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human
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human/dist/tfjs.esm.js

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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Available gradients found: ${Object.keys(i)}.`);let u=t(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=s.inputs[l];if(!Kr(u.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(r[c.id]==null)r[c.id]=u;else{let p=r[c.id];r[c.id]=n(p,u),p.dispose()}}}}var g1=20,Mm=3,Sw=7;function x1(r,e,t,n){let o=Bs(e),s=Oj(r,e,t,o),a=e.length,i=tg(r,e,t,o,s),l=["Tensor"];return n&&(l.push(` dtype: ${t}`),l.push(` rank: ${a}`),l.push(` shape: [${e}]`),l.push(" values:")),l.push(i.map(u=>" "+u).join(`
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o=++this.pendingBackendInitId,s=n.then(a=>o<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,Vn(`Initialization of backend ${e} failed`),Vn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Vn(`Initialization of backend ${e} failed`),Vn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(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((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{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(e,t){let n=this.state.tensorInfo.get(t),o=n.backend,s=this.readSync(t),a=o.refCount(t);o.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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o=this.backend.numDataIds(),s=0;n.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=o-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],o=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=Dw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Dw(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let y=Om(d,this.backendName);E(y!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let w=this.backend.numDataIds();l=y.kernelFunc({inputs:h,attrs:g,backend:this.backend});let x=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,w,x);let k=x.map(C=>{if(C.rank!=null)return C;let{dataId:A,shape:$,dtype:R}=C;return this.makeTensorFromDataId(A,$,R)});if(o){let C=this.getTensorsForGradient(d,h,k);n=this.saveTensorsForBackwardMode(C)}return k}}else{let{forwardFunc:d}=e,h=g=>{!o||(n=g.map(y=>this.keep(this.clone(y))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let y=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,y),y}}let{inputs:c,attrs:p}=e,m=Dw(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),o&&this.addTapeNode(u,c,t,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let o=bw(e);if(o!=null){let s=o.inputsToSave||[],a=o.outputsToSave||[],i;o.saveAllInputs?(E(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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n=_(r,"x","batchToSpaceND"),o=e.reduce((i,l)=>i*l);E(n.rank>=1+e.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(n.shape[0]%o==0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${o}`);let s={x:n},a={blockShape:e,crops:t};return T.runKernel(Ws,s,a)}var ou=I({batchToSpaceND_:w4});function CN(r){let e;return r.rank===0||r.rank===1?e=O(r,[1,1,1,r.size]):r.rank===2?e=O(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=O(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function k4(r,e,t,n,o,s){s==null&&(s=.001);let a=_(r,"x","batchNorm"),i=_(e,"mean","batchNorm"),l=_(t,"variance","batchNorm"),u;o!=null&&(u=_(o,"scale","batchNorm"));let c;n!=null&&(c=_(n,"offset","batchNorm")),E(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),ai(a,i,l,c,u,s)}var fk=I({batchNorm2d_:_4});function v4(r,e,t,n,o,s){let a=_(r,"x","batchNorm"),i=_(e,"mean","batchNorm"),l=_(t,"variance","batchNorm"),u;o!=null&&(u=_(o,"scale","batchNorm"));let c;return n!=null&&(c=_(n,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),ai(a,i,l,c,u,s)}var dk=I({batchNorm3d_:v4});function C4(r,e,t,n,o,s){let a=_(r,"x","batchNorm"),i=_(e,"mean","batchNorm"),l=_(t,"variance","batchNorm"),u;o!=null&&(u=_(o,"scale","batchNorm"));let c;return n!=null&&(c=_(n,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),ai(a,i,l,c,u,s)}var hk=I({batchNorm4d_:C4});function S4(r,e,t){let n=_(r,"x","bincount"),o=_(e,"weights","bincount");E(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},a={size:t};return T.runKernel(yc,s,a)}var Hm=I({bincount_:S4});function I4(r,e){let t=_(r,"s0","broadcastArgs","int32"),n=_(e,"s1","broadcastArgs","int32");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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o.data(),[l,u]=[a.length/s,s],c=pw("bool",l);for(let p=0;p<l;p++){let m=p*u,f=a.subarray(m,m+u),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<t;h++)if(d[h].index===i[p]){c[p]=1;break}}return r!==n&&n.dispose(),e!==o&&o.dispose(),vr(c,o.shape,"bool")}var sNe=Vq;var lo={};He(lo,{conv2d:()=>qN,depthwiseConv2d:()=>KN,matMul:()=>XN});function Wq(r,e,t,n,o,s="NHWC",a){let i=r;r.rank===3&&(i=O(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=O(e,[1,e.shape[0],e.shape[1],e.shape[2]])),E(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),E(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),E(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s==="NHWC"?i.shape[3]:i.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];E(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth 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jq({x:r,filter:e,strides:t,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",_u(T.state.gradientDepth,l)===!1){let C=Dn(r,e,t,n,o,s,a);return i!=null&&(C=Y(C,i)),ku(C,l,u,c)}let p=_(r,"x","conv2d"),m=_(e,"filter","conv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=O(p,[1,p.shape[0],p.shape[1],p.shape[2]])),E(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&E(ot(n),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),E(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),E(Cr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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$=(M,V)=>{let[W,G,U,H]=V,K=bu(O(M,U.shape),U,s),re,X;if(!t&&!n?(re=Me(K,G,!1,!0),X=Me(W,K,!0,!1)):!t&&n?(re=Me(K,G,!1,!1),X=Me(K,W,!0,!1)):t&&!n?(re=Me(G,K,!1,!0),X=Me(W,K,!1,!1)):(re=Me(G,K,!0,!0),X=Me(K,W,!0,!0)),o!=null){let ne=wu(H,K);return[re,X,ne]}else return[re,X]},R={a:x,b:k,bias:C,preluActivationWeights:A},P={transposeA:t,transposeB:n,activation:s,leakyreluAlpha:i};return o==null?Qr((V,W,G)=>{let U=T.runKernel(ri,R,P);return G([V,W,U]),{value:O(U,w),gradFunc:$}})(x,k):Qr((V,W,G,U)=>{let H=T.runKernel(ri,R,P);return U([V,W,H,G]),{value:O(H,w),gradFunc:$}})(x,k,C)}var XN=I({fusedMatMul_:qq});function Kq(r){return Tg(r,.54,.46)}var YN=I({hammingWindow_:Kq});function Xq(r){return Tg(r,.5,.5)}var $g=I({hannWindow_:Xq});function Yq(r,e,t,n=!1,o=0){let s=0,a=[];for(;s+e<=r.size;)a.push(Oe(r,s,e)),s+=t;if(n)for(;s<r.size;){let i=s+e-r.size,l=tt([Oe(r,s,e-i),_s([i],o)]);a.push(l),s+=t}return a.length===0?pi([],[0,e]):O(tt(a),[a.length,e])}var Dg=I({frame_:Yq});function 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a=_(r,"boxes","nonMaxSuppression"),i=_(e,"scores","nonMaxSuppression"),l=uo(a,i,t,n,o,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,m={boxes:a,scores:i},f={maxOutputSize:u,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(ea,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var lT=I({nonMaxSuppressionPadded_:pK});async function mK(r,e,t,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let a=_(r,"boxes","nonMaxSuppressionAsync"),i=_(e,"scores","nonMaxSuppressionAsync"),l=uo(a,i,t,n,o,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,[m,f]=await Promise.all([a.data(),i.data()]),{selectedIndices:d,validOutputs:h}=Fg(m,f,u,c,p,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:At(d,"int32"),validOutputs:ce(h,"int32")}}var uT=mK;function fK(r,e,t=!1,n=!1){let o=_(r,"images","resizeBilinear");E(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new 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fT=I({transform_:xK});function yK(r,e,t){E(e%1==0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1==0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let n=_(r,"a","bandPart");E(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,a]=n.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=O(Sa(0,s,1,"int32"),[-1,1]),l=Sa(0,a,1,"int32"),u=le(i,l),c=Fr(Hn(u,ce(+e,"int32")),Un(u,ce(-t,"int32"))),p=ht([s,a],n.dtype);return O(nr(Nr(O(n,[-1,s,a])).map(m=>Et(c,m,p))),o)}var dT=I({bandPart_:yK});function bK(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s<r.length;++s)E(r[s].shape[0]===o,()=>`Gram-Schmidt: 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t=r.shape[0],n=r.shape[1],o=pp(t),s=hn(r),a=pi([[1]],[1,1]),i=hn(a),l=t>=n?n:t;for(let u=0;u<l;++u){let c=s,p=i,m=o;[i,s,o]=T.tidy(()=>{let f=Oe(s,[u,u],[t-u,1]),d=Ig(f),h=Oe(s,[u,u],[1,1]),g=Et(Ht(h,0),pi([[-1]]),pi([[1]])),y=le(h,F(g,d)),w=ue(f,y);w.shape[0]===1?i=hn(a):i=tt([a,Oe(w,[1,0],[w.shape[0]-1,w.shape[1]])],0);let x=Ke(ue(Me(g,y),d)),k=Oe(s,[u,0],[t-u,n]),C=F(x,i),A=Ve(i);if(u===0)s=le(k,Me(C,Me(A,k)));else{let P=le(k,Me(C,Me(A,k)));s=tt([Oe(s,[0,0],[u,n]),P],0)}let $=Ve(C),R=Oe(o,[0,u],[t,o.shape[1]-u]);if(u===0)o=le(R,Me(Me(R,i),$));else{let P=le(R,Me(Me(R,i),$));o=tt([Oe(o,[0,0],[t,u]),P],1)}return[i,s,o]}),Ae([c,p,m])}return!e&&t>n&&(o=Oe(o,[0,0],[t,n]),s=Oe(s,[0,0],[n,n])),[o,s]})}var xT=I({qr_:wK});var qt;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(qt||(qt={}));function kK(r,e,t=qt.SUM_BY_NONZERO_WEIGHTS){let n=_(r,"losses","computeWeightedLoss"),o=null;e!=null&&(o=_(e,"weights","computeWeightedLoss"));let s=o==null?n:F(n,o);if(t===qt.NONE)return s;if(t===qt.SUM)return me(s);if(t===qt.MEAN){if(o==null)return Ct(s);{let a=n.size/o.size,i=ue(me(s),me(o));return a>1?ue(i,ce(a)):i}}if(t===qt.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ue(me(s),ce(n.size));{let a=F(o,rr(n.shape)),i=J(me(ci(a,ce(0))),"float32");return ue(me(s),i)}}throw Error(`Unknown reduction: ${t}`)}var Pr=I({computeWeightedLoss_:kK});function _K(r,e,t,n=qt.SUM_BY_NONZERO_WEIGHTS){let o=_(r,"labels","absoluteDifference"),s=_(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=_(t,"weights","absoluteDifference")),$t(o.shape,s.shape,"Error in absoluteDifference: ");let i=Tt(le(o,s));return Pr(i,a,n)}var yT=I({absoluteDifference_:_K});function vK(r,e,t,n,o=qt.SUM_BY_NONZERO_WEIGHTS){let 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s=_(r,"labels","logLoss"),a=_(e,"predictions","logLoss"),i=null;t!=null&&(i=_(t,"weights","logLoss")),$t(s.shape,a.shape,"Error in logLoss: ");let l=ce(1),u=ce(n),c=Ke(F(s,Ir(Y(a,u)))),p=F(le(l,s),Ir(Y(le(l,a),u))),m=le(c,p);return Pr(m,i,o)}var _T=I({logLoss_:IK});function NK(r,e,t,n=qt.SUM_BY_NONZERO_WEIGHTS){let o=_(r,"labels","meanSquaredError"),s=_(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=_(t,"weights","meanSquaredError")),$t(o.shape,s.shape,"Error in meanSquaredError: ");let i=df(o,s);return Pr(i,a,n)}var vT=I({meanSquaredError_:NK});function TK(r,e){let t=_(r,"labels","sigmoidCrossEntropyWithLogits"),n=_(e,"logits","sigmoidCrossEntropyWithLogits");$t(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Or(n),s=F(n,t),a=uu(tr(Ke(Tt(n))));return Y(le(o,s),a)}function EK(r,e,t,n=0,o=qt.SUM_BY_NONZERO_WEIGHTS){let s=_(r,"multiClassLabels","sigmoidCrossEntropy"),a=_(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=_(t,"weights","sigmoidCrossEntropy")),$t(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=ce(n),c=ce(1),p=ce(.5);s=Y(F(s,le(c,u)),F(p,u))}let l=TK(s,a);return Pr(l,i,o)}var CT=I({sigmoidCrossEntropy_:EK});function AK(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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s={skipEmpty:t},a={input:n,delimiter:o},i=T.runKernel(qc,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var $T=I({stringSplit_:MK});function LK(r,e){let t=_(r,"input","stringToHashBucketFast","string"),n={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let o={input:t};return T.runKernel(Kc,o,n)}var DT=I({stringToHashBucketFast_:LK});var vRe={fft:gu,ifft:fl,rfft:xu,irfft:ff},TRe={hammingWindow:YN,hannWindow:$g,frame:Dg,stft:ZN},bn={flipLeftRight:QN,grayscaleToRGB:eT,resizeNearestNeighbor:pT,resizeBilinear:cT,rotateWithOffset:tT,cropAndResize:JN,nonMaxSuppression:rT,nonMaxSuppressionAsync:sT,nonMaxSuppressionWithScore:iT,nonMaxSuppressionWithScoreAsync:aT,nonMaxSuppressionPadded:lT,nonMaxSuppressionPaddedAsync:uT,threshold:mT,transform:fT},RT={bandPart:dT,gramSchmidt:hT,qr:xT},tFe={absoluteDifference:yT,computeWeightedLoss:Pr,cosineDistance:bT,hingeLoss:wT,huberLoss:kT,logLoss:_T,meanSquaredError:vT,sigmoidCrossEntropy:CT,softmaxCrossEntropy:ST},gf={sparseFillEmptyRows:IT,sparseReshape:NT,sparseSegmentMean:TT,sparseSegmentSum:ET},Pg={stringNGrams:AT,stringSplit:$T,stringToHashBucketFast:DT};var Wr=class extends hg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ae(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return yg(e,t)}dispose(){this.iterations_!=null&&Ae(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ce(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var xp=class extends Wr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=T.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:z(()=>Ie(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:z(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;z(()=>{let c=Y(F(l,this.rho),F(We(i),1-this.rho)),p=F(ue(St(Y(u,this.epsilon)),St(Y(l,this.epsilon))),i),m=Y(F(u,this.rho),F(We(p),1-this.rho));l.assign(c),u.assign(m);let f=Y(F(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};xp.className="Adadelta";gn(xp);var yp=class extends Wr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=T.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:z(()=>_s(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;z(()=>{let l=Y(i,We(a));i.assign(l);let u=Y(F(ue(a,St(Y(l,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};yp.className="Adagrad";gn(yp);var bp=class extends Wr{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],z(()=>{this.accBeta1=ce(t).variable(),this.accBeta2=ce(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=le(1,this.accBeta1),o=le(1,this.accBeta2);t.forEach((s,a)=>{let 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g=Y(F(ue(d,Y(St(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(F(this.accBeta1,this.beta1)),this.accBeta2.assign(F(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),z(()=>{this.accBeta1.assign(yn(this.beta1,this.iterations_+1)),this.accBeta2.assign(yn(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};bp.className="Adam";gn(bp);var wp=class extends Wr{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],z(()=>{this.iteration=ce(0).variable(),this.accBeta1=ce(t).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);z(()=>{let n=le(1,this.accBeta1),o=ue(-this.learningRate,Y(F(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};wp.className="Adamax";gn(wp);var dl=class extends Wr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=T.registeredVariables[n];z(()=>{let i=Y(F(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Found: ${this.outputs.map(w=>w.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let w of this.outputs){let x=w.sourceLayer,k=w.nodeIndex,C=w.tensorIndex;this.outputLayers.push(x),this.outputLayersNodeIndices.push(k),this.outputLayersTensorIndices.push(C)}for(let w of this.inputs){let x=w.sourceLayer,k=w.nodeIndex,C=w.tensorIndex;qn(k===0,"input layer has >1 nodes"),qn(C===0,"input layer has >1 tensors"),this.inputLayers.push(x),this.inputLayersNodeIndices.push(k),this.inputLayersTensorIndices.push(C)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let w=0;w<this.inputLayers.length;w++){let x=this.inputLayers[w];if(!(x instanceof di))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qf.className="ThresholdedReLU";ee.registerClass(qf);var Kf=class extends Pe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wf().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Fe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Kf.className="Softmax";ee.registerClass(Kf);function kl(r,e,t){if(typeof r=="number")return co(r,e);if(r.length!==e)throw new L(`The ${t} argument must be an integer or tuple of ${e} integers. 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Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function _n(r,e,t,n,o=1){if(r==null)return r;let s=e+(e-1)*(o-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+n-1)/n)}function Es(r,e,t,n){if(r==null)return null;if(n==="valid")r=r*e+Ss([t-e,0]);else if(n==="same")r=r*e;else throw new L(`Unsupport padding mode: ${n}.`);return r}function Xf(r,e){return z(()=>(Rt(e),e==="channelsFirst"?Ve(r,[0,2,3,1]):r))}function J_(r,e){return z(()=>(Rt(e),e==="channelsFirst"?Ve(r,[0,2,3,4,1]):r))}function Y5(r,e,t,n=1,o="valid",s,a=1){return z(()=>{if(s==null&&(s=tn()),Rt(s),r.shape.length!==3)throw new L(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new L(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new L(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ve(r,[0,2,1])),o==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=qm(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=nn(i,t)),i})}function $A(r,e,t,n=[1,1],o="valid",s,a,i=null){return z(()=>{if(s==null&&(s=tn()),Rt(s),r.rank!==3&&r.rank!==4)throw new L(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new L(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=Xf(r,s);if(o==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=lo.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ve(l,[0,3,1,2])),l})}function Z5(r,e,t,n=[1,1,1],o="valid",s,a){return z(()=>{if(s==null&&(s=tn()),Rt(s),r.rank!==4&&r.rank!==5)throw new L(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new L(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=J_(r,s);if(o==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Xm(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=nn(i,t)),s==="channelsFirst"&&(i=Ve(i,[0,4,1,2,3])),i})}var Vp=class extends Pe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Vp.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=kl(t.kernelSize,e,"kernelSize"),this.strides=kl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,rn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Ts(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=kl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new L(`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 L(`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 L(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(qn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Bg(e.kernelSize,"number",1,3))throw new L(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ns(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Du=class extends Vp{constructor(e,t){super(e,t);this.kernel=null,Du.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Fe(e);let n,o=this.bias==null?null:this.bias.read(),s=Vg(this.activation.getClassName());if(s!=null&&this.rank===2)n=$A(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=Y5(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=$A(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Z5(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Xe(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=_n(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let o=[e[0]];return this.dataFormat==="channelsLast"?(o=o.concat(t),o.push(this.filters)):(o.push(this.filters),o=o.concat(t)),o}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Mt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new L(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_l=class extends Du{constructor(e){super(2,e);_l.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Bg(e.kernelSize,"number",1,2))throw new L(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};_l.className="Conv2D";ee.registerClass(_l);var vl=class extends Du{constructor(e){super(3,e);vl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new L(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};vl.className="Conv3D";ee.registerClass(vl);var Yf=class extends _l{constructor(e){super(e);if(this.inputSpec=[new _t({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new L(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Xe(e),e.length!==4)throw new L("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new _t({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Fe(e);if(n.shape.length!==4)throw new L(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Es(l,m,c,this.padding),h=Es(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ve(n,[0,2,3,1]));let y=Km(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Ve(y,[0,3,1,2])),this.bias!=null&&(y=nn(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=Xe(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=Es(t[o],l,a,this.padding),t[s]=Es(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Yf.className="Conv2DTranspose";ee.registerClass(Yf);var Zf=class extends vl{constructor(e){super(e);if(this.inputSpec=[new _t({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new L(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Xe(e),e.length!==5)throw new L("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new _t({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Fe(e);if(n.shape.length!==5)throw new L(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i,l;this.dataFormat==="channelsFirst"?(l=2,a=3,i=4):(l=1,a=2,i=3);let u=o[l],c=o[a],p=o[i],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],y=this.strides[2],w=Es(u,h,m,this.padding),x=Es(c,g,f,this.padding),k=Es(p,y,d,this.padding),C=[s,w,x,k,this.filters];this.dataFormat!=="channelsLast"&&(n=Ve(n,[0,2,3,4,1]));let A=_k(n,this.kernel.read(),C,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Ve(A,[0,4,1,2,3])),this.bias!==null&&(A=nn(A,this.bias.read(),this.dataFormat)),this.activation!