}call(t,e){return t=Mt(t),el(t,this.alpha.read())}getConfig(){let t={alphaInitializer:Ee(this.alphaInitializer),alphaRegularizer:me(this.alphaRegularizer),alphaConstraint:We(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}};Xm.className="PReLU";et.registerClass(Xm);var Ym=class extends Bt{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new Nt(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=Mt(t);return Zs(n)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Ym.className="ELU";et.registerClass(Ym);var Zm=class extends Bt{constructor(t){super(t==null?{}:t),this.DEFAULT_THETA=1,t==null&&(t={}),this.theta=t.theta==null?this.DEFAULT_THETA:t.theta}call(t,e){let n=Mt(t);return M(n,Z(Ge(n,this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){let t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="ThresholdedReLU";et.registerClass(Zm);var Jm=class extends Bt{constructor(t){super(t==null?{}:t),this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new qm().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){let n=Mt(t);return this.softmax(n,this.axis)}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};Jm.className="Softmax";et.registerClass(Jm);function cu(r,t,e){if(typeof r=="number")return ko(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(r=Wt(r,[0,2,1])),o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=Yu(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=xn(a,e)),a})}function J$(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=hn()),Le(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=tg(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Co.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Wt(u,[0,3,1,2])),u})}function S8(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=hn()),Le(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=LS(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=gh(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=xn(a,e)),s==="channelsFirst"&&(a=Wt(a,[0,4,1,2,3])),a})}var Fc=class extends Bt{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Fc.verifyArgs(e),this.rank=t,er(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Nt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=cu(e.kernelSize,t,"kernelSize"),this.strides=cu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,gn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Le(this.dataFormat),this.activation=ui(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ue(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=cu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Qn("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!sy(t.kernelSize,"number",1,3))throw new z(`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:li(this.activation),useBias:this.useBias,biasInitializer:Ee(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:We(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},pu=class extends Fc{constructor(t,e){super(t,e),this.kernel=null,pu.verifyArgs(e),this.filters=e.filters,er(this.filters,"filters"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ue(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Jt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=Mt(t);let n,o=this.bias==null?null:this.bias.read(),s=iy(this.activation.getClassName());if(s!=null&&this.rank===2)n=J$(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=I8(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=J$(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=S8(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Nt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Jt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<n.length;++s){let i=An(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(i)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:Ee(this.kernelInitializer),kernelRegularizer:me(this.kernelRegularizer),kernelConstraint:We(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},pl=class extends pu{constructor(t){super(2,t),pl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!sy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};pl.className="Conv2D";et.registerClass(pl);var ml=class extends pu{constructor(t){super(3,t),ml.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};ml.className="Conv3D";et.registerClass(ml);var Qm=class extends pl{constructor(t){if(super(t),this.inputSpec=[new ve({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Jt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ve({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Mt(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=ci(u,m,c,this.padding),h=ci(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Wt(n,[0,2,3,1]));let y=Zu(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Wt(y,[0,3,1,2])),this.bias!=null&&(y=xn(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(t){t=Jt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=ci(e[o],u,i,this.padding),e[s]=ci(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};Qm.className="Conv2DTranspose";et.registerClass(Qm);var tf=class extends ml{constructor(t){if(super(t),this.inputSpec=[new ve({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Jt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ve({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Mt(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],y=this.strides[2],b=ci(l,h,m,this.padding),w=ci(c,g,f,this.padding),v=ci(p,y,d,this.padding),k=[s,b,w,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Wt(n,[0,2,3,4,1]));let E=PI(n,this.kernel.read(),k,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Wt(E,[0,4,1,2,3])),this.bias!==null&&(E=xn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Jt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=ci(e[o],c,a,this.padding),e[s]=ci(e[s],p,u,this.padding),e[i]=ci(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className="Conv3DTranspose";et.registerClass(tf);var Xy=class extends pu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ue(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ue(e.pointwiseConstraint)}build(t){if(t=Jt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let n=t[e],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let a=0;a<this.rank;++a)s.push(1);s.push(n*this.depthMultiplier,this.filters);let i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new ve({ndim:this.rank+2,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{t=Mt(t);let n;if(this.rank===1)throw new Nt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Wt(t,[0,2,3,1])),n=$h(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=xn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Wt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Ee(this.depthwiseInitializer),t.pointwiseInitializer=Ee(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=We(this.depthwiseConstraint),t.pointwiseConstraint=We(this.pointwiseConstraint),t}};Xy.className="SeparableConv";var ef=class extends Xy{constructor(t){super(2,t)}};ef.className="SeparableConv2D";et.registerClass(ef);var mu=class extends pu{constructor(t){super(1,t),mu.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"&&!sy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};mu.className="Conv1D";et.registerClass(mu);var rf=class extends Bt{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Mt(t),this.dataFormat==="channelsLast"){let n=Uh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Cropping2D";et.registerClass(rf);var nf=class extends Bt{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,u$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=Mt(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Wt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i]);return Wt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};nf.className="UpSampling2D";et.registerClass(nf);function k8(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=hn()),Le(o);let i=tg(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ys(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Wt(i,[0,3,1,2])),i})}var of=class extends Fc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ue(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Jt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=Mt(t);let n=k8(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=xn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Jt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=An(e,this.kernelSize[0],this.padding,this.strides[0]),i=An(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Ee(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=We(this.depthwiseRegularizer),t}};of.className="DepthwiseConv2D";et.registerClass(of);function zS(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function BS(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Jr(2,u));if(t=Wt(t,l),s!=null)throw new Nt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Z(Z(o,"bool"),"float32"),o.rank===u-1&&(o=fr(o,-1)),o=Wt(o,l)),n&&(t=ir(t,0),o!=null&&(o=ir(o,0)));let c=[],p,m=e,f=t.shape[0],d=vr(t),h;o!=null&&(h=vr(o));for(let y=0;y<f;++y){let b=d[y],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let v=B(()=>{let k=h[y],E=lt(dr(k),k),$=J(M(w[0],k),M(m[0],E)),D=m.map((R,P)=>J(M(w[1][P],k),M(R,E)));return{output:$,newStates:D}});p=v.output,m=v.newStates}a&&c.push(p)}let g;return a&&(g=Je(c,1)),[p,g,m]})}var $n=class extends Bt{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Pc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,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 ve({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 Jr(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;n<t;++n)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new Nt("Constants support is not implemented in RNN yet.");hy(t)&&(t=t[0]),t=t;let n=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new ve({shape:[n,null,...o]});let s=[t[0]].concat(t.slice(2));this.cell.build(s);let i;if(Array.isArray(this.cell.stateSize)?i=this.cell.stateSize:i=[this.cell.stateSize],this.stateSpec!=null){if(!x.arraysEqual(this.stateSpec.map(a=>a.shape[a.shape.length-1]),i))throw new z(`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=i.map(a=>new ve({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ye([n,o])):this.states_=[ye([n,this.cell.stateSize])];else if(t==null)_t(this.states_),this.keptStates!=null&&(_t(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ye([n,o])):this.states_[0]=ye([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):_t(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,a=[n,i];if(!x.arraysEqual(s.shape,a))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Pe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=zS(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new ve({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof Qr){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Mt(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 z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=BS((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ye(t.shape);return e=mt(e,[1,2]),e=ll(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?cy(e,[1,n]):e):this.cell.stateSize>1?[cy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$n.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,t,e)}static fromConfig(t,e,n={}){let o=e.cell,s=vn(o,n);return new t(Object.assign(e,{cell:s}))}};$n.className="RNN";et.registerClass($n);var fl=class extends Bt{},Oc=class extends fl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,er(this.units,"units"),this.activation=ui(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Cc([1,ii([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Cc([1,ii([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Jt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hl({ones:()=>dr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hl({ones:()=>dr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Eo(M(t,i),this.kernel.read()):s=Eo(t,this.kernel.read()),this.bias!=null&&(s=xn(s,this.bias.read())),a!=null&&(n=M(n,a));let u=J(s,Eo(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:li(this.activation),useBias:this.useBias,kernelInitializer:Ee(this.kernelInitializer),recurrentInitializer:Ee(this.recurrentInitializer),biasInitializer:Ee(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}};Oc.className="SimpleRNNCell";et.registerClass(Oc);var sf=class extends $n{constructor(t){t.cell=new Oc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};sf.className="SimpleRNN";et.registerClass(sf);var Mc=class extends fl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,er(this.units,"units"),this.activation=ui(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ui(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Cc([1,ii([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Cc([1,ii([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Jt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hl({ones:()=>dr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hl({ones:()=>dr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0<this.dropout&&this.dropout<1&&(t=M(t,s[0]));let c=Eo(t,this.kernel.read());this.useBias&&(c=xn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=ur(p,[2*this.units,this.units],p.rank-1),d=Eo(o,m),[h,g,y]=ur(c,3,c.rank-1),[b,w]=ur(d,2,d.rank-1);a=this.recurrentActivation.apply(J(h,b)),u=this.recurrentActivation.apply(J(g,w));let v=Eo(M(u,o),f);l=this.activation.apply(J(y,v));let k=J(M(a,o),M(J(1,Zt(a)),l));return[k,k]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:li(this.activation),recurrentActivation:li(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ee(this.kernelInitializer),recurrentInitializer:Ee(this.recurrentInitializer),biasInitializer:Ee(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}};Mc.className="GRUCell";et.registerClass(Mc);var af=class extends $n{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Mc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};af.className="GRU";et.registerClass(af);var dl=class extends fl{constructor(t){super(t),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=t.units,er(this.units,"units"),this.activation=ui(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ui(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Cc([1,ii([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Cc([1,ii([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Jt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new iu().apply([i]),m=s.apply([i*2]);return kS(kS(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hl({ones:()=>dr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hl({ones:()=>dr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0<this.dropout&&this.dropout<1&&(t=M(t,i[0]));let m=Eo(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,a[0])),m=J(m,Eo(o,this.recurrentKernel.read())),this.useBias&&(m=xn(m,this.bias.read()));let[f,d,h,g]=ur(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=J(M(l,s),M(u,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=M(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:li(this.activation),recurrentActivation:li(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ee(this.kernelInitializer),recurrentInitializer:Ee(this.recurrentInitializer),biasInitializer:Ee(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}};dl.className="LSTMCell";et.registerClass(dl);var lf=class extends $n{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new dl(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className="LSTM";et.registerClass(lf);var Pc=class extends fl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){hy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{si(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign({},t,o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(vn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return qh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;i<n.weights.length;++i)e.push([n.weights[i],s[i]])}Bm(e)}};Pc.className="StackedRNNCells";et.registerClass(Pc);function hl(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):my(t(),e),a=()=>su(i,t,n);return!o||o<=1?Pe(a().clone()):Array(o).fill(void 0).map(a).map(l=>Pe(l.clone()))}var N8=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)t.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(e[n[o]]=r[n[o]]);return e};var Yy=class extends $n{constructor(t){if(t.unroll)throw new Nt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Nt("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new ve({ndim:5})]}call(t,e){return B(()=>{if(this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ye(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("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(()=>ye(s)):this.states_=[ye(s)];else if(t==null)_t(this.states_),this.keptStates!=null&&(_t(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ye(s)):this.states_[0]=ye(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):_t(this.states_);for(let a=0;a<this.states_.length;++a){let u=t[a],l=s;if(!x.arraysEqual(u.shape,l))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${u.shape}`);this.states_[a]=u}}this.states_=this.states_.map(a=>Pe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=An(l,o[0],s,i[0],a[0]),m=An(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Yy.className="ConvRNN2D";var Lc=class extends dl{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign({},t,{units:e})),this.filters=e,er(this.filters,"filters"),this.kernelSize=cu(n,2,"kernelSize"),this.kernelSize.forEach(u=>er(u,"kernelSize")),this.strides=cu(o||1,2,"strides"),this.strides.forEach(u=>er(u,"strides")),this.padding=s||"valid",gn(this.padding),this.dataFormat=i||"channelsLast",Le(this.dataFormat),this.dilationRate=cu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>er(u,"dilationRate"))}build(t){var e;t=Jt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=lr([c]),g=l.apply([c*2]);return Em([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hl({ones:()=>dr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(st,it,ft)=>!it||!it[ft]?st:M(it[ft],st),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hl({ones:()=>dr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),y=l(s,d,2),b=l(s,d,3),w=3,[v,k,E,$]=ur(this.kernel.read(),a,w),[D,R,P,W]=this.useBias?ur(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,v,D,this.padding),p=this.inputConv(p,k,R,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,$,W,this.padding);let[U,q,K,j]=ur(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,U),g=this.recurrentConv(g,q),y=this.recurrentConv(y,K),b=this.recurrentConv(b,j);let Q=this.recurrentActivation.apply(J(c,h)),rt=this.recurrentActivation.apply(J(p,g)),X=J(M(rt,i),M(Q,this.activation.apply(J(m,y)))),ot=M(this.recurrentActivation.apply(J(f,b)),this.activation.apply(X));return[ot,ot,X]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=N8(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(t,e,n,o){let s=pn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?xn(s,n,this.dataFormat):s}recurrentConv(t,e){return pn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Lc.className="ConvLSTM2DCell";et.registerClass(Lc);var uf=class extends Yy{constructor(t){let e=new Lc(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}};uf.className="ConvLSTM2D";et.registerClass(uf);var zc=class extends Bt{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return n}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return su(()=>my(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};zc.className="Dropout";et.registerClass(zc);var cf=class extends zc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};cf.className="SpatialDropout1D";et.registerClass(cf);var pf=class extends Bt{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,er(this.units,"units"),this.activation=ui(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ue(t.kernelConstraint),this.biasConstraint=Ue(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Jt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Jt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t),o=iy(this.activation.getClassName()),s;return o!=null?s=Eo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Eo(n,this.kernel.read()),this.bias!=null&&(s=xn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:li(this.activation),useBias:this.useBias,kernelInitializer:Ee(this.kernelInitializer),biasInitializer:Ee(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),biasConstraint:We(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};pf.className="Dense";et.registerClass(pf);var mf=class extends Bt{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Jt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],_o(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=Wt(n,o)}return d$(n)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}};mf.className="Flatten";et.registerClass(mf);var ff=class extends Bt{constructor(t){super(t),this.supportsMasking=!0,this.activation=ui(t.activation)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t);return this.activation.apply(n)})}getConfig(){let t={activation:li(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};ff.className="Activation";et.registerClass(ff);var df=class extends Bt{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return 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n=Mt(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return O(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};hf.className="Reshape";et.registerClass(hf);var gf=class extends Bt{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=Jr(1,t.dims.length+1);if(!x.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new ve({ndim:this.dims.length+1})]}computeOutputShape(t){t=Jt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Wt(Mt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};gf.className="Permute";et.registerClass(gf);var xf=class extends Bt{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=Mt(t),o=-1;return Yl(vo(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t),o=-1,s=!0,i=Yl(vo(n,this.maskValue),o,s);return M(n,Z(i,n.dtype))})}};xf.className="Masking";et.registerClass(xf);var yf=class extends Bt{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let 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gl=class extends Bt{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new Nt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let n=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],i=e[o];if(s==null||i==null||s<0||i<0)n.push(null);else if(s===1)n.push(i);else if(i===1)n.push(s);else{if(s!==i)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(s)}}return n}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Jt(t)]),t=t,t.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. 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t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Concatenate";et.registerClass(Sf);function eg(r,t){for(;r<0;)r+=t;return r}function T8(r,t,e){if(r.shape.length>3||t.shape.length>3)throw new Nt("batchDot is not implemented for tensors of 4D or higher rank yet");if(x.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),x.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new Nt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;l<i;++l)u.push(1);t=O(t,t.shape.concat(u))}else if(o>n){i=o-n;let u=[];for(let l=0;l<i;++l)u.push(1);r=O(r,r.shape.concat(u))}else i=0;let a;if(r.shape.length===2&&t.shape.length===2)s[0]===s[1]?a=mt(M(r,t),s[0]):a=mt(M(Wt(r,[1,0]),t),s[1]);else{let u=s[0]!==r.shape.length-1,l=s[1]===t.shape.length-1;a=Lt(r,t,u,l)}if(i>0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);a=zr(a,l)}return a.shape.length===1&&(a=fr(a,1)),a})}var kf=class extends gl{constructor(t){super(t),this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){x.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>eg(s,t[i].shape.length)):o=[eg(this.axes,e.shape.length),eg(this.axes,n.shape.length)],this.normalize&&(e=Kh(e,o[0]),n=Kh(n,o[1])),T8(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[eg(this.axes,t.length),eg(this.axes,e.length)],n}computeOutputShape(t){x.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};kf.className="Dot";et.registerClass(kf);var Nf=class extends Bt{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t);return su(()=>J(Am(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Nf.className="GaussianNoise";et.registerClass(Nf);var Tf=class extends Bt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Mt(t);return this.rate>0&&this.rate<1?su(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return M(n,Am(n.shape,1,s))},()=>n,e.training||!1):n})}};Tf.className="GaussianDropout";et.registerClass(Tf);var _f=class extends Bt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Mt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return su(()=>{let s=Mt(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=Tn(ei(n),this.rate);l=Ic(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=J(M(s,l),M(J(l,-1),u));return J(M(m,c),p)},()=>Mt(t),e.training||!1)}return t})}};_f.className="AlphaDropout";et.registerClass(_f);function rg(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=EI(r,t,e,n,o,s);else if(r.rank===3)i=AI(r,t,e,n,o,s);else if(r.rank===4)i=$I(r,t,e,n,o,s);else throw new Nt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function _8(r,t,e,n,o=.001){return B(()=>{let s=xm(r,n),i=s.mean,a=s.variance;return[rg(r,i,a,e,t,o),i,a]})}function E8(r,t,e,n,o=.001){return B(()=>{let s=xm(r,n),i=s.mean,a=s.variance,u=[];for(let d of Jr(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=O(i,u),c=O(a,u),p=t==null?null:O(t,u),m=e==null?null:O(e,u);return[rg(r,l,c,m,p,o),i,a]})}function A8(r,t,e,n,o=.001){return x.arraysEqual(n.slice().sort(),Jr(0,r.rank-1))?