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

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o=++this.pendingBackendInitId,s=r.then(c=>o<this.pendingBackendInitId?!1:(this.registry[t]=c,this.pendingBackendInit=null,!0)).catch(c=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${t} failed`),console.warn(c.stack||c.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(r){return console.warn(`Initialization of backend ${t} failed`),console.warn(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],{success:o,asyncInit:s}=this.initializeBackend(r);if(s||o)return{name:r,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let r=this.state.tensorInfo.get(e),o=r.backend,s=this.readSync(e);o.disposeData(e),r.backend=t,t.move(e,s,r.shape,r.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let r=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=t}let o;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,r){t();try{let o=r();return e(),o}catch(o){throw e(),o}}nextTensorId(){return bc.nextTensorId++}nextVariableId(){return bc.nextVariableId++}clone(t){let e=this.makeTensorFromDataId(t.dataId,t.shape,t.dtype),r={x:t},o=c=>({x:()=>{let l="float32",p={x:c},f={dtype:l};return X.runKernelFunc(m=>m.cast(c,l),p,null,cc,f)}}),s=[];return this.addTapeNode(this.state.activeScope.name,r,[e],o,s,{}),e}runKernel(t,e,r,o,s){let c=null,l=null;return this.runKernelFunc(c,e,l,t,r,o,s)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,r){let o=this.backend.numDataIds(),s=0;r.forEach(p=>{s+=p.dtype==="complex64"?3:1});let c=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],l=o-e-s-c;if(l>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${l} data ids) after running '${t}'`)}runKernelFunc(t,e,r,o,s,c,l){let p,f=[],m=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let y=this.state.numBytes,b=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let v,T=Cx(o,this.backendName),N;if(T!=null)v=()=>{let D=this.backend.numDataIds();N=T.kernelFunc({inputs:e,attrs:s,backend:this.backend});let I=Array.isArray(N)?N:[N];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,D,I);let P=I.map(({dataId:E,shape:L,dtype:B})=>this.makeTensorFromDataId(E,L,B));if(m){let E=this.getTensorsForGradient(o,e,P);if(E==null){l==null&&(l=[]);let L=P.filter((B,q)=>l[q]);E=(c||[]).slice().concat(L)}f=this.saveTensorsForBackwardMode(E)}return P};else{let D=I=>{if(!m)return;f=I.map(P=>this.keep(this.clone(P)))};v=()=>{let I=this.backend.numDataIds();N=this.tidy(()=>t(this.backend,D));let P=Array.isArray(N)?N:[N];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,I,P),P}}let S;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?p=v():(S=this.profiler.profileKernel(o,e,()=>v()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(S),p=S.outputs)}),m&&this.addTapeNode(o,e,p,r,f,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-y,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-b,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(D=>e[D]!=null?e[D].shape:null),outputShapes:p.map(D=>D.shape),kernelTimeMs:S.timeMs,extraInfo:S.extraInfo}),Array.isArray(N)?p:p[0]}saveTensorsForBackwardMode(t){let e=t.map(r=>this.keep(this.clone(r)));return e}getTensorsForGradient(t,e,r){let o=Sx(t);if(o!=null){let s=o.inputsToSave||[],c=o.outputsToSave||[],l;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an 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r=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++,r===0){this.state.numDataBuffers++;let o=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(o=t.size*Pb(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:o,refCount:0}),this.state.numBytes+=o}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof Ku||this.track(t)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;this.state.numTensors--,t.dtype==="string"&&this.state.numStringTensors--;let e=this.state.tensorInfo.get(t.dataId),r=e.refCount;r<=1?(t.dtype!=="complex64"&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let o of 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c=this.state.activeScope.track[s];!c.kept&&!r.has(c.id)&&c.dispose()}let o=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(s=>{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,r,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof ot,()=>"The result y returned by f() must be a tensor.");let c=gP(this.state.activeTape,e,s);if(!o&&c.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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WN=n=>ct().getBool("IS_BROWSER")&&(!Array.isArray(n)&&n.startsWith(Ki.URL_SCHEME))?zP(n.slice(Ki.URL_SCHEME.length)):null;Ze.registerSaveRouter(WN),Ze.registerLoadRouter(WN);function zP(n){return new Ki(n)}function WP(n){return n.startsWith(Ki.URL_SCHEME)?n.slice(Ki.URL_SCHEME.length):n}class VP{constructor(){this.indexedDB=Wx()}async listModels(){return new Promise((t,e)=>{let r=this.indexedDB.open(jf,zx);r.onupgradeneeded=()=>Vx(r),r.onsuccess=()=>{let o=r.result,s=o.transaction(ei,"readonly"),c=s.objectStore(ei),l=c.getAll();l.onsuccess=()=>{let p={};for(let f of l.result)p[f.modelPath]=f.modelArtifactsInfo;t(p)},l.onerror=p=>(o.close(),e(l.error)),s.oncomplete=()=>o.close()},r.onerror=o=>e(r.error)})}async removeModel(t){return t=WP(t),new Promise((e,r)=>{let o=this.indexedDB.open(jf,zx);o.onupgradeneeded=()=>Vx(o),o.onsuccess=()=>{let s=o.result,c=s.transaction(ei,"readwrite"),l=c.objectStore(ei),p=l.get(t),f;p.onsuccess=()=>{if(p.result==null)return s.close(),r(new Error(`Cannot 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n==null?null:n.rank===0?Q(n,[n.size]):n.rank===1?n:n.rank===2?Q(n,[1,1,n.shape[0],n.shape[1]]):n.rank===3?Q(n,[1,n.shape[0],n.shape[1],n.shape[2]]):n}let ea=j({batchNorm_:IL});function EL(n,t,e,r,o,s){let c=M(n,"x","batchNorm"),l=M(t,"mean","batchNorm"),p=M(e,"variance","batchNorm"),f;o!