==null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=Xe(e);let t=e.slice(),n,o,s,a;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,a=4):(n=4,o=1,s=2,a=3);let i=this.kernelSize[0],l=this.kernelSize[1],u=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return t[n]=this.filters,t[o]=Es(t[o],c,i,this.padding),t[s]=Es(t[s],p,l,this.padding),t[a]=Es(t[a],m,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Zf.className="Conv3DTranspose";ee.registerClass(Zf);var Q_=class extends Du{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new L("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new L("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new L(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Lt(t.depthwiseConstraint),this.pointwiseInitializer=mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Lt(t.pointwiseConstraint)}build(e){if(e=Xe(e),e.length<this.rank+2)throw new L(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new L(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new _t({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Fe(e);let n;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ve(e,[0,2,3,1])),n=Jk(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=nn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ve(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseConstraint),e.pointwiseConstraint=Mt(this.pointwiseConstraint),e}};Q_.className="SeparableConv";var Jf=class extends Q_{constructor(e){super(2,e)}};Jf.className="SeparableConv2D";ee.registerClass(Jf);var Ru=class extends Du{constructor(e){super(1,e);Ru.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Bg(e.kernelSize,"number",1,1))throw new L(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Ru.className="Conv1D";ee.registerClass(Ru);var Qf=class extends Pe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let n=Sf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Sf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Sf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Sf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="Cropping2D";ee.registerClass(Qf);var ed=class extends Pe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WE(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Fe(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ve(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?bn.resizeNearestNeighbor(n,[s,a]):bn.resizeBilinear(n,[s,a]);return Ve(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?bn.resizeNearestNeighbor(n,[s,a]):bn.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ed.className="UpSampling2D";ee.registerClass(ed);function J5(r,e,t=[1,1],n="valid",o,s){return z(()=>{o==null&&(o=tn()),Rt(o);let a=Xf(r,o);if(r.rank!==4)throw new L(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new L(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=ka(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=Ve(a,[0,3,1,2])),a})}var td=class extends Vp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=Xe(e),e.length<4)throw new L(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new L(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Fe(e);let n=J5(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=nn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=_n(t,this.kernelSize[0],this.padding,this.strides[0]),a=_n(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseRegularizer),e}};td.className="DepthwiseConv2D";ee.registerClass(td);function ev(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new L("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function tv(r,e,t,n=!1,o,s,a=!1,i=!1){return z(()=>{let l=e.shape.length;if(l<3)throw new L(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Gr(2,l));if(e=Ve(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===l-1&&(o=gr(o,-1)),o=Ve(o,u)),n&&(e=lr(e,0),o!=null&&(o=lr(o,0)));let c=[],p,m=t,f=e.shape[0],d=Nr(e),h;o!=null&&(h=Nr(o));for(let y=0;y<f;++y){let w=d[y],x=z(()=>r(w,m));if(o==null)p=x[0],m=x[1];else{let k=z(()=>{let C=h[y],A=le(xr(C),C),$=Y(F(x[0],C),F(m[0],A)),R=m.map((P,M)=>Y(F(x[1][M],C),F(P,A)));return{output:$,newStates:R}});p=k.output,m=k.newStates}i&&c.push(p)}let g;return i&&(g=nr(c,1)),[p,g,m]})}var On=class extends Pe{constructor(e){super(e);let t;if(e.cell==null)throw new L("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Gp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new L("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new _t({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Xg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Ne("Constants support is not implemented in RNN yet.");Xg(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new _t({shape:[n,null,...o]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!b.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new L(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new _t({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new Fn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new L("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=>ht([n,o])):this.states_=[ht([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ht([n,o])):this.states_[0]=ht([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!b.arraysEqual(s.shape,i))throw new L(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Dt(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=ev(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new _t({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof on){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Fe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new L(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:o},u=tv((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return z(()=>{let t=ht(e.shape);return t=me(t,[1,2]),t=Ea(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gg(t,[1,n]):t):this.cell.stateSize>1?[Gg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===On.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=sn(o,n);return new e(Object.assign(t,{cell:s}))}};On.className="RNN";ee.registerClass(On);var Cl=class extends Pe{},Wp=class extends Cl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Ts(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Cu([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new L(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Da({ones:()=>xr(e),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Da({ones:()=>xr(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=go(F(e,a),this.kernel.read()):s=go(e,this.kernel.read()),this.bias!=null&&(s=nn(s,this.bias.read())),i!=null&&(n=F(n,i));let l=Y(s,go(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ns(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Wp.className="SimpleRNNCell";ee.registerClass(Wp);var rd=class extends On{constructor(e){e.cell=new Wp(e);super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};rd.className="SimpleRNN";ee.registerClass(rd);var jp=class extends Cl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new L("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Ts(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ts(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Cu([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new L(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Da({ones:()=>xr(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Da({ones:()=>xr(o),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=F(e,s[0]));let c=go(e,this.kernel.read());this.useBias&&(c=nn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=F(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=mr(p,[2*this.units,this.units],p.rank-1),d=go(o,m),[h,g,y]=mr(c,3,c.rank-1),[w,x]=mr(d,2,d.rank-1);i=this.recurrentActivation.apply(Y(h,w)),l=this.recurrentActivation.apply(Y(g,x));let k=go(F(l,o),f);u=this.activation.apply(Y(y,k));let C=Y(F(i,o),F(Y(1,Ke(i)),u));return[C,C]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ns(this.activation),recurrentActivation:Ns(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};jp.className="GRUCell";ee.registerClass(jp);var nd=class extends On{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new jp(e);super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};nd.className="GRU";ee.registerClass(nd);var Sl=class extends Cl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Ts(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ts(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Cu([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Xe(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends wn{apply(l,u){let c=s.apply([a]),p=new Nu().apply([a]),m=s.apply([a*2]);return k_(k_(c,p),m)}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new L(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Da({ones:()=>xr(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Da({ones:()=>xr(o),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=F(e,a[0]));let m=go(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=F(o,i[0])),m=Y(m,go(o,this.recurrentKernel.read())),this.useBias&&(m=nn(m,this.bias.read()));let[f,d,h,g]=mr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Y(F(u,s),F(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=F(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ns(this.activation),recurrentActivation:Ns(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Sl.className="LSTMCell";ee.registerClass(Sl);var od=class extends On{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Sl(e);super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};od.className="LSTM";ee.registerClass(od);var Gp=class extends Cl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){Xg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{Cs(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(sn(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Rf(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}Pp(t)}};Gp.className="StackedRNNCells";ee.registerClass(Gp);function Da(r){let{ones:e,rate:t,training:n=!1,count:o=1}=r,s=()=>Hg(e(),t),a=()=>hl(s,e,n);return!o||o<=1?Dt(a().clone()):Array(o).fill(void 0).map(a).map(l=>Dt(l.clone()))}var Q5=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var rv=class extends On{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new _t({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new L("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=ht(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new Fn("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 L("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(()=>ht(s)):this.states_=[ht(s)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_[0]=ht(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!b.arraysEqual(l.shape,u))throw new L(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=_n(u,o[0],s,a[0],i[0]),m=_n(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};rv.className="ConvRNN2D";var Up=class extends Sl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=kl(n,2,"kernelSize"),this.kernelSize.forEach(l=>Kt(l,"kernelSize")),this.strides=kl(o||1,2,"strides"),this.strides.forEach(l=>Kt(l,"strides")),this.padding=s||"valid",rn(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=kl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Kt(l,"dilationRate"))}build(e){var t;e=Xe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new L(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends wn{apply(m,f){let d=u.apply([c]),h=rr([c]),g=u.apply([c*2]);return Ip([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new L(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Da({ones:()=>xr(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(Q,se,pe)=>!se||!se[pe]?Q:F(se[pe],Q),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Da({ones:()=>xr(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),y=u(s,d,2),w=u(s,d,3),x=3,[k,C,A,$]=mr(this.kernel.read(),i,x),[R,P,M,V]=this.useBias?mr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,k,R,this.padding),p=this.inputConv(p,C,P,this.padding),m=this.inputConv(m,A,M,this.padding),f=this.inputConv(f,$,V,this.padding);let[W,G,U,H]=mr(this.recurrentKernel.read(),i,x);h=this.recurrentConv(h,W),g=this.recurrentConv(g,G),y=this.recurrentConv(y,U),w=this.recurrentConv(w,H);let K=this.recurrentActivation.apply(Y(c,h)),re=this.recurrentActivation.apply(Y(p,g)),X=Y(F(re,a),F(K,this.activation.apply(Y(m,y)))),ne=F(this.recurrentActivation.apply(Y(f,w)),this.activation.apply(X));return[ne,ne,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Q5(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=Dn(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?nn(s,n,this.dataFormat):s}recurrentConv(e,t){return Dn(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Up.className="ConvLSTM2DCell";ee.registerClass(Up);var sd=class extends rv{constructor(e){let t=new Up(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};sd.className="ConvLSTM2D";ee.registerClass(sd);var Hp=class extends Pe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Fe(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return hl(()=>Hg(n,this.rate,s,this.seed),()=>n,o)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Hp.className="Dropout";ee.registerClass(Hp);var id=class extends Hp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};id.className="SpatialDropout1D";ee.registerClass(id);var ad=class extends Pe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Ts(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lt(e.kernelConstraint),this.biasConstraint=Lt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Xe(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Xe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Fe(e),o=Vg(this.activation.getClassName()),s;return o!=null?s=go(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=go(n,this.kernel.read()),this.bias!=null&&(s=nn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ns(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ad.className="Dense";ee.registerClass(ad);var ld=class extends Pe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Xe(e);for(let t of e.slice(1))if(t==null)throw new L(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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z(()=>(e=Fe(e),HE(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};cd.className="RepeatVector";ee.registerClass(cd);var pd=class extends Pe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[l];if(this.isUnknown(u))if(a===null)a=l;else throw new L("Can only specifiy one unknown dimension.");else s*=u}let i=ho(e);if(a!==null){if(s===0||i%s!=0)throw new L(n);o[a]=i/s}else if(i!==s)throw new L(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Fe(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return O(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};pd.className="Reshape";ee.registerClass(pd);var md=class extends Pe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Gr(1,e.dims.length+1);if(!b.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new _t({ndim:this.dims.length+1})]}computeOutputShape(e){e=Xe(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wd.className="Concatenate";ee.registerClass(wd);function kd(r,e){for(;r<0;)r+=e;return r}function e8(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(b.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),b.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return z(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=O(e,e.shape.concat(l))}else if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);r=O(r,r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=me(F(r,e),s[0]):i=me(F(Ve(r,[1,0]),e),s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=Me(r,e,l,u)}if(a>0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=en(i,u)}return i.shape.length===1&&(i=gr(i,1)),i})}var _d=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){b.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new L(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new L(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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Pe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Fe(e);return hl(()=>Y(Np(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};vd.className="GaussianNoise";ee.registerClass(vd);var Cd=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Fe(e);return this.rate>0&&this.rate<1?hl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return F(n,Np(n.shape,1,s))},()=>n,t.training||!1):n})}};Cd.className="GaussianDropout";ee.registerClass(Cd);var Sd=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Fe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return hl(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=Un(vs(n),this.rate);u=Su(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate,m=Y(F(s,u),F(Y(u,-1),l));return Y(F(m,c),p)},()=>Fe(e),t.training||!1)}return e})}};Sd.className="AlphaDropout";ee.registerClass(Sd);function Id(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=fk(r,e,t,n,o,s);else if(r.rank===3)a=dk(r,e,t,n,o,s);else if(r.rank===4)a=hk(r,e,t,n,o,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function t8(r,e,t,n,o=.001){return z(()=>{let s=fp(r,n),a=s.mean,i=s.variance;return[Id(r,a,i,t,e,o),a,i]})}function r8(r,e,t,n,o=.001){return z(()=>{let s=fp(r,n),a=s.mean,i=s.variance,l=[];for(let d of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new L(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new _t({ndim:e.length,axes:{[t]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,o=Fe(e),s=o.shape,a=s.length,i=Gr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=co(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!b.arraysEqual(c,Gr(0,a).slice(0,a-1)),m=()=>{if(p){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Mt(this.betaConstraint),gammaConstraint:Mt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Nd.className="BatchNormalization";ee.registerClass(Nd);var Td=class extends Pe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=mt(e.betaInitializer||"zeros"),this.gammaInitializer=mt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Xe(e);let t=e.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]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==fo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[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(e,t){let n=Fe(e),o=n.shape,s=o.length;return z(()=>{let a=!0,{mean:i,variance:l}=fp(n,this.axis,a),u=co(1,s);for(let h of this.axis)u[h]=o[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?O(h,u):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 i=Rr(i,f),l=Rr(l,f),p=Rr(p,d),m=Rr(m,d),Id(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Td.className="LayerNormalization";ee.registerClass(Td);function o8(r,e,t){return z(()=>{if(r.rank!==4)throw new L(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new L("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=tn()),t!=="channelsLast"&&t!=="channelsFirst")throw new L(`Unknown data format: ${t}. 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s==="max"?a=pu(r,e,t,i):a=nu(r,e,t,i),o==="channelsFirst"&&(a=Ve(a,[0,3,1,2])),a})}function DA(r,e,t,n,o,s){return z(()=>{Rt(o),b_(s),rn(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=tn()),s==null&&(s="max"),r=J_(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=rf(r,e,t,i):a=Um(r,e,t,i),o==="channelsFirst"&&(a=Ve(a,[0,4,1,2,3])),a})}var nv=class extends Pe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new L(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=_n(t,this.poolSize[0],this.padding,this.strides[0]),n=_n(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Dd=class extends ov{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),rn(o),mx(e,t,n,o,s,"max")}};Dd.className="MaxPooling2D";ee.registerClass(Dd);var Rd=class extends ov{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),rn(o),mx(e,t,n,o,s,"avg")}};Rd.className="AveragePooling2D";ee.registerClass(Rd);var sv=class extends Pe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new L(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),rn(this.padding),this.inputSpec=[new _t({ndim:5})]}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=_n(t,this.poolSize[0],this.padding,this.strides[0]),n=_n(n,this.poolSize[1],this.padding,this.strides[1]),o=_n(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Fd=class extends sv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),rn(o),DA(e,t,n,o,s,"max")}};Fd.className="MaxPooling3D";ee.registerClass(Fd);var Od=class extends sv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),rn(o),DA(e,t,n,o,s,"avg")}};Od.className="AveragePooling3D";ee.registerClass(Od);var iv=class extends Pe{constructor(e){super(e);this.inputSpec=[new _t({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},Pd=class extends iv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Fe(e);return Ct(n,1)})}};Pd.className="GlobalAveragePooling1D";ee.registerClass(Pd);var Md=class extends iv{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Fe(e);return Vr(n,1)})}};Md.className="GlobalMaxPooling1D";ee.registerClass(Md);var av=class extends Pe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new _t({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ld=class extends av{call(e,t){return z(()=>{let n=Fe(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};Ld.className="GlobalAveragePooling2D";ee.registerClass(Ld);var zd=class extends av{call(e,t){return z(()=>{let n=Fe(e);return this.dataFormat==="channelsLast"?Vr(n,[1,2]):Vr(n,[2,3])})}};zd.className="GlobalMaxPooling2D";ee.registerClass(zd);var lv=class extends Pe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=sn(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Bd=class extends lv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Xe(e),e.length<3)throw new L(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Xe(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return z(()=>(e=Fe(e),tv((a,i)=>[Fe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Bd.className="TimeDistributed";ee.registerClass(Bd);function s8(r){fi(VE,"BidirectionalMergeMode",r)}var i8="concat",Vd=class extends lv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=sn(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=sn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?i8:e.mergeMode,s8(this.mergeMode),e.weights)throw new Ne("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[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()):yr(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=ev(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new