_8(r,t,e,n,o):E8(r,t,e,n,o)}var Ef=class extends Bt{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.movingMeanInitializer=de(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=de(t.movingVarianceInitializer||"ones"),this.betaConstraint=Ue(t.betaConstraint),this.gammaConstraint=Ue(t.gammaConstraint),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer)}build(t){t=Jt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new ve({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=Mt(t),s=o.shape,i=s.length,a=Jr(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=ko(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!x.arraysEqual(c,Jr(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ee(this.betaInitializer),gammaInitializer:Ee(this.gammaInitializer),movingMeanInitializer:Ee(this.movingMeanInitializer),movingVarianceInitializer:Ee(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:We(this.betaConstraint),gammaConstraint:We(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Ef.className="BatchNormalization";et.registerClass(Ef);var Af=class extends Bt{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Jt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==To(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=Mt(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=xm(n,this.axis,!0),l=ko(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?O(h,l):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return a=kr(a,f),u=kr(u,f),p=kr(p,d),m=kr(m,d),rg(n,a,u,m,p,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ee(this.betaInitializer),gammaInitializer:Ee(this.gammaInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}};Af.className="LayerNormalization";et.registerClass(Af);function $8(r,t,e){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=hn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new ve({ndim:4})]}computeOutputShape(t){t=Jt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>$8(Mt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};$f.className="ZeroPadding2D";et.registerClass($f);function rb(r,t,e,n,o,s){return B(()=>{Le(o),CS(s),gn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=hn()),s==null&&(s="max"),r=tg(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Qa(r,t,e,a):i=qa(r,t,e,a),o==="channelsFirst"&&(i=Wt(i,[0,3,1,2])),i})}function Q$(r,t,e,n,o,s){return B(()=>{Le(o),CS(s),gn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=hn()),s==null&&(s="max"),r=LS(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Th(r,t,e,a):i=fh(r,t,e,a),o==="channelsFirst"&&(i=Wt(i,[0,4,1,2,3])),i})}var Zy=class extends Bt{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(er(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);er(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,gn(this.padding),this.inputSpec=[new ve({ndim:3})]}computeOutputShape(t){t=Jt(t);let e=An(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=ll(Mt(t),2);let n=this.poolingFunction(Mt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Df=class extends Zy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),rb(t,e,n,o,s,"max")}};Df.className="MaxPooling1D";et.registerClass(Df);var Rf=class extends Zy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),rb(t,e,n,o,s,"avg")}};Rf.className="AveragePooling1D";et.registerClass(Rf);var Jy=class extends Bt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];er(this.poolSize,"poolSize"),er(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),gn(this.padding),this.inputSpec=[new ve({ndim:4})]}computeOutputShape(t){t=Jt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Mt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Ff=class extends Jy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),rb(t,e,n,o,s,"max")}};Ff.className="MaxPooling2D";et.registerClass(Ff);var Of=class extends Jy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),rb(t,e,n,o,s,"avg")}};Of.className="AveragePooling2D";et.registerClass(Of);var Qy=class extends Bt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];er(this.poolSize,"poolSize"),er(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),gn(this.padding),this.inputSpec=[new ve({ndim:5})]}computeOutputShape(t){t=Jt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),o=An(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Mt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Mf=class extends Qy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),Q$(t,e,n,o,s,"max")}};Mf.className="MaxPooling3D";et.registerClass(Mf);var Pf=class extends Qy{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),gn(o),Q$(t,e,n,o,s,"avg")}};Pf.className="AveragePooling3D";et.registerClass(Pf);var tb=class extends Bt{constructor(t){super(t),this.inputSpec=[new ve({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Nt}},Lf=class extends tb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Mt(t);return xe(n,1)})}};Lf.className="GlobalAveragePooling1D";et.registerClass(Lf);var zf=class extends tb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Mt(t);return Dr(n,1)})}};zf.className="GlobalMaxPooling1D";et.registerClass(zf);var eb=class extends Bt{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),this.inputSpec=[new ve({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Nt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends eb{call(t,e){return B(()=>{let n=Mt(t);return this.dataFormat==="channelsLast"?xe(n,[1,2]):xe(n,[2,3])})}};Bf.className="GlobalAveragePooling2D";et.registerClass(Bf);var Vf=class extends eb{call(t,e){return B(()=>{let n=Mt(t);return this.dataFormat==="channelsLast"?Dr(n,[1,2]):Dr(n,[2,3])})}};Vf.className="GlobalMaxPooling2D";et.registerClass(Vf);var nb=class extends Bt{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=vn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Gf=class extends nb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Jt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Jt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=Mt(t),BS((i,a)=>[Mt(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Gf.className="TimeDistributed";et.registerClass(Gf);function D8(r){qi(a$,"BidirectionalMergeMode",r)}var R8="concat",Wf=class extends nb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=vn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=vn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?R8:t.mergeMode,D8(this.mergeMode),t.weights)throw new Nt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):_r(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=zS(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new ve({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new Nt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof Qr;for(let l of i)if(l instanceof Qr!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=ir(s,1));let 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AD=(r,t,e)=>{switch(r.op){case"Equal":return[Sr(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[vo(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[Ge(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[Tn(I("a",r,t,e),I("b",r,t,e))];case"Less":return[ec(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[_n(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[Nr(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[Ja(I("a",r,t,e))];case"LogicalOr":return[nc(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[_e(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return x.isPromise(o)?o.then(s=>[].concat(s)):[].concat(o)}var sg=class{constructor(t={},e={},n={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let n=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(n))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function fk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>Cn(m)[0]),c=[];n!=null&&(c=n.map(m=>Cn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((dk(m)||rZ(m)||nZ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zD(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>Cn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var Q7=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],tZ=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],eZ=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function dk(r){return Q7.indexOf(r.op)>=0}function rZ(r){return tZ.indexOf(r.op)>=0}function nZ(r){return eZ.indexOf(r.op)>=0}var Bc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!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(n=>{this._functionExecutorMap[n]=new Bc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),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 e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let n=fk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return zD(this.graph,this.weightMap,n)}execute(t,e){t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[Cn(p)[0]]),s=e.map(p=>Cn(p)[0]),i=s.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));let l={},c={};return B(()=>{let p=new sg(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(t).forEach(h=>{let[g,y]=Cn(h),b=[];b[y]=t[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<u.length;h++){let g=u[h];if(!m[g.name]){let y=mk(g,m,p,this._resourceManager);if(x.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);m[g.name]=y,this.checkTensorForDisposal(g.name,g,m,p,f,s,d)}}return this.parent==null&&p.dispose(f),e.map(h=>Cr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category==="control"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!=="control"){let l=cD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];if(p===1){if(!this.keepTensorForDebug)c.dispose();else{let[m,f]=Do(e.name,o);this.intermediateTensors[m]?this.intermediateTensors[m][f]=c:(this.intermediateTensors[m]=[],this.intermediateTensors[m][f]=c)}delete a[c.id]}else p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(t=>this.intermediateTensors[t].forEach(e=>e.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(t=>{this.tensorsMap[t].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let t in this.intermediateTensors)this.intermediateTensors[t].forEach(e=>e.dispose()),delete this.intermediateTensors[t]}async _executeAsync(t,e,n=!1,o={},s={}){n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepTensorForDebug=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let i=new sg(this.weightMap,o,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(t,i,e,n);let a=e.map(c=>Cr(c,this.tensorsMap,i)),u=a.map(c=>c.id),l=Object.keys(t).map(c=>t[c].id);return this.keepIds=new Set([...u,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&i.dispose(this.keepIds),a}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[Cn(w)[0]]),a=n.map(w=>Cn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=fk(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[v,k]=Cn(w),E=[];E[k]=t[w],d[v]=E});let h={},g=this.getFrozenTensorIds(d),y={};for(;f.length>0;){let w=this.processStack(i,f,e,d,y,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=u.filter(w=>!dk(w)&&!Cr(w.name,d,e)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&I("isConstant",p.node,o,n)&&([m]=Do(p.node.name,n)),o[p.node.name]==null){let f=mk(p.node,o,n,this._resourceManager);m||([m]=Do(p.node.name,n));let d=n.currentContext;x.isPromise(f)?c.push(f.then(h=>(o[m]=h,n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Do(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=Cn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);x.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){let e={};for(let n in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];e[o.name]=t[n]}else e[n]=t[n];return e}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=Cn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null?this._signature.outputs[e].name:e,{})}checkOutputs(t){t.forEach(e=>{let[n]=Cn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Ib=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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e=Mr.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(Mr.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=await this.handler.load();return this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=Mr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Bc(og.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=og.Instance.transformGraph(t.modelInitializer);this.initializer=new Bc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(t,e){if(typeof t=="string"){let n=Mr.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof Pt)&&!Array.isArray(t))return 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TextDecoder;else{let{StringDecoder:e}=Ck();t=r instanceof e}return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Pt)&&!(r instanceof Promise)&&!t)}function sR(r){return r==null||fZ(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Pt||x.isTypedArray(r)}function fZ(r){return r===null||typeof r!="object"&&typeof r!="function"}function iR(r){return rR(r,dZ)}function dZ(r){return r instanceof Pt?{value:r.clone(),recurse:!1}:du(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var Hf=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new 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this.set(e,this.pop()),n}};var Gc=class extends Hf{constructor(){super(Gc.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,e=new Array(t),n=this.length();for(let o=0;o<n;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Gc.INITIAL_CAPACITY=32;function Mk(r){return new Sk(r)}function ig(r){return new kk(r)}function lR(r,t){return new Eb(r,t)}function uR(r,t=xl.FAIL){return new Fk(r,t)}var rr=class{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],n=await t.next();for(;!n.done;)e.push(n.value),n=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),n=t(e.value);for(;!e.done&&n;)e=await this.next(),n=t(e.value)}handleErrors(t){return new Dk(this,t)}filter(t){return new Ak(this,t)}map(t){return new $k(this,t)}mapAsync(t){return new _b(this,t)}serialMapAsync(t){return new _b(this,t).serial()}flatmap(t){return new Rk(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new Ek(this,t,e)}columnMajorBatch(t,e=!0,n=Ik){return this.rowMajorBatch(t,e).map(s=>nR(s,n))}concatenate(t,e){return new Eb(Mk([this,t]),e)}take(t){return t<0||t==null?this:new _k(this,t)}skip(t){return t<0||t==null?this:new Tk(this,t)}prefetch(t){return new Ab(this,t)}shuffle(t,e){return new Ok(this,t,e)}serial(){return new Nk(this)}},Sk=class extends rr{constructor(t){super(),this.items=t,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 t=this.items[this.trav];return this.trav++,{value:iR(t),done:!1}}},kk=class extends rr{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Nk=class extends rr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Tk=class extends rr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;_t(t.value)}return this.upstream.next()}},_k=class extends rr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Ek=class extends rr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Ak=class extends rr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;_t(t.value)}}},$k=class extends rr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=fo.getTensorsInContainer(t.value),n=this.transform(t.value),o=fo.getTensorsInContainer(n);for(let s of e)fo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Dk=class extends rr{constructor(t,e){super(),this.upstream=t,this.handler=e,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 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rr{constructor(t,e=xl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof rr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await Tb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case xl.FAIL:throw new Error(`Zipped streams should have the same length. 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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var $b='"',ag=Symbol("out"),fR=Symbol("field"),Db=Symbol("quote"),Pk=Symbol("quoteafterquote"),dR=Symbol("quoteinquote"),Kf=class extends mi{constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new qf(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(x.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&x.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(x.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!(this.configuredColumnsOnly&&!a)){let u=e[s],l=null;if(u==="")if(a&&a.default!==void 0)l=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${t}`);l=void 0}else{let c=Number(u);if(isNaN(c))a&&a.dtype==="bool"?l=this.getBoolean(u):l=u;else if(!a||!a.dtype)l=c;else switch(a.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(u);break;default:l=c}}a&&a.isLabel?o[i]=l:n[i]=l}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let n=[],o=0,s=t.length,i=ag;for(let a=0;a<s;a++)switch(i){case ag:switch(t.charAt(a)){case $b:o=a+1,i=Db;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=ag;break;default:i=fR,o=a;break}break;case fR:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=ag,o=a+1;break;default:}break;case Db:switch(t.charAt(a)){case $b:i=Pk;break;default:}break;case Pk:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=ag,o=a+1;break;case $b:i=Db;break;default:i=dR;break}break;case dR:switch(t.charAt(a)){case $b:i=Db;break;default:}break;default:}if(i===Pk?n.push(t.substring(o,s-1)):n.push(t.substring(o)),e&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var jf=class extends rr{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!V().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new jf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let 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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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 e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(x.sizeFromShape(e));return n.set(t,n.length-t.length),Ar(n,e)}};var Xf=class extends rr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Re([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Wi([i,s,u,a],[1,4])}else this.cropBox=Wi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!V().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new Xf(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&x.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Ex.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=fr(Z(t,"float32"),0),n;n=dn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return O(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var Yf=class{};var lg=class extends rr{split(t){return new Lk(this,t)}},Lk=class extends lg{constructor(t,e){super(),this.upstream=t,this.impl=new zk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zk=class extends Wc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var Rb=class extends rr{decodeUTF8(){return new Bk(this)}},Bk=class extends lg{constructor(t){super(),this.upstream=t,this.impl=new Vk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Vk=class extends Wc{constructor(t){if(super(),this.upstream=t,V().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=Ck();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return V().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var Zf=class extends Rb{constructor(t,e={}){super(),this.file=t,this.options=e,x.assert(t instanceof Uint8Array||(V().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof 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Yf{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return Fb(this.url)?new Jf(this.url,this.fileOptions).iterator():hR(this.url,this.fileOptions)}};function gR(r,t={}){return new Kf(new Qf(r),t)}function xR(r){let t=ig(r);return Dn(async()=>t)}function yR(r){return Dn(async()=>{let t=await r();return ig(()=>t.next())})}async function bR(r,t){return Xf.create(r,t)}async function wR(r){return jf.create(r)}var Gk="3.15.0";function nt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&x.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yZ=Gr.whereImpl,hu=class extends jo{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ji(this,go())}nextDataId(){return hu.nextDataId++}write(t,e,n){this.firstUse&&(this.firstUse=!1,V().get("IS_NODE")&&S.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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bZ=r=>{let{x:t}=r.inputs,e=r.backend;nt(t,"abs");let n=new Float32Array(x.sizeFromShape(t.shape)),o=e.data.get(t.dataId).values;return n=Wk(o),e.makeOutput(n,t.shape,t.dtype)},CR={kernelName:bi,backendName:"cpu",kernelFunc:bZ};function te(r){return(t,e,n,o,s)=>{let i=S.assertAndGetBroadcastShape(t,e),a=i.length,u=x.computeStrides(i),l=x.sizeFromShape(i),c=x.getTypedArrayFromDType(s,l),p=t.length,m=e.length,f=x.computeStrides(t),d=x.computeStrides(e),h=S.getBroadcastDims(t,i),g=S.getBroadcastDims(e,i);if(h.length+g.length===0)for(let y=0;y<c.length;++y)c[y]=r(n[y%n.length],o[y%o.length]);else for(let y=0;y<c.length;++y){let b=x.indexToLoc(y,a,u),w=b.slice(-p);h.forEach($=>w[$]=0);let v=x.locToIndex(w,p,f),k=b.slice(-m);g.forEach($=>k[$]=0);let E=x.locToIndex(k,m,d);c[y]=r(n[v],o[E])}return[c,i]}}function Ir(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=e.makeTensorInfo(n.shape,"complex64"),u=e.data.get(a.dataId);return 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,v=c.effectiveFilterDepth,k=c.effectiveFilterHeight,E=c.effectiveFilterWidth,$=v-1-c.padInfo.front,D=E-1-c.padInfo.left,R=k-1-c.padInfo.top,P=It(s.shape,"float32"),W=1/(d*h*g),U=e.bufferSync(o);for(let q=0;q<c.batchSize;++q)for(let K=0;K<c.inChannels;++K)for(let j=0;j<c.inDepth;++j)for(let Q=0;Q<c.inHeight;++Q)for(let rt=0;rt<c.inWidth;++rt){let X=j-$,ot=Q-R,st=rt-D,it=0;for(let ft=0;ft<v;ft+=y){let at=(X+ft)/p;if(!(at<0||at>=c.outDepth||Math.floor(at)!==at))for(let xt=0;xt<k;xt+=b){let dt=(ot+xt)/m;if(!(dt<0||dt>=c.outHeight||Math.floor(dt)!==dt))for(let bt=0;bt<E;bt+=w){let kt=(st+bt)/f;if(kt<0||kt>=c.outWidth||Math.floor(kt)!==kt)continue;it+=U.get(q,at,dt,kt,K)}}}P.set(it*W,q,j,Q,rt,K)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var IF={kernelName:Tp,backendName:"cpu",kernelFunc:o9};function s9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;nt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,v=y-1-c.padInfo.top,k=It(i.shape,"float32"),E=1/(f*d),$=e.data.get(o.dataId).values,D=It(o.shape,"float32",$);for(let R=0;R<c.batchSize;++R)for(let P=0;P<c.inChannels;++P)for(let W=0;W<c.inHeight;++W)for(let U=0;U<c.inWidth;++U){let q=W-v,K=U-w,j=0;for(let Q=0;Q<y;Q+=h){let rt=(q+Q)/p;if(!(rt<0||rt>=c.outHeight||Math.floor(rt)!==rt))for(let X=0;X<b;X+=g){let ot=(K+X)/m;if(ot<0||ot>=c.outWidth||Math.floor(ot)!==ot)continue;j+=D.get(R,rt,ot,P)}}k.set(j*E,R,W,U,P)}return e.makeTensorInfo(k.shape,k.dtype,k.values)}var SF={kernelName:Np,backendName:"cpu",kernelFunc:s9};function i9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;x.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),nt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,y=f.length,b=m.length,w=p.length,v=0,k=0,E=0,$=0;for(let D=0;D<c.length;++D)h[D]=d[v++]+(c[D]-p[k++])*f[E++]/Math.sqrt(m[$++]+l),v>=g&&(v=0),k>=w&&(k=0),E>=y&&(E=0),$>=b&&($=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var kF={kernelName:fs,backendName:"cpu",kernelFunc:i9};function a9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;nt([o],"batchToSpaceND");let a=s.reduce((y,b)=>y*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=ne({inputs:{x:o},backend:e,attrs:{shape:u}}),d=He({inputs:{x:f},backend:e,attrs:{perm:l}}),h=ne({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var NF={kernelName:wi,backendName:"cpu",kernelFunc:a9};function l9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=rd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var TF={kernelName:_p,backendName:"cpu",kernelFunc:l9};function u9(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var _F={kernelName:Ep,backendName:"cpu",kernelFunc:u9};var c9=Et(ao,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),EF={kernelName:ao,backendName:"cpu",kernelFunc:c9};var p9=r=>{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(x.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;l<a.length;l++){let c=a[l],p=u[l];n[l]=Math.hypot(c,p)}return e.makeOutput(n,t.shape,"float32")},AF={kernelName:Dl,backendName:"cpu",kernelFunc:p9};function Yi(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var $F={kernelName:Gp,backendName:"cpu",kernelFunc:Yi};function gu(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,t[0].shape)[0],i=S.computeOutShape(t.map(h=>h.shape),s);if(x.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let a=t.filter(h=>x.sizeFromShape(h.shape)>0);if(a.length===1)return qr({inputs:{x:a[0]},backend:e});let u=a.map(h=>h.shape);if(S.assertParamsConsistent(u,s),a[0].dtype==="complex64"){let h=a.map(v=>Ro({inputs:{input:v},backend:e})),g=a.map(v=>Yi({inputs:{input:v},backend:e})),y=gu({inputs:h,backend:e,attrs:{axis:s}}),b=gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:y,imag:b},backend:e});return h.forEach(v=>e.disposeIntermediateTensorInfo(v)),g.forEach(v=>e.disposeIntermediateTensorInfo(v)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),w}let l=a.map(h=>{let g=x.sizeFromShape(h.shape.slice(s));return ne({inputs:{x:h},backend:e,attrs:{shape:[-1,g]}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Uc(c,i,t[0].dtype,p),f=S.computeOutShape(a.