=null&&(f=M(o,"scale","batchNorm"));let m;return r!=null&&(m=M(r,"offset","batchNorm")),_(c.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${c.rank}.`),_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${l.rank}.`),_(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),f!=null&&_(f.rank===2||f.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${f.rank}.`),m!=null&&_(m.rank===2||m.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${m.rank}.`),ea(c,l,p,m,f,s)}let b_=j({batchNorm2d_:EL});function DL(n,t,e,r,o,s){let c=M(n,"x","batchNorm"),l=M(t,"mean","batchNorm"),p=M(e,"variance","batchNorm"),f;o!=null&&(f=M(o,"scale","batchNorm"));let m;return r!=null&&(m=M(r,"offset","batchNorm")),_(c.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${c.rank}.`),_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${l.rank}.`),_(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),f!=null&&_(f.rank===3||f.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${f.rank}.`),m!=null&&_(m.rank===3||m.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${m.rank}.`),ea(c,l,p,m,f,s)}let x_=j({batchNorm3d_:DL});function AL(n,t,e,r,o,s){let c=M(n,"x","batchNorm"),l=M(t,"mean","batchNorm"),p=M(e,"variance","batchNorm"),f;o!=null&&(f=M(o,"scale","batchNorm"));let m;return r!=null&&(m=M(r,"offset","batchNorm")),_(c.rank===4,()=>`Error in batchNorm4D: x 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l=M(n,"x","conv2d"),p=M(t,"filter","conv2d"),f=l,m=!1;l.rank===3&&(m=!0,f=Q(l,[1,l.shape[0],l.shape[1],l.shape[2]])),_(f.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${f.rank}.`),_(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),c!=null&&_(gt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${c} but got pad ${r}.`);let y=o==="NHWC"?f.shape[3]:f.shape[1];_(y===p.shape[2],()=>`Error in conv2d: depth of input (${y}) must match input depth for filter ${p.shape[2]}.`),_(fn(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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requires input tensor to have a rank >= 2, but got rank ${n.rank}`),n.rank===2)return dC(n,t);{let e=n.shape.slice(0,n.shape.length-2).reduce((p,f)=>p*f),r=mo(Q(n,[e,n.shape[n.shape.length-2],n.shape[n.shape.length-1]]),0),o=[],s=[];r.forEach(p=>{let[f,m]=dC(p,t);o.push(f),s.push(m)});let c=Q(ur(o,0),n.shape),l=Q(ur(s,0),n.shape);return[c,l]}}function dC(n,t=!1){return X.tidy(()=>{_(n.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${n.shape.length}D Tensor.`);let e=n.shape[0],r=n.shape[1],o=hd(e),s=ni(n),c=ui([[1]],[1,1]),l=ni(c),p=e>=r?r:e;for(let f=0;f<p;++f){let m=s,y=l,b=o;[l,s,o]=X.tidy(()=>{let v=ce(s,[f,f],[e-f,1]),T=Ed(v),N=ce(s,[f,f],[1,1]),S=Xn(Fr(N,0),ui([[-1]]),ui([[1]])),D=Dt(N,nt(S,T)),I=Bt(v,D);I.shape[0]===1?l=ni(c):l=Qe([c,ce(I,[1,0],[I.shape[0]-1,I.shape[1]])],0);let P=tn(Bt(ge(S,D),T)),E=ce(s,[f,0],[e-f,r]),L=nt(P,l),B=Kt(l);if(f===0)s=Dt(E,ge(L,ge(B,E)));else{let Z=Dt(E,ge(L,ge(B,E)));s=Qe([ce(s,[0,0],[f,r]),Z],0)}let q=Kt(L),H=ce(o,[0,f],[e,o.shape[1]-f]);if(f===0)o=Dt(H,ge(ge(H,l),q));else{let Z=Dt(H,ge(ge(H,l),q));o=Qe([ce(o,[0,0],[e,f]),Z],1)}return[l,s,o]}),Xt([m,y,b])}return!t&&e>r&&(o=ce(o,[0,0],[e,r]),s=ce(s,[0,0],[r,r])),[o,s]})}let eW=j({qr_:tW});(function(n){n[n.NONE=0]="NONE",n[n.MEAN=1]="MEAN",n[n.SUM=2]="SUM",n[n.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(i.Reduction||(i.Reduction={}));function nW(n,t,e=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let r=M(n,"losses","computeWeightedLoss"),o=null;t!=null&&(o=M(t,"weights","computeWeightedLoss"));let s=o==null?r:nt(r,o);if(e===i.Reduction.NONE)return s;if(e===i.Reduction.SUM)return zt(s);if(e===i.Reduction.MEAN){if(o==null)return en(s);{let c=r.size/o.size,l=Bt(zt(s),zt(o));return c>1?Bt(l,Et(c)):l}}if(e===i.Reduction.SUM_BY_NONZERO_WEIGHTS){if(o==null)return Bt(zt(s),Et(r.size));{let c=nt(o,ho(r.shape)),l=$t(zt(ci(c,Et(0))),"float32");return Bt(zt(s),l)}}throw Error(`Unknown reduction: ${e}`)}let ms=j({computeWeightedLoss_:nW});function rW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),c=null;e!=null&&(c=M(e,"weights","absoluteDifference")),W(o.shape,s.shape,"Error in absoluteDifference: ");let l=bn(Dt(o,s));return ms(l,c,r)}let oW=j({absoluteDifference_:rW});function sW(n,t,e,r,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","cosineDistance"),c=M(t,"predictions","cosineDistance"),l=null;r!=null&&(l=M(r,"weights","cosineDistance")),W(s.shape,c.shape,"Error in cosineDistance: ");let p=Et(1),f=Dt(p,zt(nt(s,c),e,!0));return ms(f,l,o)}let iW=j({cosineDistance_:sW});function aW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),c=null;e!