L("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new 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i;return this.mergeMode==="concat"?i=Ip([o,s]):this.mergeMode==="sum"?i=Y(o,s):this.mergeMode==="ave"?i=F(.5,Y(o,s)):this.mergeMode==="mul"?i=F(o,s):this.mergeMode==null&&(i=[o,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Cs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Cs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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qA=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[Y(v("a",r,e,t),v("b",r,e,t))];case"AddN":return[nk(v("tensors",r,e,t))];case"FloorMod":case"Mod":return[zk(v("a",r,e,t),v("b",r,e,t))];case"Mul":return[F(v("a",r,e,t),v("b",r,e,t))];case"RealDiv":case"Div":return[ue(v("a",r,e,t),v("b",r,e,t))];case"DivNoNan":return[Ik(v("a",r,e,t),v("b",r,e,t))];case"FloorDiv":return[jm(v("a",r,e,t),v("b",r,e,t))];case"Sub":return[le(v("a",r,e,t),v("b",r,e,t))];case"Minimum":return[Ca(v("a",r,e,t),v("b",r,e,t))];case"Maximum":return[Rn(v("a",r,e,t),v("b",r,e,t))];case"Pow":return[yn(v("a",r,e,t),v("b",r,e,t))];case"SquaredDifference":return[df(v("a",r,e,t),v("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KA=(r,e,t)=>{switch(r.op){case"Abs":case"ComplexAbs":return[Tt(v("x",r,e,t))];case"Acos":return[tk(v("x",r,e,t))];case"Acosh":return[rk(v("x",r,e,t))];case"Asin":return[sk(v("x",r,e,t))];case"Asinh":return[ik(v("x",r,e,t))];case"Atan":return[ak(v("x",r,e,t))];case"Atan2":return[lk(v("x",r,e,t),v("y",r,e,t))];case"Atanh":return[uk(v("x",r,e,t))];case"Ceil":return[xk(v("x",r,e,t))];case"Complex":return[$n(v("real",r,e,t),v("imag",r,e,t))];case"Cos":return[iu(v("x",r,e,t))];case"Cosh":return[Ym(v("x",r,e,t))];case"Elu":return[_a(v("x",r,e,t))];case"Erf":return[Tk(v("x",r,e,t))];case"Exp":return[tr(v("x",r,e,t))];case"Expm1":return[Ek(v("x",r,e,t))];case"Floor":return[va(v("x",r,e,t))];case"Log":return[Ir(v("x",r,e,t))];case"Log1p":return[uu(v("x",r,e,t))];case"Imag":return[au(v("x",r,e,t))];case"Neg":return[Ke(v("x",r,e,t))];case"Reciprocal":return[Zk(v("x",r,e,t))];case"Real":return[ml(v("x",r,e,t))];case"Relu":return[Or(v("x",r,e,t))];case"Round":return[sf(v("x",r,e,t))];case"Selu":return[lf(v("x",r,e,t))];case"Sigmoid":return[Jr(v("x",r,e,t))];case"Sin":return[uf(v("x",r,e,t))];case"Sign":return[e_(v("x",r,e,t))];case"Sinh":return[cf(v("x",r,e,t))];case"Softplus":return[ui(v("x",r,e,t))];case"Sqrt":return[St(v("x",r,e,t))];case"Square":return[We(v("x",r,e,t))];case"Tanh":return[wa(v("x",r,e,t))];case"Tan":return[r_(v("x",r,e,t))];case"ClipByValue":return[Sr(v("x",r,e,t),v("clipValueMin",r,e,t),v("clipValueMax",r,e,t))];case"Relu6":return[of(v("x",r,e,t))];case"Rsqrt":return[af(fr(r.inputNames[0],e,t))];case"Prod":return[nf(v("x",r,e,t),v("axes",r,e,t))];case"LeakyRelu":return[lu(v("x",r,e,t),v("alpha",r,e,t))];case"Prelu":return[fu(v("x",r,e,t),v("alpha",r,e,t))];case"IsNan":return[Ak(fr(r.inputNames[0],e,t))];default:throw 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Pv=class{constructor(e,t,n,o,s,a,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ce(0),Dt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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this.trav++,{value:L$(e),done:!1}}},j$=class extends Zt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},G$=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},U$=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},H$=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},q$=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},K$=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ae(e.value)}}},X$=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=io.getTensorsInContainer(e.value),n=this.transform(e.value),o=io.getTensorsInContainer(n);for(let s of t)io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Y$=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},e0=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=io.getTensorsInContainer(e.value),n=await this.transform(e.value),o=io.getTensorsInContainer(n);for(let s of t)io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Yp=class extends Zt{constructor(){super();this.outputQueue=new Xp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Z$=class extends Yp{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=io.getTensorsInContainer(e.value),n=this.transform(e.value),o=io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)io.isTensorInList(s,o)||s.dispose();return!0}},t0=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Ra;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Ra||(Ra={}));var J$=class extends Zt{constructor(e,t=Ra.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function o(a){return a instanceof Zt?{value:a.next().then(l=>(t++,l.done&&n++,l.value)),recurse:!1}:{value:null,recurse:!0}}let s=await $x(this.iterators,o);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ra.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ra.SHORTEST:return{value:null,done:!0};case Ra.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},r0=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new jd(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},Q$=class extends r0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=z$.alea(n||b.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var xi=class{constructor(){this.size=null}batch(e,t=!0){let n=this;b.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let o;return this.size===1/0||this.size==null?o=this.size:t?o=Math.ceil(this.size/e):o=Math.floor(this.size/e),vn(async()=>(await n.iterator()).columnMajorBatch(e,t,_7),o)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,vn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,vn(async()=>(await t.iterator()).filter(o=>z(()=>e(o))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return vn(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return vn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return vn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,vn(async()=>{let o=Gd(async()=>({value:await t.iterator(),done:!1}));return B$(o.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,vn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=eD.alea(t||b.now().toString());return vn(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await o.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,vn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};xi.MAX_BUFFER_SIZE=1e4;function vn(r,e=null){return new class extends xi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function tD(r){return vn(async()=>Qv(r),r.length)}function rD(r){if(!Nl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return vn(async()=>{let t=await $x(r,n=>{if(n instanceof xi)return{value:n.iterator(),recurse:!1};if(Nl(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return V$(t,Ra.SHORTEST)},e)}function _7(r){if(r===null)return null;let e=r[0];return M$(e)?{value:v7(r),recurse:!1}:{value:null,recurse:!0}}function v7(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof je?nr(r):vr(r)}var Ud=class extends xi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var Kd=class extends Zt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(j().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Kd(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(b.sizeFromShape(t));return n.set(e,n.length-e.length),vr(n,t)}};var Xd=class extends Zt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=At([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=pi([a,s,l,i],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(j().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Xd(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&b.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=cg.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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============================
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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t.makeTensorInfo(o.shape,o.dtype,h)}var wR={kernelName:Vo,backendName:"cpu",kernelFunc:f9};function d9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;te([o],"batchToSpaceND");let i=s.reduce((y,w)=>y*w),l=S.getReshaped(o.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(o.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=Ze({inputs:{x:o},backend:t,attrs:{shape:l}}),d=Jt({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Ze({inputs:{x:d},backend:t,attrs:{shape:c}}),g=ko({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var kR={kernelName:Ws,backendName:"cpu",kernelFunc:d9};function h9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=Qp(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var 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t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var LR={kernelName:vc,backendName:"cpu",kernelFunc:T9};function E9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;b.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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VR={kernelName:Cc,backendName:"cpu",kernelFunc:A9};function $9(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n;te([o,s],"depthwiseConv2DNativeBackpropInput");let p=b.computeStrides(o.shape),m=b.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new ct(f.inShape,"float32"),h=d.values,[g,y,w]=d.strides,x=t.data.get(o.dataId).values,[k,C,A]=p,$=t.data.get(s.dataId).values,[R,P,M]=m,{batchSize:V,filterHeight:W,filterWidth:G,inChannels:U,inHeight:H,inWidth:K,outChannels:re,outHeight:X,outWidth:ne,strideHeight:Q,strideWidth:se}=f,pe=W-1-f.padInfo.top,ie=G-1-f.padInfo.left,fe=re/U;for(let de=0;de<V;++de)for(let ge=0;ge<U;++ge)for(let we=0;we<H;++we){let $e=we-pe,Ce=Math.max(0,Math.ceil($e/Q)),Be=Math.min(X,(W+$e)/Q);for(let qe=0;qe<K;++qe){let lt=qe-ie,It=Math.max(0,Math.ceil(lt/se)),Nt=Math.min(ne,(G+lt)/se),Ue=0;for(let pt=Ce;pt<Be;++pt){let ft=pt*Q-$e;for(let Bt=It;Bt<Nt;++Bt){let 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GR={kernelName:Qa,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:n,filter:o}=r,{strides:s,pad:a,dilations:i}=t,l=e,u=l.data.get(n.dataId).values,c=n.shape.length,p=l.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:y,outWidth:w,padInfo:x,strideHeight:k,strideWidth:C,filterHeight:A,filterWidth:$,dilationHeight:R,dilationWidth:P,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,a,"NHWC",i),V=b.sizeFromShape(M),W=M.length,G=b.getArrayFromDType(n.dtype,V);for(let H=0;H<f;++H)for(let K=0;K<y;++K){let re=K*k-x.top;for(let X=0;X<w;++X){let ne=X*C-x.left;for(let Q=0;Q<g;++Q){let se=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<A;++ie){let fe=re+ie*R;if(fe>=0&&fe<d)for(let de=0;de<$;++de){let ge=ne+de*P;if(ge>=0&&ge<h){let we=b.locToIndex([H,fe,ge,Q],c,b.computeStrides(n.shape)),$e=b.locToIndex([ie,de,Q],m,b.computeStrides(o.shape)),Ce=u[we]+p[$e];Ce>se&&(se=Ce)}}}let 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bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
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}
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#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="",e="attribute",t="varying",n="varying",o="texture2D",s="gl_FragColor",a="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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int getFlatIndex(ivec3 coords) {
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
}
`}function lm(){return`
int getFlatIndex(ivec3 coords) {
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}
`}var ly=`
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:RO}=S;function FO(r,e,t){let n=[];if(r.forEach(f=>{let d=b.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};`)),t.enableShapeUniforms){let{uniformShape:h}=uy(t.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;`)}}),t.enableShapeUniforms){switch(e.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;")}t.customUniforms&&t.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>LZ(f,e,t.packedInputs,t.enableShapeUniforms)).join(`
`),a=e.texShape,i=zt(),l=VZ(i),u,c,p=GZ(i);return e.isPacked?(u=zZ(e.logicalShape,a,t.enableShapeUniforms),c=jZ(i)):(u=BZ(e.logicalShape,a,t.enableShapeUniforms),c=WZ(i)),t.packedInputs&&(p+=KZ),[p,l,c,o,u,s,t.userCode].join(`
`)}function um(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return iJ(r,e);case 1:return lJ(r,e);case 2:return cJ(r,e);case 3:return mJ(r,e);case 4:return dJ(r,e);case 5:return hJ(r);case 6:return gJ(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function OO(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return sJ(r);case 1:return aJ(r,e);case 2:return uJ(r,e);case 3:return pJ(r,e);default:return fJ(r,e)}}function LZ(r,e,t=!1,n){let o="";t?o+=OO(r,n):o+=um(r,n);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?o+=xJ(r,e):o+=yJ(r,e)),o}function zZ(r,e,t){switch(r.length){case 0:return PO();case 1:return XZ(r,e,t);case 2:return nJ(r,e,t);case 3:return ZZ(r,e,t);default:return QZ(r,e,t)}}function BZ(r,e,t){switch(r.length){case 0:return PO();case 1:return YZ(r,e,t);case 2:return oJ(r,e,t);case 3:return JZ(r,e,t);case 4:return eJ(r,e,t);case 5:return tJ(r,e);case 6:return rJ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function VZ(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function WZ(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function jZ(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function GZ(r){return`${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);
}
${UZ}
${HZ}
${qZ}
`}var UZ=`
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);
}
`,HZ=`
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);
}
`,qZ=`
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);
}
`,KZ=`
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 PO(){return`
int getOutputCoords() {
return 0;
}
`}function XZ(r,e,t){let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return n[0]===1?t?`
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?t?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function YZ(r,e,t){return e[0]===1?t?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?t?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`}function ZZ(r,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 JZ(r,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${ju(["r","c","d"],r)}
return ivec3(r, c, d);
}
`;let n=Rs(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function QZ(r,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),a=s,i="",l="b, r, c";for(let u=2;u<r.length-1;u++)a*=r[r.length-u-1],i=`
int b${u} = index / ${a};
index -= b${u} * ${a};
`+i,l=`b${u}, `+l;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec${r.length}(${l});
}
`}function eJ(r,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${ju(["r","c","d","d2"],r)}
return ivec4(r, c, d, d2);
}
`;let n=Rs(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function tJ(r,e){let t=Rs(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function rJ(r,e){let t=Rs(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function nJ(r,e,t){let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(b.arraysEqual(r,e))return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let o=Math.ceil(r[1]/2);return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function oJ(r,e,t){return b.arraysEqual(r,e)?t?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:r[1]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function Gu(r){return`offset${r}`}function sJ(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=zt();return`
vec4 ${t}() {
return ${n.texture2D}(${e}, halfCR);
}
`}function iJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let a=Gu(t);if(e)return`
float ${n}() {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a});
return sampleTexture(${t}, uv);
}
`;let[i,l]=r.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${a});
return sampleTexture(${t}, uv);
}
`}function aJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=zt();if(e)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${t}, uv);
}
`;let a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function lJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`
float ${n}(int index) {
${cm(r)}
}
`;let o=r.shapeInfo.texShape,s=o[0],a=o[1];if(a===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=Gu(t);return a===1?e?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0]));
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);
return sampleTexture(${t}, uv);
}
`:s===1?e?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t}TexShape[1]), 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:e?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], index + ${i});
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function uJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],l=zt();if(s!=null&&b.arraysEqual(t,s))return e?`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${l.texture2D}(${n}, uv);
}
`:`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${a}.0);
return ${l.texture2D}(${n}, uv);
}
`;if(e)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 ${l.texture2D}(${n}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${n}, uv);
}
`}function cJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&b.arraysEqual(t,s)){if(e)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:a,keptDims:i}=b.squeezeShape(t),l=a;if(l.length<t.length){let m=pm(r,l),f=["row","col"];return`
${um(m,e)}
float ${o}(int row, int col) {
return ${o}(${mm(f,i)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${cm(r)}
}
`;let u=s[0],c=s[1],p=Gu(n);return c===1?e?`
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, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?e?`
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, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:e?`
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 * ${t[1]} + col + ${p};
vec2 uv = uvFromFlat(${u}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function pJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let m=t.slice(1),f=[1,2],d=pm(r,m),h=["b","row","col"];return`
${OO(d,e)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${mm(h,f)});
}
`}let i=zt();if(e)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 ${i.texture2D}(${n}, uv);
}
`;let l=a[0],u=a[1],c=Math.ceil(t[2]/2),p=c*Math.ceil(t[1]/2);return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${p}, ${c}, b, row, col);
return ${i.texture2D}(${n}, uv);
}
`}function mJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[1]*t[2],a=t[2],{newShape:i,keptDims:l}=b.squeezeShape(t),u=i;if(u.length<t.length){let h=pm(r,u),g=["row","col","depth"];return`
${um(h,e)}
float ${o}(int row, int col, int depth) {
return ${o}(${mm(g,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${a}, 1)));
${cm(r)}
}
`;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return e?`
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 ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(m===a&&f==null)return e?`
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 ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let d=Gu(n);return e?`
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 * ${a} + 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 * ${a} + depth + ${d};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function fJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=zt();if(e)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${t}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${t}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${t}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}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 ${o.texture2D}(${t}, uv);
}
`;let s=r.shapeInfo.logicalShape,a=s.length,i=r.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],p=Math.ceil(s[a-1]/2),m=p*Math.ceil(s[a-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<a-1;h++)f=`int b${h}, `+f,m*=s[a-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}, ${u});
return ${o.texture2D}(${t}, uv);
}
`}function dJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[3],a=t[2]*s,i=t[1]*a,{newShape:l,keptDims:u}=b.squeezeShape(t);if(l.length<t.length){let w=pm(r,l),x=["row","col","depth","depth2"];return`
${um(w,e)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${mm(x,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, 1)));
${cm(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===i&&c==null)return e?`
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 ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===s&&c==null)return e?`
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 ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;let y=Gu(n);return e?`
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 * ${i} + col * ${a} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${m}, ${f}, index + ${y});
return sampleTexture(${n}, uv);
}
`}function hJ(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=b.squeezeShape(e);if(l.length<e.length){let h=pm(r,l),g=["row","col","depth","depth2","depth3"];return`
${um(h)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${mm(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${o})) +
depth3;
${cm(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(f===o&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;let d=Gu(t);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 * ${i} + col * ${a} + depth * ${s} +
depth2 * ${o} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${t}, uv);
}
`}function gJ(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=b.squeezeShape(e);if(o.length<e.length){let g=pm(r,o),y=["row","col","depth","depth2","depth3","depth4"];return`
${um(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${mm(y,s)});
}
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;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}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${cm(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;if(d===a&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;let h=Gu(t);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 * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${t}, uv);