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var DF={kernelName:vi,backendName:"cpu",kernelFunc:gu};function bN(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;nt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,y=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",v=new pe(m.outShape,o.dtype),k=x.computeStrides(o.shape),E=x.computeStrides(s.shape),$=k[0],D=w?k[1]:k[2],R=w?k[2]:1,P=w?1:k[1],W=v.strides[0],U=w?v.strides[1]:v.strides[2],q=w?v.strides[2]:1,K=w?1:v.strides[1],j=e.data.get(o.dataId).values,Q=e.data.get(s.dataId).values,rt=v.values;for(let X=0;X<m.batchSize;++X){let ot=X*$,st=X*W;for(let it=0;it<m.outHeight;++it){let ft=st+it*U,at=it*m.strideHeight-b;for(let xt=0;xt<f;++xt){let dt=at+xt*h;if(dt<0||dt>=m.inHeight)continue;let bt=xt*E[0],kt=ot+dt*D;for(let At=0;At<m.outWidth;++At){let Dt=ft+At*q,qt=At*m.strideWidth-y;for(let Kt=0;Kt<d;++Kt){let ce=qt+Kt*g;if(ce<0||ce>=m.inWidth)continue;let Ot=bt+Kt*E[1],$e=kt+ce*R,Se=Ot;for(let ae=0;ae<m.inChannels;++ae){let je=j[$e+ae*P];for(let Fe=0;Fe<m.outChannels;++Fe)rt[Dt+Fe*K]+=je*Q[Se+Fe];Se+=m.outChannels}}}}}}return e.makeTensorInfo(v.shape,v.dtype,rt)}var RF={kernelName:rs,backendName:"cpu",kernelFunc:bN};function m9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n;nt([o,s],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,y=m.dataFormat==="channelsLast",b=new pe(m.filterShape,"float32"),w=m.padInfo.left,v=m.padInfo.top,k=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,$=new pe(o.shape,o.dtype,k),D=new pe(s.shape,s.dtype,E);for(let R=0;R<h;++R){let P=Math.max(0,Math.ceil((v-R)/f)),W=Math.min(m.outHeight,(m.inHeight+v-R)/f);for(let U=0;U<g;++U){let q=Math.max(0,Math.ceil((w-U)/d)),K=Math.min(m.outWidth,(m.inWidth+w-U)/d);for(let j=0;j<m.inChannels;++j)for(let Q=0;Q<m.outChannels;++Q){let rt=0;for(let X=0;X<m.batchSize;++X)for(let ot=P;ot<W;++ot){let st=R+ot*f-v;for(let it=q;it<K;++it){let ft=U+it*d-w;y?rt+=$.get(X,st,ft,j)*D.get(X,ot,it,Q):rt+=$.get(X,j,st,ft)*D.get(X,Q,ot,it)}}b.set(rt,R,U,j,Q)}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var FF={kernelName:$p,backendName:"cpu",kernelFunc:m9};function f9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n;nt([o,s],"conv2dBackpropInput");let p=x.computeStrides(s.shape),m=x.computeStrides(o.shape),f=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,f),h=new pe(d.inShape,"float32"),g=h.values,y=e.data.get(o.dataId).values,b=e.data.get(s.dataId).values,[w,v,k]=p,{batchSize:E,filterHeight:$,filterWidth:D,inChannels:R,inHeight:P,inWidth:W,outChannels:U,outHeight:q,outWidth:K,strideHeight:j,strideWidth:Q}=d;f=d.dataFormat;let rt=$-1-d.padInfo.top,X=D-1-d.padInfo.left,ot=f==="channelsLast",st=h.strides[0],it=ot?h.strides[1]:h.strides[2],ft=ot?h.strides[2]:1,at=ot?1:h.strides[1],xt=m[0],dt=ot?m[1]:m[2],bt=ot?m[2]:1,kt=ot?1:m[1];for(let At=0;At<E;++At)for(let Dt=0;Dt<R;++Dt)for(let qt=0;qt<P;++qt){let Kt=qt-rt,ce=Math.max(0,Math.ceil(Kt/j)),Ot=Math.min(q,($+Kt)/j);for(let $e=0;$e<W;++$e){let Se=$e-X,ae=Math.max(0,Math.ceil(Se/Q)),je=Math.min(K,(D+Se)/Q),Fe=0;for(let ze=ce;ze<Ot;++ze){let Bo=ze*j-Kt;for(let Fr=ae;Fr<je;++Fr){let nn=Fr*Q-Se,on=xt*At+dt*ze+bt*Fr,jr=w*($-1-Bo)+v*(D-1-nn)+k*Dt;for(let oo=0;oo<U;++oo){let Fn=y[on+kt*oo],Vo=b[jr+oo];Fe+=Fn*Vo}}}let rn=st*At+it*qt+ft*$e+at*Dt;g[rn]=Fe}}return e.makeTensorInfo(h.shape,h.dtype,h.values)}var OF={kernelName:ns,backendName:"cpu",kernelFunc:f9};function d9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;nt([o,s],"conv3d");let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=l,y=g.front,b=g.left,w=g.top,v=new pe(l.outShape,o.dtype),k=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,$=v.values,D=x.computeStrides(o.shape),R=x.computeStrides(s.shape);for(let P=0;P<l.batchSize;++P){let W=P*D[0],U=P*v.strides[0];for(let q=0;q<l.outDepth;++q){let K=U+q*v.strides[1],j=q*l.strideDepth-y;for(let Q=0;Q<c;++Q){let rt=j+Q*f;if(rt<0||rt>=l.inDepth)continue;let X=Q*R[0],ot=W+rt*D[1];for(let st=0;st<l.outHeight;++st){let it=K+st*v.strides[2],ft=st*l.strideHeight-w;for(let at=0;at<p;++at){let xt=ft+at*d;if(xt<0||xt>=l.inHeight)continue;let dt=X+at*R[1],bt=ot+xt*D[2];for(let kt=0;kt<l.outWidth;++kt){let At=it+kt*l.outChannels,Dt=kt*l.strideWidth-b;for(let qt=0;qt<m;++qt){let Kt=Dt+qt*h;if(Kt<0||Kt>=l.inWidth)continue;let ce=dt+qt*R[2],Ot=bt+Kt*l.inChannels,$e=ce;for(let Se=0;Se<l.inChannels;++Se){let ae=k[Ot+Se];for(let je=0;je<l.outChannels;++je)$[At+je]+=ae*E[$e+je];$e+=l.outChannels}}}}}}}}return e.makeTensorInfo(v.shape,v.dtype,v.values)}var MF={kernelName:Rl,backendName:"cpu",kernelFunc:d9};function h9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;nt([o,s],"conv3dBackpropFilterV2");let l=x.computeStrides(o.shape),c=x.computeStrides(s.shape),p=S.computeConv3DInfo(o.shape,u,i,1,a),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new pe(p.filterShape,"float32"),w=b.values,[v,k,E,$]=b.strides,D=e.data.get(s.dataId).values,[R,P,W,U]=c,q=e.data.get(o.dataId).values,[K,j,Q,rt]=l,X=p.padInfo.front,ot=p.padInfo.left,st=p.padInfo.top;for(let it=0;it<h;++it){let ft=Math.max(0,Math.ceil((X-it)/m)),at=Math.min(p.outDepth,(p.inDepth+X-it)/m),xt=it*v;for(let dt=0;dt<g;++dt){let bt=Math.max(0,Math.ceil((st-dt)/f)),kt=Math.min(p.outHeight,(p.inHeight+st-dt)/f),At=dt*k+xt;for(let Dt=0;Dt<y;++Dt){let qt=Math.max(0,Math.ceil((ot-Dt)/d)),Kt=Math.min(p.outWidth,(p.inWidth+ot-Dt)/d),ce=Dt*E+At;for(let Ot=0;Ot<p.inChannels;++Ot){let $e=Ot*$+ce;for(let Se=0;Se<p.outChannels;++Se){let ae=0;for(let je=0;je<p.batchSize;++je){let Fe=je*K,rn=je*R;for(let ze=ft;ze<at;++ze){let Fr=(it+ze*m-X)*j+Fe,nn=ze*P+rn;for(let on=bt;on<kt;++on){let oo=(dt+on*f-st)*Q+Fr,Fn=on*W+nn;for(let Vo=qt;Vo<Kt;++Vo){let Nl=(Dt+Vo*d-ot)*rt+oo,ku=Vo*U+Fn;ae+=q[Nl+Ot]*D[ku+Se]}}}}w[$e+Se]=ae}}}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var PF={kernelName:Dp,backendName:"cpu",kernelFunc:h9};function g9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;nt([o],"conv3dBackpropInputV2");let l=x.computeStrides(o.shape),c=x.computeStrides(s.shape),p=S.computeConv3DInfo(u,s.shape,a,1,i),m=new pe(p.inShape,"float32"),f=m.values,[d,h,g,y]=m.strides,b=e.data.get(o.dataId).values,[w,v,k,E]=l,$=e.data.get(s.dataId).values,[D,R,P,W]=c,{batchSize:U,filterDepth:q,filterHeight:K,filterWidth:j,inChannels:Q,inDepth:rt,inHeight:X,inWidth:ot,outChannels:st,outDepth:it,outHeight:ft,outWidth:at,strideDepth:xt,strideHeight:dt,strideWidth:bt}=p,kt=q-1-p.padInfo.front,At=K-1-p.padInfo.top,Dt=j-1-p.padInfo.left;for(let qt=0;qt<U;++qt)for(let Kt=0;Kt<Q;++Kt)for(let ce=0;ce<rt;++ce){let Ot=ce-kt,$e=Math.max(0,Math.ceil(Ot/xt)),Se=Math.min(it,(q+Ot)/xt);for(let ae=0;ae<X;++ae){let je=ae-At,Fe=Math.max(0,Math.ceil(je/dt)),rn=Math.min(ft,(K+je)/dt);for(let ze=0;ze<ot;++ze){let Bo=ze-Dt,Fr=Math.max(0,Math.ceil(Bo/bt)),nn=Math.min(at,(j+Bo)/bt),on=0;for(let jr=$e;jr<Se;++jr){let oo=jr*xt-Ot;for(let Fn=Fe;Fn<rn;++Fn){let Vo=Fn*dt-je;for(let so=Fr;so<nn;++so){let Nl=so*bt-Bo,ku=w*qt+v*jr+k*Fn+E*so,ap=D*(q-1-oo)+R*(K-1-Vo)+P*(j-1-Nl)+W*Kt;for(let gr=0;gr<st;++gr){let lp=b[ku+gr],up=$[ap+gr];on+=lp*up}}}}f[d*qt+h*ce+g*ae+y*ze+Kt]=on}}}return e.makeTensorInfo(m.shape,m.dtype,m.values)}var LF={kernelName:Rp,backendName:"cpu",kernelFunc:g9};var x9=Et(os,r=>Math.cos(r)),zF={kernelName:os,backendName:"cpu",kernelFunc:x9};var y9=Et(ss,r=>Math.cosh(r)),BF={kernelName:ss,backendName:"cpu",kernelFunc:y9};function b9(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,y=It([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,v=e.data.get(o.dataId).values,k=x.computeStrides(o.shape),E=x.computeStrides(y.shape);for(let $=0;$<d;$++){let D=$*4,R=b[D],P=b[D+1],W=b[D+2],U=b[D+3],q=w[$];if(q>=c)continue;let K=h>1?(W-R)*(p-1)/(h-1):0,j=g>1?(U-P)*(m-1)/(g-1):0;for(let Q=0;Q<h;Q++){let rt=h>1?R*(p-1)+Q*K:.5*(R+W)*(p-1);if(rt<0||rt>p-1){for(let X=0;X<g;X++)for(let ot=0;ot<f;ot++){let st=ot+X*E[2]+Q*E[1]+$*E[0];y.values[st]=l}continue}if(u==="bilinear"){let X=Math.floor(rt),ot=Math.ceil(rt),st=rt-X;for(let it=0;it<g;it++){let ft=g>1?P*(m-1)+it*j:.5*(P+U)*(m-1);if(ft<0||ft>m-1){for(let bt=0;bt<f;bt++){let kt=bt+it*E[2]+Q*E[1]+$*E[0];y.values[kt]=l}continue}let at=Math.floor(ft),xt=Math.ceil(ft),dt=ft-at;for(let bt=0;bt<f;bt++){let kt=bt+at*k[2]+X*k[1]+q*k[0],At=v[kt];kt=bt+xt*k[2]+X*k[1]+q*k[0];let Dt=v[kt];kt=bt+at*k[2]+ot*k[1]+q*k[0];let qt=v[kt];kt=bt+xt*k[2]+ot*k[1]+q*k[0];let Kt=v[kt],ce=At+(Dt-At)*dt,Ot=qt+(Kt-qt)*dt;kt=bt+it*E[2]+Q*E[1]+$*E[0],y.values[kt]=ce+(Ot-ce)*st}}}else for(let X=0;X<g;++X){let ot=g>1?P*(m-1)+X*j:.5*(P+U)*(m-1);if(ot<0||ot>m-1){for(let ft=0;ft<f;ft++){let at=ft+X*E[2]+Q*E[1]+$*E[0];y.values[at]=l}continue}let st=Math.round(ot),it=Math.round(rt);for(let ft=0;ft<f;ft++){let at=ft+st*k[2]+it*k[1]+q*k[0],xt=ft+X*E[2]+Q*E[1]+$*E[0];y.values[xt]=v[at]}}}}return e.makeTensorInfo(y.shape,y.dtype,y.values)}var VF={kernelName:ca,backendName:"cpu",kernelFunc:b9};function w9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;nt(o,"cumprod");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=He({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ar(l.dtype,"int32"),m=x.makeOnesTypedArray(x.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(y,b)=>y+d-b-1:(y,b)=>y+b;for(let y=0;y<f.length;y+=d)for(let b=0;b<d;b++){let w=h(y,b);if(b===0)m[w]=i?1:f[w];else{let v=h(y,b-1);m[w]=i?f[v]*m[v]:f[w]*m[v]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let y=S.getUndoAxesPermutation(u),b=He({inputs:{x:g},backend:e,attrs:{perm:y}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var GF={kernelName:ua,backendName:"cpu",kernelFunc:w9};function v9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;nt(o,"cumsum");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=He({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ar(l.dtype,"int32"),m=x.makeZerosTypedArray(x.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(y,b)=>y+d-b-1:(y,b)=>y+b;for(let y=0;y<f.length;y+=d)for(let b=0;b<d;b++){let w=h(y,b);if(b===0)m[w]=i?0:f[w];else{let v=h(y,b-1);m[w]=i?f[v]+m[v]:f[w]+m[v]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let y=S.getUndoAxesPermutation(u),b=He({inputs:{x:g},backend:e,attrs:{perm:y}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var WF={kernelName:is,backendName:"cpu",kernelFunc:v9};function C9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=rd(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=Mb(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var UF={kernelName:Fp,backendName:"cpu",kernelFunc:C9};function I9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n;x.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let y=0;y<a;++y)for(let b=0;b<p;++b){let w=Math.floor(b/s),v=b%s;for(let k=0;k<m;++k){let E=Math.floor(k/s),$=k%s,D=(v*s+$)*f;for(let R=0;R<f;++R){let W=R+D+c*(E+l*(w+u*y));h[g++]=d[W]}}}return e.makeTensorInfo([a,p,m,f],o.dtype,h)}var HF={kernelName:pa,backendName:"cpu",kernelFunc:I9};function wN(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n;nt([o,s],"depthwiseConv2DNative");let c=x.computeStrides(o.shape),p=x.computeStrides(s.shape),m=u;m==null&&(m=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(i,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:y,padInfo:b}=f,w=b.left,v=b.top,k=f.outChannels/f.inChannels,E=new pe(f.outShape,o.dtype),$=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,R=E.values;for(let P=0;P<f.batchSize;++P){let W=P*c[0],U=P*E.strides[0];for(let q=0;q<f.outHeight;++q){let K=U+q*E.strides[1],j=q*f.strideHeight-v;for(let Q=0;Q<d;++Q){let rt=j+Q*g;if(rt<0||rt>=f.inHeight)continue;let X=Q*p[0],ot=W+rt*c[1];for(let st=0;st<f.outWidth;++st){let it=K+st*E.strides[2],ft=st*f.strideWidth-w;for(let at=0;at<h;++at){let xt=ft+at*y;if(xt<0||xt>=f.inWidth)continue;let dt=X+at*p[1],bt=ot+xt*f.inChannels,kt=it,At=dt;for(let Dt=0;Dt<f.inChannels;++Dt){let qt=$[bt+Dt];for(let Kt=0;Kt<k;++Kt)R[kt+Kt]+=qt*D[At+Kt];kt+=k,At+=k}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}var qF={kernelName:as,backendName:"cpu",kernelFunc:wN};function S9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n;nt([o,s],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new pe(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,w=p.outChannels/p.inChannels,v=e.data.get(o.dataId).values,k=new pe(o.shape,o.dtype,v),E=e.data.get(s.dataId).values,$=new pe(s.shape,s.dtype,E);for(let D=0;D<d;++D){let R=Math.max(0,Math.ceil((b-D)/m)),P=Math.min(p.outHeight,(p.inHeight+b-D)/m);for(let W=0;W<h;++W){let U=Math.max(0,Math.ceil((y-W)/f)),q=Math.min(p.outWidth,(p.inWidth+y-W)/f);for(let K=0;K<p.outChannels;++K){let j=Math.trunc(K/w),Q=K%w,rt=0;for(let X=0;X<p.batchSize;++X)for(let ot=R;ot<P;++ot){let st=D+ot*m-b;for(let it=U;it<q;++it){let ft=W+it*f-y;rt+=k.get(X,st,ft,j)*$.get(X,ot,it,K)}}g.set(rt,D,W,j,Q)}}}return e.makeTensorInfo(g.shape,g.dtype,g.values)}var KF={kernelName:Op,backendName:"cpu",kernelFunc:S9};function k9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n;nt([o,s],"depthwiseConv2DNativeBackpropInput");let p=x.computeStrides(o.shape),m=x.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),d=new pe(f.inShape,"float32"),h=d.values,[g,y,b]=d.strides,w=e.data.get(o.dataId).values,[v,k,E]=p,$=e.data.get(s.dataId).values,[D,R,P]=m,{batchSize:W,filterHeight:U,filterWidth:q,inChannels:K,inHeight:j,inWidth:Q,outChannels:rt,outHeight:X,outWidth:ot,strideHeight:st,strideWidth:it}=f,ft=U-1-f.padInfo.top,at=q-1-f.padInfo.left,xt=rt/K;for(let dt=0;dt<W;++dt)for(let bt=0;bt<K;++bt)for(let kt=0;kt<j;++kt){let At=kt-ft,Dt=Math.max(0,Math.ceil(At/st)),qt=Math.min(X,(U+At)/st);for(let Kt=0;Kt<Q;++Kt){let ce=Kt-at,Ot=Math.max(0,Math.ceil(ce/it)),$e=Math.min(ot,(q+ce)/it),Se=0;for(let ae=Dt;ae<qt;++ae){let je=ae*st-At;for(let Fe=Ot;Fe<$e;++Fe){let rn=Fe*it-ce,ze=v*dt+k*ae+E*Fe,Bo=D*(U-1-je)+R*(q-1-rn)+P*bt;for(let Fr=0;Fr<xt;++Fr){let nn=bt*xt+Fr,on=w[ze+nn],jr=$[Bo+Fr];Se+=on*jr}}}h[g*dt+y*kt+b*Kt+bt]=Se}}return e.makeTensorInfo(d.shape,d.dtype,d.values)}var jF={kernelName:Mp,backendName:"cpu",kernelFunc:k9};function N9(r){let{inputs:t,backend:e}=r,{x:n}=t,o=x.sizeFromShape(n.shape),s=e.data.get(n.dataId).values,i=It([o,o],n.dtype),a=i.values;for(let l=0;l<s.length;l++)a[l*o+l]=s[l];let u=[...n.shape,...n.shape];return e.makeTensorInfo(u,i.dtype,i.values)}var XF={kernelName:Pp,backendName:"cpu",kernelFunc:N9};var YF={kernelName:Fl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:y,outWidth:b,padInfo:w,strideHeight:v,strideWidth:k,filterHeight:E,filterWidth:$,dilationHeight:D,dilationWidth:R,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),W=x.sizeFromShape(P),U=P.length,q=x.getArrayFromDType(n.dtype,W);for(let j=0;j<f;++j)for(let Q=0;Q<y;++Q){let rt=Q*v-w.top;for(let X=0;X<b;++X){let ot=X*k-w.left;for(let st=0;st<g;++st){let it=Number.MIN_SAFE_INTEGER;for(let at=0;at<E;++at){let xt=rt+at*D;if(xt>=0&&xt<d)for(let dt=0;dt<$;++dt){let bt=ot+dt*R;if(bt>=0&&bt<h){let kt=x.locToIndex([j,xt,bt,st],c,x.computeStrides(n.shape)),At=x.locToIndex([at,dt,st],m,x.computeStrides(o.shape)),Dt=l[kt]+p[At];Dt>it&&(it=Dt)}}}let 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Ft=V();Ft.registerFlag("HAS_WEBGL",()=>Ft.getNumber("WEBGL_VERSION")>0);Ft.registerFlag("WEBGL_VERSION",()=>lw(2)?2:lw(1)?1:0);Ft.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ft.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ft.get("WEBGL_VERSION")===2);Ft.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ft.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ft.registerFlag("WEBGL_PACK",()=>Ft.getBool("HAS_WEBGL"));Ft.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_CLIP",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_PACK_REDUCE",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_CONV_IM2COL",()=>Ft.getBool("WEBGL_PACK"));Ft.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>WN(Ft.getNumber("WEBGL_VERSION")));Ft.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>UN(Ft.getNumber("WEBGL_VERSION")));Ft.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Ft.getNumber("WEBGL_VERSION");return r===0?0:HN(r)});Ft.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ft.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!jl.isMobile());Ft.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>qN(Ft.getNumber("WEBGL_VERSION")));Ft.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ft.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ft.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ft.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>KN(Ft.getNumber("WEBGL_VERSION")));Ft.registerFlag("WEBGL_FENCE_API_ENABLED",()=>jN(Ft.getNumber("WEBGL_VERSION")));Ft.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ft.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ft.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Ft.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>jl.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Ft.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ft.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ft.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ft.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function qe(){let r,t,e,n,o,s,i,a,u,l;return V().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",t="in",e="out",n="in",o="texture",s="outputColor",i="out vec4 outputColor;",a=`
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(r="",t="attribute",e="varying",n="varying",o="texture2D",s="gl_FragColor",i="",a=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function di(r,t,e="index"){let n=x.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function Yc(r,t,e="index"){let n=x.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function AQ(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PM(r,t,e="index"){let n=r.map((s,i)=>i),o=AQ(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function pd(r){let t=x.computeStrides(r).map(e=>e.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function md(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var uw=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;var{getBroadcastDims:LM}=S;function zM(r,t,e){let n=[];if(r.forEach(f=>{let d=x.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=cw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>$Q(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
`),i=t.texShape,a=qe(),u=FQ(a),l,c,p=PQ(a);return t.isPacked?(l=DQ(t.logicalShape,i,e.enableShapeUniforms),c=MQ(a)):(l=RQ(t.logicalShape,i,e.enableShapeUniforms),c=OQ(a)),e.packedInputs&&(p+=VQ),[p,u,c,o,l,s,e.userCode].join(`
`)}function dd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return QQ(r,t);case 1:return ett(r,t);case 2:return ntt(r,t);case 3:return stt(r,t);case 4:return att(r,t);case 5:return ltt(r);case 6:return utt(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BM(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return JQ(r);case 1:return ttt(r,t);case 2:return rtt(r,t);case 3:return ott(r,t);default:return itt(r,t)}}function $Q(r,t,e=!1,n){let o="";e?o+=BM(r,n):o+=dd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=ctt(r,t):o+=ptt(r,t)),o}function DQ(r,t,e){switch(r.length){case 0:return VM();case 1:return GQ(r,t,e);case 2:return YQ(r,t,e);case 3:return UQ(r,t,e);default:return qQ(r,t,e)}}function RQ(r,t,e){switch(r.length){case 0:return VM();case 1:return WQ(r,t,e);case 2:return ZQ(r,t,e);case 3:return HQ(r,t,e);case 4:return KQ(r,t,e);case 5:return jQ(r,t);case 6:return XQ(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function FQ(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function OQ(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function MQ(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function PQ(r){return`${r.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${r.varyingFs} vec2 resultUV;
${r.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${r.defineSpecialNaN}
${r.defineSpecialInf}
${r.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${LQ}
${zQ}
${BQ}
`}var LQ=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,zQ=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,BQ=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,VQ=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function VM(){return`
int getOutputCoords() {
return 0;
}
`}function GQ(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?e?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:e?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function WQ(r,t,e){return t[0]===1?e?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?e?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:e?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function UQ(r,t,e){if(e)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec3(b, r, c);
}
`}function HQ(r,t,e){if(e)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Yc(["r","c","d"],r)}
return ivec3(r, c, d);
}
`;let n=di(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function qQ(r,t,e){if(e)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
int b${l} = index / ${i};
index -= b${l} * ${i};
`+a,u=`b${l}, `+u;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${a}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec${r.length}(${u});
}
`}function KQ(r,t,e){if(e)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Yc(["r","c","d","d2"],r)}
return ivec4(r, c, d, d2);
}
`;let n=di(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function jQ(r,t){let e=di(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function XQ(r,t){let e=di(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function YQ(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(x.arraysEqual(r,t))return e?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let o=Math.ceil(r[1]/2);return e?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function ZQ(r,t,e){return x.arraysEqual(r,t)?e?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:r[1]===1?e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function Zc(r){return`offset${r}`}function JQ(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=qe();return`
vec4 ${e}() {
return ${n.texture2D}(${t}, halfCR);
}
`}function QQ(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
float ${n}() {
return sampleTexture(${e}, halfCR);
}
`;let i=Zc(e);if(t)return`
float ${n}() {
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${i});
return sampleTexture(${e}, uv);
}
`;let[a,u]=r.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${a}, ${u}, ${i});
return sampleTexture(${e}, uv);
}
`}function ttt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=qe();if(t)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${e}, uv);
}
`;let i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${e}, uv);
}
`}function ett(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
float ${n}(int index) {
${hd(r)}
}
`;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${e}, halfCR);
}
`;let a=Zc(e);return i===1?t?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${e}TexShape[0]));
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${s}.0);
return sampleTexture(${e}, uv);
}
`:s===1?t?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${e}TexShape[1]), 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:t?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], index + ${a});
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${a});
return sampleTexture(${e}, uv);
}
`}function rtt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=qe();if(s!=null&&x.arraysEqual(e,s))return t?`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${u.texture2D}(${n}, uv);
}
`:`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);
return ${u.texture2D}(${n}, uv);
}
`;if(t)return`
vec4 ${o}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${u.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(e[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function ntt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&x.arraysEqual(e,s)){if(t)return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`;let m=s[0],f=s[1];return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:i,keptDims:a}=x.squeezeShape(e),u=i;if(u.length<e.length){let m=gd(r,u),f=["row","col"];return`
${dd(m,t)}
float ${o}(int row, int col) {
return ${o}(${xd(f,a)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
${hd(r)}
}
`;let l=s[0],c=s[1],p=Zc(n);return c===1?t?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?t?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${e[1]} + col + ${p};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function ott(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=gd(r,m),h=["b","row","col"];return`
${BM(d,t)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${xd(h,f)});
}
`}let a=qe();if(t)return`
vec4 ${o}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${a.texture2D}(${n}, uv);
}
`;let u=i[0],l=i[1],c=Math.ceil(e[2]/2),p=c*Math.ceil(e[1]/2);return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${u}, ${l}, ${p}, ${c}, b, row, col);
return ${a.texture2D}(${n}, uv);
}
`}function stt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[1]*e[2],i=e[2],{newShape:a,keptDims:u}=x.squeezeShape(e),l=a;if(l.length<e.length){let h=gd(r,l),g=["row","col","depth"];return`
${dd(h,t)}
float ${o}(int row, int col, int depth) {