=null&&(c=M(e,"weights","hingeLoss")),W(o.shape,s.shape,"Error in hingeLoss: ");let l=Et(1);o=Dt(nt(Et(2),o),l);let p=Vo(Dt(l,nt(o,s)));return ms(p,c,r)}let cW=j({hingeLoss_:aW});function lW(n,t,e,r=1,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","huberLoss"),c=M(t,"predictions","huberLoss"),l=null;e!=null&&(l=M(e,"weights","huberLoss")),W(s.shape,c.shape,"Error in huberLoss: ");let p=Et(r),f=bn(Dt(c,s)),m=oa(f,p),y=Dt(f,m),b=Tt(nt(Et(.5),De(m)),nt(p,y));return ms(b,l,o)}let uW=j({huberLoss_:lW});function pW(n,t,e,r=1e-7,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","logLoss"),c=M(t,"predictions","logLoss"),l=null;e!=null&&(l=M(e,"weights","logLoss")),W(s.shape,c.shape,"Error in logLoss: ");let p=Et(1),f=Et(r),m=tn(nt(s,wr(Tt(c,f)))),y=nt(Dt(p,s),wr(Tt(Dt(p,c),f))),b=Dt(m,y);return ms(b,l,o)}let hW=j({logLoss_:pW});function fW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),c=null;e!=null&&(c=M(e,"weights","meanSquaredError")),W(o.shape,s.shape,"Error in meanSquaredError: ");let l=gp(o,s);return ms(l,c,r)}let dW=j({meanSquaredError_:fW});function mW(n,t){let e=M(n,"labels","sigmoidCrossEntropyWithLogits"),r=M(t,"logits","sigmoidCrossEntropyWithLogits");W(e.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Vo(r),s=nt(r,e),c=dd(Ar(tn(bn(r))));return Tt(Dt(o,s),c)}function gW(n,t,e,r=0,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"multiClassLabels","sigmoidCrossEntropy"),c=M(t,"logits","sigmoidCrossEntropy"),l=null;if(e!=null&&(l=M(e,"weights","sigmoidCrossEntropy")),W(s.shape,c.shape,"Error in sigmoidCrossEntropy: "),r>0){let f=Et(r),m=Et(1),y=Et(.5);s=Tt(nt(s,Dt(m,f)),nt(y,f))}let p=mW(s,c);return ms(p,l,o)}let yW=j({sigmoidCrossEntropy_:gW});function bW(n,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${e}`);let r=zo((o,s,c)=>{let l=!0,p=_w(s,[e],l),f=Dt($t(s,"float32"),p);c([o,f]);let m=tn(nt(f,o)),y=zt(m,[e]),b=(v,T)=>{let[N,S]=T,D=Rn(v.shape,[e]);return[nt(Q(v,D),Dt($t(N,"float32"),Ar(S))),nt(Q(v,D),Dt(Ar(S),$t(N,"float32")))]};return{value:y,gradFunc:b}});return r(n,t)}function xW(n,t,e,r=0,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"onehotLabels","softmaxCrossEntropy"),c=M(t,"logits","softmaxCrossEntropy"),l=null;if(e!=null&&(l=M(e,"weights","softmaxCrossEntropy")),W(s.shape,c.shape,"Error in softmaxCrossEntropy: "),r>0){let f=Et(r),m=Et(1),y=Et(s.shape[1]);s=Tt(nt(s,Dt(m,f)),Bt(f,y))}let p=bW(s,c);return ms(p,l,o)}let wW=j({softmaxCrossEntropy_:xW});let vW={fft:dp,ifft:Rc,rfft:mp,irfft:Sd},TW={hammingWindow:xz,hannWindow:lC,frame:uC,stft:kz},pi={flipLeftRight:Sz,resizeNearestNeighbor:fC,resizeBilinear:hC,rotateWithOffset:Iz,cropAndResize:_z,nonMaxSuppression:Dz,nonMaxSuppressionAsync:Bz,nonMaxSuppressionWithScore:Wz,nonMaxSuppressionWithScoreAsync:Gz,nonMaxSuppressionPadded:qz,nonMaxSuppressionPaddedAsync:jz},mC={bandPart:Jz,gramSchmidt:Qz,qr:eW},kW={absoluteDifference:oW,computeWeightedLoss:ms,cosineDistance:iW,hingeLoss:cW,huberLoss:uW,logLoss:hW,meanSquaredError:dW,sigmoidCrossEntropy:yW,softmaxCrossEntropy:wW};class gs extends Qi{minimize(t,e=!1,r){let{value:o,grads:s}=this.computeGradients(t,r);if(r!=null){let c=r.map(l=>({name:l.name,tensor:s[l.name]}));this.applyGradients(c)}else this.applyGradients(s);return Xt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Nw(t,e)}dispose(){this.iterations_!=null&&Xt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Et(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}}Object.defineProperty(gs,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class bp extends gs{constructor(t,e,r=null){super();this.learningRate=t,this.rho=e,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${r}/accum_grad`,variable:rt(()=>re(s).variable(c))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${r}/accum_var`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedGrads[o].variable,f=this.accumulatedUpdates[o].variable;rt(()=>{let m=Tt(nt(p,this.rho),nt(De(l),1-this.rho)),y=nt(Bt(Pn(Tt(f,this.epsilon)),Pn(Tt(p,this.epsilon))),l),b=Tt(nt(f,this.rho),nt(De(y),1-this.rho));p.assign(m),f.assign(b);let v=Tt(nt(y,-this.learningRate),s);s.assign(v)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Xt(this.accumulatedGrads.map(t=>t.variable)),Xt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,r=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}}bp.className="Adadelta",vt(bp);class xp extends gs{constructor(t,e=.1){super();this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulatedGrads[o]==null){let p=!1;this.accumulatedGrads[o]={originalName:`${r}/accumulator`,variable:rt(()=>Cc(s.shape,this.initialAccumulatorValue).variable(p))}}let c=Array.isArray(t)?t[o].tensor:t[r];if(c==null)return;let l=this.accumulatedGrads[o].variable;rt(()=>{let p=Tt(l,De(c));l.assign(p);let f=Tt(nt(Bt(c,Pn(Tt(p,X.backend.epsilon()))),-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Xt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}}xp.className="Adagrad",vt(xp);class wp extends gs{constructor(t,e,r,o=null){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],rt(()=>{this.accBeta1=Et(e).variable(),this.accBeta2=Et(r).