}
`}function cm(r){let e=r.name,t=b.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function xJ(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=RO(r.shapeInfo.logicalShape,e.logicalShape),l=ze(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(w=>`coords.${p[w+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((w,x)=>`coords.${p[x+u]}`).join(", ");let f="return outputValue;",h=b.sizeFromShape(r.shapeInfo.logicalShape)===1,y=b.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!y)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!y)a===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:f=`
return vec4(outputValue.x);
`;else if(i.length){let w=s-2,x=s-1;i.indexOf(w)>-1&&i.indexOf(x)>-1?f="return vec4(outputValue.x);":i.indexOf(w)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${o}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${n}(${m});
${f}
}
`}function yJ(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&b.arraysEqual(a,s))return`
float ${o}() {
return sampleTexture(${t}, resultUV);
}
`;let u=ze(l),c=RO(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${o}() {
${u} coords = getOutputCoords();
${m}
return get${n}(${d});
}
`}function ze(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 ${r} is not yet supported`)}function uy(r,e,t){let{newShape:n,keptDims:o}=b.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):n,l=!r&&s>1&&!b.arraysEqual(e,t)&&n.length<s||a;return{useSqueezeShape:l,uniformShape:l?i:e,keptDims:o}}function pm(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function mm(r,e){return e.map(t=>r[t]).join(", ")}function MO(r,e,t,n){let o=t.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:e.variableNames[k],shapeInfo:C}}),s=o.map(x=>x.shapeInfo),a={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},i=FO(o,a,e),l=r.createProgram(i),u=null,c=r.getUniformLocation(l,"NAN",!1);j().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(l,"INFINITY",!1));let p=!1,m={},f={},d={};for(let x=0;x<e.variableNames.length;x++){let k=e.variableNames[x];m[k]=r.getUniformLocation(l,k,p),m[`offset${k}`]=r.getUniformLocation(l,`offset${k}`,p),e.enableShapeUniforms&&(f[`${k}Shape`]=r.getUniformLocation(l,`${k}Shape`,p),d[`${k}TexShape`]=r.getUniformLocation(l,`${k}TexShape`,p))}let h,g,y;e.enableShapeUniforms&&(h=r.getUniformLocation(l,"outShape",p),y=r.getUniformLocation(l,"outShapeStrides",p),g=r.getUniformLocation(l,"outTexShape",p));let w=[];return e.customUniforms&&e.customUniforms.forEach((x,k)=>{w[k]=r.getUniformLocation(l,x.name,p)}),{program:e,source:i,webGLProgram:l,uniformLocations:m,customUniformLocations:w,inShapeInfos:s,outShapeInfo:a,infLoc:u,nanLoc:c,inShapesLocations:f,inTexShapesLocations:d,outShapeLocation:h,outShapeStridesLocation:y,outTexShapeLocation:g}}function LO(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,n)=>{let o=t.logicalShape,s=e[n],a=s.shape;if(!b.arraysEqual(o,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!b.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function zO(r,e,t,n,o){e.program.enableShapeUniforms||(LO(e.inShapeInfos,t),LO([e.outShapeInfo],[n]));let s=n.texData.texture,a=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),j().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((l,u)=>{let c=e.program.variableNames[u],p=e.uniformLocations[c],m=e.uniformLocations[`offset${c}`],f=e.inShapesLocations[`${c}Shape`],d=e.inTexShapesLocations[`${c}TexShape`];if(f){let{uniformShape:h}=uy(e.program.packedInputs,l.shape,l.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,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(b.sizeFromShape(l.shape)<2)r.gl.uniform1f(p,l.uniformValues[0]);else{let h=l.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(p,h)}return}l.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,l.texData.slice.flatOffset),r.setInputMatrixTexture(l.texData.texture,p,u)}});let i=e.outShapeLocation;if(i)switch(n.shape.length){case 1:r.gl.uniform1iv(i,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(i,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(i,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(i,new Int32Array(n.shape));break;default:break}if(e.outShapeStridesLocation){let l=b.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(l));break;case 3:r.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(l));break;case 4:r.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(l));break;default:break}}e.outTexShapeLocation&&r.gl.uniform2i(e.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),e.program.customUniforms&&o&&e.program.customUniforms.forEach((l,u)=>{let c=e.customUniformLocations[u],p=o[u];if(l.type==="float")r.gl.uniform1fv(c,p);else if(l.type==="vec2")r.gl.uniform2fv(c,p);else if(l.type==="vec3")r.gl.uniform3fv(c,p);else if(l.type==="vec4")r.gl.uniform4fv(c,p);else if(l.type==="int")r.gl.uniform1iv(c,p);else if(l.type==="ivec2")r.gl.uniform2iv(c,p);else if(l.type==="ivec3")r.gl.uniform3iv(c,p);else if(l.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),r.executeProgram()}function BO(r,e,t){let n="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let l=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=uy(r.packedInputs,a.shape,l),m="",f="",d="";if(c.length===1&&r.packedInputs){let C=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];m=`${C[0]>1}_${C[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let C=b.computeStrides(c);d=`${C[0]===l[1]}_${C[C.length-1]===l[1]}`}let h=a.shape.length,g=c.length===2&&b.arraysEqual(a.shape,l),y=b.sizeFromShape(a.shape)===1,w=S.getBroadcastDims(a.shape,t.shape),x=!r.packedInputs&&h===t.shape.length&&b.arraysEqual(l,t.texData.texShape),k=r.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${h}_${x}_${u?p:""}_${c.length}_${y}_${w}_${g}_${m}_${f}_${d}_${k}_${i}`}else{let l=a.isUniform?"uniform":a.texData.texShape;n+=`${a.shape}_${l}_${i}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${j().getNumber("WEBGL_VERSION")}`,s}function jt(r){return j().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var cC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=El.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=zt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?ju(["r","c","d"],e):Rs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}};var pC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=El.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=zt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?ju(["r","c","d"],e):Rs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}};var mC=class{constructor(e){this.variableNames=["A"],this.outTexUsage=zr.DOWNLOAD;let t=zt();this.outputShape=e,this.userCode=`
${ly}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}};var fC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=zr.DOWNLOAD;let t=zt();this.outputShape=e,this.userCode=`
${ly}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}};var dC=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=zt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let o="result";t&&(o="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?lm():am(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${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 hC=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=zt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let o="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let l=a*2+i;o+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
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 = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${l}] = values[0];
} else if (offset == 1) {
result[${l}] = values[1];
} else if (offset == 2) {
result[${l}] = values[2];
} else {
result[${l}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?lm():am(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${o}
${n.output} = ${s};
}
`}};var VO={};He(VO,{bindVertexProgramAttributeStreams:()=>CC,createBufferFromOutputTexture:()=>NC,createFloat16MatrixTexture:()=>wC,createFloat16PackedMatrixTexture:()=>vC,createFloat32MatrixTexture:()=>bC,createIndexBuffer:()=>yC,createPackedMatrixTexture:()=>_C,createUnsignedBytesMatrixTexture:()=>kC,createVertexBuffer:()=>xC,createVertexShader:()=>gC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>EC,downloadFloat32MatrixFromBuffer:()=>TC,downloadMatrixFromPackedOutputTexture:()=>$C,downloadPackedMatrixFromBuffer:()=>AC,getInternalFormatForFloat16MatrixTexture:()=>py,getInternalFormatForFloat16PackedMatrixTexture:()=>dy,getInternalFormatForFloat32MatrixTexture:()=>cy,getInternalFormatForPackedMatrixTexture:()=>fy,getInternalFormatForUnsignedBytesMatrixTexture:()=>my,uploadDenseMatrixToTexture:()=>SC,uploadPixelDataToTexture:()=>IC});function gC(r){let e=zt(),t=`${e.version}
precision highp float;
${e.attribute} vec3 clipSpacePos;
${e.attribute} vec2 uv;
${e.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return G0(r,t)}function xC(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return K0(r,e)}function yC(r){let e=new Uint16Array([0,1,2,2,1,3]);return X0(r,e)}function fh(r,e,t,n,o,s){Z0(e,t);let a=Y0(r),i=r.TEXTURE_2D;return ke(r,()=>r.bindTexture(i,a)),ke(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ke(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ke(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),ke(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),ke(r,()=>r.texImage2D(i,0,n,e,t,0,o,s,null)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function cy(r){return r.internalFormatFloat}function bC(r,e,t,n){let[o,s]=Wu(e,t);return fh(r,o,s,cy(n),n.textureFormatFloat,r.FLOAT)}function py(r){return r.internalFormatHalfFloat}function wC(r,e,t,n){let[o,s]=Wu(e,t);return fh(r,o,s,py(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function my(r){return r.downloadTextureFormat}function kC(r,e,t,n){let[o,s]=Wu(e,t);return fh(r,o,s,my(n),r.RGBA,r.UNSIGNED_BYTE)}function fy(r){return r.internalFormatPackedFloat}function _C(r,e,t,n){let[o,s]=bi(e,t);return fh(r,o,s,fy(n),r.RGBA,r.FLOAT)}function dy(r){return r.internalFormatPackedHalfFloat}function vC(r,e,t,n){let[o,s]=bi(e,t);return fh(r,o,s,dy(n),r.RGBA,n.textureTypeHalfFloat)}function CC(r,e,t){let n=0,o=3*4,s=3*4+2*4;return ke(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),ry(r,e,"clipSpacePos",t,3,s,n)&&ry(r,e,"uv",t,2,s,o)}function SC(r,e,t,n,o,s){ke(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function IC(r,e,t){ke(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function NC(r,e,t,n){let o=r.createBuffer();ke(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let i=4*4*e*t;return ke(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ke(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ke(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function TC(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function EC(r,e,t,n){let[o,s]=Wu(e,t),a=4,i=new Uint8Array(SO(e*t,a));return ke(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function AC(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(IO(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function $C(r,e,t){let n=new Float32Array(e*t*4);return ke(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var hy=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=j().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,W0(t,e)):this.gl=Yn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(j().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=sm(this.gl,s),Mn(this.gl,a))this.textureHalfFloatExtension=sm(this.gl,a);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Mn(this.gl,o))this.colorBufferHalfFloatExtension=sm(this.gl,o);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=xC(this.gl),this.indexBuffer=yC(this.gl),this.framebuffer=J0(this.gl),this.textureConfig=uh(this.gl,this.textureHalfFloatExtension)}get debug(){return j().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. 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this.throwIfDisposed(),vC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),_C(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(ny(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>EC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return AC(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return TC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=NC(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(j().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=o.clientWaitSync(s,0,0);return a===o.ALREADY_SIGNALED||a===o.CONDITION_SATISFIED},t=s}else j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$C(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=U0(t,e);this.vertexShader==null&&(this.vertexShader=gC(t));let o=H0(t);return ke(t,()=>t.attachShader(o,this.vertexShader)),ke(t,()=>t.attachShader(o,n)),q0(t,o),this.debug&&ch(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=CC(t,this.program,this.vertexBuffer)),o}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ch(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Q0(this.gl,e,t):eC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),tC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=bi(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ch(this.gl,this.program),im(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=sm(this.gl,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await b.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=bJ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&b.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ph(this.gl,e,this.framebuffer),this.debug&&im(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ph(this.gl,this.outputTexture,this.framebuffer),this.debug&&im(this.gl)):ny(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let o=this.gl;ph(o,e,this.framebuffer),this.debug&&im(o),this.outputTexture=e,ke(o,()=>o.viewport(0,0,t,n)),ke(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,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 bJ(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{addImpl:WO,bincountImpl:gy,bincountReduceImpl:jO,ceilImpl:GO,concatImpl:UO,equalImpl:HO,expImpl:qO,expm1Impl:KO,floorImpl:XO,gatherNdImpl:YO,gatherV2Impl:ZO,greaterImpl:JO,greaterEqualImpl:QO,lessImpl:eP,lessEqualImpl:tP,linSpaceImpl:rP,logImpl:nP,maxImpl:oP,maximumImpl:sP,minimumImpl:iP,multiplyImpl:aP,negImpl:lP,notEqualImpl:uP,prodImpl:cP,rangeImpl:pP,rsqrtImpl:mP,sigmoidImpl:fP,simpleAbsImpl:xy,sliceImpl:dP,sparseFillEmptyRowsImpl:hP,sparseReshapeImpl:gP,sparseSegmentReductionImpl:yy,sqrtImpl:xP,stridedSliceImpl:yP,stringNGramsImpl:bP,stringSplitImpl:wP,stringToHashBucketFastImpl:kP,subImpl:_P,tileImpl:vP,topKImpl:CP,transposeImpl:Uu,uniqueImpl:SP}=Yx;function DC(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Xt(r,e){return e===1?[r]:DC(r,e)}function IP(r,e){if(r===1)return"rc";let t="";for(let n=0;n<r;n++)t+=e[n],n<r-1&&(t+=",");return t}var RC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=Xt("rc",t),o=ze(t),s=kJ(t,e,n),a=_J(t,e[e.length-1],e[e.length-2],n),i=vJ(e,n);this.userCode=`
void main() {
${o} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${i}));
}
}
`}}};function wJ(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function kJ(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function _J(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
int r = ${o[0]};
int c = ${o[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${e};
bool rEdge = rp1 >= ${t};
`}function vJ(r,e){let t=r.length,n=wJ(t,e);return t===1?`getA(rc),
rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${n[0]}),
cEdge ? 0. : getA(${n[1]}),
rEdge ? 0. : getA(${n[2]}),
rEdge || cEdge ? 0. : getA(${n[3]})`}var dh=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=jt(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=`
${CJ(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?lm():am(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function CJ(r,e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${e?DO(["r","c","d"],"inputShape"):Rs(["r","c","d"],r)}
return ivec3(r, c, d);
}
`}var FC=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let o=TP(t,n),s=EP(e,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=NP(e,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return o===Tr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===Tr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===Tr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===Tr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===Tr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,o){if(this.freeTextures==null)return;let s=TP(n,o),a=EP(t,s,o);a in this.freeTextures||(this.freeTextures[a]=[]);let i=NP(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),l=j().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function SJ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function NP(r,e,t,n,o){let s=IJ(e,n),a;if(o){let[l,u]=bi(r[0],r[1]);a=l*u}else{let[l,u]=Wu(r[0],r[1]);a=l*u}let i=SJ(t,s);return a*i}function IJ(r,e){switch(r){case Tr.PACKED_2X2_FLOAT32:return fy(e);case Tr.PACKED_2X2_FLOAT16:return dy(e);case Tr.UNPACKED_FLOAT32:return cy(e);case Tr.UNPACKED_FLOAT16:return py(e);case Tr.PACKED_4X1_UNSIGNED_BYTE:return my(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function NJ(r){return j().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Tr.PACKED_2X2_FLOAT32:Tr.UNPACKED_FLOAT32:r?Tr.PACKED_2X2_FLOAT16:Tr.UNPACKED_FLOAT16}function TP(r,e){if(r===zr.UPLOAD)return Tr.PACKED_2X2_FLOAT32;if(r===zr.RENDER||r==null)return NJ(e);if(r===zr.DOWNLOAD||r===zr.PIXELS)return Tr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function EP(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Cn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},br="if (isnan(x)) return x;",AP="return x;",OC="return abs(x);";var $P="return (x >= 0.0) ? x : (exp(x) - 1.0);",DP=br+`
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`,RP=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
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vec4 result;
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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;
`,MP=`
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;
`,LP=`
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;
`,zP="return 1.0 / (1.0 + exp(-1.0 * x));",Fs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var PC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Xt("rc",t),o=ze(t),s=IP(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${i}));
}
`}};var TJ=Mr.whereImpl,EJ=1e-7,AJ=1e-4,by={};function $J(r){return r in by||(by[r]={}),by[r]}var DJ=j().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),RJ=600;function FJ(){return j().global.screen==null?1024:j().global.screen.height*j().global.screen.width*window.devicePixelRatio*RJ/1024/1024}var Hu=class extends Ls{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!j().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Yn(j().getNumber("WEBGL_VERSION"));this.binaryCache=$J(j().getNumber("WEBGL_VERSION")),this.gpgpu=new hy(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new FC(this.gpgpu),this.numMBBeforeWarning=FJ(),this.texData=new Ka(this,ks())}nextDataId(){return Hu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((j().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||j().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=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:t,dtype:n,values:e,usage:zr.UPLOAD,refCount:1}),o}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,o,s){if(j().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:o,values:t,usage:zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:o,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Fs(i,hh):m=new Cn(i,hh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(o==="string")return n;let u=this.activeTimers!=null,c;u&&(c=b.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(e);return u&&(this.downloadWaitMs+=b.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Fs(o,hh):d=new Cn(o,hh);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!j().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&j().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&j().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...lh(o))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=b.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let d=this.gpgpu.gl;ke(d,()=>d.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ks().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>b.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Se(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!j0(n))throw j().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=b.sizeFromShape(t);if(j().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...lh(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=j().getBool("WEBGL_PACK")&&o===!0,i=a?mh(t):t,l=a?new fC(i):new mC(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=b.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=b.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=b.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:b.now(),endMs:null}}endTimer(e){return j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=b.now(),e)}async getQueryTime(e){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=DJ){return j().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&b.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return TJ(e.shape,t)}packedUnaryOp(e,t,n){let o=new Fs(e.shape,t),s=this.compileAndRun(o,[e],n);return ks().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=xy(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(j().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,OC,e.dtype);let t=new Cn(e.shape,OC),n=this.compileAndRun(t,[e]);return ks().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&b.isString(n[0])){let s=n.map(a=>b.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return ks().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new PC(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new RC(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ma(e.shape),...La(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ma(t),...La(t)],a=new dh(s,n),i=!0,l=[n],u=this.runWebGLProgram(a,[o],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=mh(o),i,l=lh(a);n?i=new pC(a):i=new cC(a);let u=!0,c=[l],p=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,c,u);return{dtype:s,shape:o,dataId:p.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===El.DENSE){let g=lh(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),b.sizeFromShape(a.shape)===0)return i.values=b.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&b.sizeFromShape(g.shape)<=j().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}else if(!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Al(y.shape,g.shape)){let w=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),w.shape=x}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=BO(e,u,c),m=this.getAndSaveBinary(p,()=>MO(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),zO(this.gpgpu,m,u,c,o),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=j().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=b.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!j().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,n,o,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,o,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(j().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!j().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=j().getBool("DEBUG");j().set("DEBUG",!1);let t=this.abs(ce(1e-8)).dataSync()[0];if(j().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EJ:AJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=b.now());let p=t.texShape;if(p==null&&(p=rC(n,l),t.texShape=p),s!=null){let m=mh(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=bi(p[0],p[1]),f=new hC(m,g)):f=new dC(m,g);let y=this.makeTensorInfo([h,d],o);g?this.texData.get(y.dataId).usage=zr.PIXELS:this.texData.get(y.dataId).usage=zr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),d,h,s);let w=[[h,d]],x=!0,k=this.runWebGLProgram(f,[y],o,w,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=b.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=OJ(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,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(e,t,o)}computeBytes(e,t){return e[0]*e[1]*b.bytesPerElement(t)}};Hu.nextDataId=0;function OJ(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var PJ="3.9.0";function BP(){j().set("WEBGL_FORCE_F16_TEXTURES",!0)}Ql.isBrowser()&&ap("webgl",()=>new Hu,2);var Cwt={forceHalfFloat:BP};var wy=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var _o=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var $l=`