return ${o}(${xd(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${hd(r)}
}
`;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return t?`
float ${o}(int row, int col, int depth) {
int stride1 = ${n}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&f==null)return t?`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let d=Zc(n);return t?`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${n}Shape[1] * ${n}Shape[2];
int stride1 = ${n}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${d};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${d};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function itt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=qe();if(t)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${e}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${e}, uv);
}
`;let s=r.shapeInfo.logicalShape,i=s.length,a=r.shapeInfo.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=Math.ceil(s[i-1]/2),m=p*Math.ceil(s[i-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<i-1;h++)f=`int b${h}, `+f,m*=s[i-h-1],d=`b${h} * ${m} + `+d;return`
vec4 ${n}(${f}) {
int index = ${d};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
return ${o.texture2D}(${e}, uv);
}
`}function att(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[3],i=e[2]*s,a=e[1]*i,{newShape:u,keptDims:l}=x.squeezeShape(e);if(u.length<e.length){let b=gd(r,u),w=["row","col","depth","depth2"];return`
${dd(b,t)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${xd(w,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${s}, 1)));
${hd(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===a&&c==null)return t?`
float ${o}(int row, int col, int depth, int depth2) {
${d}
${h}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===s&&c==null)return t?`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${e[1]*e[2]}, ${e[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;let y=Zc(n);return t?`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${d}
${h}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${m}, ${f}, index + ${y});
return sampleTexture(${n}, uv);
}
`}function ltt(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,a=t[1]*i,{newShape:u,keptDims:l}=x.squeezeShape(t);if(u.length<t.length){let h=gd(r,u),g=["row","col","depth","depth2","depth3"];return`
${dd(h)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${xd(g,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${s}, ${o})) +
depth3;
${hd(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===a&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;if(f===o&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;let d=Zc(e);return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} + depth * ${s} +
depth2 * ${o} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${e}, uv);
}
`}function utt(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=x.squeezeShape(t);if(o.length<t.length){let g=gd(r,o),y=["row","col","depth","depth2","depth3","depth4"];return`
${dd(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${xd(y,s)});
}
`}let i=t[5],a=t[4]*i,u=t[3]*a,l=t[2]*u,c=t[1]*l;if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${l}, ${u}, ${a})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${hd(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${l}, ${u}, ${a}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${e}, uv);
}
`;if(d===i&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${e}, uv);
}
`;let h=Zc(e);return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${l} + depth * ${u} +
depth2 * ${a} + depth3 * ${i} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${e}, uv);
}
`}function hd(r){let t=r.name,e=x.sizeFromShape(r.shapeInfo.logicalShape);return e<2?`return ${t};`:`
for (int i = 0; i < ${e}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function ctt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=LM(r.shapeInfo.logicalShape,t.logicalShape),u=Vt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`
`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=x.sizeFromShape(r.shapeInfo.logicalShape)===1,y=x.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!y)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!y)i===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:f=`
return vec4(outputValue.x);
`;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f="return vec4(outputValue.x);":a.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${o}() {
${u} coords = getOutputCoords();
${c}
vec4 outputValue = get${n}(${m});
${f}
}
`}function ptt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&x.arraysEqual(i,s))return`
float ${o}() {
return sampleTexture(${e}, resultUV);
}
`;let l=Vt(u),c=LM(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${o}() {
${l} coords = getOutputCoords();
${m}
return get${n}(${d});
}
`}function Vt(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function cw(r,t,e){let{newShape:n,keptDims:o}=x.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!x.arraysEqual(t,e)&&n.length<s||i;return{useSqueezeShape:u,uniformShape:u?a:t,keptDims:o}}function gd(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function xd(r,t){return t.map(e=>r[e]).join(", ")}function WM(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=zM(o,i,t),u=$N(r.gl,a),l=r.createProgram(u);return V().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},XN(r,t,l))}function XN(r,t,e){let n={},o={},s={},i=[],a,u,l,c=null,p=null;p=r.getUniformLocation(e,"NAN",!1),V().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(e,"INFINITY",!1));let m=!1;for(let f=0;f<t.variableNames.length;f++){let d=t.variableNames[f];n[d]=r.getUniformLocation(e,d,m),n[`offset${d}`]=r.getUniformLocation(e,`offset${d}`,m),t.enableShapeUniforms&&(o[`${d}Shape`]=r.getUniformLocation(e,`${d}Shape`,m),s[`${d}TexShape`]=r.getUniformLocation(e,`${d}TexShape`,m))}return t.enableShapeUniforms&&(a=r.getUniformLocation(e,"outShape",m),l=r.getUniformLocation(e,"outShapeStrides",m),u=r.getUniformLocation(e,"outTexShape",m)),t.customUniforms&&t.customUniforms.forEach((f,d)=>{i[d]=r.getUniformLocation(e,f.name,m)}),{uniformLocations:n,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:o,inTexShapesLocations:s,outShapeLocation:a,outShapeStridesLocation:l,outTexShapeLocation:u}}function GM(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!x.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!x.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function UM(r,t,e,n,o){t.program.enableShapeUniforms||(GM(t.inShapeInfos,e),GM([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),V().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN),e.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],m=t.uniformLocations[`offset${c}`],f=t.inShapesLocations[`${c}Shape`],d=t.inTexShapesLocations[`${c}TexShape`];if(f){let{uniformShape:h}=cw(t.program.packedInputs,u.shape,u.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(f,new Int32Array(h));break;case 2:r.gl.uniform2iv(f,new Int32Array(h));break;case 3:r.gl.uniform3iv(f,new Int32Array(h));break;case 4:r.gl.uniform4iv(f,new Int32Array(h));break;default:break}}if(d&&r.gl.uniform2i(d,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(x.sizeFromShape(u.shape)<2)r.gl.uniform1f(p,u.uniformValues[0]);else{let h=u.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(p,h)}return}u.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,u.texData.slice.flatOffset),r.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let a=t.outShapeLocation;if(a)switch(n.shape.length){case 1:r.gl.uniform1iv(a,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(a,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(a,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(a,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let u=x.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:r.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:r.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&r.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&o&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=o[l];if(u.type==="float")r.gl.uniform1fv(c,p);else if(u.type==="vec2")r.gl.uniform2fv(c,p);else if(u.type==="vec3")r.gl.uniform3fv(c,p);else if(u.type==="vec4")r.gl.uniform4fv(c,p);else if(u.type==="int")r.gl.uniform1iv(c,p);else if(u.type==="ivec2")r.gl.uniform2iv(c,p);else if(u.type==="ivec3")r.gl.uniform3iv(c,p);else if(u.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),r.executeProgram()}function HM(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=cw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=x.computeStrides(c);d=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&x.arraysEqual(i.shape,u),y=x.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&x.arraysEqual(u,e.texData.texShape),v=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${y}_${b}_${g}_${m}_${f}_${d}_${v}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${V().getNumber("WEBGL_VERSION")}`,s}function De(r){return V().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var pw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Yc(["r","c","d"],t):di(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${e.output} = result;
}
`}};var mw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Yc(["r","c","d"],t):di(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${e.output} = result;
}
`}};var fw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Kr.DOWNLOAD;let e=qe();this.outputShape=t,this.userCode=`
${uw}
void main() {
float x = getAAtOutCoords();
${e.output} = encode_float(x);
}
`}};var dw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Kr.DOWNLOAD;let e=qe();this.outputShape=t,this.userCode=`
${uw}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${e.output} = encode_float(x);
}
`}};var hw=class{constructor(t,e=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let o="result";e&&(o="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?md():pd(t)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${o}, 0., 0., 0.);
}
`}};var gw=class{constructor(t,e=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let o="",s="result";e&&(s="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){let u=i*2+a;o+=`
localCoords = coords;
if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${t[2]}`}) {
localCoords[2] += ${a};
if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${t[1]}`}) {
localCoords[1] += ${i};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${u}] = values[0];
} else if (offset == 1) {
result[${u}] = values[1];
} else if (offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?md():pd(t)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${o}
${n.output} = ${s};
}
`}};var mT={};Yt(mT,{bindVertexProgramAttributeStreams:()=>oT,createBufferFromOutputTexture:()=>aT,createFloat16MatrixTexture:()=>tT,createFloat16PackedMatrixTexture:()=>nT,createFloat32MatrixTexture:()=>QN,createIndexBuffer:()=>JN,createPackedMatrixTexture:()=>rT,createUnsignedBytesMatrixTexture:()=>eT,createVertexBuffer:()=>ZN,createVertexShader:()=>YN,downloadByteEncodedFloatMatrixFromOutputTexture:()=>uT,downloadFloat32MatrixFromBuffer:()=>lT,downloadMatrixFromPackedOutputTexture:()=>pT,downloadPackedMatrixFromBuffer:()=>cT,getInternalFormatForFloat16MatrixTexture:()=>yw,getInternalFormatForFloat16PackedMatrixTexture:()=>vw,getInternalFormatForFloat32MatrixTexture:()=>xw,getInternalFormatForPackedMatrixTexture:()=>ww,getInternalFormatForUnsignedBytesMatrixTexture:()=>bw,uploadDenseMatrixToTexture:()=>sT,uploadPixelDataToTexture:()=>iT});function YN(r){let t=qe(),e=`${t.version}
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${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return AN(r,e)}function ZN(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return FN(r,t)}function JN(r){let t=new Uint16Array([0,1,2,2,1,3]);return ON(r,t)}function Cg(r,t,e,n,o,s){PN(t,e);let i=MN(r),a=r.TEXTURE_2D;return vt(r,()=>r.bindTexture(a,i)),vt(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),vt(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),vt(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),vt(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),V().getNumber("WEBGL_VERSION")===1?vt(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):vt(r,()=>r.texStorage2D(a,1,n,t,e)),vt(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function xw(r){return r.internalFormatFloat}function QN(r,t,e,n){let[o,s]=Xc(t,e);return Cg(r,o,s,xw(n),n.textureFormatFloat,r.FLOAT)}function yw(r){return r.internalFormatHalfFloat}function tT(r,t,e,n){let[o,s]=Xc(t,e);return 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;vt(t,()=>t.finish()),vt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),vt(t,()=>t.deleteFramebuffer(this.framebuffer)),vt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),vt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),vt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),QN(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),tT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),eT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),iT(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),sT(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return 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t=dtt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),!(this.itemsToPoll.length>1)&&x.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),wg(this.gl,t,this.framebuffer),this.debug&&ud(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(wg(this.gl,this.outputTexture,this.framebuffer),this.debug&&ud(this.gl)):aw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;wg(o,t,this.framebuffer),this.debug&&ud(o),this.outputTexture=t,vt(o,()=>o.viewport(0,0,e,n)),vt(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),vt(this.gl,()=>this.gl.scissor(t,e,n,o))}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 dtt(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:qM,bincountImpl:Cw,bincountReduceImpl:KM,ceilImpl:jM,concatImpl:XM,equalImpl:YM,expImpl:ZM,expm1Impl:JM,floorImpl:QM,gatherNdImpl:tP,gatherV2Impl:eP,greaterImpl:rP,greaterEqualImpl:nP,lessImpl:oP,lessEqualImpl:sP,linSpaceImpl:iP,logImpl:aP,maxImpl:lP,maximumImpl:uP,minimumImpl:cP,multiplyImpl:pP,negImpl:mP,notEqualImpl:fP,prodImpl:dP,rangeImpl:hP,rsqrtImpl:gP,sigmoidImpl:xP,simpleAbsImpl:Iw,sliceImpl:yP,sparseFillEmptyRowsImpl:bP,sparseReshapeImpl:wP,sparseSegmentReductionImpl:Sw,sqrtImpl:vP,stridedSliceImpl:CP,stringNGramsImpl:IP,stringSplitImpl:SP,stringToHashBucketFastImpl:kP,subImpl:NP,tileImpl:TP,topKImpl:_P,transposeImpl:Qc,uniqueImpl:EP}=Yb;function fT(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function nr(r,t){return t===1?[r]:fT(r,t)}function AP(r,t){if(r===1)return"rc";let e="";for(let n=0;n<r;n++)e+=t[n],n<r-1&&(e+=",");return e}var kw=class{constructor(t){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.enableShapeUniforms=De(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let e=nr("rc",this.rank),n=Vt(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${o}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)s=`${t[t.length-1-i]},`+s;e.push(s)}return e}getOutOfBoundsCondition(t){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n<this.rank;n++)e+=`${t[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(e+="||");return e}getSetup(t){if(this.rank===1)return"";let e=t.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],o=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${e[0]};
int c = ${e[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${o};
`}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),
cEdge ? 0. : getA(${e[1]}),
rEdge ? 0. : getA(${e[2]}),
rEdge || cEdge ? 0. : getA(${e[3]})`}};var yd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
${s}
${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${o}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${o>0?"}":""}
`}this.userCode=`
${htt(e,this.enableShapeUniforms)}
${this.enableShapeUniforms?md():pd(t)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]};
${n}
setOutput(result);
}
`}};function htt(r,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?PM(["r","c","d"],"inputShape"):di(["r","c","d"],r)}
return ivec3(r, c, d);
}
`}var Nw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=DP(e,n),s=RP(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=$P(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Rr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Rr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Rr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Rr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Rr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=DP(n,o),i=RP(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=$P(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function gtt(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function $P(r,t,e,n,o){let s=xtt(t,n),i;if(o){let[u,l]=Zi(r[0],r[1]);i=u*l}else{let[u,l]=Xc(r[0],r[1]);i=u*l}let a=gtt(e,s);return i*a}function xtt(r,t){switch(r){case Rr.PACKED_2X2_FLOAT32:return ww(t);case Rr.PACKED_2X2_FLOAT16:return vw(t);case Rr.UNPACKED_FLOAT32:return xw(t);case Rr.UNPACKED_FLOAT16:return yw(t);case Rr.PACKED_4X1_UNSIGNED_BYTE:return bw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function ytt(r){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Rr.PACKED_2X2_FLOAT32:Rr.UNPACKED_FLOAT32:r?Rr.PACKED_2X2_FLOAT16:Rr.UNPACKED_FLOAT16}function DP(r,t){if(r===Kr.UPLOAD)return Rr.PACKED_2X2_FLOAT32;if(r===Kr.RENDER||r==null)return ytt(t);if(r===Kr.DOWNLOAD||r===Kr.PIXELS)return Rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function RP(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var en=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${e}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},hr="if (isnan(x)) return x;",FP="return x;",dT="return abs(x);";var OP="return (x >= 0.0) ? x : (exp(x) - 1.0);",MP=hr+`
return (x < 0.0) ? 0.0 : x;
`,PP=hr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,tp="return x;",LP="return 1.0 / (1.0 + exp(-1.0 * x));";var BP="return x;",VP=`
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;
`,GP=`
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;
`,WP=`
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;
`,UP="return 1.0 / (1.0 + exp(-1.0 * x));",eo=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${e}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var Tw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let e=t.length,n=nr("rc",e),o=Vt(e),s=AP(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${a}));
}
`}};var wtt=Gr.whereImpl,vtt=1e-7,Ctt=1e-4,_w={};function Itt(r){return r in _w||(_w[r]={}),_w[r]}var Stt=V().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ktt=600;function Ntt(){return V().global.screen==null?1024:V().global.screen.height*V().global.screen.width*window.devicePixelRatio*ktt/1024/1024}var bu=class extends jo{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,!V().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof Jc)e=t;else{let n=Wn(V().getNumber("WEBGL_VERSION"),t);e=new Jc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wn(V().getNumber("WEBGL_VERSION"));e=new Jc(n),this.binaryCache=Itt(V().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Nw(this.gpgpu),this.numMBBeforeWarning=Ntt(),this.texData=new Ji(this,go())}nextDataId(){return bu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(t,e,n){if((V().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||V().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Kr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(V().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Kr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new eo(a,tp):m=new en(a,tp);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=x.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=x.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new eo(o,tp):d=new en(o,tp);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(V().getBool("DEBUG")&&!V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&V().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&V().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...xg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=x.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;vt(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&go().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new eo(s,tp):f=new en(s,tp);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=go().makeTensorFromDataId(c.dataId,c.shape,c.dtype),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId),n=e;if(t.dtype==="string")try{n=e.map(o=>x.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return It(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let e=0;e<t.length;e++){let n=t[e];if(!EN(n))throw V().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=x.sizeFromShape(e);if(V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...xg(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=V().getBool("WEBGL_PACK")&&o===!0,a=i?vg(e):e,u=i?new dw(a):new fw(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=x.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=x.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=x.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(t){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=x.now(),t)}async getQueryTime(t){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&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:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=Stt){return V().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&x.sizeFromShape(n.shape)<e)}getGPGPUContext(){return this.gpgpu}where(t){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let e=t.dataSync();return wtt(t.shape,e)}packedUnaryOp(t,e,n){let o=new eo(t.shape,e),s=this.compileAndRun(o,[t],n);return go().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=Iw(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,o)}if(V().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,dT,t.dtype);let e=new en(t.shape,dT),n=this.compileAndRun(e,[t]);return go().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&x.isString(n[0])){let s=n.map(i=>x.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){let{dataId:o}=this.makeTensorInfo(t,e,n);return go().makeTensorFromDataId(o,t,e,this)}unpackTensor(t){let e=new Tw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new kw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[vl(t.shape),...Cl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[vl(e),...Cl(e)],i=new yd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=x.sizeFromShape(s),f=e[0]*e[1]*4;x.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=vg(s),u;o?u=new mw(a):u=new pw(a);let l=!0,c=[e!=null?e:xg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===xu.DENSE){let y=i!=null?i:xg(t.outputShape);u.texShape=y.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),x.sizeFromShape(a.shape)===0)return u.values=x.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(y=>{if(y.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(y.dataId);if(b.texture==null){if(!t.packedInputs&&x.sizeFromShape(y.shape)<=V().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:y.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=y.shape)}if(this.uploadToGPU(y.dataId),!!b.isPacked!=!!t.packedInputs)y=b.isPacked?this.unpackTensor(y):this.packTensor(y),l.push(y),b=this.texData.get(y.dataId);else if(b.isPacked&&!yu(b.shape,y.shape)){let w=y,v=y.shape;y.shape=b.shape,y=this.packedReshape(y,v),l.push(y),b=this.texData.get(y.dataId),w.shape=v}return{shape:y.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HM(t,c,p),f=this.getAndSaveBinary(m,()=>WM(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||UM(this.gpgpu,f,c,p,o),l.forEach(y=>this.disposeIntermediateTensorInfo(y)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=V().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let y=x.now();y-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=y)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let y=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),y}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(V().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=V().getBool("DEBUG");V().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(V().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?vtt:Ctt}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=x.now());let p=e.texShape;if(p==null&&(p=GN(n,u),e.texShape=p),s!=null){let m=vg(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Zi(p[0],p[1])),u?f=new gw(m,g):f=new hw(m,g);let y=g?[h,d]:p,b=this.makeTensorInfo(y,o),w=this.texData.get(b.dataId);g?w.usage=Kr.PIXELS:w.usage=Kr.UPLOAD,w.texShape=y,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let v=[[h,d]],k=!0,E=this.runWebGLProgram(f,[b],o,v,k),$=this.texData.get(E.dataId);e.texShape=$.texShape,e.isPacked=$.isPacked,e.usage=$.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=$.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=x.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return this.releaseGPUData(t),e!=null&&(n.values=Ttt(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*x.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Vh(),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?(sw(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.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=XN(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}};bu.nextDataId=0;function Ttt(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<e.length;++n)e[n]=Math.round(r[n]);return e}else throw new Error(`Unknown dtype ${t}`)}var HP="3.15.0";function qP(){V().set("WEBGL_FORCE_F16_TEXTURES",!0)}jl.isBrowser()&&fm("webgl",()=>new bu,2);var YSe={forceHalfFloat:qP};var Ew=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var ro=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${t}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var wu=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;var Po=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=De(s);let i="";if(o)if(s===0||x.sizeFromShape(this.outputShape)===1)i=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(i=`