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Dt(1,this.accBeta2);e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:rt(()=>re(l).variable(p))}),this.accumulatedSecondMoment[c]==null&&(this.accumulatedSecondMoment[c]={originalName:`${s}/v`,variable:rt(()=>re(l).variable(p))});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedSecondMoment[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=Tt(nt(y,this.beta2),nt(De(f),1-this.beta2)),T=Bt(b,r),N=Bt(v,o);m.assign(b),y.assign(v);let S=Tt(nt(Bt(T,Tt(Pn(N),this.epsilon)),-this.learningRate),l);l.assign(S)}),this.accBeta1.assign(nt(this.accBeta1,this.beta1)),this.accBeta2.assign(nt(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Xt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),rt(()=>{this.accBeta1.assign(fo(this.beta1,this.iterations_+1)),this.accBeta2.assign(fo(this.beta2,this.iterations_+1))});let e=t.length/2,r=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}}wp.className="Adam",vt(wp);class vp extends gs{constructor(t,e,r,o=null,s=0){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],rt(()=>{this.iteration=Et(0).variable(),this.accBeta1=Et(e).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Bt(-this.learningRate,Tt(nt(this.iteration,this.decay),1));e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:re(l).variable(p)}),this.accumulatedWeightedInfNorm[c]==null&&(this.accumulatedWeightedInfNorm[c]={originalName:`${s}/v`,variable:re(l).variable(p)});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedWeightedInfNorm[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=nt(y,this.beta2),T=bn(f),N=Xr(v,T);m.assign(b),y.assign(N);let S=Tt(nt(Bt(o,r),Bt(b,Tt(N,this.epsilon))),l);l.assign(S)}),this.iteration.assign(Tt(this.iteration,1)),this.accBeta1.assign(nt(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Xt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}}vp.className="Adamax",vt(vp);class Mc extends gs{constructor(t){super();this.learningRate=t,this.setLearningRate(t)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=Array.isArray(t)?t[o].tensor:t[r];if(s==null)return;let c=X.registeredVariables[r];rt(()=>{let l=Tt(nt(this.c,s),c);c.assign(l)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Sn(Et(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}}Mc.className="SGD",vt(Mc);class Tp extends Mc{constructor(t,e,r=!1){super(t);this.learningRate=t,this.momentum=e,this.useNesterov=r,this.accumulations=[],this.m=Et(this.momentum)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulations[o]==null){let p=!1;this.accumulations[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(p))}}let c=this.accumulations[o].variable,l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;rt(()=>{let p,f=Tt(nt(this.m,c),l);this.useNesterov?p=Tt(nt(this.c,Tt(l,nt(f,this.m))),s):p=Tt(nt(this.c,f),s),c.assign(f),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Xt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}}Tp.className="Momentum",vt(Tp);class kp extends 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Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new Go(`Layer ${this.name} is not connected, no input to return.`);return pr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Go(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Go(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. 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Input received: ${t}`);for(let r=0;r<t.length;r++){let o=t[r],s=e[r];if(s==null)continue;let c=o.rank;if(s.ndim!=null&&c!==s.ndim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${c}`);if(s.maxNDim!=null&&c>s.maxNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${c}`);if(s.minNDim!=null&&c<s.minNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${c}.`);if(s.dtype!=null&&o.dtype!==s.dtype)throw new Y(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${o.dtype}.`);if(s.axes){let l=o.shape;for(let p in s.axes){let f=Number(p),m=s.axes[p],y=f>=0?l[f]:l[l.length+f];if(m!=null&&[m,null].indexOf(y)===-1)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected axis ${f} of input shape to have value ${m} but got shape ${l}.`)}}if(s.shape!=null)for(let l=0;l<s.shape.length;++l){let p=s.shape[l],f=o.shape[l];if(p!=null&&f!=null&&p!==f)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${o.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let r=ze(t),o=!0;for(let c of r)if(!(c instanceof xo)){o=!1;break}let s=!0;for(let c of r)if(c instanceof xo){s=!1;break}if(o===s)throw new Y("Arguments to apply() must be all SymbolicTensors or all Tensors");return fa(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let c=[];for(let l of ze(t))c.push(l.shape);this.build(pr(c)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let c=this.call(t,e),l=ze(c),p=[];for(let f of l)r.indexOf(f)!==-1&&(f=f.clone()),p.push(f);if(c=pr(p),this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}else{let c=xG(t),l=this.computeOutputShape(c),p,f=wG(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?c[0]:c),l!=null&&l.length>0&&Array.isArray(l[0])?p=l.map((m,y)=>new xo(f,m,this,ze(t),e,this.name,y)):p=new xo(f,l,this,ze(t),e,this.name),this.addInboundNode(t,p,null,null,c,l,e),this._refCount++,this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return p}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape==null)return;if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((r,o)=>{r!=null&&t[o]!=null&&t[o]!