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 Os=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=jt(s);let a="";if(o)if(s===0||b.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${ze(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let l=Xt("coords",s);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${l[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${l[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Yt(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var VP={kernelName:to,backendName:"webgl",kernelFunc:Yt};function Sn(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Yt({inputs:{x:n},backend:t}),l=Yt({inputs:{x:o},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var WP={kernelName:bc,backendName:"webgl",kernelFunc:Sn};var MC="return (a < 0.) ? b * a : a;",LC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function MJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",b.createScalarValue(s,"float32")),i=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(LC,o.shape,a.shape):new _o(MC,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var jP={kernelName:jo,backendName:"webgl",kernelFunc:MJ};var zC="return (a < 0.) ? b * a : a;",BC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function LJ(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(BC,n.shape,o.shape):new _o(zC,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)}var GP={kernelName:rs,backendName:"webgl",kernelFunc:LJ};var ky="if (isnan(x)) return x;",UP=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,HP=`
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 _e({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=j().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Fs(a.shape,e):c=new Cn(a.shape,r),i.runWebGLProgram(c,[a],l)}}function st({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(x=>{let[k,C]=x,A={dataId:k.dataId,dtype:k.dtype,shape:l.shape},$={dataId:C.dataId,dtype:C.dtype,shape:u.shape},R=new _o(r,l.shape,u.shape);return c.runWebGLProgram(R,[A,$],pr(k.dtype,C.dtype))}),w=Sn({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),w}let p=s||pr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&o!=null){let d=c.texData.get(l.dataId).values,h=c.texData.get(u.dataId).values,g=l.dtype==="string"?S.fromUint8ToStringArray(d):d,y=l.dtype==="string"?S.fromUint8ToStringArray(h):h,[w,x]=o(l.shape,u.shape,g,y,p),k=c.makeTensorInfo(x,p),C=c.texData.get(k.dataId);return C.values=w,k}let m=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Os(e,l.shape,u.shape,t):f=new _o(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function Dl(r,e=!1){if(r==="linear")return e?OP:AP;if(r==="relu")return e?MP:DP;if(r==="elu")return e?PP:$P;if(r==="relu6")return e?LP:RP;if(r==="prelu")return e?BC:zC;if(r==="leakyrelu")return e?LC:MC;if(r==="sigmoid")return e?zP:FP;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var gh=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=jt(this.outputShape.length);let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";i&&(l?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:g=`vec4 activation(vec4 x) {
${i}
}`,y="result = activation(result);");let w=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let x="rc.x",k="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(k=`int(min(float(rc.x), ${t[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 = ${x};
int batchB = ${k};
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);
${w}
${y}
setOutput(result);
}
`}};var VC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},_y=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var qP="return a * b;";function xh(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new _y(VC.REAL,n.shape,o.shape),c=new _y(VC.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=Sn({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=aP(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Os(qP,n.shape,o.shape):a=new _o(qP,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var KP={kernelName:Jo,backendName:"webgl",kernelFunc:xh};function XP(r,e,t){let n=[Ma(r.shape),...La(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Ma(e),...La(e)],a=new dh(s,n),i=!0,l=[n],u=t.runWebGLProgram(a,[o],r.dtype,l,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function ae(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=b.sizeFromShape(o.shape),l=b.inferFromImplicitShape(s,i),u=b.sizeFromShape(l);b.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!Al(o.shape,l)&&!(c.texture!==null&&Al(c.shape,l))?XP(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var YP={kernelName:Xs,backendName:"webgl",kernelFunc:ae};var vy=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${b.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}};var WC=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${l}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,f="vec4";t==="all"?(i="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):t==="any"&&(i="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${u});
}
`}};function zJ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function Ln(r,e,t,n){let o=zJ(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new vy({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new vy({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new WC({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var jC=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=ze(this.rank),s=BJ(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function BJ(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var GC=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=ze(this.rank),s=DC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${l}) {
result[1] = ${u};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${l}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Rl(r,e,t){let n=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GC(r.shape,e):new jC(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function ZP(r,e,t,n){let o=e,s=r.shape.length,a=b.parseAxisParam(o,r.shape),i=a,l=S.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Rl(r,l,n),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=S.expandShapeToKeepDim(p,a));let d=b.sizeFromShape(m),g=b.sizeFromShape(r.shape)/d,y=ae({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),w=Zl(r.dtype),x=Ln(y,w,"sum",n),k=ae({inputs:{x},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(c),k}function qu(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return ZP(o,s,a,t)}var JP={kernelName:ms,backendName:"webgl",kernelFunc:qu};function Ft(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=Uu(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Rl(o,s,a);return u}var QP={kernelName:ys,backendName:"webgl",kernelFunc:Ft};var UC=1e3;function Ku({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),y=b.sizeFromShape(h),w=b.sizeFromShape(g),x=y===w||y===1||w===1;b.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let C=(y>w?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);b.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let A=t?[y,p,f]:[y,f,p],$=n?[w,d,m]:[w,m,d],R=ae({inputs:{x:r},backend:o,attrs:{shape:A}}),P=ae({inputs:{x:e},backend:o,attrs:{shape:$}}),M=[R,P],V=Math.max(y,w),W=t?R.shape[1]:R.shape[2],G=s!=null,U=a!=null,H=l==="leakyrelu",K=l!=null?Dl(l,!0):null,re=G||U||H||K!=null,X;if((f===1||d===1)&&W>UC&&re===!1){let Q=R,se=P;t&&(Q=Ft({inputs:{x:R},backend:o,attrs:{perm:[0,2,1]}}),M.push(Q)),n&&(se=Ft({inputs:{x:P},backend:o,attrs:{perm:[0,2,1]}}),M.push(se));let pe=d!==1,ie=d===1,fe=Q;pe&&(fe=ae({inputs:{x:Q},backend:o,attrs:{shape:[V,W,1]}}),M.push(fe));let de=d===1?2:1,ge=se;ie&&(ge=ae({inputs:{x:se},backend:o,attrs:{shape:[V,1,W]}}),M.push(ge));let we=xh({inputs:{a:fe,b:ge},backend:o});X=qu({inputs:{x:we},backend:o,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let Q=pr(r.dtype,e.dtype),se=new gh(A,$,[V,f,d],t,n,G,K,U,H),pe=[R,P];if(s!=null&&pe.push(s),U&&pe.push(a),H){let ie=o.makeTensorInfo([],"float32",b.createScalarValue(i,"float32"));pe.push(ie),M.push(ie)}X=o.runWebGLProgram(se,pe,Q)}let ne=ae({inputs:{x:X},backend:o,attrs:{shape:C}});M.push(X);for(let Q of M)o.disposeIntermediateTensorInfo(Q);return ne}function VJ(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return Ku({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var eM={kernelName:ri,backendName:"webgl",kernelFunc:VJ};var tM="return abs(x);";function WJ(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=xy(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fs(n.shape,tM):o=new Cn(n.shape,tM),t.runWebGLProgram(o,[n],n.dtype)}var rM={kernelName:Vs,backendName:"webgl",kernelFunc:WJ};var jJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,GJ=_e({opSnippet:jJ}),nM={kernelName:Ni,backendName:"webgl",kernelFunc:GJ};var UJ=br+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,HJ=_e({opSnippet:UJ}),oM={kernelName:Ti,backendName:"webgl",kernelFunc:HJ};var sM="return a + b;",qJ=st({opSnippet:sM,packedOpSnippet:sM,supportsComplex:!0,cpuKernelImpl:WO}),iM={kernelName:Wn,backendName:"webgl",kernelFunc:qJ};var HC=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${o};
setOutput(result);
}
`}};var qC=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${o};
setOutput(result);
}
`}};function Cy(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Yt({inputs:{x:n[0]},backend:t});if(n.length>j().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=Cy({inputs:n.slice(0,l),backend:t}),c=Cy({inputs:n.slice(l),backend:t});return Cy({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>pr(l,u)),s=n.map(l=>l.shape),i=j().getBool("WEBGL_PACK")?new qC(n[0].shape,s):new HC(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var aM={kernelName:So,backendName:"webgl",kernelFunc:Cy};function KJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Ft({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ae({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Ln(h,h.dtype,"all",t),y;if(a){let w=S.expandShapeToKeepDim(m,l);y=ae({inputs:{x:g},backend:t,attrs:{shape:w}})}else y=ae({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var lM={kernelName:Ei,backendName:"webgl",kernelFunc:KJ};function XJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Ft({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ae({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Ln(h,h.dtype,"any",t),y;if(a){let w=S.expandShapeToKeepDim(m,l);y=ae({inputs:{x:g},backend:t,attrs:{shape:w}})}else y=ae({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var uM={kernelName:Ai,backendName:"webgl",kernelFunc:XJ};var KC=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${o}; i++) {
int inIdx = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var XC=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,b.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=ze(l),c=Xt("coords",l),p,m;if(a===1){m=l+1;let R=ze(m);p=`
${R} sourceLocR = ${R}(${c.join()}, 0);
++${c[l-1]};
${R} sourceLocG = ${R}(${c.join()}, 0);
++${c[l-2]};
${R} sourceLocA = ${R}(${c.join()}, 0);
--${c[l-1]};
${R} sourceLocB = ${R}(${c.join()}, 0);
--${c[l-2]};`}else m=l,p=`
${u} sourceLocR = coords;
++${c[l-1]};
${u} sourceLocG = coords;
++${c[l-2]};
${u} sourceLocA = coords;
--${c[l-1]};
${u} sourceLocB = coords;
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Xt("sourceLocR",m-1).concat("inIdx.r"),y=Xt("sourceLocG",m-1).concat("inIdx.g"),w=Xt("sourceLocB",m-1).concat("inIdx.b"),x=Xt("sourceLocA",m-1).concat("inIdx.a"),k=n==="max"?"greaterThan":"lessThan",C=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${w.join()}),
getBestIndicesAChannel(${x.join()})));`,A=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${y.join()}) : 0.,
hasNextRow ? getAChannel(${w.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${x.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() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${A};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${C}
vec4 candidate = ${A};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${k}(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 cM(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new KC(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=cM(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function pM(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=S.computeOptimalWindowSize(s),i=new XC(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=pM(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Sy(r,e,t,n){let o=[t];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!j().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,l=e;i&&(l=r.unpackTensor(e),s.push(l));let[u,c]=S.computeOutAndReduceShapes(l.shape,o),p=b.sizeFromShape(c),m=ae({inputs:{x:l},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=cM(r,m,n);s.push(f);let d=ae({inputs:{x:f},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return pM(r,e,n)}function YJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=b.parseAxisParam(s,o.shape),i=S.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Ft({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Sy(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var mM={kernelName:Io,backendName:"webgl",kernelFunc:YJ};function ZJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=b.parseAxisParam(s,o.shape),i=S.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Ft({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Sy(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var fM={kernelName:Xa,backendName:"webgl",kernelFunc:ZJ};var JJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,QJ=_e({opSnippet:JJ}),dM={kernelName:$i,backendName:"webgl",kernelFunc:QJ};var eQ=br+"return log(x + sqrt(x * x + 1.0));",tQ=_e({opSnippet:eQ}),hM={kernelName:Di,backendName:"webgl",kernelFunc:tQ};var rQ=br+`
return atan(x);
`,nQ=_e({opSnippet:rQ}),gM={kernelName:Ri,backendName:"webgl",kernelFunc:nQ};var oQ=UP+`
return atan(a, b);
`,sQ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+HP+`
return result;
`,iQ=st({opSnippet:oQ,packedOpSnippet:sQ}),xM={kernelName:Oi,backendName:"webgl",kernelFunc:iQ};var aQ=br+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lQ=_e({opSnippet:aQ}),yM={kernelName:Fi,backendName:"webgl",kernelFunc:lQ};var wi=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,w="0.0";if(h||(w="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?g:y:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let C=Math.floor(a/4)*4,A=a%4,$=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${w};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${w});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${C}; 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 + ${C};
if (${A===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${A===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${$}
} else if (${A===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${$}
}
}
setOutput(${k});
}
`}},Xu=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,y=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",k="0.0";if(x||(k="-1.0 / 1e-20"),n){let M=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${y}, ${w});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${M} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let C="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let $=Math.floor(a/4)*4,R=a%4,P=`
if (${x}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${C}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${y}, ${w});
const float initializationValue = ${k};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${k});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${$}; 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)
);
${P}
}
int xC = xCCorner + ${$};
if (${R===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${P}
} else if (${R===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${P}
} else if (${R===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${P}
}
}
setOutput(${A});
}
}
`}};function uQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Ds(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;b.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&b.arraysEqual(c.inShape,c.outShape))return Yt({inputs:{x:o},backend:t});let p=new wi(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var bM={kernelName:No,backendName:"webgl",kernelFunc:uQ};function cQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new Xu(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var wM={kernelName:Ya,backendName:"webgl",kernelFunc:cQ};var YC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},ZC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,y=1/(t*n*o);this.userCode=`
const ivec3 pads = ivec3(${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 += ${l}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function pQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new ZC(m);return t.runWebGLProgram(f,[o],a.dtype)}var kM={kernelName:xc,backendName:"webgl",kernelFunc:pQ};function mQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;Ds([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=S.computePool2DInfo(a.shape,i,l,1,u),p=new YC(c);return t.runWebGLProgram(p,[o],a.dtype)}var _M={kernelName:gc,backendName:"webgl",kernelFunc:mQ};function fQ(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return Ku({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var vM={kernelName:To,backendName:"webgl",kernelFunc:fQ};var JC=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(S.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var QC=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}};var dQ=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;b.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),b.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),b.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=j().getBool("WEBGL_PACK_NORMALIZATION")?new QC(n.shape,o.shape,s.shape,c,p,l):new JC(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},CM={kernelName:Vo,backendName:"webgl",kernelFunc:dQ};var eS=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ze(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=hQ(this.rank),o,s=e.map((a,i)=>`sourceLoc.${tS[i]} = start[${i}] + coords.${tS[i]};`);o=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${o}
setOutput(getSource(${n}));
}
`}},tS=["x","y","z","w","u","v"];function hQ(r){if(r===1)return"sourceLoc";if(r<=6)return tS.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var rS=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ze(this.rank),n=Xt("coords",this.rank),o=Xt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${a};
--${o[this.rank-1]};
}
`,l=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${l}
setOutput(result);
}