${Vt(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?i+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:i+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let u=nr("coords",s);this.enableShapeUniforms?i+=`
bool nextRowOutOfBounds =
(${u[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${u[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:i+=`
bool nextRowOutOfBounds =
(${u[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${u[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${t}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${i}
setOutput(result);
}
`}};function Ke(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var KP={kernelName:lo,backendName:"webgl",kernelFunc:Ke};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=Ke({inputs:{x:n},backend:e}),u=Ke({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var jP={kernelName:Ap,backendName:"webgl",kernelFunc:Rn};var hT="return (a < 0.) ? b * a : a;",gT=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function _tt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",x.createScalarValue(s,"float32")),a=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(gT,o.shape,i.shape):new ro(hT,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var XP={kernelName:hs,backendName:"webgl",kernelFunc:_tt};var xT="return (a < 0.) ? b * a : a;",yT=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Ett(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(yT,n.shape,o.shape):new ro(xT,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var YP={kernelName:_s,backendName:"webgl",kernelFunc:Ett};var Lo="if (isnan(x)) return x;",ZP=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,JP=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ct({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=V().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new eo(i.shape,t):c=new en(i.shape,r),a.runWebGLProgram(c,[i],u)}}function le({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[v,k]=w,E={dataId:v.dataId,dtype:v.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},D=new ro(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,$],ar(v.dtype,k.dtype))}),b=Rn({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||ar(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,y=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,y,p),v=c.makeTensorInfo(w,p),k=c.texData.get(v.dataId);return k.values=b,v}let m=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Po(t,u.shape,l.shape,e):f=new ro(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function vu(r,t=!1){if(r==="linear")return t?BP:FP;if(r==="relu")return t?GP:MP;if(r==="elu")return t?VP:OP;if(r==="relu6")return t?WP:PP;if(r==="prelu")return t?yT:xT;if(r==="leakyrelu")return t?gT:hT;if(r==="sigmoid")return t?UP:LP;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var bd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=De(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";a&&(u?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${a}
}`:l?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${a}
}`:g=`vec4 activation(vec4 x) {
${a}
}`,y="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",v="rc.x";t[0]<e[0]?w=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(v=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
${g}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${w};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${h[0]});
result += (${d[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${y}
setOutput(result);
}
`}};var bT={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Ig=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${t}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var QP="return a * b;";function Sg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new Ig(bT.REAL,n.shape,o.shape),c=new Ig(bT.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=pP(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Po(QP,n.shape,o.shape):i=new ro(QP,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var tL={kernelName:Ss,backendName:"webgl",kernelFunc:Sg};function eL(r,t,e){let n=[vl(r.shape),...Cl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[vl(t),...Cl(t)],i=new yd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=x.sizeFromShape(o.shape),u=x.inferFromImplicitShape(s,a),l=x.sizeFromShape(u);x.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!yu(o.shape,u)&&!(c.texture!==null&&yu(c.shape,u))?eL(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var rL={kernelName:Ti,backendName:"webgl",kernelFunc:ut};var kg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${x.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${a}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${a};
if (${u===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}};var Aw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
if (${e==="sum"}) {
sumValue += dot(values, ones);
} else if (${e==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${u}(values, minMaxValue);
if (${e==="min"} || ${e==="max"}) {
minMaxValue = ${u}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,f="vec4";e==="all"?(a="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):e==="any"&&(a="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${a};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${a});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${l});
}
`}};function $tt(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Hn(r,t,e,n){let o=$tt(r.shape),s=r;for(let i=0;i<o.length;i++){let{inSize:a,windowSize:u,outSize:l}=o[i],c,p;e==="mean"?c=i===0?new kg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new kg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new Aw({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},e),p=s,s=n.runWebGLProgram(c,[s],t),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var $w=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[e[i]];this.outputShape=n,this.rank=n.length;let o=Vt(this.rank),s=Dtt(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Dtt(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o<r.length;o++)n[r[o]]=e[o];return n.join()}var Dw=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(t.length);for(let c=0;c<n.length;c++)n[c]=t[e[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Vt(this.rank),s=fT("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${i.slice(-2).join()})`,u=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${u}) {
result[1] = ${l};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${u}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Cu(r,t,e){let n=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dw(r.shape,t):new $w(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function nL(r,t,e,n){let o=t,s=r.shape.length,i=x.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Cu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=ut({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=Wu(r.dtype),w=Hn(y,b,"sum",n),v=ut({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),v}function ep(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return nL(o,s,i,e)}var oL={kernelName:Ls,backendName:"webgl",kernelFunc:ep};function Ae(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=Qc(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=Cu(o,s,i);return l}var sL={kernelName:Us,backendName:"webgl",kernelFunc:Ae};var wT=1e3;function rp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),v=Pr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);x.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let k=e?[y,p,f]:[y,f,p],E=n?[b,d,m]:[b,m,d],$=ut({inputs:{x:r},backend:o,attrs:{shape:k}}),D=ut({inputs:{x:t},backend:o,attrs:{shape:E}}),R=[$,D],P=Math.max(y,b),W=e?$.shape[1]:$.shape[2],U=s!=null,q=i!=null,K=u==="leakyrelu",j=u!=null?vu(u,!0):null,Q=U||q||K||j!=null,rt;if((f===1||d===1)&&W>wT&&Q===!1){let ot=$,st=D;e&&(ot=Ae({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),R.push(ot)),n&&(st=Ae({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),R.push(st));let it=d!==1,ft=d===1,at=ot;it&&(at=ut({inputs:{x:ot},backend:o,attrs:{shape:[P,W,1]}}),R.push(at));let xt=d===1?2:1,dt=st;ft&&(dt=ut({inputs:{x:st},backend:o,attrs:{shape:[P,1,W]}}),R.push(dt));let bt=Sg({inputs:{a:at,b:dt},backend:o});rt=ep({inputs:{x:bt},backend:o,attrs:{axis:xt,keepDims:!0}}),R.push(bt)}else{let ot=ar(r.dtype,t.dtype),st=new bd(k,E,[P,f,d],e,n,U,j,q,K),it=[$,D];if(s!=null&&it.push(s),q&&it.push(i),K){let ft=o.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));it.push(ft),R.push(ft)}rt=o.runWebGLProgram(st,it,ot)}let X=ut({inputs:{x:rt},backend:o,attrs:{shape:v}});R.push(rt);for(let ot of R)o.disposeIntermediateTensorInfo(ot);return X}function Rtt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return rp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var iL={kernelName:Fi,backendName:"webgl",kernelFunc:Rtt};var aL="return abs(x);";function Ftt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Iw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new eo(n.shape,aL):o=new en(n.shape,aL),e.runWebGLProgram(o,[n],n.dtype)}var lL={kernelName:bi,backendName:"webgl",kernelFunc:Ftt};var Ott=hr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,Mtt=Ct({opSnippet:Ott}),uL={kernelName:ta,backendName:"webgl",kernelFunc:Mtt};var Ptt=hr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Ltt=Ct({opSnippet:Ptt}),cL={kernelName:ea,backendName:"webgl",kernelFunc:Ltt};var pL="return a + b;",ztt=le({opSnippet:pL,packedOpSnippet:pL,supportsComplex:!0,cpuKernelImpl:qM}),mL={kernelName:Xn,backendName:"webgl",kernelFunc:ztt};var Rw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${o};
setOutput(result);
}
`}};var Fw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${o};
setOutput(result);
}
`}};function Ow(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return Ke({inputs:{x:n[0]},backend:e});if(n.length>V().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=Ow({inputs:n.slice(0,u),backend:e}),c=Ow({inputs:n.slice(u),backend:e});return Ow({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ar(u,l)),s=n.map(u=>u.shape),a=V().getBool("WEBGL_PACK")?new Fw(n[0].shape,s):new Rw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var fL={kernelName:Zo,backendName:"webgl",kernelFunc:Ow};function Btt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Ae({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"all",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var dL={kernelName:ra,backendName:"webgl",kernelFunc:Btt};function Vtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Ae({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"any",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var hL={kernelName:na,backendName:"webgl",kernelFunc:Vtt};var Mw=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${o}; i++) {
int inIdx = ${u};
float candidate = getA(batch, inIdx);
if (candidate ${a} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var Pw=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=Vt(u),c=nr("coords",u),p,m;if(i===1){m=u+1;let D=Vt(m);p=`
${D} sourceLocR = ${D}(${c.join()}, 0);
++${c[u-1]};
${D} sourceLocG = ${D}(${c.join()}, 0);
++${c[u-2]};
${D} sourceLocA = ${D}(${c.join()}, 0);
--${c[u-1]};
${D} sourceLocB = ${D}(${c.join()}, 0);
--${c[u-2]};`}else m=u,p=`
${l} sourceLocR = coords;
++${c[u-1]};
${l} sourceLocG = coords;
++${c[u-2]};
${l} sourceLocA = coords;
--${c[u-1]};
${l} sourceLocB = coords;
--${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=nr("sourceLocR",m-1).concat("inIdx.r"),y=nr("sourceLocG",m-1).concat("inIdx.g"),b=nr("sourceLocB",m-1).concat("inIdx.b"),w=nr("sourceLocA",m-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",k=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${w.join()})));`,E=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${y.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,$=o?"":`
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${f.join()}),
vec2(${f.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${h.join()}) {
return getChannel(getA(${f.join()}),
vec2(${f.slice(-2).join()}));
}
${$}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};
bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${e};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${E};
for (int i = 0; i < ${e}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${E};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(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 gL(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Mw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=gL(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function xL(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Pw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=xL(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Lw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!V().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=x.sizeFromShape(c),m=ut({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=gL(r,m,n);s.push(f);let d=ut({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return xL(r,t,n)}function Gtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=x.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Ae({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=Lw(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var yL={kernelName:Jo,backendName:"webgl",kernelFunc:Gtt};function Wtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=x.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Ae({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=Lw(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var bL={kernelName:Al,backendName:"webgl",kernelFunc:Wtt};var Utt=hr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,Htt=Ct({opSnippet:Utt}),wL={kernelName:oa,backendName:"webgl",kernelFunc:Htt};var qtt=hr+"return log(x + sqrt(x * x + 1.0));",Ktt=Ct({opSnippet:qtt}),vL={kernelName:sa,backendName:"webgl",kernelFunc:Ktt};var jtt=hr+`
return atan(x);
`,Xtt=Ct({opSnippet:jtt}),CL={kernelName:ia,backendName:"webgl",kernelFunc:Xtt};var Ytt=ZP+`
return atan(a, b);
`,Ztt=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+JP+`
return result;
`,Jtt=le({opSnippet:Ytt,packedOpSnippet:Ztt}),IL={kernelName:la,backendName:"webgl",kernelFunc:Jtt};var Qtt=hr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,tet=Ct({opSnippet:Qtt}),SL={kernelName:aa,backendName:"webgl",kernelFunc:tet};var hi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,y=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
const ivec2 strides = ivec2(${a}, ${u});
const ivec2 pads = ivec2(${f}, ${d});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.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 ${D} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?g:y:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let w="max",v=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(v="avgValue / count");let k=Math.floor(i/4)*4,E=i%4,$=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${a}, ${u});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${b};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${$}
}
int xC = xCCorner + ${k};
if (${E===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${E===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${$}
} else if (${E===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${$}
}
}
setOutput(${v});
}
`}},Iu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,y=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",v="0.0";if(w||(v="-1.0 / 1e-20"),n){let P=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${u}, ${l});
const ivec3 pads = ivec3(${g}, ${y}, ${b});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${P} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let k="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let $=Math.floor(i/4)*4,D=i%4,R=`
if (${w}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${u}, ${l});
const ivec3 pads = ivec3(${g}, ${y}, ${b});
const float initializationValue = ${v};
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 >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${$}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${R}
}
int xC = xCCorner + ${$};
if (${D===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${R}
} else if (${D===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${R}
} else if (${D===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${R}
}
}
setOutput(${E});
}
}
`}};function eet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;fi(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;x.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ke({inputs:{x:o},backend:e});let p=new hi(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var kL={kernelName:Qo,backendName:"webgl",kernelFunc:eet};function ret(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Iu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var NL={kernelName:$l,backendName:"webgl",kernelFunc:ret};var zw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${u};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Bw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,y=1/(e*n*o);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${y});
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 += ${u}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${t.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 net(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Bw(m);return e.runWebGLProgram(f,[o],i.dtype)}var TL={kernelName:Tp,backendName:"webgl",kernelFunc:net};function oet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;fi([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new zw(c);return e.runWebGLProgram(p,[o],i.dtype)}var _L={kernelName:Np,backendName:"webgl",kernelFunc:oet};function set(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return rp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var EL={kernelName:ts,backendName:"webgl",kernelFunc:set};var Vw=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${a};
float scale = ${u};
float inv = scale * inversesqrt(variance + float(${i}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var Gw=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
vec4 offset = ${a};
vec4 scale = ${u};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${i}));
setOutput((x - mean) * inv + offset);
}
`}};var iet=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;x.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=V().getBool("WEBGL_PACK_NORMALIZATION")?new Gw(n.shape,o.shape,s.shape,c,p,u):new Vw(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},AL={kernelName:fs,backendName:"webgl",kernelFunc:iet};var Ww=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=aet(this.rank),o,s=t.map((i,a)=>`sourceLoc.${vT[a]} = start[${a}] + coords.${vT[a]};`);o=`
${e} sourceLoc;
${e} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${o}
setOutput(getSource(${n}));
}
`}},vT=["x","y","z","w","u","v"];function aet(r){if(r===1)return"sourceLoc";if(r<=6)return vT.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Uw=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Vt(this.rank),n=nr("coords",this.rank),o=nr("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`
result.x = ${i};
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${i};
--${o[this.rank-1]};
}
`,u=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${t[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${i};
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${i};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${e} coords = getOutputCoords();
${e} sourceLoc;
${l}
vec4 result = vec4(0.);
${a}
${u}
setOutput(result);
}
`}};function uet(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Ve.computeFlatOffset(t,x.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function gi(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Ve.parseSliceParams(o,s,i);if(Ve.assertParamsValid(o,a,u),x.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=yP(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Ve.isSliceContinous(o.shape,a,u);if(l||!c){let p=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Uw(u):new Ww(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),uet(o,a,u,e)}var $L={kernelName:Ei,backendName:"webgl",kernelFunc:gi};var cet=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;x.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=ut({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Ae({inputs:{x:d},backend:e,attrs:{perm:l}}),g=ut({inputs:{x:h},backend:e,attrs:{shape:c}}),y=gi({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),y},DL={kernelName:wi,backendName:"webgl",kernelFunc:cet};function pet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Cw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var RL={kernelName:_p,backendName:"webgl",kernelFunc:pet};function met(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var FL={kernelName:Ep,backendName:"webgl",kernelFunc:met};var fet="return float(a != b);",CT=le({opSnippet:fet,cpuKernelImpl:fP,dtype:"bool"}),OL={kernelName:Na,backendName:"webgl",kernelFunc:CT};function Il(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return Ke({inputs:{x:o.complexTensorInfos.real},backend:e})}var ML={kernelName:Xp,backendName:"webgl",kernelFunc:Il};var det="return float(int(x));";function PL(r,t){let e=new en(r.shape,det),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function IT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Ke({inputs:{x:o},backend:e});let i=ye(o.shape),a=IT({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Rn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Il({inputs:{input:o},backend:e}),a=IT({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!x.hasEncodingLoss(o.dtype,s)){let i=Ke({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return PL(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),u=CT({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var LL={kernelName:io,backendName:"webgl",kernelFunc:IT};var zL="return ceil(x);",het=Ct({opSnippet:zL,packedOpSnippet:zL,cpuKernelImpl:jM}),BL={kernelName:es,backendName:"webgl",kernelFunc:het};var Hw=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}};var qw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function get(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;V().getBool("WEBGL_PACK_CLIP")?a=new qw(o.shape):a=new Hw(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var VL={kernelName:ao,backendName:"webgl",kernelFunc:get};var Kw=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function GL(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xet(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new Kw(n.shape),i=[GL(n,o.complexTensorInfos.real),GL(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var WL={kernelName:Dl,backendName:"webgl",kernelFunc:xet};var jw=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i<e.length;i++)e[i]=e[i-1]+t[i][1];let n=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<e.length;i++){let a=e[i-1];n.push(`else if (yC < ${e[i]}) setOutput(getT${i}(yR, yC-${a}));`)}let o=e.length,s=e[e.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}};var Yw=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=Vt(o),i=nr("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h<u.length;h++)u[h]=u[h-1]+t[h][e];let l=a[e],c=a.slice(-2),p=a.join(),m=`if (${l} < ${u[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<u.length;h++){let g=u[h-1];m+=`
if (${l} < ${u[h]} && ${l} >= ${u[h-1]}) {
return getChannel(
getT${h}(${Xw(a,l,g)}),
vec2(${Xw(c,l,g)}));
}`}let f=u.length,d=u[u.length-1];m+=`
return getChannel(
getT${f}(${Xw(a,l,d)}),
vec2(${Xw(c,l,d)}));`,this.userCode=`
float getValue(${a.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${i}), 0., 0., 0.);
${i[o-1]} = ${i[o-1]} + 1;
if (${i[o-1]} < ${n[o-1]}) {
result.g = getValue(${i});
}
${i[o-2]} = ${i[o-2]} + 1;
if (${i[o-2]} < ${n[o-2]}) {
result.a = getValue(${i});
}
${i[o-1]} = ${i[o-1]} - 1;
if (${i[o-2]} < ${n[o-2]} &&
${i[o-1]} < ${n[o-1]}) {
result.b = getValue(${i});
}
setOutput(result);
}