==r&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Go(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let r=JSON.stringify(e.outputShapes);t.indexOf(r)===-1&&t.push(r)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new Go(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Jr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return rm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return Iv(t?this.trainableWeights:this.weights)}setWeights(t){rt(()=>{let e=this.weights;if(e.length!==t.length)throw new Y(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let r=[],o=Iv(e);for(let s=0;s<o.length;++s){let c=o[s],l=e[s],p=t[s];if(!lt(c.shape,p.shape))throw new Y(`Layer weight shape ${c.shape} not compatible with provided weight shape ${p.shape}`);r.push([l,p])}Ev(r)})}addWeight(t,e,r,o,s,c,l){if(this._addedWeightNames.indexOf(t)!==-1)throw new Y(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(o=je("zeros"));let p=o.apply(e,r),f=new bo(p,r,t,c,l);return p.dispose(),s!=null&&this.addLoss(()=>s.apply(f.read())),c==null&&(c=!0),c?this._trainableWeights.push(f):this._nonTrainableWeights.push(f),f}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){if(t==null||Array.isArray(t)&&t.length===0)return;t=ze(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t)}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,r,o,s,c,l=null){let p=ze(t);e=ze(e),r=ze(r),o=ze(o),s=nm(s),c=nm(c);let f=[],m=[],y=[];for(let b of p)f.push(b.sourceLayer),m.push(b.nodeIndex),y.push(b.tensorIndex);new om({outboundLayer:this,inboundLayers:f,nodeIndices:m,tensorIndices:y,inputTensors:p,outputTensors:e,inputMasks:r,outputMasks:o,inputShapes:s,outputShapes:c},l);for(let b=0;b<e.length;b++)e[b].sourceLayer=this,e[b].nodeIndex=this.inboundNodes.length-1,e[b].tensorIndex=b}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function xG(n){n=ze(n);let t=[];for(let e of n)t.push(e.shape);return pr(t)}function wG(n){return"float32"}function XC(n,t,e){if((t==null||e!=null&&e>0)&&(t=n.sourceLayer,e=n.nodeIndex),t.inboundNodes.length===0)return[n];{let r=t.inboundNodes[e];if(r.inboundLayers.length===0)return r.inputTensors;{let o=[];for(let s=0;s<r.inboundLayers.length;s++){let c=r.inputTensors[s],l=r.inboundLayers[s],p=r.nodeIndices[s],f=XC(c,l,p);for(let m of f)o.indexOf(m)===-1&&o.push(m)}return o}}}class Gc extends ye{constructor(t){super({dtype:t.dtype,name:t.name!=null?t.name:em("input").toString()});if(t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new Y("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new Y("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new Y("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=t.dtype||"float32";this.batchInputShape=e,this.dtype=r,this.inputSpec=[{shape:e}];let o=new xo(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new om({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new Y(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Gc.className="InputLayer",vt(Gc);function YC(n){if(n.batchShape==null&&n.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Found: ${this.outputs.map(I=>I.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let I of this.outputs){let P=I.sourceLayer,E=I.nodeIndex,L=I.tensorIndex;this.outputLayers.push(P),this.outputLayersNodeIndices.push(E),this.outputLayersTensorIndices.push(L)}for(let I of this.inputs){let P=I.sourceLayer,E=I.nodeIndex,L=I.tensorIndex;Pr(E===0,"input layer has >1 nodes"),Pr(L===0,"input layer has >1 tensors"),this.inputLayers.push(P),this.inputLayersNodeIndices.push(E),this.inputLayersTensorIndices.push(L)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let I=0;I<this.inputLayers.length;I++){let P=this.inputLayers[I];if(!(P instanceof Gc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${t.inputs}. Input ${I} (0-based) originates from layer type ${P.getClassName()}.`);this.inputNames.push(P.name),this.feedInputShapes.push(P.batchInputShape),this.feedInputNames.push(P.name)}for(let I of this.outputLayers)this.outputNames.push(I.name);this.internalInputShapes=this.inputs.map(I=>I.shape),this.internalOutputShapes=this.outputs.map(I=>I.shape);let e={},r={},o={},s={},c={},l=[],p=(I,P,E,L,B,q)=>{(L==null||B==null||q==null)&&(L=I.sourceLayer,B=I.nodeIndex,q=I.tensorIndex);let H=L.inboundNodes[B];if(E.indexOf(H)!==-1)throw new Jr(`The tensor ${I.name} at layer "${L.name}" is part of a cycle.`);if(P.indexOf(H)!==-1)return;this.containerNodes.add(vo.nodeKey(L,B)),L.id in c||(c[L.id]=Object.keys(c).length),E.indexOf(H)===-1&&E.push(H);let Z=H.inboundLayers.length;for(let J=0;J<Z;J++){let it=H.inputTensors[J],pt=H.inboundLayers[J],ht=H.nodeIndices[J],dt=H.tensorIndices[J];p(it,P,E,pt,ht,dt)}for(P.push(H);E.indexOf(H)>=0;)E.splice(E.indexOf(H),1);l.push(H)},f=[],m=[];for(let I of this.outputs)p(I,f,m);let y=l.slice().reverse();for(let I of y){r[I.id]=I,I.id in e||(e[I.id]=0);let P=e[I.id],E=o[I.outboundLayer.id]==null?0:o[I.outboundLayer.id];P=Math.max(P,E),o[I.outboundLayer.id]=P,s[I.outboundLayer.id]=I.outboundLayer,e[I.id]=P;for(let L=0;L<I.inboundLayers.length;L++){let B=I.inboundLayers[L],q=I.nodeIndices[L],H=B.inboundNodes[q],Z=e[H.id]==null?0:e[H.id];e[H.id]=Math.max(P+1,Z),r[H.id]=H}}let b={};for(let I in e){let P=e[I];P in b||(b[P]=[]),b[P].push(r[I])}let v={};for(let I in o){let P=o[I];P in v||(v[P]=[]),v[P].push(s[I])}let T=Object.keys(v).map(I=>parseInt(I,10)).sort(Gd);this.layers=[];for(let I of T){let P=v[I];P.sort((E,L)=>{let B=c[E.id],q=c[L.id];return B<q?