`}};function gQ(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ar.computeFlatOffset(e,b.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function Ps(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=ar.parseSliceParams(o,s,a);if(ar.assertParamsValid(o,i,l),b.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=dP(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=ar.isSliceContinous(o.shape,i,l);if(u||!c){let p=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rS(l):new eS(l),m=[i];return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),gQ(o,i,l,t)}var SM={kernelName:Zs,backendName:"webgl",kernelFunc:Ps};var xQ=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;b.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((w,x)=>w*x),l=S.getReshaped(o.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(o.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=[],d=ae({inputs:{x:o},backend:t,attrs:{shape:l}}),h=Ft({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ae({inputs:{x:h},backend:t,attrs:{shape:c}}),y=Ps({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(w=>t.disposeIntermediateTensorInfo(w)),y},IM={kernelName:Ws,backendName:"webgl",kernelFunc:xQ};function yQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=gy(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var NM={kernelName:yc,backendName:"webgl",kernelFunc:yQ};var bQ="return float(a != b);",nS=st({opSnippet:bQ,cpuKernelImpl:uP,dtype:"bool"}),TM={kernelName:Ji,backendName:"webgl",kernelFunc:nS};function za(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Yt({inputs:{x:o.complexTensorInfos.real},backend:t})}var EM={kernelName:Lc,backendName:"webgl",kernelFunc:za};var wQ="return float(int(x));";function AM(r,e){let t=new Cn(r.shape,wQ),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function oS(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Yt({inputs:{x:o},backend:t});let a=ht(o.shape),i=oS({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=Sn({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=za({inputs:{input:o},backend:t}),i=oS({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!b.hasEncodingLoss(o.dtype,s)){let a=Yt({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return AM(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",b.getTypedArrayFromDType("bool",1)),l=nS({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var $M={kernelName:Qn,backendName:"webgl",kernelFunc:oS};var DM="return ceil(x);",kQ=_e({opSnippet:DM,packedOpSnippet:DM,cpuKernelImpl:GO}),RM={kernelName:Eo,backendName:"webgl",kernelFunc:kQ};var sS=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}};var iS=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function _Q(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;j().getBool("WEBGL_PACK_CLIP")?i=new iS(o.shape):i=new sS(o.shape);let l=[[s],[a]];return t.runWebGLProgram(i,[o],o.dtype,l)}var FM={kernelName:eo,backendName:"webgl",kernelFunc:_Q};var aS=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function OM(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function vQ(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new aS(n.shape),a=[OM(n,o.complexTensorInfos.real),OM(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var PM={kernelName:Za,backendName:"webgl",kernelFunc:vQ};var lS=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}};var uS=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=ze(o),a=Xt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
return getChannel(
getT${h}(${Iy(i,u,g)}),
vec2(${Iy(c,u,g)}));
}`}let f=l.length,d=l[l.length-1];m+=`
return getChannel(
getT${f}(${Iy(i,u,d)}),
vec2(${Iy(c,u,d)}));`,this.userCode=`
float getValue(${i.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[o-1]} = ${a[o-1]} + 1;
if (${a[o-1]} < ${n[o-1]}) {
result.g = getValue(${a});
}
${a[o-2]} = ${a[o-2]} + 1;
if (${a[o-2]} < ${n[o-2]}) {
result.a = getValue(${a});
}
${a[o-1]} = ${a[o-1]} - 1;
if (${a[o-2]} < ${n[o-2]} &&
${a[o-1]} < ${n[o-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Iy(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function Yu(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Yt({inputs:{x:o.complexTensorInfos.imag},backend:t})}var MM={kernelName:$c,backendName:"webgl",kernelFunc:Yu};function Zu(r,e,t){let n=r[0].dtype;if(n==="complex64"){let c=r.map(h=>za({inputs:{input:h},backend:t})),p=r.map(h=>Yu({inputs:{input:h},backend:t})),m=Zu(c,e,t),f=Zu(p,e,t),d=Sn({inputs:{real:m,imag:f},backend:t});return c.forEach(h=>t.disposeIntermediateTensorInfo(h)),p.forEach(h=>t.disposeIntermediateTensorInfo(h)),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}let o=t.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let c=r.map(y=>{let w=b.sizeFromShape(y.shape.slice(e));return ae({inputs:{x:y},backend:t,attrs:{shape:[-1,w]}})}),p=c.map(y=>({vals:t.readSync(y.dataId),shape:y.shape})),m=S.computeOutShape(c.map(y=>y.shape),1),f=c[0].shape[0]===1,d=UO(p,m,n,f),h=S.computeOutShape(r.map(y=>y.shape),e),g=t.makeTensorInfo(h,n,d);return c.forEach(y=>t.disposeIntermediateTensorInfo(y)),g}if(r.length>j().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),p=Zu(r.slice(0,c),e,t),m=Zu(r.slice(c),e,t),f=Zu([p,m],e,t);return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let c=new uS(r.map(p=>p.shape),e);return t.runWebGLProgram(c,r,n)}let{tensors2D:s,outShape:a}=CQ(r,e,t),i=new lS(s.map(c=>c.shape)),l=t.runWebGLProgram(i,s,n);s.forEach(c=>t.disposeIntermediateTensorInfo(c));let u=ae({inputs:{x:l},attrs:{shape:a},backend:t});return t.disposeIntermediateTensorInfo(l),u}function CQ(r,e,t){let n=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ae({inputs:{x:s},attrs:{shape:[-1,b.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:n}}function cS(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=b.parseAxisParam(o,e[0].shape)[0],a=S.computeOutShape(e.map(u=>u.shape),s);if(b.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>b.sizeFromShape(u.shape)>0);if(i.length===1)return Yt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return S.assertParamsConsistent(l,s),Zu(i,s,t)}var LM={kernelName:js,backendName:"webgl",kernelFunc:cS};var yh=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",y=g?1:2,w=g?2:3,x=g?3:1,k="",C="";n&&(o?k=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?k=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:k=`
float activation(float x) {
${n}
}
`,C="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${k}
const ivec2 strides = ivec2(${l}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${x}];
ivec2 xRCCorner =
ivec2(coords[${y}], coords[${w}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${A}
${C}
setOutput(result);
}
`}},pS=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${o});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${p}; wF++) {
int xF = xFCorner + wF * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var mS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let{dataFormat:n}=t,o=zt(),s=n==="channelsLast",a=s?0:1,i=s?1:2,l=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)u+=`
blockIndex = rc.y + ${p};
pos = rc.x + ${c};
${l}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${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;
${u}
${o.output} = result;
}
`}};function Ny({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,y=[];if(!((p===1||m===1)&&c>UC)&&u.isPacked&&f&&u.texture!=null&&l[2]%2!=0&&b.arraysEqual(u.shape.slice(-3),l.slice(-3))){let k=l[0]*l[1]*(l[2]+1),C={dataId:r.dataId,shape:[1,k,t.inChannels],dtype:r.dtype},A=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,b.assert(Al(u.shape,C.shape),()=>`packed reshape ${u.shape} to ${C.shape} isn't free`);let $=ae({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});y.push($);let R=Ku({a:C,b:$,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),P=n.texData.get(R.dataId);b.assert(P.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=A,P.shape=t.outShape,g=Yt({inputs:{x:R},backend:n}),g.shape=t.outShape,y.push(R)}else{let k=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],C=ae({inputs:{x:r},backend:n,attrs:{shape:[1,k,t.inChannels]}}),A=ae({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Ku({a:C,b:A,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ae({inputs:{x:$},backend:n,attrs:{shape:t.outShape}}),y.push(C),y.push(A),y.push($)}for(let k of y)n.disposeIntermediateTensorInfo(k);return g}function Ty({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,y=[h,g],w=!0,x=!1,k=[],C=ae({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),A=ae({inputs:{x:e},backend:n,attrs:{shape:[1,h,b.sizeFromShape(e.shape)/h]}});k.push(C),k.push(A);let $=new mS(y,t),R=[C.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],P=n.runWebGLProgram($,[C],"float32",R),M=ae({inputs:{x:P},backend:n,attrs:{shape:[1,y[0],y[1]]}});k.push(P),k.push(M);let V=o!=null,W=s!=null,G=i==="leakyrelu",U=i?Dl(i,!0):null,H=new gh(M.shape,A.shape,[1,g,t.outChannels],w,x,V,U,W,G),K=[M,A];if(o&&K.push(o),W&&K.push(s),G){let Q=n.makeTensorInfo([],"float32",b.createScalarValue(a,"float32"));K.push(Q),k.push(Q)}let re=n.runWebGLProgram(H,K,"float32"),X=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],ne=ae({inputs:{x:re},backend:n,attrs:{shape:X}});k.push(re);for(let Q of k)n.disposeIntermediateTensorInfo(Q);return ne}function SQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Ny({x:o,filter:s,convInfo:m,backend:t});else if(j().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=Ty({x:o,filter:s,convInfo:m,backend:t});else{let h=new yh(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=ae({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var zM={kernelName:Ao,backendName:"webgl",kernelFunc:SQ};var fS=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},dS=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},hS=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},gS=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${u}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function IQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new fS(m);return t.runWebGLProgram(f,[o,s],"float32")}var BM={kernelName:wc,backendName:"webgl",kernelFunc:IQ};function NQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new dS(m);return t.runWebGLProgram(f,[o,s],"float32")}var VM={kernelName:$o,backendName:"webgl",kernelFunc:NQ};function TQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=S.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new pS(u);return t.runWebGLProgram(c,[o,s],"float32")}var WM={kernelName:Ja,backendName:"webgl",kernelFunc:TQ};function EQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=S.computeConv3DInfo(o.shape,l,a,1,i),c=new hS(u);return t.runWebGLProgram(c,[o,s],"float32")}var jM={kernelName:kc,backendName:"webgl",kernelFunc:EQ};function AQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,i,1,a),c=new gS(u);return t.runWebGLProgram(c,[o,s],"float32")}var GM={kernelName:_c,backendName:"webgl",kernelFunc:AQ};var $Q=ky+`
return cos(x);
`,DQ=_e({opSnippet:$Q}),UM={kernelName:Do,backendName:"webgl",kernelFunc:DQ};var RQ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,FQ=_e({opSnippet:RQ}),HM={kernelName:Ro,backendName:"webgl",kernelFunc:FQ};var xS=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,y,w]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[x,k,C]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${x});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${y};
float width_scale = ${k};
float in_y = ${w};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${C};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var OQ=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new xS(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},qM={kernelName:Pi,backendName:"webgl",kernelFunc:OQ};var Ey=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${KM(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${ze(o)} coords = getOutputCoords();
int end = ${XM(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${l};
${XM(o,"coords")} = idx;
val += getX(${KM(o,"coords")});
}
setOutput(val);
}
`}};function KM(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function XM(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function PQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=S.getAxesPermutation([s],l),c=o;u!=null&&(c=Ft({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=S.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Yt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Ey(c.shape,!1,i),g=[[d]],y=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(y)}if(a){let d=new Ey(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=S.getUndoAxesPermutation(u),h=Ft({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var YM={kernelName:Fo,backendName:"webgl",kernelFunc:PQ};function MQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=gy(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=jO(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var ZM={kernelName:vc,backendName:"webgl",kernelFunc:MQ};var yS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function LQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;b.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new yS(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var JM={kernelName:Mi,backendName:"webgl",kernelFunc:LQ};var bh=class{constructor(e,t=!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=e.outShape,this.enableShapeUniforms=jt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,l=e.outChannels/e.inChannels,u="",c="";n&&(o?u=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?u=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:u=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${u}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${l};
int q = d2 - d1 * ${l};
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 < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${p}
${c}
setOutput(result);
}
`}};var wh=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=jt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,l=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,p=e.filterWidth,m=p,f=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let y=0;y<p;y++)f+=`
vec4 xTexelC${y*2};
int xTexelC${y*2}Ready;
vec4 xTexelC${y*2+1};
int xTexelC${y*2+1}Ready;
vec4 xC${y};`;for(let y=0;y<c;y++){for(let w=0;w<p;w++)f+=`
xTexelC${w*2} = vec4(0.0);
xTexelC${w*2}Ready = 0;
xTexelC${w*2+1} = vec4(0.0);
xTexelC${w*2+1}Ready = 0;
xC${w} = vec4(0.0);`;f+=`
xR = xRCorner + ${y} * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let w=0;w<(m+1)/2;w++){let x=w*2;if(f+=`
xC = xCCorner + ${x*u};
`,l===1){if(x<p&&(i%2==1?(f+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
`,u===1&&x>0?f+=`
xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.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${x} = vec4(previous.zw, xTexelC${x}.xy);
} else {
xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);
}
`):f+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
xC${x} = xTexelC${x};
`,x+1<p)){let k=i%2==0?b.nearestLargerEven(u):u;u%2==0&&i%2==1||u%2!=0&&i%2!=1?(f+=`
xCOffset = xC + imod(pads[1], 2) + ${k};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x+1}.zw = vec2(0.0);
}
xTexelC${x+1}Ready = 1;
}
`,u>1&&(f+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xCOffset, d1);
xTexelC${x}Ready = 1;
}
`),f+=`
xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);
`):k===1?f+=`
xC${x+1} = xTexelC${x};
`:f+=`
xCOffset = xC + ${k};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x+1}.zw = vec2(0.0);
}
xTexelC${x+1}Ready = 1;
}
xC${x+1} = xTexelC${x+1};
`}}else x<p&&(i%2==1?(f+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {
xTexelC${x+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${x+1}.zw = vec2(0.0);
}
xTexelC${x+1}Ready = 1;
}
xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
`,x+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${x+1} = vec4(xTexelC${x+1}.xy, final.xy);
`)):(f+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x+1}.zw = vec2(0.);
}
xTexelC${x+1}Ready = 1;
}
xC${x} = vec4(
xTexelC${x}.xy, xTexelC${x+1}.xy);
`,x+1<p&&(f+=`
xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
`)));x<p&&(f+=`
wTexel = getW(${y}, ${x}, d1, q);
dotProd += xC${x} * vec4(wTexel.xz, wTexel.xz);
`,x+1<p&&(f+=`
wTexel = getW(${y}, ${x+1}, d1, q);
dotProd += xC${x+1} * vec4(wTexel.xz, wTexel.xz);
`))}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=t?"result += getBiasAtOutCoords();":"";t&&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 / ${a};
int q = d2 - d1 * ${a};
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 zQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),b.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;j().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new wh(p):m=new bh(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return t.runWebGLProgram(m,[o,s],"float32",f)}var QM={kernelName:Oo,backendName:"webgl",kernelFunc:zQ};var bS=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},wS=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function BQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new bS(p);return t.runWebGLProgram(m,[o,s],"float32")}var eL={kernelName:Cc,backendName:"webgl",kernelFunc:BQ};function VQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new wS(p);return t.runWebGLProgram(m,[o,s],"float32")}var tL={kernelName:Sc,backendName:"webgl",kernelFunc:VQ};var kS=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function WQ(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=b.sizeFromShape(n.shape),a=ae({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new kS(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ae({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var rL={kernelName:Ic,backendName:"webgl",kernelFunc:WQ};var _S=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function jQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=S.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new _S(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=ae({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var nL={kernelName:Qa,backendName:"webgl",kernelFunc:jQ};function GQ(r){let{inputs:e,backend:t,attrs:n}=r,{equation:o}=n,s=e,{allDims:a,summedDims:i,idDims:l}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(a.length,l,s);let{path:u,steps:c}=S.getEinsumComputePath(i,l),p=c.length,m=null,f=a.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:y,expandDims:w}=S.getEinsumPermutation(f,l[g]),x;S.isIdentityPermutation(y)?x=s[g]:(x=Ft({inputs:{x:s[g]},backend:t,attrs:{perm:y}}),d.push(x));let k=x.shape.slice();for(let C=0;C<w.length;++C)k.splice(w[C],0,1);b.arraysEqual(x.shape,k)||(x=ae({inputs:{x},backend:t,attrs:{shape:k}}),d.push(x)),m===null?m=x:(m=xh({inputs:{a:x,b:m},backend:t}),d.push(m))}h<p-1&&(u[h]>=0&&(m=qu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var oL={kernelName:Nc,backendName:"webgl",kernelFunc:GQ};var UQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",HQ=`
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;
`,qQ=_e({opSnippet:UQ,packedOpSnippet:HQ}),sL={kernelName:Mo,backendName:"webgl",kernelFunc:qQ};var KQ="return (b >= 1.0) ? a : a * (b + 1.0);",XQ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,YQ=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Os(XQ,n.shape,o.shape):new _o(KQ,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},iL={kernelName:Tc,backendName:"webgl",kernelFunc:YQ};var ZQ=`
return vec4(equal(a, b));
`,JQ="return float(a == b);",QQ=st({opSnippet:JQ,packedOpSnippet:ZQ,dtype:"bool",cpuKernelImpl:HO}),aL={kernelName:zi,backendName:"webgl",kernelFunc:QQ};var eee=`
// 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 = ${S.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,tee=_e({opSnippet:eee}),lL={kernelName:Li,backendName:"webgl",kernelFunc:tee};var uL="return exp(x);",vS=_e({opSnippet:uL,packedOpSnippet:uL,cpuKernelImpl:qO}),cL={kernelName:Lo,backendName:"webgl",kernelFunc:vS};function Ay(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(b.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),ae({inputs:{x:s},backend:n,attrs:{shape:i}})}var pL={kernelName:Gs,backendName:"webgl",kernelFunc:Ay};var mL="return exp(x) - 1.0;",ree=_e({opSnippet:mL,packedOpSnippet:mL,cpuKernelImpl:KO}),fL={kernelName:Bi,backendName:"webgl",kernelFunc:ree};var $y=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${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) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function Dy(r,e,t){let n=t.texData.get(r.dataId),o=b.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,i=ae({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new $y("real",l,e),c=new $y("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=Sn({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ae({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function nee(r){let{inputs:e,backend:t}=r,{input:n}=e;return Dy(n,!1,t)}var dL={kernelName:Ec,backendName:"webgl",kernelFunc:nee};var CS=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function Ba(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||b.inferDtype(o),s==="string"){let a=b.getArrayFromDType(s,b.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new CS(n,o),i=[[o]];return e.runWebGLProgram(a,[],s,i)}}var hL={kernelName:el,backendName:"webgl",kernelFunc:Ba};var SS=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var gL={kernelName:Vi,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new SS(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var xL="return floor(x);",oee=_e({opSnippet:xL,packedOpSnippet:xL,cpuKernelImpl:XO}),yL={kernelName:zo,backendName:"webgl",kernelFunc:oee};var see=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,iee=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,aee=st({opSnippet:see,packedOpSnippet:iee,dtype:"int32"}),bL={kernelName:Bo,backendName:"webgl",kernelFunc:aee};var IS=class{constructor(e){this.variableNames=["A"];let t=zt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var NS=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=zt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}};var wL={kernelName:Rm,backendName:"webgl",kernelFunc:lee},fm;function lee(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[l,u]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[u,l],p=[u,l,s];(i||a)&&(fm==null&&(fm=document.createElement("canvas").getContext("2d")),fm.canvas.width=l,fm.canvas.height=u,fm.drawImage(o,0,0,l,u),o=fm.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=zr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),o);let f=j().getBool("WEBGL_PACK")?new NS(p):new IS(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function uee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),y,w=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=Ny({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(j().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)y=Ty({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let k=a!=null,C=i!=null,A=f==="leakyrelu",$=f?Dl(f,!1):null,R=new yh(g,k,$,C,A),P=[o,s];if(a&&P.push(a),i&&P.push(i),A){let M=t.makeTensorInfo([],"float32",b.createScalarValue(d,"float32"));P.push(M),w.push(M)}y=t.runWebGLProgram(R,P,"float32")}let x=ae({inputs:{x:y},backend:t,attrs:{shape:g.outShape}});return w.push(y),w.forEach(k=>t.disposeIntermediateTensorInfo(k)),x}var kL={kernelName:ni,backendName:"webgl",kernelFunc:uee};function cee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),b.assert(S.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),y=j().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,w=m?Dl(m,y):null,x=[o,s],k=a!=null,C=i!=null,A=m==="leakyrelu";if(k&&x.push(a),C&&x.push(i),A){let M=t.makeTensorInfo([],"float32",b.createScalarValue(f,"float32"));x.push(M),d.push(M)}let $;y?$=new wh(g,k,w,C,A):$=new bh(g,k,w,C,A);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=t.runWebGLProgram($,x,"float32",R);return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),P}var _L={kernelName:oi,backendName:"webgl",kernelFunc:cee};var TS=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=ze(t.length),s=ze(n.length),a=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 * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function pee(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],i=b.sizeFromShape(n.shape),[l,u,c,p]=S.prepareAndValidate(n,o),m=ae({inputs:{x:o},backend:t,attrs:{shape:[u,a]}}),f=ae({inputs:{x:n},backend:t,attrs:{shape:[b.sizeFromShape(n.shape)/c,c]}});if(t.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let y=t.readSync(o.dataId),w=t.bufferSync(n),x=YO(y,w,n.dtype,u,a,c,p,n.shape,i);return t.makeTensorInfo(l,n.dtype,x.values)}let d=new TS(a,p,[u,c]),h=t.runWebGLProgram(d,[f,m],f.dtype),g=ae({inputs:{x:h},backend:t,attrs:{shape:l}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var vL={kernelName:Wi,backendName:"webgl",kernelFunc:pee};var ES=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ze(this.rank),o=mee(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}};function mee(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function AS(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=b.parseAxisParam(a,o.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=b.sizeFromShape(s.shape),p=[],m=ae({inputs:{x:o},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=ae({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let w=t.bufferSync(f),x=t.bufferSync(m),k=ZO(x,w,d);return p.forEach(C=>t.disposeIntermediateTensorInfo(C)),t.makeTensorInfo(u.outputShape,k.dtype,k.values)}let h=new ES(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=ae({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(w=>t.disposeIntermediateTensorInfo(w)),y}var CL={kernelName:Us,backendName:"webgl",kernelFunc:AS};var fee="return float(a > b);",dee=`