`}};function Xw(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function np(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return Ke({inputs:{x:o.complexTensorInfos.imag},backend:e})}var UL={kernelName:Gp,backendName:"webgl",kernelFunc:np};function op(r,t,e){let n=r[0].dtype;if(n==="complex64"){let c=r.map(h=>Il({inputs:{input:h},backend:e})),p=r.map(h=>np({inputs:{input:h},backend:e})),m=op(c,t,e),f=op(p,t,e),d=Rn({inputs:{real:m,imag:f},backend:e});return c.forEach(h=>e.disposeIntermediateTensorInfo(h)),p.forEach(h=>e.disposeIntermediateTensorInfo(h)),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let c=r.map(y=>{let b=x.sizeFromShape(y.shape.slice(t));return ut({inputs:{x:y},backend:e,attrs:{shape:[-1,b]}})}),p=c.map(y=>({vals:e.readSync(y.dataId),shape:y.shape})),m=S.computeOutShape(c.map(y=>y.shape),1),f=c[0].shape[0]===1,d=XM(p,m,n,f),h=S.computeOutShape(r.map(y=>y.shape),t),g=e.makeTensorInfo(h,n,d);return c.forEach(y=>e.disposeIntermediateTensorInfo(y)),g}if(r.length>V().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),p=op(r.slice(0,c),t,e),m=op(r.slice(c),t,e),f=op([p,m],t,e);return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),f}if(V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let c=new Yw(r.map(p=>p.shape),t);return e.runWebGLProgram(c,r,n)}let{tensors2D:s,outShape:i}=yet(r,t,e),a=new jw(s.map(c=>c.shape)),u=e.runWebGLProgram(a,s,n);s.forEach(c=>e.disposeIntermediateTensorInfo(c));let l=ut({inputs:{x:u},attrs:{shape:i},backend:e});return e.disposeIntermediateTensorInfo(u),l}function yet(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>ut({inputs:{x:s},attrs:{shape:[-1,x.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function ST(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),s);if(x.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let a=t.filter(l=>x.sizeFromShape(l.shape)>0);if(a.length===1)return Ke({inputs:{x:a[0]},backend:e});let u=a.map(l=>l.shape);return S.assertParamsConsistent(u,s),op(a,s,e)}var HL={kernelName:vi,backendName:"webgl",kernelFunc:ST};var wd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",y=g?1:2,b=g?2:3,w=g?3:1,v="",k="";n&&(o?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,k="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${u}, ${l});
const ivec2 pads = ivec2(${i}, ${a});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${w}];
ivec2 xRCCorner =
ivec2(coords[${y}], coords[${b}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${E}
${k}
setOutput(result);
}
`}},Zw=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${i}, ${a});
const ivec3 pads = ivec3(${e}, ${n}, ${o});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${p}; wF++) {
int xF = xFCorner + wF * ${u};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var Jw=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let{dataFormat:n}=e,o=qe(),s=n==="channelsLast",i=s?0:1,a=s?1:2,u=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${t[1]} && pos < ${t[0]}) {`,l="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)l+=`
blockIndex = rc.y + ${p};
pos = rc.x + ${c};
${u}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${i}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${a}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${c*2+p}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+p}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${o.output} = result;
}
`}};function Qw({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,y=[];if(!((p===1||m===1)&&c>wT)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&x.arraysEqual(l.shape.slice(-3),u.slice(-3))){let v=u[0]*u[1]*(u[2]+1),k={dataId:r.dataId,shape:[1,v,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,x.assert(yu(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=ut({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});y.push($);let D=rp({a:k,b:$,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),R=n.texData.get(D.dataId);x.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,R.shape=e.outShape,g=Ke({inputs:{x:D},backend:n}),g.shape=e.outShape,y.push(D)}else{let v=f?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=ut({inputs:{x:r},backend:n,attrs:{shape:[1,v,e.inChannels]}}),E=ut({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),$=rp({a:k,b:E,transposeA:d,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=ut({inputs:{x:$},backend:n,attrs:{shape:e.outShape}}),y.push(k),y.push(E),y.push($)}for(let v of y)n.disposeIntermediateTensorInfo(v);return g}function tv({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,y=[h,g],b=!0,w=!1,v=[],k=ut({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=ut({inputs:{x:t},backend:n,attrs:{shape:[1,h,x.sizeFromShape(t.shape)/h]}});v.push(k),v.push(E);let $=new Jw(y,e),D=[k.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=n.runWebGLProgram($,[k],"float32",D),P=ut({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});v.push(R),v.push(P);let W=o!=null,U=s!=null,q=a==="leakyrelu",K=a?vu(a,!0):null,j=new bd(P.shape,E.shape,[1,g,e.outChannels],b,w,W,K,U,q),Q=[P,E];if(o&&Q.push(o),U&&Q.push(s),q){let st=n.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));Q.push(st),v.push(st)}let rt=n.runWebGLProgram(j,Q,"float32"),X=d?[1,m,p,e.outChannels]:[1,e.outChannels,m,p],ot=ut({inputs:{x:rt},backend:n,attrs:{shape:X}});v.push(rt);for(let st of v)n.disposeIntermediateTensorInfo(st);return ot}function bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Qw({x:o,filter:s,convInfo:m,backend:e});else if(V().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=tv({x:o,filter:s,convInfo:m,backend:e});else{let h=new wd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=ut({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var qL={kernelName:rs,backendName:"webgl",kernelFunc:bet};var ev=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.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 < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
if (${i}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},rv=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
if (${i}) {
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);
}
`}},nv=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.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 < ${t.batchSize}; b++) {
for (int yF = 0; yF < ${t.outDepth}; yF++) {
int xF = wF + yF * ${e} - ${s};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${i};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${a};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},ov=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${u}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${e}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${e} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new ev(m);return e.runWebGLProgram(f,[o,s],"float32")}var KL={kernelName:$p,backendName:"webgl",kernelFunc:wet};function vet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new rv(m);return e.runWebGLProgram(f,[o,s],"float32")}var jL={kernelName:ns,backendName:"webgl",kernelFunc:vet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new Zw(l);return e.runWebGLProgram(c,[o,s],"float32")}var XL={kernelName:Rl,backendName:"webgl",kernelFunc:Cet};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new nv(l);return e.runWebGLProgram(c,[o,s],"float32")}var YL={kernelName:Dp,backendName:"webgl",kernelFunc:Iet};function ket(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new ov(l);return e.runWebGLProgram(c,[o,s],"float32")}var ZL={kernelName:Rp,backendName:"webgl",kernelFunc:ket};var Net=Lo+`
return cos(x);
`,Tet=Ct({opSnippet:Net}),JL={kernelName:os,backendName:"webgl",kernelFunc:Tet};var _et=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,Eet=Ct({opSnippet:_et}),QL={kernelName:ss,backendName:"webgl",kernelFunc:Eet};var sv=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,y,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,v,k]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${w});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${i}) {
return;
}
float height_scale = ${y};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var Aet=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new sv(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},t3={kernelName:ca,backendName:"webgl",kernelFunc:Aet};var Ng=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=t;let o=t.length,s=e?"1.0":`getX(${e3(o,"coords")})`,i=t[t.length-1],a="",u="";e?(a=n?`end != ${i-1}`:"end != 0",u=n?"end + 1":"end - 1"):(a=n?`end + pow2 < ${i}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${Vt(o)} coords = getOutputCoords();
int end = ${r3(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${u};
${r3(o,"coords")} = idx;
val *= getX(${e3(o,"coords")});
}
setOutput(val);
}
`}};function e3(r,t){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${r} is not yet supported`)}function r3(r,t){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative product for rank ${r} is not yet supported`)}function $et(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length,l=S.getAxesPermutation([s],u),c=o;l!=null&&(c=Ae({inputs:{x:o},backend:e,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Ke({inputs:{x:c},backend:e});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Ng(c.shape,!1,a),g=[[d]],y=f;f=e.runWebGLProgram(h,[f],f.dtype,g),e.disposeIntermediateTensorInfo(y)}if(i){let d=new Ng(c.shape,i,a),h=f;f=e.runWebGLProgram(d,[f],f.dtype),e.disposeIntermediateTensorInfo(h)}if(l!=null){let d=S.getUndoAxesPermutation(l),h=Ae({inputs:{x:f},backend:e,attrs:{perm:d}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(c),h}return f}var n3={kernelName:ua,backendName:"webgl",kernelFunc:$et};var Tg=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=t;let o=t.length,s=e?"0.0":`getX(${o3(o,"coords")})`,i=t[t.length-1],a="",u="";e?(a=n?`end != ${i-1}`:"end != 0",u=n?"end + 1":"end - 1"):(a=n?`end + pow2 < ${i}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${Vt(o)} coords = getOutputCoords();
int end = ${s3(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${u};
${s3(o,"coords")} = idx;
val += getX(${o3(o,"coords")});
}
setOutput(val);
}
`}};function o3(r,t){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function s3(r,t){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function Det(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length,l=S.getAxesPermutation([s],u),c=o;l!=null&&(c=Ae({inputs:{x:o},backend:e,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Ke({inputs:{x:c},backend:e});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Tg(c.shape,!1,a),g=[[d]],y=f;f=e.runWebGLProgram(h,[f],f.dtype,g),e.disposeIntermediateTensorInfo(y)}if(i){let d=new Tg(c.shape,i,a),h=f;f=e.runWebGLProgram(d,[f],f.dtype),e.disposeIntermediateTensorInfo(h)}if(l!=null){let d=S.getUndoAxesPermutation(l),h=Ae({inputs:{x:f},backend:e,attrs:{perm:d}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(c),h}return f}var i3={kernelName:is,backendName:"webgl",kernelFunc:Det};function Ret(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Cw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KM(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var a3={kernelName:Fp,backendName:"webgl",kernelFunc:Ret};var iv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,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 / ${e};
int offset_h = imod(h, ${e});
int in_w = w / ${e};
int offset_w = imod(w, ${e});
int offset_d = (offset_h * ${e} + 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 Fet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new iv(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var l3={kernelName:pa,backendName:"webgl",kernelFunc:Fet};var vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=De(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${u};
int q = d2 - d1 * ${u};
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 < ${i}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${a}; 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}
${c}
setOutput(result);
}
`}};var Cd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=De(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let y=0;y<p;y++)f+=`
vec4 xTexelC${y*2};
int xTexelC${y*2}Ready;
vec4 xTexelC${y*2+1};
int xTexelC${y*2+1}Ready;
vec4 xC${y};`;f+=`
for (int r = 0; r < ${c}; r++) {
`;for(let y=0;y<p;y++)f+=`
xTexelC${y*2} = vec4(0.0);
xTexelC${y*2}Ready = 0;
xTexelC${y*2+1} = vec4(0.0);
xTexelC${y*2+1}Ready = 0;
xC${y} = vec4(0.0);`;f+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let y=0;y<(m+1)/2;y++){let b=y*2;if(f+=`
xC = xCCorner + ${b*l};
`,u===1){if(b<p&&(a%2===1?(f+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b}Ready = 1;
}
`,l===1&&b>0?f+=`
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
`:f+=`
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);
}
`):f+=`
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<p)){let w=a%2===0?x.nearestLargerEven(l):l;l%2===0&&a%2===1||l%2!==0&&a%2!==1?(f+=`
xCOffset = xC + imod(pads[1], 2) + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1}Ready = 1;
}
`,l>1&&(f+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
xTexelC${b}Ready = 1;
}
`),f+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
`):w===1?f+=`
xC${b+1} = xTexelC${b};
`:f+=`
xCOffset = xC + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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${b+1};
`}}else b<p&&(a%2===1?(f+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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<p&&(f+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
`)):(f+=`
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${b+1}.xy);
`,b+1<p&&(f+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<p&&(f+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<p&&(f+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}f+=`
}
`,f+=`
}
`;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?d=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:d=`vec4 activation(vec4 x) {
${n}
}`,h="result = activation(result);");let g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${d}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${f}
vec4 result = dotProd - vec4(0.000000000000001);
${g}
${h}
setOutput(result);
}
`}};function Oet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Cd(p):m=new vd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var u3={kernelName:as,backendName:"webgl",kernelFunc:Oet};var av=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.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 * ${i} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},lv=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`
const ivec2 pads = ivec2(${i}, ${a});
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 < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.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 < ${u}; dm++) {
int d2 = d1 * ${u} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new av(p);return e.runWebGLProgram(m,[o,s],"float32")}var c3={kernelName:Op,backendName:"webgl",kernelFunc:Met};function Pet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new lv(p);return e.runWebGLProgram(m,[o,s],"float32")}var p3={kernelName:Mp,backendName:"webgl",kernelFunc:Pet};var uv=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Let(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=x.sizeFromShape(n.shape),i=ut({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new uv(s),u=e.runWebGLProgram(a,[i],i.dtype),l=ut({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var m3={kernelName:Pp,backendName:"webgl",kernelFunc:Let};var cv=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
const ivec2 strides = ivec2(${s}, ${i});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${a}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${e}) {
for (int w = 0; w < ${u}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new cv(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=ut({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var f3={kernelName:Fl,backendName:"webgl",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:y,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(y)?w=s[g]:(w=Ae({inputs:{x:s[g]},backend:e,attrs:{perm:y}}),d.push(w));let v=w.shape.slice();for(let k=0;k<b.length;++k)v.splice(b[k],0,1);x.arraysEqual(w.shape,v)||(w=ut({inputs:{x:w},backend:e,attrs:{shape:v}}),d.push(w)),m===null?m=w:(m=Sg({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=ep({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var d3={kernelName:Lp,backendName:"webgl",kernelFunc:Bet};var Vet="return (x >= 0.0) ? x : (exp(x) - 1.0);",Get=`
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;
`,Wet=Ct({opSnippet:Vet,packedOpSnippet:Get}),h3={kernelName:us,backendName:"webgl",kernelFunc:Wet};var Uet="return (b >= 1.0) ? a : a * (b + 1.0);",Het=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,qet=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(Het,n.shape,o.shape):new ro(Uet,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},g3={kernelName:zp,backendName:"webgl",kernelFunc:qet};var Ket=`
return vec4(equal(a, b));
`,jet="return float(a == b);",Xet=le({opSnippet:jet,packedOpSnippet:Ket,dtype:"bool",cpuKernelImpl:YM}),x3={kernelName:fa,backendName:"webgl",kernelFunc:Xet};var Yet=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${S.ERF_P};
float a1 = ${S.ERF_A1};
float a2 = ${S.ERF_A2};
float a3 = ${S.ERF_A3};
float a4 = ${S.ERF_A4};
float a5 = ${S.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Zet=Ct({opSnippet:Yet}),y3={kernelName:ma,backendName:"webgl",kernelFunc:Zet};var Jet=Lo+`
return exp(x);
`,Qet=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,kT=Ct({opSnippet:Jet,packedOpSnippet:Qet,cpuKernelImpl:ZM,dtype:"float32"}),b3={kernelName:cs,backendName:"webgl",kernelFunc:kT};function pv(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(x.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),ut({inputs:{x:s},backend:n,attrs:{shape:a}})}var w3={kernelName:Ci,backendName:"webgl",kernelFunc:pv};var v3="return exp(x) - 1.0;",trt=Ct({opSnippet:v3,packedOpSnippet:v3,cpuKernelImpl:JM}),C3={kernelName:da,backendName:"webgl",kernelFunc:trt};var _g=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${a}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${o});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${o}; 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) / ${i};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function mv(r,t,e){let n=e.texData.get(r.dataId),o=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=ut({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new _g("real",u,t),c=new _g("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=ut({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function ert(r){let{inputs:t,backend:e}=r,{input:n}=t;return mv(n,!1,e)}var I3={kernelName:Bp,backendName:"webgl",kernelFunc:ert};var fv=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function Sl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||x.inferDtype(o),s==="string"){let i=x.getArrayFromDType(s,x.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new fv(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var S3={kernelName:Ol,backendName:"webgl",kernelFunc:Sl};var dv=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${e} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${e}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var k3={kernelName:ha,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new dv(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var N3="return floor(x);",rrt=Ct({opSnippet:N3,packedOpSnippet:N3,cpuKernelImpl:QM}),T3={kernelName:ps,backendName:"webgl",kernelFunc:rrt};var nrt=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,ort=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,srt=le({opSnippet:nrt,packedOpSnippet:ort,dtype:"int32"}),_3={kernelName:ms,backendName:"webgl",kernelFunc:srt};var hv=class{constructor(t){this.variableNames=["A"];let e=qe(),[n,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
vec4 values = ${e.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var gv=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=qe(),[n,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.0, ${n}.0);
vec4 values = ${e.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${e.output} = result;
}
`}};var E3={kernelName:Hd,backendName:"webgl",kernelFunc:irt},Id;function irt(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];(a||i)&&(Id==null&&(Id=document.createElement("canvas").getContext("2d")),Id.canvas.width=u,Id.canvas.height=l,Id.drawImage(o,0,0,u,l),o=Id.canvas);let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Kr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=V().getBool("WEBGL_PACK")?new gv(p):new hv(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=Qw({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(V().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)y=tv({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let v=i!=null,k=a!=null,E=f==="leakyrelu",$=f?vu(f,!1):null,D=new wd(g,v,$,k,E),R=[o,s];if(i&&R.push(i),a&&R.push(a),E){let P=e.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));R.push(P),b.push(P)}y=e.runWebGLProgram(D,R,"float32")}let w=ut({inputs:{x:y},backend:e,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>e.disposeIntermediateTensorInfo(v)),w}var A3={kernelName:Oi,backendName:"webgl",kernelFunc:art};function lrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?vu(m,y):null,w=[o,s],v=i!=null,k=a!=null,E=m==="leakyrelu";if(v&&w.push(i),k&&w.push(a),E){let P=e.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));w.push(P),d.push(P)}let $;y?$=new Cd(g,v,b,k,E):$=new vd(g,v,b,k,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=e.runWebGLProgram($,w,"float32",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),R}var $3={kernelName:Mi,backendName:"webgl",kernelFunc:lrt};var xv=class{constructor(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;let o=Vt(e.length),s=Vt(n.length),i=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${i};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function urt(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=x.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=ut({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=ut({inputs:{x:n},backend:e,attrs:{shape:[x.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let y=e.readSync(o.dataId),b=e.bufferSync(n),w=tP(y,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new xv(i,p,[l,c]),h=e.runWebGLProgram(d,[f,m],f.dtype),g=ut({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var D3={kernelName:ga,backendName:"webgl",kernelFunc:urt};var yv=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=Vt(this.rank),o=crt(t,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${o}));
}
`}};function crt(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("index"):n.push(`${e[o]}`);return n.join()}function NT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n,u=x.parseAxisParam(i,o.shape)[0];if(V().get("DEBUG")){let b=e.readSync(s.dataId),w=o.shape[u];for(let v=0;v<b.length;++v){let k=b[v];x.assert(k<=w-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=x.sizeFromShape(s.shape),p=[],m=ut({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=ut({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),v=eP(w,b,d);return p.forEach(k=>e.disposeIntermediateTensorInfo(k)),e.makeTensorInfo(l.outputShape,v.dtype,v.values)}let h=new yv(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=ut({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),y}var R3={kernelName:Ii,backendName:"webgl",kernelFunc:NT};var prt="return float(a > b);",mrt=`
return vec4(greaterThan(a, b));
`,frt=le({opSnippet:prt,packedOpSnippet:mrt,cpuKernelImpl:rP,dtype:"bool"}),F3={kernelName:xa,backendName:"webgl",kernelFunc:frt};var drt="return float(a >= b);",hrt=`
return vec4(greaterThanEqual(a, b));
`,grt=le({opSnippet:drt,packedOpSnippet:hrt,dtype:"bool",cpuKernelImpl:nP}),O3={kernelName:ds,backendName:"webgl",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return mv(n,!0,e)}var M3={kernelName:Vp,backendName:"webgl",kernelFunc:xrt};var yrt="return float(!isnan(x) && !isinf(x));",brt=Ct({opSnippet:yrt,dtype:"bool"}),P3={kernelName:ya,backendName:"webgl",kernelFunc:brt};var wrt="return float(isinf(x));",vrt=Ct({opSnippet:wrt,dtype:"bool"}),L3={kernelName:ba,backendName:"webgl",kernelFunc:vrt};var Crt="return float(isnan(x));",Irt=Ct({opSnippet:Crt,dtype:"bool"}),z3={kernelName:wa,backendName:"webgl",kernelFunc:Irt};var Srt="return float(a < b);",krt=`
return vec4(lessThan(a, b));
`,Nrt=le({opSnippet:Srt,packedOpSnippet:krt,cpuKernelImpl:oP,dtype:"bool"}),B3={kernelName:va,backendName:"webgl",kernelFunc:Nrt};var Trt="return float(a <= b);",_rt=`
return vec4(lessThanEqual(a, b));
`,Ert=le({opSnippet:Trt,packedOpSnippet:_rt,cpuKernelImpl:sP,dtype:"bool"}),V3={kernelName:Ca,backendName:"webgl",kernelFunc:Ert};function Art(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=iP(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var G3={kernelName:Wp,backendName:"webgl",kernelFunc:Art};var $rt=Lo+`
return x < 0.0 ? 0./0. : log(x);
`,Drt=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,Rrt=Ct({opSnippet:$rt,packedOpSnippet:Drt,cpuKernelImpl:aP}),W3={kernelName:gs,backendName:"webgl",kernelFunc:Rrt};var Frt=Lo+`
return log(1.0 + x);
`,Ort=Ct({opSnippet:Frt}),U3={kernelName:Ia,backendName:"webgl",kernelFunc:Ort};var Mrt="return float(a >= 1.0 && b >= 1.0);",Prt=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,Lrt=le({opSnippet:Mrt,packedOpSnippet:Prt,dtype:"bool"}),H3={kernelName:Sa,backendName:"webgl",kernelFunc:Lrt};var zrt="return float(!(x >= 1.0));",Brt=Ct({opSnippet:zrt}),q3={kernelName:Ou,backendName:"webgl",kernelFunc:Brt};var Vrt="return float(a >= 1.0 || b >= 1.0);",Grt=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Wrt=le({opSnippet:Vrt,packedOpSnippet:Grt,dtype:"bool"}),K3={kernelName:Mu,backendName:"webgl",kernelFunc:Wrt};var bv=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${i}; j <= ${i}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${a}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${u};