-1:B>q?1:0});for(let E of P)E instanceof vo&&this.internalContainerRefs.push(E),this.layers.push(E)}this.layersByDepth=v,T=Object.keys(b).map(I=>parseInt(I,10)).sort(Gd);let N=this.inputs.slice(),S=[];for(let I of T)for(let P of b[I]){let E=P.outboundLayer;if(E!=null){for(let L of P.inputTensors)if(N.indexOf(L)===-1)throw new Jr(`Graph disconnected: cannot obtain value for tensor ${L} at layer "${E.name}". 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.layers)e.push(...r.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let r={},o=0;for(let c of this.layers)for(let l of c.weights){if(r[l.originalName]!=null)throw new Y(`Duplicate weight name: ${l.originalName}`);r[l.originalName]=l,o++}let s=[];for(let c in t){let l=c;if(r[c]==null){let p=c.split("/"),f=p.slice(0,-2).concat([p[p.length-1]]);l=f.join("/")}if(r[l]!=null)s.push([r[l],t[c]]);else if(e)throw new Y(`Provided weight data has no target variable: ${c}`);delete r[l]}if(e){let c=[];for(let l in r)c.push(l);if(c.length>0)throw new Y(`${c.length} of ${o} weights are not set: ${c}`)}Ev(s)}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${fm}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let r=Mv(this.updatedConfig());return e?JSON.stringify(r):r}call(t,e){return rt(()=>{t=ze(t);let r=new yi;for(let o=0;o<this.inputs.length;++o)r.add(this.inputs[o],t[o]);return Op(this.outputs,r,e)})}computeMask(t,e){return rt(()=>{t=ze(t);let r;return e==null?r=pa(null,t.length):r=ze(e),this.runInternalGraph(t,r)[1]})}computeOutputShape(t){let e=nm(t);if(e.length!==this.inputLayers.length)throw new Y(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let l=0;l<e.length;l++){let p=this.inputLayers[l],f=e[l],m=p.name+"_0_0";r[m]=f}let o=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(Gd);if(o.length>1)for(let l of o){let p=this.nodesByDepth[l];for(let f of p){let m=f.outboundLayer;if(this.inputLayers.map(N=>N.id).indexOf(m.id)!==-1)continue;let y=[];for(let N=0;N<f.inboundLayers.length;N++){let S=f.inboundLayers[N],D=f.nodeIndices[N],I=f.tensorIndices[N],P=`${S.name}_${D}_${I}`,E=r[P];y.push(E)}let b=m.computeOutputShape(pr(y)),v=nm(b),T=m.inboundNodes.indexOf(f);for(let N=0;N<v.length;N++){let S=`${m.name}_${T}_${N}`;r[S]=v[N]}}}let s=[],c=[];for(let l=0;l<this.outputLayers.length;l++){let p=this.outputLayers[l],f=this.outputLayersNodeIndices[l],m=this.outputLayersTensorIndices[l],y=`${p.name}_${f}_${m}`;c.push(y)}for(let l=0;l<c.length;l++){let p=c[l];Pr(p in r),s.push(r[p])}return pr(s)}runInternalGraph(t,e){e==null&&(e=pa(null,t.length));let r={};for(let p=0;p<this.inputs.length;++p){let f=this.inputs[p],m=t[p],y=e[p];r[f.id]=[m,y]}let o=Object.keys(this.nodesByDepth).map(p=>parseInt(p,10)).sort(Gd);for(let p of o){let f=this.nodesByDepth[p];for(let m of f){let y=m.outboundLayer,b=m.inputTensors,v=m.outputTensors,T=new Array;for(let N of b)N.id in r&&T.push(r[N.id]);if(T.length===b.length){let N={},S,D,I,P;if(m.callArgs!=null&&(N=m.callArgs),T.length===1){let[E,L]=T[0];N.mask==null&&(N.mask=L),I=ze(y.call(E,N)),P=ze(y.computeMask(E,L)),S=[E],D=[L]}else S=T.map(E=>E[0]),D=T.map(E=>E[1]),N.mask==null&&(N.mask=D),I=ze(y.call(S,N)),P=ze(y.computeMask(S,D));if(y.activityRegularizer)throw new Ut("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let E=0;E<v.length;++E){let L=v[E],B=I[E],q=P[E];r[L.id]=[B,q]}}}}let s=[],c=[],l=[];for(let p of this.outputs){Pr(p.id in r,`Could not compute output ${p.name} : ${p.id}`);let[f,m]=r[p.id];l.push(f.shape),s.push(f),c.push(m)}return[s,c,l]}buildNodeConversionMap(t){let e={},r;for(let o of this.layers){r=o instanceof vo?1:0;for(let s=0;s<o.inboundNodes.length;s++){let c=vo.nodeKey(o,s);this.containerNodes.has(c)&&(e[c]=r,r+=1)}}return e}getLayer(t,e){if(e!=null){if(this.layers.length<=e)throw new Y(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}else if(t==null)throw new Y("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===t)return r;throw new Y(`No such layer: ${t}`)}calculateLosses(){return rt(()=>{let t=[];for(let e of this.layers)for(let r=0;r<e.inboundNodes.length;++r){let o=vo.nodeKey(e,r);this.containerNodes.has(o)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),r=[];for(let c of this.layers){let l=c.getClassName(),p=c.getConfig(),f=[];for(let y=0;y<c.inboundNodes.length;y++){let b=c.inboundNodes[y],v=vo.nodeKey(c,y),T={};if(this.containerNodes.has(v)){if(b.callArgs)try{JSON.stringify(b.callArgs),T=b.callArgs}catch(N){console.warn(`Layer ${c.name} was passed non-serializable keyword arguments: ${b.callArgs}. 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Provided ${e} not understood: ${JSON.stringify(n)}`)}function mS(n,t){return dS(n,t,"classWeight")}function lst(n,t){return dS(n,t,"sampleWeight")}async function gS(n,t,e,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=rt(()=>{if(n.shape.length===1)return n.clone();if(n.shape.length===2)if(n.shape[1]>1){let l=1;return n.argMax(l)}else{if(n.shape[1]===1)return n.reshape([n.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${n.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${n.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Xt(o);let c=[];return s.forEach(l=>{if(e[l]==null)throw new Error(`classWeight must contain all classes in the training data. 