return vec4(greaterThan(a, b));
`,hee=st({opSnippet:fee,packedOpSnippet:dee,cpuKernelImpl:JO,dtype:"bool"}),SL={kernelName:ji,backendName:"webgl",kernelFunc:hee};var gee="return float(a >= b);",xee=`
return vec4(greaterThanEqual(a, b));
`,yee=st({opSnippet:gee,packedOpSnippet:xee,dtype:"bool",cpuKernelImpl:QO}),IL={kernelName:Wo,backendName:"webgl",kernelFunc:yee};function bee(r){let{inputs:e,backend:t}=r,{input:n}=e;return Dy(n,!0,t)}var NL={kernelName:Ac,backendName:"webgl",kernelFunc:bee};var wee="return float(!isnan(x) && !isinf(x));",kee=_e({opSnippet:wee,dtype:"bool"}),TL={kernelName:Gi,backendName:"webgl",kernelFunc:kee};var _ee="return float(isinf(x));",vee=_e({opSnippet:_ee,dtype:"bool"}),EL={kernelName:Ui,backendName:"webgl",kernelFunc:vee};var Cee="return float(isnan(x));",See=_e({opSnippet:Cee,dtype:"bool"}),AL={kernelName:Hi,backendName:"webgl",kernelFunc:See};var Iee="return float(a < b);",Nee=`
return vec4(lessThan(a, b));
`,Tee=st({opSnippet:Iee,packedOpSnippet:Nee,cpuKernelImpl:eP,dtype:"bool"}),$L={kernelName:qi,backendName:"webgl",kernelFunc:Tee};var Eee="return float(a <= b);",Aee=`
return vec4(lessThanEqual(a, b));
`,$ee=st({opSnippet:Eee,packedOpSnippet:Aee,cpuKernelImpl:tP,dtype:"bool"}),DL={kernelName:Ki,backendName:"webgl",kernelFunc:$ee};function Dee(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=rP(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var RL={kernelName:Dc,backendName:"webgl",kernelFunc:Dee};var Ree=`if (x < 0.0) return NAN;
return log(x);`,Fee=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,Oee=_e({opSnippet:Ree,packedOpSnippet:Fee,cpuKernelImpl:nP}),FL={kernelName:Go,backendName:"webgl",kernelFunc:Oee};var Pee="return log(1.0 + x);",Mee=_e({opSnippet:Pee}),OL={kernelName:Xi,backendName:"webgl",kernelFunc:Mee};var Lee="return float(a >= 1.0 && b >= 1.0);",zee=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,Bee=st({opSnippet:Lee,packedOpSnippet:zee,dtype:"bool"}),PL={kernelName:Yi,backendName:"webgl",kernelFunc:Bee};var Vee="return float(!(x >= 1.0));",Wee=_e({opSnippet:Vee}),ML={kernelName:jl,backendName:"webgl",kernelFunc:Wee};var jee="return float(a >= 1.0 || b >= 1.0);",Gee=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Uee=st({opSnippet:jee,packedOpSnippet:Gee,dtype:"bool"}),LL={kernelName:Gl,backendName:"webgl",kernelFunc:Uee};var $S=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}};var DS=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * 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 - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${l};
setOutput(result);
}
`}};var Hee=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=j().getBool("WEBGL_PACK_NORMALIZATION")?new DS(o.shape,s,a,i,l):new $S(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},zL={kernelName:tl,backendName:"webgl",kernelFunc:Hee};var RS=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${o}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${o})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var qee=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new RS(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},BL={kernelName:Rc,backendName:"webgl",kernelFunc:qee};function VL(r,e,t,n){let o=b.sizeFromShape(e),a=b.sizeFromShape(r.shape)/o,i=ae({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Ln(i,r.dtype,"max",n),u=ae({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function FS(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let x=t.texData.get(f.dataId).values,k=new Array(i);for(let $=0;$<k.length;$++)k[$]=o.shape[c[$]];let C=Uu(x,o.shape,o.dtype,c,k);f=t.makeTensorInfo(k,o.dtype);let A=t.texData.get(f.dataId);A.values=C}else f=Rl(o,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("max",u,i);let[d,h]=S.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=S.expandShapeToKeepDim(d,l));let y;if(m){let x=t.texData.get(f.dataId).values,k=oP(x,b.sizeFromShape(h),g,o.dtype);y=t.makeTensorInfo(g,o.dtype);let C=t.texData.get(y.dataId);C.values=k}else y=VL(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),y}var WL={kernelName:Uo,backendName:"webgl",kernelFunc:FS};var Kee=wy+`
return max(a, b);
`,Xee=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+$l+`
return result;
`,Yee=st({opSnippet:Kee,packedOpSnippet:Xee,cpuKernelImpl:sP}),jL={kernelName:Ho,backendName:"webgl",kernelFunc:Yee};function Zee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Ds(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;b.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&b.arraysEqual(c.inShape,c.outShape))return Yt({inputs:{x:o},backend:t});let p=new wi(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var GL={kernelName:qo,backendName:"webgl",kernelFunc:Zee};function Jee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new Xu(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var UL={kernelName:rl,backendName:"webgl",kernelFunc:Jee};var OS=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},PS=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${l};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Qee(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new Xu(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new PS(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var HL={kernelName:Oc,backendName:"webgl",kernelFunc:Qee};function ete(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;Ds([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new wi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new OS(m),y=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),y}var qL={kernelName:Fc,backendName:"webgl",kernelFunc:ete};function KL(r,e,t,n){let o=new wi(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new wi(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var XL={kernelName:Pc,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;b.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];b.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,o,s,u,a),[p,m]=KL(n,i,c,l);return[p,m]}};function YL(r,e,t,n){let o=b.sizeFromShape(e),a=b.sizeFromShape(r.shape)/o,i=ae({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Ln(i,"float32","mean",n),u=ae({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var ZL={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=b.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let k=a.texData.get(d.dataId).values,C=new Array(i);for(let R=0;R<C.length;R++)C[R]=n.shape[c[R]];let A=Uu(k,n.shape,n.dtype,c,C);d=a.makeTensorInfo(C,n.dtype);let $=a.texData.get(d.dataId);$.values=A}else d=Rl(n,c,a);f.push(d),u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=S.computeOutAndReduceShapes(d.shape,u),y=h;o&&(y=S.expandShapeToKeepDim(h,l));let w=YL(d,g,y,a);for(let x of f)a.disposeIntermediateTensorInfo(x);return w}};function tte(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Ft({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ae({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Ln(h,h.dtype,"min",t),y;if(a){let w=S.expandShapeToKeepDim(m,l);y=ae({inputs:{x:g},backend:t,attrs:{shape:w}})}else y=ae({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var JL={kernelName:Xo,backendName:"webgl",kernelFunc:tte};var rte=wy+`
return min(a, b);
`,nte=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+$l+`
return result;
`,ote=st({opSnippet:rte,packedOpSnippet:nte,cpuKernelImpl:iP}),QL={kernelName:Yo,backendName:"webgl",kernelFunc:ote};var MS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let o=e.length,s=ze(o),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),u=n==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${s} coords = outC - start;
setOutput(getX(${l}));
}
`}};var LS=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=ze(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Xt("rc",o),u=Xt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {
${d}
result[2] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${u.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var ste=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new LS(n.shape,o,s):new MS(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},ez={kernelName:Zo,backendName:"webgl",kernelFunc:ste};var ite=`if (b == 0.0) return NAN;
return mod(a, b);`,ate=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+$l+`
return result;
`,lte=st({opSnippet:ite,packedOpSnippet:ate}),tz={kernelName:Zi,backendName:"webgl",kernelFunc:lte};var zS=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}};var ute=`
if (a == b) {
return 1.0;
};
return a / b;`,cte=`
// 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;
`,BS=st({opSnippet:ute,packedOpSnippet:cte,checkOutOfBounds:!0}),rz={kernelName:Po,backendName:"webgl",kernelFunc:BS};var nz="return a - b;",VS=st({opSnippet:nz,packedOpSnippet:nz,supportsComplex:!0,cpuKernelImpl:_P}),oz={kernelName:hs,backendName:"webgl",kernelFunc:VS};function WS(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=b.parseAxisParam([s],o.shape),i=FS({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=S.expandShapeToKeepDim(i.shape,a),u=ae({inputs:{x:i},backend:t,attrs:{shape:l}}),c=VS({inputs:{a:o,b:u},backend:t}),p=vS({inputs:{x:c},backend:t}),m=qu({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ae({inputs:{x:m},backend:t,attrs:{shape:l}}),d=BS({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var sz={kernelName:fs,backendName:"webgl",kernelFunc:WS};function pte(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:WS({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new zS(u,c,s),m=[[a]],f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var iz={kernelName:Mc,backendName:"webgl",kernelFunc:pte};var az="return -x;";function mte(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=lP(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fs(n.shape,az):o=new Cn(n.shape,az),t.runWebGLProgram(o,[n],n.dtype)}var lz={kernelName:Hs,backendName:"webgl",kernelFunc:mte};var fte=Mr.nonMaxSuppressionV3Impl;function dte(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=fte(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var uz={kernelName:Qi,backendName:"webgl",kernelFunc:dte};var hte=Mr.nonMaxSuppressionV4Impl;function gte(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=hte(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var cz={kernelName:ea,backendName:"webgl",kernelFunc:gte};var xte=Mr.nonMaxSuppressionV5Impl;function yte(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:y}=xte(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var pz={kernelName:ta,backendName:"webgl",kernelFunc:yte};var jS=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${n}),
float(index == coords.y)));
}
`}};var bte=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=b.sizeFromShape(o.shape),u=new jS(l,s,a,i),c=ae({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ae({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},mz={kernelName:Qo,backendName:"webgl",kernelFunc:bte};function kh(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=za({inputs:{input:n},backend:t}),s=kh({inputs:{x:o},backend:t}),a=Yu({inputs:{input:n},backend:t}),i=kh({inputs:{x:a},backend:t}),l=Sn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Ba({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var fz={kernelName:ti,backendName:"webgl",kernelFunc:kh};function dz(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=za({inputs:{input:n},backend:t}),s=dz({inputs:{x:o},backend:t}),a=Yu({inputs:{input:n},backend:t}),i=kh({inputs:{x:a},backend:t}),l=Sn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Ba({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var hz={kernelName:qs,backendName:"webgl",kernelFunc:dz};function wte(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return Ay({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{b.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),b.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Ay({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=cS({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var gz={kernelName:Ks,backendName:"webgl",kernelFunc:wte};var GS=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=ze(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${l}));
}
}
`}};var US=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=ze(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Xt("rc",o),u=Xt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
if(${c}) {
`,o===1?"":`}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${l[o-1]} += 1;
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${p});
}
`;d+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var HS=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n;if(b.sizeFromShape(o.shape)===0){let u=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ba({backend:t,attrs:{shape:u,value:a,dtype:o.dtype}})}let i=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new US(o.shape,s,a):new GS(o.shape,s,a),l=[[a]];return t.runWebGLProgram(i,[o],o.dtype,l)},xz={kernelName:es,backendName:"webgl",kernelFunc:HS};var kte=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,_te=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+$l+`
return result;
`,vte=st({opSnippet:kte,packedOpSnippet:_te}),yz={kernelName:ts,backendName:"webgl",kernelFunc:vte};function Cte(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=b.parseAxisParam(s,o.shape),c=u,p=S.getAxesPermutation(c,i),m=o;p!=null&&(m=Ft({inputs:{x:o},backend:t,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,i),l.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=cP(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,y,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=b.sizeFromShape(h),y=ae({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),w=Zl(o.dtype),x=Ln(y,w,"prod",t);f=ae({inputs:{x},backend:t,attrs:{shape:d}}),l.push(y),l.push(x)}if(a){l.push(f);let d=S.expandShapeToKeepDim(f.shape,u);f=ae({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var bz={kernelName:ra,backendName:"webgl",kernelFunc:Cte};var qS=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=pP(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},wz={kernelName:nl,backendName:"webgl",kernelFunc:qS};var Ste="return 1.0 / x;",Ite=_e({opSnippet:Ste}),kz={kernelName:na,backendName:"webgl",kernelFunc:Ite};var Nte=br+`
return (x < 0.0) ? 0.0 : x;
`,Tte=`
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;
`,Ete=_e({opSnippet:Nte,packedOpSnippet:Tte}),_z={kernelName:ns,backendName:"webgl",kernelFunc:Ete};var Ate=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,$te=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Dte=_e({opSnippet:Ate,packedOpSnippet:$te}),vz={kernelName:ss,backendName:"webgl",kernelFunc:Dte};var KS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}};var XS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function Rte(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=j().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new XS(o.shape,l,u,s,a):new KS(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var Cz={kernelName:os,backendName:"webgl",kernelFunc:Rte};var YS=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Fte(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new YS(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var Sz={kernelName:Bc,backendName:"webgl",kernelFunc:Fte};var ZS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};var JS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=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]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${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 < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Ote(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=j().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new JS(o.shape,l,u,s,a):new ZS(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var Iz={kernelName:ol,backendName:"webgl",kernelFunc:Ote};var QS=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Pte(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new QS(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var Nz={kernelName:zc,backendName:"webgl",kernelFunc:Pte};var eI=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=ze(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var tI=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let o=Xt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ze(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${l(o.slice())};
if(${s}){
result.g = ${u(o.slice())};
}
if(${a}) {
result.b = ${c(o.slice())};
if(${s}) {
result.a = ${p(o.slice())};
}
}
setOutput(result);
}
`;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((w,x)=>f(x,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Mte(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=b.parseAxisParam(s,o.shape);if(a===0)return Yt({inputs:{x:o},backend:t});let l=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tI(o.shape,i):new eI(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var Tz={kernelName:is,backendName:"webgl",kernelFunc:Mte};var rI=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],o=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${s}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var Ez={kernelName:ma,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new rI(n.shape,s),[u,c]=S.getImageCenter(a,n.shape[1],n.shape[2]),p=[[u,c,Math.sin(o),Math.cos(o)]];return i.runWebGLProgram(l,[n],n.dtype,p)}};var Lte=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,zte=_e({opSnippet:Lte}),Az={kernelName:as,backendName:"webgl",kernelFunc:zte};var Bte="return inversesqrt(x);",Vte=_e({opSnippet:Bte,cpuKernelImpl:mP}),$z={kernelName:ls,backendName:"webgl",kernelFunc:Vte};var _h=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=ze(s.length),u=ze(a.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=t>1?"strides[j]":"strides";this.userCode=`
${l} strides = ${l}(${s});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Wte(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=S.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=ae({inputs:{x:o},backend:t,attrs:{shape:[l,i]}}),d=ae({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new _h(l,i,f.shape.length,d.shape.length,c,m),y=t.runWebGLProgram(g,[d,f,h],d.dtype),w=ae({inputs:{x:y},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(h),w}var Dz={kernelName:oa,backendName:"webgl",kernelFunc:Wte};var nI=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);o=l.join(),s=u.join()}let a=ze(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function jte(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new nI(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],pr(o.dtype,s.dtype))}var Rz={kernelName:Ys,backendName:"webgl",kernelFunc:jte};var Gte=`
// 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);
`,Ute=_e({opSnippet:Gte}),Fz={kernelName:sa,backendName:"webgl",kernelFunc:Ute};var Oz="return 1.0 / (1.0 + exp(-1.0 * x));",Hte=_e({opSnippet:Oz,packedOpSnippet:Oz,cpuKernelImpl:fP}),Pz={kernelName:cs,backendName:"webgl",kernelFunc:Hte};var qte=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Kte=_e({opSnippet:qte}),Mz={kernelName:aa,backendName:"webgl",kernelFunc:Kte};var Xte=ky+`
return sin(x);
`,Yte=_e({opSnippet:Xte}),Lz={kernelName:us,backendName:"webgl",kernelFunc:Yte};var Zte=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Jte=_e({opSnippet:Zte}),zz={kernelName:ia,backendName:"webgl",kernelFunc:Jte};var Qte=`
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;