setOutput(val);
}
`}};var wv=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${i};
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 = - ${i}; j <= ${i}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
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 * ${u};
setOutput(result);
}
`}};var Urt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=V().getBool("WEBGL_PACK_NORMALIZATION")?new wv(o.shape,s,i,a,u):new bv(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},j3={kernelName:Ml,backendName:"webgl",kernelFunc:Urt};var vv=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${e})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${e} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${o}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${o})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var Hrt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new vv(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},X3={kernelName:Up,backendName:"webgl",kernelFunc:Hrt};function Y3(r,t,e,n){let o=x.sizeFromShape(t),i=x.sizeFromShape(r.shape)/o,a=ut({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,r.dtype,"max",n),l=ut({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function TT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,v=new Array(a);for(let $=0;$<v.length;$++)v[$]=o.shape[c[$]];let k=Qc(w,o.shape,o.dtype,c,v);f=e.makeTensorInfo(v,o.dtype);let E=e.texData.get(f.dataId);E.values=k}else f=Cu(o,c,e);l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("max",l,a);let[d,h]=S.computeOutAndReduceShapes(f.shape,l),g=d;i&&(g=S.expandShapeToKeepDim(d,u));let y;if(m){let w=e.texData.get(f.dataId).values,v=lP(w,x.sizeFromShape(h),g,o.dtype);y=e.makeTensorInfo(g,o.dtype);let k=e.texData.get(y.dataId);k.values=v}else y=Y3(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),y}var Z3={kernelName:xs,backendName:"webgl",kernelFunc:TT};var qrt=Ew+`
return max(a, b);
`,Krt=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wu+`
return result;
`,jrt=le({opSnippet:qrt,packedOpSnippet:Krt,cpuKernelImpl:uP}),J3={kernelName:ys,backendName:"webgl",kernelFunc:jrt};function Xrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;fi(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;x.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ke({inputs:{x:o},backend:e});let p=new hi(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var Q3={kernelName:bs,backendName:"webgl",kernelFunc:Xrt};function Yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Iu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var tz={kernelName:Pl,backendName:"webgl",kernelFunc:Yrt};var Cv=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${e}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${i}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${t.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 * ${i} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Iv=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${e}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${a}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Zrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Iu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new Iv(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var ez={kernelName:qp,backendName:"webgl",kernelFunc:Zrt};function Jrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;fi([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new hi(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new Cv(m),y=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),y}var rz={kernelName:Hp,backendName:"webgl",kernelFunc:Jrt};function nz(r,t,e,n){let o=new hi(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new hi(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var oz={kernelName:Kp,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;x.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];x.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=nz(n,a,c,u);return[p,m]}};function sz(r,t,e,n){let o=x.sizeFromShape(t),i=x.sizeFromShape(r.shape)/o,a=ut({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,"float32","mean",n),l=ut({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var iz={kernelName:ws,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=x.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let v=i.texData.get(d.dataId).values,k=new Array(a);for(let D=0;D<k.length;D++)k[D]=n.shape[c[D]];let E=Qc(v,n.shape,n.dtype,c,k);d=i.makeTensorInfo(k,n.dtype);let $=i.texData.get(d.dataId);$.values=E}else d=Cu(n,c,i);f.push(d),l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("sum",l,a);let[h,g]=S.computeOutAndReduceShapes(d.shape,l),y=h;o&&(y=S.expandShapeToKeepDim(h,u));let b=sz(d,g,y,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function Qrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Ae({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"min",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var az={kernelName:vs,backendName:"webgl",kernelFunc:Qrt};var tnt=Ew+`
return min(a, b);
`,ent=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wu+`
return result;
`,rnt=le({opSnippet:tnt,packedOpSnippet:ent,cpuKernelImpl:cP}),lz={kernelName:Cs,backendName:"webgl",kernelFunc:rnt};var Sv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=Vt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${i};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${i});
${s} end = ${s}(${a});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${s} coords = outC - start;
setOutput(getX(${u}));
}
`}};var kv=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=Vt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=nr("rc",o),l=nr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${p});
}
rc = outputLoc;
${u[o-2]} += 1;
if(${u[o-2]} < ${this.outputShape[o-2]}) {
${d}
result[2] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${l.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${i});
const ${s} end = ${s}(${a});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var nnt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kv(n.shape,o,s):new Sv(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},uz={kernelName:Is,backendName:"webgl",kernelFunc:nnt};var ont=`if (b == 0.0) return NAN;
return mod(a, b);`,snt=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+wu+`
return result;
`,int=le({opSnippet:ont,packedOpSnippet:snt}),cz={kernelName:ka,backendName:"webgl",kernelFunc:int};var Nv=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,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 < ${e-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${e-1}));
}
`}};var ant=`
if (a == b) {
return 1.0;
};
return a / b;`,lnt=`
// 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;
`,_T=le({opSnippet:ant,packedOpSnippet:lnt,checkOutOfBounds:!0}),pz={kernelName:ls,backendName:"webgl",kernelFunc:_T};var mz="return a - b;",ET=le({opSnippet:mz,packedOpSnippet:mz,supportsComplex:!0,cpuKernelImpl:NP}),fz={kernelName:Vs,backendName:"webgl",kernelFunc:ET};function AT(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=x.parseAxisParam([s],o.shape),a=TT({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=ut({inputs:{x:a},backend:e,attrs:{shape:u}}),c=ET({inputs:{a:o,b:l},backend:e}),p=kT({inputs:{x:c},backend:e}),m=ep({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=ut({inputs:{x:m},backend:e,attrs:{shape:u}}),d=_T({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var dz={kernelName:zs,backendName:"webgl",kernelFunc:AT};function unt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:AT({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new Nv(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var hz={kernelName:jp,backendName:"webgl",kernelFunc:unt};var cnt=hr+`
return -x;
`,pnt=`
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 mnt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=mP(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new eo(n.shape,pnt):o=new en(n.shape,cnt),e.runWebGLProgram(o,[n],n.dtype)}var gz={kernelName:Si,backendName:"webgl",kernelFunc:mnt};var fnt=Gr.nonMaxSuppressionV3Impl;function dnt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=fnt(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var xz={kernelName:Ta,backendName:"webgl",kernelFunc:dnt};var hnt=Gr.nonMaxSuppressionV4Impl;function gnt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=hnt(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var yz={kernelName:_a,backendName:"webgl",kernelFunc:gnt};var xnt=Gr.nonMaxSuppressionV5Impl;function ynt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:y}=xnt(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var bz={kernelName:Ea,backendName:"webgl",kernelFunc:ynt};var Tv=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${n}),
float(index == coords.y)));
}
`}};var bnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{depth:s,onValue:i,offValue:a}=n,u=x.sizeFromShape(o.shape),l=new Tv(u,s,i,a),c=ut({inputs:{x:o},backend:e,attrs:{shape:[u]}}),p=e.runWebGLProgram(l,[c],o.dtype);e.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ut({inputs:{x:p},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(p),f},wz={kernelName:ks,backendName:"webgl",kernelFunc:bnt};function Eg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Il({inputs:{input:n},backend:e}),s=Eg({inputs:{x:o},backend:e}),i=np({inputs:{input:n},backend:e}),a=Eg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var vz={kernelName:Ri,backendName:"webgl",kernelFunc:Eg};function Cz(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Il({inputs:{input:n},backend:e}),s=Cz({inputs:{x:o},backend:e}),i=np({inputs:{input:n},backend:e}),a=Eg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var Iz={kernelName:ki,backendName:"webgl",kernelFunc:Cz};function wnt(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return pv({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=pv({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=ST({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var Sz={kernelName:Ni,backendName:"webgl",kernelFunc:wnt};var _v=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Vt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${i};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${i});
${s} end = ${s}(${a});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${u}));
}
}
`}};var Ev=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Vt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=nr("rc",o),l=nr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;
if(${c}) {
`,o===1?"":`}
rc = outputLoc;
${u[o-2]} += 1;
if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${u[o-1]} += 1;
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${l.join()}), ${p});
}
`;d+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${i});
const ${s} end = ${s}(${a});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var $T=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(x.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Sl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ev(o.shape,s,i):new _v(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},kz={kernelName:Ns,backendName:"webgl",kernelFunc:$T};var vnt=`
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);
`,Cnt=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+wu+`
return result;
`,Int=le({opSnippet:vnt,packedOpSnippet:Cnt}),Nz={kernelName:Ts,backendName:"webgl",kernelFunc:Int};function Snt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=x.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Ae({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=dP(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,y,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=x.sizeFromShape(h),y=ut({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Wu(o.dtype),w=Hn(y,b,"prod",e);f=ut({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(y),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=ut({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var Tz={kernelName:Aa,backendName:"webgl",kernelFunc:Snt};var DT=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=hP(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},_z={kernelName:Ll,backendName:"webgl",kernelFunc:DT};var knt="return 1.0 / x;",Nnt=Ct({opSnippet:knt}),Ez={kernelName:$a,backendName:"webgl",kernelFunc:Nnt};var Tnt=hr+`
return (x < 0.0) ? 0.0 : x;
`,_nt=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Ent=Ct({opSnippet:Tnt,packedOpSnippet:_nt}),Az={kernelName:Es,backendName:"webgl",kernelFunc:Ent};var Ant=hr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,$nt=`
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;
`,Dnt=Ct({opSnippet:Ant,packedOpSnippet:$nt}),$z={kernelName:$s,backendName:"webgl",kernelFunc:Dnt};var Av=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}};var $v=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
${u}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${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 Rnt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new $v(o.shape,u,l,s,i):new Av(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var Dz={kernelName:As,backendName:"webgl",kernelFunc:Rnt};var Dv=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${i}) {
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 >= ${a}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Fnt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new Dv(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var Rz={kernelName:Zp,backendName:"webgl",kernelFunc:Fnt};var Rv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};var Fv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
${u}.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 = ${f};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Ont(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Fv(o.shape,u,l,s,i):new Rv(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var Fz={kernelName:zl,backendName:"webgl",kernelFunc:Ont};var Ov=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${i}) {
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 >= ${a}) {
continue;
}
float sourceFracRow =
float(${u[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${u[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Mnt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new Ov(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var Oz={kernelName:Yp,backendName:"webgl",kernelFunc:Mnt};var Mv=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${t[0]} - coord - 1));
}
`;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=Vt(n);this.userCode=`
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var Pv=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=nr("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Vt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${t[0]} - rc - 1),
${t[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),
${t[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${u(o.slice())};
if(${s}){
result.g = ${l(o.slice())};
}
if(${i}) {
result.b = ${c(o.slice())};
if(${s}) {
result.a = ${p(o.slice())};
}
}
setOutput(result);
}
`;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Pnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=x.parseAxisParam(s,o.shape);if(i===0)return Ke({inputs:{x:o},backend:e});let u=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pv(o.shape,a):new Mv(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var Mz={kernelName:Ds,backendName:"webgl",kernelFunc:Pnt};var Lv=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=`
vec3 fill = vec3(${e.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${s}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var Pz={kernelName:Va,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new Lv(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Lnt=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,znt=Ct({opSnippet:Lnt}),Lz={kernelName:Rs,backendName:"webgl",kernelFunc:znt};var Bnt="return inversesqrt(x);",Vnt=Ct({opSnippet:Bnt,cpuKernelImpl:gP}),zz={kernelName:Fs,backendName:"webgl",kernelFunc:Vnt};var Sd=class{constructor(t,e,n,o,s,i,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let u=Vt(s.length),l=Vt(i.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=e>1?"strides[j]":"strides";this.userCode=`
${u} strides = ${u}(${s});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${t}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${e}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Gnt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=ut({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=ut({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g=new Sd(u,a,f.shape.length,d.shape.length,c,m),y=e.runWebGLProgram(g,[d,f,h],d.dtype),b=ut({inputs:{x:y},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(h),b}var Bz={kernelName:Da,backendName:"webgl",kernelFunc:Gnt};var zv=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c<e.length;c++)l.push(`${a[c]}`),c<t&&u.push(`${a[c]}`);o=u.join(),s=l.join()}let i=Vt(n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function Wnt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new zv(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ar(o.dtype,s.dtype))}var Vz={kernelName:_i,backendName:"webgl",kernelFunc:Wnt};var Unt=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${S.SELU_SCALEALPHA};
float scale = ${S.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Hnt=Ct({opSnippet:Unt}),Gz={kernelName:Ra,backendName:"webgl",kernelFunc:Hnt};var qnt=Lo+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,Knt=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,jnt=Ct({opSnippet:qnt,packedOpSnippet:Knt,cpuKernelImpl:xP}),Wz={kernelName:Ms,backendName:"webgl",kernelFunc:jnt};var Xnt=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Ynt=Ct({opSnippet:Xnt}),Uz={kernelName:Oa,backendName:"webgl",kernelFunc:Ynt};var Znt=Lo+`
return sin(x);
`,Jnt=Ct({opSnippet:Znt}),Hz={kernelName:Os,backendName:"webgl",kernelFunc:Jnt};var Qnt=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,tot=Ct({opSnippet:Qnt}),qz={kernelName:Fa,backendName:"webgl",kernelFunc:tot};var eot=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,rot=Ct({opSnippet:eot}),Kz={kernelName:Ma,backendName:"webgl",kernelFunc:rot};var not=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;x.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((y,b)=>y*b),u=[[0,0]];u.push(...i);for(let y=1+s.length;y<o.shape.length;++y)u.push([0,0]);let l=[],c=$T({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,s,a,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,a,!1),d=ut({inputs:{x:c},backend:e,attrs:{shape:p}}),h=Ae({inputs:{x:d},backend:e,attrs:{perm:m}}),g=ut({inputs:{x:h},backend:e,attrs:{shape:f}});return l.push(c),l.push(d),l.push(h),l.forEach(y=>e.disposeIntermediateTensorInfo(y)),g},jz={kernelName:Ai,backendName:"webgl",kernelFunc:not};function oot(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=bP(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var Xz={kernelName:Bl,backendName:"webgl",kernelFunc:oot};function sot(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=wP(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var Yz={kernelName:Pa,backendName:"webgl",kernelFunc:sot};function iot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Sw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var Zz={kernelName:Vl,backendName:"webgl",kernelFunc:iot};function aot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Sw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var Jz={kernelName:Gl,backendName:"webgl",kernelFunc:aot};function lot(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(s,o,a),m=!1,f=new Sd(l,u,o.shape.length,s.shape.length,c,[p,1],m),d=e.runWebGLProgram(f,[s,o,i],s.dtype),h=ut({inputs:{x:d},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),h}var Qz={kernelName:Jp,backendName:"webgl",kernelFunc:lot};function uot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=x.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=gi({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var tB={kernelName:$i,backendName:"webgl",kernelFunc:uot};var eB="return sqrt(x);",cot=Ct({opSnippet:eB,packedOpSnippet:eB,cpuKernelImpl:vP}),rB={kernelName:Ps,backendName:"webgl",kernelFunc:cot};var pot="return x * x;",mot=Ct({opSnippet:pot}),nB={kernelName:Wl,backendName:"webgl",kernelFunc:mot};var oB="return (a - b) * (a - b);",fot=le({opSnippet:oB,packedOpSnippet:oB}),sB={kernelName:Bs,backendName:"webgl",kernelFunc:fot};function dot({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=hr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new en(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var iB={kernelName:uo,backendName:"webgl",kernelFunc:dot};var Bv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Vt(n.length),i=Vt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${t});
${s} strides = ${s}(${e});
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}};function hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:y,begin:b,end:w,strides:v}=Ve.sliceInfo(o.shape,s,i,a,u,l,c,p,m),k;if(h)k=ut({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||y){x.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let $=Ve.computeOutShape(b,w,v),D=gi({inputs:{x:o},backend:e,attrs:{begin:b,size:$}});k=ut({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),R=It(o.shape,o.dtype,D),P=CP(f,R,v,b);k=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new Bv(b,v,f);k=e.runWebGLProgram(D,[o],o.dtype)}let E=ut({inputs:{x:k},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(k),E}var aB={kernelName:La,backendName:"webgl",kernelFunc:hot};function got(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=IP(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var lB={kernelName:Qp,backendName:"webgl",kernelFunc:got};function xot(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{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 a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=SP(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var uB={kernelName:tm,backendName:"webgl",kernelFunc:xot};function yot(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=kP(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var cB={kernelName:em,backendName:"webgl",kernelFunc:yot};var bot="return tan(x);",wot=Ct({opSnippet:bot}),pB={kernelName:Gs,backendName:"webgl",kernelFunc:wot};var vot=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Cot=Ct({opSnippet:vot}),mB={kernelName:Ws,backendName:"webgl",kernelFunc:Cot};var Vv=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[i]*e[i];this.outputShape=n,this.rank=n.length;let o=Vt(this.rank),s=Iot(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Iot(r){let t=r.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${e[o]}, ${r[o]})`);return n.join()}function RT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>x.decodeString(m)):u,c=It(o.shape,o.dtype,l),p=TP(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Vv(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var fB={kernelName:Yn,backendName:"webgl",kernelFunc:RT};var Gv=class{constructor(t){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=t,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));
}
}
`}},Wv=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,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 sp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function dB(r){let t=1;for(;t<r;)t*=2;return t}function Sot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n,a=V().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=V().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=o.shape,c=l[l.length-1];if(e.shouldExecuteOnCPU([o])||c<a||s>u){let P=e.readSync(o.dataId),[W,U]=_P(P,l,o.dtype,s,i);return[e.makeTensorInfo(W.shape,W.dtype,W.values),e.makeTensorInfo(U.shape,U.dtype,U.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Sl({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=x.sizeFromShape(l)/c,g=ut({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&sp(e,f);let y=dB(s),b=dB(c),w=null,v=()=>w===null?[g,g]:[g,w],k=(P,W,U)=>{let q=v(),K=new Gv(U),Q=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[W]],rt=w;w=e.runWebGLProgram(K,q,"int32",Q),sp(e,rt)};for(let P=1;P<y;P*=2){let W=P*2;for(let U=P;U>=1;U/=2)k(W,U,[h,b])}for(let P=b;P>y;P/=2){let W=v(),U=new Wv([h,P/2]),K=[[c],[w===null?1:0],[y]],j=w;w=e.runWebGLProgram(U,W,"int32",K),sp(e,j);let Q=y/2,rt=Q*2;for(let X=Q;X>=1;X/=2)k(rt,X,w.shape)}let E=w;w=gi({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),sp(e,E);let $=NT({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});sp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=ut({inputs:{x:w},attrs:{shape:D},backend:e}),sp(e,E);let R=$;return $=ut({inputs:{x:$},attrs:{shape:D},backend:e}),sp(e,R),[$,w]}var hB={kernelName:za,backendName:"webgl",kernelFunc:Sot};var Uv=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${u} == 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 (${u} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${u} == 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 < ${t} && 0 <= coordX && coordX < ${e}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${e}));