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c=0;c<o.length;++c){if(r&&!o[c].trainable)continue;e.push({name:o[c].originalName,tensor:s[c]})}return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){let t=super.dispose();if(t.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let e=ed().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-ed().numTensors}return t}getLossIdentifiers(){let t;if(typeof this.loss=="string")t=bs(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if(typeof e!="string")throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(e=>bs(e))}else{let e=Object.keys(this.loss);t={};let r=this.loss;for(let o of e)if(typeof r[o]=="string")t[o]=bs(r[o]);else throw new Error("Serialization of non-string loss is not supported.")}return t}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[bs(pm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(t=>bs(pm(t)));{let t={};for(let e in this.metrics)t[e]=bs(pm(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(t.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(t.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(t.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let e=Pp(t.optimizer_config),r=wo(e),o;if(typeof t.loss=="string")o=ha(t.loss);else if(Array.isArray(t.loss))o=t.loss.map(c=>ha(c));else if(t.loss!=null){o={};for(let c in t.loss)o[c]=ha(t.loss[c])}let s;if(Array.isArray(t.metrics))s=t.metrics.map(c=>ha(c));else if(t.metrics!=null){s={};for(let c in t.metrics)s[c]=ha(t.metrics[c])}this.compile({loss:o,metrics:s,optimizer:r})}async save(t,e){if(typeof t=="string"){let f=Mx(t);if(f.length===0)throw new Y(`Cannot find any save handlers for URL '${t}'`);if(f.length>1)throw new Y(`Found more than one (${f.length}) save handlers for URL '${t}'`);t=f[0]}if(t.save==null)throw new Y("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ox(this.getNamedWeights(e)),o=!1,s=null,c=this.toJSON(s,o),l={modelTopology:c,format:lU,generatedBy:`TensorFlow.js tfjs-layers v${fm}`,convertedBy:null},p=e==null?!1:e.includeOptimizer;if(p&&this.optimizer!=null){l.trainingConfig=this.getTrainingConfig();let f="optimizer",{data:m,specs:y}=await Ox(await this.optimizer.getWeights(),f);r.specs.push(...y),r.data=Hf([r.data,m])}if(this.userDefinedMetadata!=null){let f=!0;uS(this.userDefinedMetadata,this.name,f),l.userDefinedMetadata=this.userDefinedMetadata}return l.weightData=r.data,l.weightSpecs=r.specs,t.save(l)}setUserDefinedMetadata(t){uS(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}}ws.className="Model",vt(ws);class NS extends ws{}NS.className="Functional",vt(NS);async function uU(n,t){"modelTopology"in n||(n={modelTopology:n}),n=n;let e=n.modelTopology;e.model_config!=null&&(e=e.model_config);let r=Pp(e),o=wo(r,t);if(n.weightsManifest!=null){let s=await JN(n.weightsManifest,n.pathPrefix,o.weights.map(l=>l.originalName)),c={};for(let l of o.weights)c[l.originalName]=s[l.originalName];o.loadWeights(c),Xt(s)}return o}async function pU(n,t){if(t==null&&(t={}),typeof n=="string"){let e=Bx(n,t);if(e.length===0)e.push(Xf(n,t));else if(e.length>1)throw new Y(`Found more than one (${e.length}) load handlers for URL '${n}'`);n=e[0]}return hU(n,void 0,t)}async function hU(n,t,e){if(e==null&&(e={}),n.load==null)throw new Y("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await n.load(),o=r.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,c=r.weightData!=null&&r.weightSpecs!=null&&s,l=wo(Pp(o),t,c),p=r.trainingConfig;if(p!=null&&l.loadTrainingConfig(p),r.userDefinedMetadata!=null&&l.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Y("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:f,optimizerWeights:m}=fU(r.weightData,r.weightSpecs);l.loadWeights(f,s),l.optimizer!=null&&m.length>0&&await l.optimizer.setWeights(m),Xt(f),Xt(m.map(y=>y.tensor))}return l}function fU(n,t){let e=qf(n,t),r={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):r[s.name]=e[s.name]}),{modelWeights:r,optimizerWeights:o}}class ga extends ws{constructor(t){super({inputs:[],outputs:[]});if(t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:em("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){let e=t.inboundNodes[0].outputTensors[0].shape;if(e.some(r=>r<0))throw new Y(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof ga||t instanceof ws,r;if(e){if(r=t,r.outputs.length!==1)throw new Y("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(t,e,r)}async evaluateDataset(t,e){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,r={}){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.fit(t,e,r)}async fitDataset(t,e){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,r={},o=!1){let s,c={};if(e instanceof Array){if(!(e[0].className!=null)||e[0].className==="Merge")throw new Y("Legacy serialization format not supported yet.");s=e}else _(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,c=e;let l=new t(c);if(!