`,ere=_e({opSnippet:Qte}),Bz={kernelName:la,backendName:"webgl",kernelFunc:ere};var tre=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;b.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((y,w)=>y*w),l=[[0,0]];l.push(...a);for(let y=1+s.length;y<o.shape.length;++y)l.push([0,0]);let u=[],c=HS({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,i,!1),d=ae({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Ft({inputs:{x:d},backend:t,attrs:{perm:m}}),g=ae({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(y=>t.disposeIntermediateTensorInfo(y)),g},Vz={kernelName:Js,backendName:"webgl",kernelFunc:tre};function rre(r){let{inputs:e,backend:t}=r,{indices:n,values:o,denseShape:s,defaultValue:a}=e;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(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${a.shape}`);let i=t.readSync(n.dataId),l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[p,m,f,d,h]=hP(i,n.shape,n.dtype,l,o.dtype,u,c);return[t.makeTensorInfo(m,n.dtype,p),t.makeTensorInfo([m[0]],o.dtype,f),t.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),t.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var Wz={kernelName:Vc,backendName:"webgl",kernelFunc:rre};function nre(r){let{inputs:e,backend:t}=r,{inputIndices:n,inputShape:o,newShape:s}=e;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 a=Array.from(t.readSync(o.dataId)),i=t.readSync(n.dataId),l=Array.from(t.readSync(s.dataId)),[u,c,p]=gP(i,n.shape,n.dtype,a,l);return[t.makeTensorInfo(c,n.dtype,u),t.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var jz={kernelName:Wc,backendName:"webgl",kernelFunc:nre};function ore(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;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 a=t.readSync(n.dataId),i=t.readSync(o.dataId),l=t.readSync(s.dataId),[u,c]=yy(a,n.shape,n.dtype,i,l,!0);return t.makeTensorInfo(c,n.dtype,u)}var Gz={kernelName:jc,backendName:"webgl",kernelFunc:ore};function sre(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;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
${s.shape}`);let a=t.readSync(n.dataId),i=t.readSync(o.dataId),l=t.readSync(s.dataId),[u,c]=yy(a,n.shape,n.dtype,i,l);return t.makeTensorInfo(c,n.dtype,u)}var Uz={kernelName:Gc,backendName:"webgl",kernelFunc:sre};function ire(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=!1,f=new _h(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=ae({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var Hz={kernelName:Uc,backendName:"webgl",kernelFunc:ire};function are(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=b.parseAxisParam(a,o.shape)[0],l=S.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Ps({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var qz={kernelName:Qs,backendName:"webgl",kernelFunc:are};var Kz="return sqrt(x);",lre=_e({opSnippet:Kz,packedOpSnippet:Kz,cpuKernelImpl:xP}),Xz={kernelName:ps,backendName:"webgl",kernelFunc:lre};var ure="return x * x;",cre=_e({opSnippet:ure}),Yz={kernelName:sl,backendName:"webgl",kernelFunc:cre};var Zz="return (a - b) * (a - b);",pre=st({opSnippet:Zz,packedOpSnippet:Zz}),Jz={kernelName:ds,backendName:"webgl",kernelFunc:pre};function mre({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=br+`
return x > 0.0 ? 1.0 : float(${e.alpha});
`,s=new Cn(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var Qz={kernelName:ro,backendName:"webgl",kernelFunc:mre};var oI=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=ze(n.length),a=ze(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function fre(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:y,outShape:w}=ar.sliceInfo(o.shape,s,a,i,l,u,c,p,m),x=ae({inputs:{x:o},backend:t,attrs:{shape:y}}),k;if(f){let A=Ps({inputs:{x},backend:t,attrs:{begin:d,size:g}});k=ae({inputs:{x:A},backend:t,attrs:{shape:w}}),t.disposeIntermediateTensorInfo(A)}else if(w.some(A=>A===0))k=t.makeTensorInfo(w,o.dtype,[]);else if(t.shouldExecuteOnCPU([x])){let R=t.texData.get(x.dataId).values,P=Se(x.shape,x.dtype,R),M=yP(w,P,h,d);k=t.makeTensorInfo(w,x.dtype,M.values)}else{let $=new oI(d,h,w);k=t.runWebGLProgram($,[x],x.dtype)}let C=ae({inputs:{x:k},backend:t,attrs:{shape:w}});return t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(k),C}var e3={kernelName:ua,backendName:"webgl",kernelFunc:fre};function dre(r){let{inputs:e,backend:t,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:a,rightPad:i,padWidth:l,preserveShortSequences:u}=n,{data:c,dataSplits:p}=e,m=t.readSync(c.dataId),f=t.readSync(p.dataId),[d,h]=bP(m,f,o,s,a,i,l,u);return[t.makeTensorInfo([d.length],"string",d),t.makeTensorInfo(p.shape,"int32",h)]}var t3={kernelName:Hc,backendName:"webgl",kernelFunc:dre};function hre(r){let{inputs:e,backend:t,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:a}=e;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(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),l=t.readSync(a.dataId)[0],[u,c,p]=wP(i,l,o),m=c.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",c),t.makeTensorInfo([2],"int32",new Int32Array(p))]}var r3={kernelName:qc,backendName:"webgl",kernelFunc:hre};function gre(r){let{inputs:e,backend:t,attrs:n}=r,{numBuckets:o}=n,{input:s}=e;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 a=t.readSync(s.dataId),i=kP(a,o);return t.makeTensorInfo(s.shape,"int32",i)}var n3={kernelName:Kc,backendName:"webgl",kernelFunc:gre};var xre="return tan(x);",yre=_e({opSnippet:xre}),o3={kernelName:gs,backendName:"webgl",kernelFunc:yre};var bre=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,wre=_e({opSnippet:bre}),s3={kernelName:xs,backendName:"webgl",kernelFunc:wre};var sI=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=ze(this.rank),s=kre(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function kre(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function iI(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let l=t.readSync(o.dataId),u=o.dtype==="string"?l.map(m=>b.decodeString(m)):l,c=Se(o.shape,o.dtype,u),p=vP(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new sI(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var i3={kernelName:jn,backendName:"webgl",kernelFunc:iI};var aI=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},lI=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function Ju(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function a3(r){let e=1;for(;e<r;)e*=2;return e}function _re(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=j().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=j().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=o.shape,c=u[u.length-1];if(t.shouldExecuteOnCPU([o])||c<i||s>l){let M=t.readSync(o.dataId),[V,W]=CP(M,u,o.dtype,s,a);return[t.makeTensorInfo(V.shape,V.dtype,V.values),t.makeTensorInfo(W.shape,W.dtype,W.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,o.dtype,[]),t.makeTensorInfo(u,"int32",[])];if(c===1)return[o,Ba({attrs:{shape:u,dtype:"int32",value:0},backend:t})];let p=t.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?t.unpackTensor(o):o,h=b.sizeFromShape(u)/c,g=ae({inputs:{x:f},attrs:{shape:[h,c]},backend:t});m&&Ju(t,f);let y=a3(s),w=a3(c),x=null,k=()=>x===null?[g,g]:[g,x],C=(M,V,W)=>{let G=k(),U=new aI(W),K=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],re=x;x=t.runWebGLProgram(U,G,"int32",K),Ju(t,re)};for(let M=1;M<y;M*=2){let V=M*2;for(let W=M;W>=1;W/=2)C(V,W,[h,w])}for(let M=w;M>y;M/=2){let V=k(),W=new lI([h,M/2]),U=[[c],[x===null?1:0],[y]],H=x;x=t.runWebGLProgram(W,V,"int32",U),Ju(t,H);let K=y/2,re=K*2;for(let X=K;X>=1;X/=2)C(re,X,x.shape)}let A=x;x=Ps({inputs:{x},backend:t,attrs:{begin:0,size:[h,s]}}),Ju(t,A);let $=AS({inputs:{x:g,indices:x},backend:t,attrs:{axis:1,batchDims:1}});Ju(t,g);let R=u.slice(0,-1);R.push(s),A=x,x=ae({inputs:{x},attrs:{shape:R},backend:t}),Ju(t,A);let P=$;return $=ae({inputs:{x:$},attrs:{shape:R},backend:t}),Ju(t,P),[$,x]}var l3={kernelName:ca,backendName:"webgl",kernelFunc:_re};var uI=class{constructor(e,t,n,o,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,l;switch(o){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${l} == 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 (${l} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function vre(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=n,[c,p,m,f]=o.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],y=new uI(p,m,a,i,l,g);return t.runWebGLProgram(y,[o,s],"float32")}var u3={kernelName:pa,backendName:"webgl",kernelFunc:vre};function Cre(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;Ds(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=SP(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var c3={kernelName:Xc,backendName:"webgl",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Ps({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),y=ae({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=y,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var p3={kernelName:ei,backendName:"webgl",kernelFunc:Sre};var cI=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="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 = ${l};
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(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
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
);
${m}
}
setOutput(${u});
}
`}};function Ire(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,segmentIds:s}=e,{numSegments:a}=n,i=o.shape.length,l=[],u=0,c=S.getAxesPermutation([u],i),p=o;c!=null&&(p=Ft({inputs:{x:o},backend:t,attrs:{perm:c}}),l.push(p),u=S.getInnerMostAxes(1,i)[0]);let m=S.segment_util.computeOutShape(p.shape,u,a),f=b.sizeFromShape([p.shape[u]]),d=ae({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=Zl(o.dtype),g=(k,C,A,$,R)=>{let P=k.shape[0],M=k.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,R),W={windowSize:V,inSize:M,batchSize:P,numSegments:R},G=new cI(W,C),U=t.compileAndRun(G,[k,A],$);if(l.push(U),U.shape[1]===R)return U;let H=qS({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),K=iI({inputs:{x:H},backend:t,attrs:{reps:[M/V]}});return l.push(H),l.push(K),g(U,C,K,$,R)},y=g(d,"unsortedSegmentSum",s,h,a),w=ae({inputs:{x:y},backend:t,attrs:{shape:m}}),x=w;if(c!=null){l.push(w);let k=S.getUndoAxesPermutation(c);x=Ft({inputs:{x},backend:t,attrs:{perm:k}})}return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),x}var m3={kernelName:il,backendName:"webgl",kernelFunc:Ire};var Nre=[zL,BL,eM,rM,nM,oM,iM,aM,lM,uM,mM,fM,dM,hM,xM,gM,yM,wM,bM,kM,_M,vM,CM,IM,NM,$M,RM,FM,PM,WP,LM,BM,VM,zM,jM,GM,WM,UM,HM,qM,YM,ZM,JM,eL,tL,QM,rL,nL,oL,sL,iL,aL,lL,cL,pL,fL,dL,hL,gL,yL,bL,wL,kL,_L,vL,CL,SL,IL,VP,NL,MM,TL,EL,AL,jP,$L,DL,RL,OL,FL,PL,ML,LL,WL,UL,GL,HL,qL,XL,jL,ZL,JL,QL,ez,tz,iz,KP,lz,uz,cz,pz,TM,mz,hz,gz,xz,yz,GP,bz,wz,EM,rz,kz,vz,_z,YP,Cz,Sz,Iz,Nz,Tz,Ez,Az,$z,Dz,Rz,Fz,Pz,Mz,Lz,zz,SM,sz,Bz,Vz,Wz,jz,Gz,Uz,Hz,qz,Xz,Yz,Jz,Qz,e3,t3,r3,n3,oz,JP,o3,s3,i3,l3,u3,QP,c3,p3,m3,fz];for(let r of Nre)Ul(r);var Ot;(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"})(Ot||(Ot={}));var Fl;(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"})(Fl||(Fl={}));var f3;function Tre(r){f3=r.wasm.cwrap(ri,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ere(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Fl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?o.shape[2]:o.shape[1],w=u?s.shape[1]:s.shape[2],x=o.shape[0],k=t.makeOutput([x,y,w],o.dtype),C=t.dataIdMap.get(k.dataId).id,A=new Uint8Array(new Int32Array(o.shape).buffer),$=new 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Please use 'NHWC'.`);let Q=n.makeOutput(h.outShape,"float32"),se=n.dataIdMap.get(Q.dataId).id,pe=i==null?0:n.dataIdMap.get(i.dataId).id;return pB(y,re,X,ne,w,C,A,k,$,R,P,M,K,V,W,G,U,H,x,g,pe,d||0,se),Q}var mB={kernelName:ni,backendName:"wasm",setupFunc:gne,kernelFunc:xne};var fB;function yne(r){fB=r.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bne(r){let{inputs:e,attrs:t,backend:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=S.computeConv2DInfo(o.shape,s.shape,l,c,u,m,!0),g=Fl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(o.dataId).id,w=n.dataIdMap.get(s.dataId).id,x=h.outChannels,k=0;if(a!=null){let ie=n.dataIdMap.get(a.dataId);if(ie.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ie.shape.length}.`);if(ie.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ie.shape}) does not match the number of output channels (${x})`);k=ie.id}let C=h.filterHeight,A=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,P=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,W=h.dilationWidth,G=h.strideHeight,U=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,re=h.batchSize,X=h.inHeight,ne=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let Q=n.makeOutput(h.outShape,"float32"),se=n.dataIdMap.get(Q.dataId).id,pe=i==null?0:n.dataIdMap.get(i.dataId).id;return fB(y,re,X,ne,w,C,A,k,$,R,P,M,K,V,W,G,U,H,x,g,pe,d||0,se),Q}var dB={kernelName:oi,backendName:"wasm",setupFunc:yne,kernelFunc:bne};var hB;function wne(r){hB=r.wasm.cwrap(Wi,null,["number","number","number","number","number","number","array","number"])}function kne(r){let{backend:e,inputs:t}=r,{params:n,indices:o}=t,[s,a,i,l]=pg.prepareAndValidate(n,o),u=e.makeOutput(s,n.dtype);if(a===0)return u;let c=o.shape,p=c[c.length-1],f=e.dataIdMap.get(n.dataId).id,h=e.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=e.dataIdMap.get(u.dataId).id;return hB(f,Ot[n.dtype],h,a,p,i,g,y),u}var gB={kernelName:Wi,backendName:"wasm",setupFunc:wne,kernelFunc:kne};var xB;function _ne(r){xB=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function vne(r){let{backend:e,inputs:t,attrs:n}=r,{x:o,indices:s}=t,{axis:a,batchDims:i}=n,l=b.parseAxisParam(a,o.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=sr({inputs:{x:o},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:e}),p=b.sizeFromShape(s.shape),m=sr({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:e}),f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],d=e.makeOutput(f,o.dtype);if(b.sizeFromShape(o.shape)===0)return d;let h=c.shape.length-1,y=e.dataIdMap.get(c.dataId).id,x=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(d.dataId).id,C=new Uint8Array(new Int32Array(b.computeStrides(c.shape)).buffer),A=new Uint8Array(new Int32Array(b.computeStrides(f)).buffer);return xB(y,Ot[o.dtype],C,h,x,u.batchSize,A,k),e.disposeData(c.dataId),e.disposeData(m.dataId),d.shape=u.outputShape,d}var yB={kernelName:Us,backendName:"wasm",setupFunc:_ne,kernelFunc:vne};var Cne=!1,bB=yt(ji,Cne,"bool");var Sne=!1,wB=yt(Wo,Sne,"bool");var kB;function Ine(r){kB=r.wasm.cwrap(jo,null,["number","number","number"])}function Nne(r){let{inputs:{x:e},attrs:{alpha:t},backend:n}=r,o=n.dataIdMap.get(e.dataId).id,s=n.makeOutput(e.shape,e.dtype);if(b.sizeFromShape(e.shape)!==0){let a=n.dataIdMap.get(s.dataId).id;kB(o,t,a)}return s}var _B={kernelName:jo,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne};var Tne=!1,vB=yt(qi,Tne,"bool");var Ene=!1,CB=yt(Ki,Ene,"bool");var SB=it(Go);var Ane=!1,IB=yt(Yi,Ane,"bool");var NB;function $ne(r){NB=r.wasm.cwrap(Uo,null,["number, number, number"])}function Dne(r){let{backend:e,inputs:t,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:a}=t,l=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=cn(a,o,e);if(f){let x=e.dataIdMap.get(c.dataId).id;u=c,l=x}let d=u.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(u.shape,p),y=b.sizeFromShape(g),w=e.makeOutput(h,a.dtype);if(b.sizeFromShape(u.shape)!==0){let x=e.dataIdMap.get(w.dataId).id;NB(l,y,x)}if(f&&e.disposeData(c.dataId),s){let x=S.expandShapeToKeepDim(w.shape,m);w.shape=x}return w}var TB={kernelName:Uo,backendName:"wasm",setupFunc:$ne,kernelFunc:Dne};var Rne=!1,EB=yt(Ho,Rne);var AB;function Fne(r){AB=r.wasm.cwrap(qo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function One(r){let{inputs:e,attrs:t,backend:n}=r,o=e.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=S.computePool2DInfo(o.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,y=c.dilationHeight,w=c.dilationWidth,x=c.strideHeight,k=c.strideWidth,C=c.inChannels,A=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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Conv2DBackpropFilter,$o as Conv2DBackpropInput,Ja as Conv3D,kc as Conv3DBackpropFilterV2,_c as Conv3DBackpropInputV2,Do as Cos,Ro as Cosh,Pi as CropAndResize,Fo as Cumsum,I_ as CustomCallback,Ka as DataStorage,vc as DenseBincount,Mi as DepthToSpace,Oo as DepthwiseConv2dNative,Cc as DepthwiseConv2dNativeBackpropFilter,Sc as DepthwiseConv2dNativeBackpropInput,Ic as Diag,Qa as Dilation2D,Dm as Dilation2DBackpropFilter,$m as Dilation2DBackpropInput,gw as ENV,fv as EarlyStopping,Nc as Einsum,Mo as Elu,Tc as EluGrad,Yh as Environment,zi as Equal,Li as Erf,Lo as Exp,Gs as ExpandDims,Bi as Expm1,Ec as FFT,el as Fill,Vi as FlipLeftRight,zo as Floor,Bo as FloorDiv,Rm as FromPixels,Vo as FusedBatchNorm,ni as FusedConv2D,oi as FusedDepthwiseConv2D,hy as GPGPUContext,Wi as GatherNd,Us as GatherV2,jv as GraphModel,ji as Greater,Wo as GreaterEqual,S_ as History,Ac as IFFT,to as Identity,$c as Imag,_t as InputSpec,Gi as IsFinite,Ui as IsInf,Hi as IsNan,Ls as KernelBackend,tl as LRN,Rc as LRNGrad,Yg as LayerVariable,Xn as LayersModel,jo as LeakyRelu,qi as Less,Ki as LessEqual,Dc as LinSpace,Go as Log,Xi as Log1p,YI as LogSoftmax,Yi as LogicalAnd,jl as LogicalNot,Gl as LogicalOr,Pu as MathBackendCPU,Hu as MathBackendWebGL,Uo as Max,qo as MaxPool,rl as MaxPool3D,Oc as MaxPool3DGrad,Fc as MaxPoolGrad,Pc as MaxPoolWithArgmax,Ho as Maximum,Ko as Mean,Xo as Min,Yo as Minimum,Zo as MirrorPad,Zi as Mod,kp as MomentumOptimizer,Mc as Multinomial,Jo as Multiply,Hs as Neg,Qi as NonMaxSuppressionV3,ea as NonMaxSuppressionV4,ta as NonMaxSuppressionV5,Ji as NotEqual,S1 as OP_SCOPE_SUFFIX,Qo as OneHot,qs as OnesLike,Wr as Optimizer,Ks as Pack,es as PadV2,fse as Pool,ts as Pow,rs as Prelu,ra as Prod,_p as RMSPropOptimizer,On as RNN,nl as Range,Iw as Rank,Lc as Real,Po as RealDiv,na as Reciprocal,qt as Reduction,ns as Relu,ss as Relu6,Xs as Reshape,os as ResizeBilinear,Bc as ResizeBilinearGrad,ol as ResizeNearestNeighbor,zc as ResizeNearestNeighborGrad,is as Reverse,ma as RotateWithOffset,as as Round,ls as Rsqrt,dl as SGDOptimizer,oa as ScatterNd,Ys as Select,sa as Selu,$a as Sequential,cs as Sigmoid,aa as Sign,us as Sin,ia as Sinh,Zs as Slice,fs as Softmax,la as Softplus,Js as SpaceToBatchND,Vc as SparseFillEmptyRows,Wc as SparseReshape,jc as SparseSegmentMean,Gc as SparseSegmentSum,Uc as SparseToDense,Qs as SplitV,ps as Sqrt,sl as Square,ds as SquaredDifference,ro as Step,ua as StridedSlice,Hc as StringNGrams,qc as StringSplit,Kc as StringToHashBucketFast,hs as Sub,ms as Sum,on as SymbolicTensor,gs as Tan,xs as Tanh,je as Tensor,ct as TensorBuffer,jn as Tile,ca as TopK,pa as Transform,ys as Transpose,Xc as Unique,ei as Unpack,il as UnsortedSegmentSum,ul as Variable,ti as ZerosLike,ri as _FusedMatMul,Tt as abs,tk as acos,rk as acosh,Y as add,nk as addN,Gm as all,lp as any,ba as argMax,ok as argMin,sk as asin,ik as asinh,ak as atan,lk as atan2,uk as atanh,nu as avgPool,Um as avgPool3d,kN as backend,S as backend_util,b4 as basicLSTMCell,ai as batchNorm,fk as batchNorm2d,dk as batchNorm3d,hk as batchNorm4d,ou as batchToSpaceND,Hm as bincount,XIe as booleanMaskAsync,gk as broadcastArgs,su as broadcastTo,cg as browser,Se as buffer,PX as callbacks,J as cast,xk as ceil,Sr as clipByValue,hn as clone,$n as complex,tt as concat,yk as concat1d,bk as concat2d,wk as concat3d,kk as concat4d,PE as constraints,qm as conv1d,Dn as conv2d,Km as conv2dTranspose,Xm as conv3d,_k as conv3dTranspose,wse as copyRegisteredKernels,iu as cos,Ym as cosh,Tg as cosineWindow,Zm as cumsum,Qr as customGrad,s0 as data,vk as denseBincount,ek as deprecationWarn,Ck as depthToSpace,ka as depthwiseConv2d,LX as deregisterOp,Ql as device_util,K4 as diag,Sk as dilation2d,nue as disableDeprecationWarnings,Ae as dispose,oue as disposeVariables,ue as div,Ik as divNoNan,rU as dot,UN as dropout,Nk as einsum,_a as elu,rue as enableDebugMode,tue as enableProdMode,HN as enclosingPowerOfTwo,ks as engine,j as env,Dr as equal,Tk as erf,tr as exp,gr as expandDims,Ek as expm1,pp as eye,gu as fft,_s as fill,cue as findBackend,pue as findBackendFactory,va as floor,jm as floorDiv,BP as forceHalfFloat,lo as fused,li as gather,jN as gatherND,pg as gather_util,lue as getBackend,bw as getGradient,Om as getKernel,Jh as getKernelsForBackend,VO as gpgpu_util,NU as grad,TU as grads,Ht as greater,Un as greaterEqual,fl as ifft,au as imag,bn as image,sNe as inTopKAsync,rA as initializers,O_ as input,$r as io,ff as irfft,xU as isFinite,bU as isInf,Ak as isNaN,Dt as keep,Mr as kernel_impls,MA as layers,lu as leakyRelu,Jm as less,Hn as lessEqual,RT as linalg,$k as linspace,m7 as loadGraphModel,K5 as loadLayersModel,Dk as localResponseNormalization,Ir as log,uu as log1p,FU as logSigmoid,Qm as logSoftmax,Pk as logSumExp,Fr as logicalAnd,cu as logicalNot,tf as logicalOr,HU as logicalXor,tFe as losses,Me as matMul,oN as math,Vr as max,pu as maxPool,rf as maxPool3d,Mk as maxPoolWithArgmax,Rn as maximum,Ct as mean,Wm as memory,JU as meshgrid,LA as metrics,mp as min,Ca as minimum,Lk as mirrorPad,zk as mod,H5 as model,zA as models,fp as moments,k1e as movingAverage,F as mul,iH as multiRNNCell,Bk as multinomial,Ke as neg,xf as nextFrame,Ig as norm,ci as notEqual,xa as oneHot,rr as ones,xr as onesLike,I as op,pH as outerProduct,xn as pad,dH as pad1d,gH as pad2d,yH as pad3d,wH as pad4d,SH as pool,yn as pow,fu as prelu,Gw as print,nf as prod,sue as profile,AH as rand,LH as randomGamma,_g as randomNormal,vs as randomUniform,Sa as range,aue as ready,ml as real,Zk as reciprocal,ap as registerBackend,X5 as registerCallbackConstructor,ZI as registerGradient,Ul as registerKernel,MX as registerOp,BA as regularizers,Or as relu,of as relu6,uue as removeBackend,O as reshape,lr as reverse,qH as reverse1d,XH as reverse2d,ZH as reverse3d,QH as reverse4d,xu as rfft,sf as round,af as rsqrt,ce as scalar,VN as scatterND,fg as scatter_util,lf as selu,Jk as separableConv2d,q5 as sequential,ee as serialization,VG as setBackend,mue as setPlatform,Hoe as setWasmPath,qoe as setWasmPaths,W0 as setWebGLContext,Qk as 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/**
* @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 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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