float mapY = mapCoord(inY, float(${t}));
if (${a} == 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 kot(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],y=new Uv(p,m,i,a,u,g);return e.runWebGLProgram(y,[o,s],"float32")}var gB={kernelName:Ba,backendName:"webgl",kernelFunc:kot};function Not(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;fi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=EP(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var xB={kernelName:rm,backendName:"webgl",kernelFunc:Not};function Tot(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;h<a;h++)h!==s&&(l[c++]=i.shape[h]);let p=[],m=new Array(a).fill(0),f=i.shape.slice();f[s]=1;let d=new Array(u);for(let h=0;h<d.length;h++){m[s]=h;let g=gi({inputs:{x:i},backend:e,attrs:{begin:m,size:f}}),y=ut({inputs:{x:g},backend:e,attrs:{shape:l}});d[h]=y,p.push(g)}return p.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var yB={kernelName:Di,backendName:"webgl",kernelFunc:Tot};var Hv=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
sumValue += dot(values, segFilter);
`,f="";s%n>0&&(f=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${u};
float getValue(int batch, int inIdx) {
${f}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${i})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${i})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; 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
);
${m}
}
int inIdx = inOffset + ${c};
if (${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
);
${m}
} else if (${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
);
${m}
} else if (${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
);
${m}
}
setOutput(${l});
}
`}};function _ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Ae({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=x.sizeFromShape([p.shape[l]]),d=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=Wu(o.dtype),g=(v,k,E,$,D)=>{let R=v.shape[0],P=v.shape[1],W=S.segment_util.segOpComputeOptimalWindowSize(P,D),U={windowSize:W,inSize:P,batchSize:R,numSegments:D},q=new Hv(U,k),K=e.compileAndRun(q,[v,E],$);if(u.push(K),K.shape[1]===D)return K;let j=DT({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),Q=RT({inputs:{x:j},backend:e,attrs:{reps:[P/W]}});return u.push(j),u.push(Q),g(K,k,Q,$,D)},y=g(d,"unsortedSegmentSum",s,h,i),b=ut({inputs:{x:y},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let v=S.getUndoAxesPermutation(c);w=Ae({inputs:{x:w},backend:e,attrs:{perm:v}})}return u.forEach(v=>e.disposeIntermediateTensorInfo(v)),w}var bB={kernelName:Ul,backendName:"webgl",kernelFunc:_ot};var Eot=[iL,lL,uL,cL,mL,fL,dL,hL,yL,bL,wL,vL,CL,IL,SL,kL,NL,TL,_L,EL,AL,DL,RL,FL,LL,BL,VL,jP,WL,HL,qL,KL,jL,XL,YL,ZL,JL,QL,t3,n3,i3,a3,l3,u3,c3,p3,m3,f3,d3,h3,g3,x3,y3,b3,w3,C3,I3,S3,k3,T3,_3,E3,A3,$3,D3,R3,F3,O3,KP,M3,UL,P3,L3,z3,XP,B3,V3,G3,W3,U3,H3,q3,K3,j3,X3,Z3,J3,Q3,tz,ez,rz,oz,iz,az,lz,uz,cz,hz,tL,gz,xz,yz,bz,OL,wz,Iz,Sz,kz,Nz,YP,Tz,_z,ML,pz,Ez,Az,$z,rL,Dz,Rz,Fz,Oz,Mz,Pz,Lz,zz,Bz,Vz,Gz,Wz,Uz,Hz,qz,$L,dz,Kz,jz,Xz,Yz,Zz,Jz,Qz,tB,rB,nB,sB,iB,aB,lB,uB,cB,fz,oL,pB,mB,fB,hB,gB,sL,xB,yB,bB,vz];for(let r of Eot)Pu(r);var Xt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(Xt||(Xt={}));var Su;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Su||(Su={}));var wB;function Aot(r){wB=r.wasm.cwrap(Fi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Su[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Pr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),v=e.makeOutput([...w,y,b],o.dtype),k=e.dataIdMap.get(v.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),$=new Uint8Array(new Int32Array(s.shape).buffer);return wB(m,E,o.shape.length,f,$,s.shape.length,u,l,g,d,h,p||0,k),v}var vB={kernelName:Fi,backendName:"wasm",setupFunc:Aot,kernelFunc:$ot};function ue(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return x.sizeFromShape(l.shape)===0||e(u,Xt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var CB=ue(bi);function Ie(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(x.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,y,c.shape.length,Xt[l.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var Dot=!0,IB=Ie(Xn,Dot);var SB;function Rot(r){SB=r.wasm.cwrap(Zo,null,["array","number","number","number"])}function Fot(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(x.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return SB(s,o.length,Xt[n.dtype],i),n}var kB={kernelName:Zo,backendName:"wasm",setupFunc:Rot,kernelFunc:Fot};function ip(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var NB={kernelName:lo,backendName:"wasm",kernelFunc:ip};var TB;function Oot(r){TB=r.wasm.cwrap(Us,null,["number","array","number","number","number","array","number"])}function no(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Pot(t.x.shape,n.perm),i=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(i=!1);let a=Mot(t.x.shape,n.perm),u={dataId:t.x.dataId,shape:o,dtype:t.x.dtype};if(i){let d=ip({inputs:t,backend:e});return d.shape=a,d}let l=e.makeOutput(a,u.dtype),c=e.dataIdMap.get(u.dataId).id,p=e.dataIdMap.get(l.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(u.shape).buffer);return TB(c,f,u.shape.length,Xt[u.dtype],p,m,s.length),l}function Mot(r,t){let e=new Array(r.length);for(let n=0;n<e.length;n++)e[n]=r[t[n]];return e}function Pot(r,t){let e=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&e.push(r[o]),r[t[o]]!==1&&n.push(t[o]);for(let o=0;o<n.length;++o){let s=-1;for(let i=0;i<n.length;++i)n[i]>=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var _B={kernelName:Us,backendName:"wasm",kernelFunc:no,setupFunc:Oot};function Sn(r,t,e){let n=r.shape,o=r.shape.length,s=x.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[a[f]];i=S.getInnerMostAxes(i.length,o),u=no({inputs:{x:r},attrs:{perm:a},backend:e});let p=e.dataIdMap.get(r.dataId).id;e.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var EB;function Lot(r){EB=r.wasm.cwrap(ra,null,["number, number, number"])}function zot(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("all",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),y=x.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(x.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;EB(u,y,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var AB={kernelName:ra,backendName:"wasm",setupFunc:Lot,kernelFunc:zot};var $B;function Bot(r){$B=r.wasm.cwrap(na,null,["number, number, number"])}function Vot(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Sn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("any",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),y=x.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(x.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;$B(u,y,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var DB={kernelName:na,backendName:"wasm",setupFunc:Bot,kernelFunc:Vot};var RB;function Got(r){RB=r.wasm.cwrap(Jo,null,["number","number","number","number","number"])}function Wot(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o}=n,{x:s}=e,i=t.dataIdMap.get(s.dataId).id,a=i,u=s,{transposed:l,axes:c,inputWasTransposed:p}=Sn(s,o,t);if(p){let y=t.dataIdMap.get(l.dataId).id;y!==i&&(u=l,a=y)}let m=u.shape.slice(0,-1),f=t.makeOutput(m,"int32"),d=t.dataIdMap.get(f.dataId).id,h=x.sizeFromShape(f.shape),g=u.shape[c[0]];return RB(a,Xt[u.dtype],h,g,d),p&&t.disposeData(l.dataId),f}var FB={kernelName:Jo,backendName:"wasm",kernelFunc:Wot,setupFunc:Got};var OB;function Uot(r){OB=r.wasm.cwrap(Qo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hot(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let v=n.makeOutput(c.outShape,"float32"),k=n.dataIdMap.get(v.dataId).id;return OB(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,y,b,w,k),v}var MB={kernelName:Qo,backendName:"wasm",setupFunc:Uot,kernelFunc:Hot};function pr(r){let{inputs:t,attrs:e}=r,{x:n}=t,{shape:o}=e,s=x.sizeFromShape(n.shape),i=x.inferFromImplicitShape(o,s);return x.assert(s===x.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var PB={kernelName:Ti,backendName:"wasm",kernelFunc:pr};var LB;function qot(r){LB=r.wasm.cwrap(ts,null,["number","array","number","number","array","number","number","number","number"])}function Kot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=x.sizeFromShape(d),y=x.sizeFromShape(h),w=Pr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);x.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${i} and transposeB=${a} must match.`);let v=i?[g,c,m]:[g,m,c],k=a?[y,f,p]:[y,p,f],E=pr({inputs:{x:o},backend:e,attrs:{shape:v}}),$=pr({inputs:{x:s},backend:e,attrs:{shape:k}}),D=e.dataIdMap.get(E.dataId).id,R=e.dataIdMap.get($.dataId).id,P=i?E.shape[2]:E.shape[1],W=a?$.shape[1]:$.shape[2],U=Math.max(g,y),q=e.makeOutput([U,P,W],E.dtype),K=e.dataIdMap.get(q.dataId).id,j=new Uint8Array(new Int32Array(E.shape).buffer),Q=new Uint8Array(new Int32Array($.shape).buffer);return LB(D,j,E.shape.length,R,Q,$.shape.length,i,a,K),e.disposeData(E.dataId),e.disposeData($.dataId),q.shape=w,q}var zB={kernelName:ts,backendName:"wasm",setupFunc:qot,kernelFunc:Kot};function zo(r){let{inputs:{x:t},attrs:{begin:e,size:n},backend:o}=r,[s,i]=Ve.parseSliceParams(t,e,n),a=Ve.isSliceContinous(t.shape,s,i),u=o.readSync(t.dataId),l=o.makeOutput(i,t.dtype),c=x.computeStrides(t.shape),p=o.dataIdMap.get(l.dataId);if(a){let d=Ve.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=u.slice(d,d+x.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+x.sizeFromShape(i))),l}if(t.dtype==="string"){let d=Kc(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)jot(u,c[0],m,s,i);else if(f===3)Xot(u,c[0],c[1],m,s,i);else if(f===4)Yot(u,c[0],c[1],c[2],m,s,i);else{let d=Kc(u,s,i,t.shape,t.dtype);m.set(d)}return l}function jot(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;l<u;l++){let c=l*t+a;e.set(r.subarray(c,c+o[1]),s),s+=o[1]}}function Xot(r,t,e,n,o,s){let i=0,a=o[0],u=o[1],l=o[2],c=a+s[0],p=u+s[1];for(let m=a;m<c;m++)for(let f=u;f<p;f++){let d=m*t+f*e+l;n.set(r.subarray(d,d+s[2]),i),i+=s[2]}}function Yot(r,t,e,n,o,s,i){let a=0,u=s[0],l=s[1],c=s[2],p=u+i[0],m=l+i[1],f=c+i[2],d=s[3];for(let h=u;h<p;h++)for(let g=l;g<m;g++)for(let y=c;y<f;y++){let b=h*t+g*e+y*n+d;o.set(r.subarray(b,b+i[3]),a),a+=i[3]}}var BB={kernelName:Ei,backendName:"wasm",kernelFunc:zo};function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n,a=s.reduce((y,b)=>y*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=pr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=no({inputs:{x:f},backend:e,attrs:{perm:l}}),h=pr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=zo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var VB={kernelName:wi,backendName:"wasm",kernelFunc:Zot};function kl(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var GB={kernelName:io,backendName:"wasm",kernelFunc:kl};var WB=ue(es);var UB;function Jot(r){UB=r.wasm.cwrap(ao,null,["number","number","number","number"])}function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return UB(a,s,i,l),u}var HB={kernelName:ao,backendName:"wasm",setupFunc:Jot,kernelFunc:Qot};function FT(r){let{inputs:t,backend:e}=r,n=x.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=S.computeOutShape(t.map(f=>f.shape),n),s=t.filter(f=>x.sizeFromShape(f.shape)>0);if(s.length===1)return ip({inputs:{x:s[0]},backend:e});let i=e.makeOutput(o,t[0].dtype);if(x.sizeFromShape(o)===0)return i;let a=s.map(f=>f.shape);if(S.assertParamsConsistent(a,n),s[0].dtype==="string"){let f=s.map(w=>{let v=x.sizeFromShape(w.shape.slice(n));return pr({inputs:{x:w},backend:e,attrs:{shape:[-1,v]}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));o=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=Uc(d,o,t[0].dtype,h),y=S.computeOutShape(s.map(w=>w.shape),n);i.shape=y;let b=e.dataIdMap.get(i.dataId);return 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est(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:m}=e,f=S.convertConv2DDataFormat(m),d=S.computeConv2DInfo(o.shape,s.shape,u,l,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,y=d.padInfo.top,b=d.padInfo.right,w=d.padInfo.bottom,v=d.padInfo.left,k=d.dilationHeight,E=d.dilationWidth,$=d.strideHeight,D=d.strideWidth,R=d.inChannels,P=d.outChannels,W=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. 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OT;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(OT||(OT={}));var QB;function ost(r){QB=r.wasm.cwrap(ca,null,["number","number","number","number","array","number","number","number","number","number"])}function sst(r){let{backend:t,inputs:e,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:i}=n,{image:a,boxes:u,boxInd:l}=e,c=u.shape[0],[p,m]=i,f=[c,p,m,a.shape[3]],d=t.dataIdMap.get(a.dataId),h;a.dtype!=="float32"&&(h=kl({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),d=t.dataIdMap.get(h.dataId));let g=d.id,y=t.dataIdMap.get(u.dataId).id,b=t.dataIdMap.get(l.dataId).id,w=t.makeOutput(f,"float32"),v=t.dataIdMap.get(w.dataId).id,k=new Uint8Array(new Int32Array(a.shape).buffer);return QB(g,y,b,c,k,p,m,OT[o],s,v),h!=null&&t.disposeData(h.dataId),w}var tV={kernelName:ca,backendName:"wasm",setupFunc:ost,kernelFunc:sst};var eV;function ist(r){eV=r.wasm.cwrap(ua,null,["number","number","number","number","number","number"])}function ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;x.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=no({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;eV(d,i?1:0,a?1:0,f,h,Xt[o.dtype]);let g=m;if(l!==null){let y=S.getUndoAxesPermutation(l);g=no({inputs:{x:m},attrs:{perm:y},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var rV={kernelName:ua,backendName:"wasm",setupFunc:ist,kernelFunc:ast};var nV;function lst(r){nV=r.wasm.cwrap(is,null,["number","number","number","number","number","number"])}function 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pst(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),y=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(x.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),v=new Uint8Array(new Int32Array(x.computeStrides(d)).buffer),k=t.dataIdMap.get(h.dataId).id;return sV(y,s,i==="NHWC"?1:0,b,o.shape.length-1,w,v,d.length,k),h}var iV={kernelName:pa,backendName:"wasm",setupFunc:cst,kernelFunc:pst};var aV;function mst(r){aV=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fst(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,y=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,E=f.strideHeight,$=f.strideWidth,D=f.inChannels,R=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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PT;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(PT||(PT={}));var qV;function Hst(r){qV=r.wasm.cwrap(Is,null,["number","array","number","number","array","array","number","number"])}function qst(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return qV(i,l,t.shape.length,Xt[t.dtype],m,f,PT[o],u),a}var KV={kernelName:Is,backendName:"wasm",kernelFunc:qst,setupFunc:Hst};var Kst=!0,jV=Ie(Ss,Kst);var XV=ue(Si);function kd(r,t){let e=new Int32Array(r.wasm.HEAPU8.buffer,t,4),n=e[0],o=e[1],s=e[2],i=e[3];return r.wasm._free(t),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:i}}var YV;function jst(r){YV=r.wasm.cwrap(Ta,"number",["number","number","number","number","number"])}function Xst(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i}=n,{boxes:a,scores:u}=e,l=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=YV(l,c,s,o,i),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=kd(t,p);return t.wasm._free(d),t.wasm._free(h),t.makeOutput([f],"int32",m)}var ZV={kernelName:Ta,backendName:"wasm",setupFunc:jst,kernelFunc:Xst};var JV;function Yst(r){JV=r.wasm.cwrap(_a,"number",["number","number","number","number","number","bool"])}function Zst(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=JV(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=kd(t,m);t.wasm._free(h);let 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iG={kernelName:Ni,backendName:"wasm",kernelFunc:oit};var aG;function sit(r){aG=r.wasm.cwrap(Ns,null,["number","array","number","number","array","array","number","number"])}function iit(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,constantValue:o}}=r,s=n.map((h,g)=>h[0]+t.shape[g]+h[1]);if(x.sizeFromShape(t.shape)===0)return MT({backend:e,attrs:{shape:s,value:o,dtype:t.dtype}});let i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),l=e.dataIdMap.get(a.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(h=>h[0]),m=n.map(h=>h[1]),f=new Uint8Array(new Int32Array(p).buffer),d=new Uint8Array(new Int32Array(m).buffer);return aG(i,c,t.shape.length,Xt[t.dtype],f,d,o,l),a}var Kv={kernelName:Ns,backendName:"wasm",kernelFunc:iit,setupFunc:sit};var ait=!1,lG=Ie(Ts,ait);var uG;function lit(r){uG=r.wasm.cwrap(_s,null,["number","number","number"])}function 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}HT=t}var yW=-1,UT=-1;function oat(r){yW=r}function sat(){if(UT===-1)throw new Error("WASM backend not initialized.");return UT}var iat="3.15.0";var aat=2;fm("wasm",async()=>{let{wasm:r}=await xW();return new Fg(r)},aat);var lat="3.15.0",uat="3.15.0",cat="3.15.0",pat="3.15.0",mat="3.15.0",fat="3.15.0",dat="3.15.0",hat="3.15.0",gat={tfjs:lat,"tfjs-core":uat,"tfjs-data":cat,"tfjs-layers":pat,"tfjs-converter":mat,"tfjs-backend-cpu":fat,"tfjs-backend-webgl":dat,"tfjs-backend-wasm":hat};export{bi as Abs,ta as Acos,ea as Acosh,Ql as AdadeltaOptimizer,tu as AdagradOptimizer,eu as AdamOptimizer,ru as AdamaxOptimizer,Xn as Add,Zo as AddN,ra as All,na as Any,Jo as ArgMax,Al as ArgMin,oa as Asin,sa as Asinh,ia as Atan,la as Atan2,aa as Atanh,Qo as AvgPool,$l as AvgPool3D,Tp as AvgPool3DGrad,Np as AvgPoolGrad,Fg as BackendWasm,ts as BatchMatMul,wi as BatchToSpaceND,_p as Bincount,Ep as BroadcastArgs,b1 as BroadcastTo,sb as Callback,yy as CallbackList,io as Cast,es as Ceil,ao as ClipByValue,Ap as Complex,Dl as ComplexAbs,vi as Concat,rs as Conv2D,$p as Conv2DBackpropFilter,ns as Conv2DBackpropInput,Rl as Conv3D,Dp as Conv3DBackpropFilterV2,Rp as Conv3DBackpropInputV2,os as Cos,ss as Cosh,ca as CropAndResize,ua as Cumprod,is as Cumsum,wy as CustomCallback,Ji as DataStorage,Fp as DenseBincount,pa as DepthToSpace,as as DepthwiseConv2dNative,Op as DepthwiseConv2dNativeBackpropFilter,Mp as DepthwiseConv2dNativeBackpropInput,Pp as Diag,Fl as Dilation2D,Ud as Dilation2DBackpropFilter,Wd as Dilation2DBackpropInput,PC as ENV,ib as EarlyStopping,Lp as Einsum,us as Elu,zp as EluGrad,Vd as Environment,fa as Equal,ma as Erf,cs as Exp,Ci as ExpandDims,da as Expm1,Bp as FFT,Ol as Fill,ha as FlipLeftRight,ps as Floor,ms as FloorDiv,Hd as FromPixels,fs as FusedBatchNorm,Oi as FusedConv2D,Mi as FusedDepthwiseConv2D,Jc as GPGPUContext,ga as GatherNd,Ii as GatherV2,Sb as GraphModel,xa as Greater,ds as GreaterEqual,by as History,Vp as IFFT,lo as Identity,Gp as Imag,ve as InputSpec,ya as IsFinite,ba as IsInf,wa as IsNan,jo as KernelBackend,Ml as LRN,Up as LRNGrad,Hh as LayerVariable,Vn as LayersModel,hs as LeakyRelu,va as Less,Ca as LessEqual,Wp as LinSpace,gs as Log,Ia as Log1p,w1 as LogSoftmax,Sa as LogicalAnd,Ou as LogicalNot,Mu as LogicalOr,bu as MathBackendWebGL,xs as Max,bs as MaxPool,Pl as MaxPool3D,qp as MaxPool3DGrad,Hp as MaxPoolGrad,Kp as MaxPoolWithArgmax,ys as Maximum,ws as Mean,vs as Min,Cs as Minimum,Is as MirrorPad,ka as Mod,nu as MomentumOptimizer,jp as Multinomial,Ss as Multiply,Si as Neg,Ta as NonMaxSuppressionV3,_a as NonMaxSuppressionV4,Ea as NonMaxSuppressionV5,Na as NotEqual,j1 as OP_SCOPE_SUFFIX,ks as OneHot,ki as OnesLike,Vr as Optimizer,ni as OptimizerConstructors,Ni as Pack,Ns as PadV2,Cat as Pool,Ts as Pow,_s as Prelu,Aa as Prod,ou as RMSPropOptimizer,$n as RNN,Ll as Range,jC as Rank,Xp as Real,ls as RealDiv,$a as Reciprocal,Qe as Reduction,Es as Relu,$s as Relu6,Ti as Reshape,As as ResizeBilinear,Zp as ResizeBilinearGrad,zl as ResizeNearestNeighbor,Yp as ResizeNearestNeighborGrad,Ds as Reverse,Va as RotateWithOffset,Rs as Round,Fs as Rsqrt,Ui as SGDOptimizer,Da as ScatterNd,_i as Select,Ra as Selu,Xi as Sequential,Ms as Sigmoid,Oa as Sign,Os as Sin,Fa as Sinh,Ei as Slice,zs as Softmax,Ma as Softplus,Ai as SpaceToBatchND,Bl as SparseFillEmptyRows,Pa as SparseReshape,Vl as SparseSegmentMean,Gl as SparseSegmentSum,Jp as SparseToDense,$i as SplitV,Ps as Sqrt,Wl as Square,Bs as SquaredDifference,uo as Step,La as StridedSlice,Qp as StringNGrams,tm as StringSplit,em as StringToHashBucketFast,Vs as Sub,Ls as Sum,Qr as SymbolicTensor,Gs as Tan,Ws as Tanh,Pt as Tensor,pe as TensorBuffer,Yn as Tile,za as TopK,Ba as Transform,Us as Transpose,rm as Unique,Di as Unpack,Ul as UnsortedSegmentSum,Ga as Variable,Ri as ZerosLike,Fi as _FusedMatMul,Ne as abs,sh as acos,ih as acosh,J as add,kI as addN,Ku as all,Yl as any,js as argMax,ah as argMin,lh as asin,uh as asinh,ch as atan,ph as atan2,mh as atanh,qa as avgPool,fh as avgPool3d,W_ as backend,S as backend_util,MH as basicLSTMCell,xo as batchNorm,EI as batchNorm2d,AI as batchNorm3d,$I as batchNorm4d,Ka as batchToSpaceND,dh as bincount,QTt as booleanMaskAsync,DI as broadcastArgs,ja as broadcastTo,Pr as broadcast_util,Ex as browser,It as buffer,u7 as callbacks,Z as cast,hh as ceil,br as clipByValue,kn as clone,zn as complex,se as concat,RI as concat1d,FI as concat2d,OI as concat3d,MI as concat4d,r$ as constraints,Yu as conv1d,pn as conv2d,Zu as conv2dTranspose,gh as conv3d,PI as conv3dTranspose,Eat as copyRegisteredKernels,Xa as cos,Ju as cosh,Hx as cosineWindow,xh as cumprod,Qu as cumsum,mn as customGrad,vR as data,LI as denseBincount,SI as deprecationWarn,yh as depthToSpace,Ys as depthwiseConv2d,f7 as deregisterOp,jl as device_util,mq as diag,bh as dilation2d,Jct as disableDeprecationWarnings,_t as dispose,Qct as disposeVariables,ct as div,wh as divNoNan,zI as dot,fE as dropout,BI as einsum,Zs as elu,Zct as enableDebugMode,Yct as enableProdMode,dE as enclosingPowerOfTwo,go as engine,V as env,Sr as equal,vh as erf,Ze as exp,fr as expandDims,Ch as expm1,gm as eye,ol as fft,Js as fill,spt as findBackend,ipt as findBackendFactory,Qs as floor,qu as floorDiv,qP as forceHalfFloat,Co as fused,yo as gather,pE as gatherND,Ax as gather_util,npt as getBackend,BC as getGradient,Kd as getKernel,hx as getKernelsForBackend,sat as getThreadsCount,mT as gpgpu_util,Vq as grad,Gq as grads,Ge as greater,Tn as greaterEqual,Gi as ifft,tc as imag,dn as image,r_t as inTopKAsync,C$ as initializers,OS as input,Mr as io,fc as irfft,VI as isFinite,GI as isInf,Ih as isNaN,Pe as keep,Gr as kernel_impls,oD as layers,Ya as leakyRelu,ec as less,_n as lessEqual,QE as linalg,WI as linspace,iZ as loadGraphModel,w8 as loadLayersModel,Sh as localResponseNormalization,wr as log,Za as log1p,UI as logSigmoid,rc as logSoftmax,Nh as logSumExp,Nr as logicalAnd,Ja as logicalNot,nc as logicalOr,XI as logicalXor,RFt as losses,Lt as matMul,S_ as math,Dr as max,Qa as maxPool,Th as maxPool3d,YI as maxPoolWithArgmax,fn as maximum,xe as mean,oh as memory,cK as meshgrid,sD as metrics,Zl as min,ti as minimum,_h as mirrorPad,Eh as mod,y8 as model,iD as models,xm as moments,I1t as movingAverage,M as mul,yK as multiRNNCell,ZI as multinomial,Zt as neg,Vh as nextFrame,wm as norm,vo as notEqual,Ks as oneHot,lr as ones,dr as onesLike,N as op,IK as outerProduct,Yr as pad,NK as pad1d,_K as pad2d,AK as pad3d,DK as pad4d,JI as pool,Zr as pow,el as prelu,mI as print,oc as prod,tpt as profile,VK as rand,XK as randomGamma,Vx as randomNormal,ei as randomUniform,rl as range,rpt as ready,Jl as real,Ah as reciprocal,fm as registerBackend,v8 as registerCallbackConstructor,C1 as registerGradient,Pu as registerKernel,m7 as registerOp,aD as regularizers,Tr as relu,ac as relu6,opt as removeBackend,O as reshape,ir as reverse,oj as reverse1d,ij as reverse2d,lj as reverse3d,cj as reverse4d,sl as rfft,lc as round,uc as rsqrt,pt as scalar,uE as scatterND,rh as scatter_util,cc as selu,$h as separableConv2d,b8 as sequential,et as serialization,oH as setBackend,apt as setPlatform,oat as setThreadsCount,rat as setWasmPath,nat as setWasmPaths,NN as setWebGLContext,aS as setdiff1dAsync,Lr as sigmoid,Dh as sign,iFt as signal,pc as sin,mc as sinh,Rt as slice,Rh as slice1d,Gx as slice2d,Fh as slice3d,ym as slice4d,Ve as slice_util,nl as softmax,bo as softplus,tl as spaceToBatchND,Bh as sparse,Ux as sparseToDense,eFt as spectral,ur as split,be as sqrt,Ut as square,dc as squaredDifference,zr as squeeze,Je as stack,ri as step,Oh as stridedSlice,ey as string,lt as sub,mt as sum,Wu as sumOutType,Mh as tan,Xs as tanh,Ar as tensor,Re as tensor1d,Wi as tensor2d,yI as tensor3d,Mj as tensor4d,Pj as tensor5d,Lj as tensor6d,fo as tensor_util,V_ as test_util,B as tidy,kr as tile,ept as time,Ph as topk,wc as train,Wt as transpose,hc as truncatedNormal,bm as unique,_at as unregisterGradient,Tat as unregisterKernel,Lh as unsortedSegmentSum,vr as unstack,ar as upcastType,x as util,Wq as valueAndGrad,Uq as valueAndGrads,lS as variable,Ox as variableGrads,gat as version,BD as version_converter,G_ as version_core,Um as version_layers,iat as version_wasm,HP as version_webgl,YSe as webgl,cd as webgl_util,_e as where,zh as whereAsync,ye as zeros,St as zerosLike};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the 'License');
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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