(l instanceof ga))throw new Ut(`Sequential.fromConfig called on non-Sequential input: ${l}`);for(let p of s){let f=void 0,m=wo(p,f,o);o&&m.setFastWeightInitDuringBuild(!0),l.add(m)}return l}set stopTraining(t){if(this.model==null)throw new Y("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new Y("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let r={};r.className=e.getClassName(),r.config=e.getConfig(),t.push(r)}return{name:this.name,layers:t}}}ga.className="Sequential",vt(ga);function dU(n){return new ws(n)}function mU(n){return new ga(n)}function gU(n,t){return t==null&&(t={}),pU(n,t)}function _S(n){return YC(n)}function yU(n,t){Lr.registerCallbackConstructor(n,t)}class kr extends Qi{getConfig(){return{}}}class CS extends kr{apply(t,e=1){return qV(t,e)}}CS.className="elu",vt(CS);class SS extends kr{apply(t){return Td(t)}}SS.className="selu",vt(SS);class $S extends kr{apply(t){return Vo(t)}}$S.className="relu",vt($S);class IS extends kr{apply(t){return rt(()=>oa(6,Vo(t)))}}IS.className="relu6",vt(IS);class ES extends kr{apply(t){return t}}ES.className="linear",vt(ES);class DS extends kr{apply(t){return Bo(t)}}DS.className="sigmoid",vt(DS);class AS extends kr{apply(t){return jV(t)}}AS.className="hardSigmoid",vt(AS);class FS extends kr{apply(t){return 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MS{constructor(t){super();Hv(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return rt(()=>{let e=xe([1]);return this.hasL1&&(e=Tt(e,zt(nt(this.l1,bn(t))))),this.hasL2&&(e=Tt(e,zt(nt(this.l2,Ap(t))))),e.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}}Mp.className="L1L2",vt(Mp);function bU(n){return Hv(n),new Mp({l1:n!=null?n.l1:null,l2:0})}function xU(n){return Hv(n),new Mp({l2:n!=null?n.l2:null,l1:0})}let BS={l1l2:"L1L2"};function Pe(n){return lv(n)}function zS(n,t={}){return Sp(n,Dr.getMap().classNameMap,t,"regularizer")}function Ke(n){if(n==null)return null;if(typeof n=="string"){let t=n in BS?BS[n]:n,e={className:t,config:{}};return zS(e)}else return n instanceof MS?n:zS(n)}class jv extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=Qt(t);let r=Vo(t);return 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Received: ${n.length} elements.`);for(let r=0;r<t;++r){let o=n[r];if(!BV(o))throw new Y(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(n)} including a non-integer number ${o}`)}return n}function To(n,t,e,r,o=1){if(n==null)return n;let s=t+(t-1)*(o-1),c;return e==="same"?c=n:c=n-s+1,Math.floor((c+r-1)/r)}function dm(n,t,e,r){if(n==null)return null;if(r==="valid")n=n*t+di([e-t,0]);else if(r==="same")n=n*t;else throw new Y(`Unsupport padding mode: ${r}.`);return n}function Qv(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,1]):n))}function WS(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,4,1]):n))}function VS(n,t,e,r=1,o="valid",s,c=1){return rt(()=>{if(s==null&&(s=go()),nn(s),n.shape.length!==3)throw new Y(`The input of a conv1dWithBias operation should be 3, but is ${n.shape.length} instead.`);if(t.shape.length!==3)throw new Y(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new Y(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(n=Kt(n,[0,2,1])),o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let l=cd(n,t,r,o==="same"?"same":"valid","NWC",c);return e!=null&&(l=qo(l,e)),l})}function ust(n,t,e=1,r="valid",o,s=1){return rt(()=>(nn(o),VS(n,t,null,e,r,o,s)))}function pst(n,t,e=[1,1],r="valid",o,s){return rt(()=>(nn(o),t0(n,t,null,e,r,o,s)))}function t0(n,t,e,r=[1,1],o="valid",s,c,l=null){return rt(()=>{if(s==null&&(s=go()),nn(s),n.rank!==3&&n.rank!==4)throw new Y(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${n.rank}.`);if(t.rank!==3&&t.rank!==4)throw new Y(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${n.rank}.`);let p=Qv(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return p=qw({x:p,filter:t,strides:r,pad:o==="same"?"same":"valid",dilations:c,dataFormat:"NHWC",bias:e,activation:l}),s==="channelsFirst"&&(p=Kt(p,[0,3,1,2])),p})}function hst(n,t,e=[1,1,1],r="valid",o,s){return rt(()=>(nn(o),GS(n,t,null,e,r,o,s)))}function GS(n,t,e,r=[1,1,1],o="valid",s,c){return rt(()=>{if(s==null&&(s=go()),nn(s),n.rank!==4&&n.rank!==5)throw new Y(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(t.rank!==4&&t.rank!==5)throw new Y(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let l=WS(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return l=gw(l,t,r,o==="same"?"same":"valid","NDHWC",c),e!=null&&(l=qo(l,e)),s==="channelsFirst"&&(l=Kt(l,[0,4,1,2,3])),l})}class mm extends ye{constructor(t,e){super(e);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",mm.verifyArgs(e),this.rank=t,$n(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ut(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Hc(e.kernelSize,t,"kernelSize"),this.strides=Hc(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,Or(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,nn(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Tn(e.biasConstraint),this.biasRegularizer=Ke(e.biasRegularizer),this.activityRegularizer=Ke(e.activityRegularizer),this.dilationRate=Hc(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new Y(`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 Y(`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 Y(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Pr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,3))throw new Y(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:bi(this.activation),useBias:this.useBias,biasInitializer:rn(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class jc extends mm{constructor(t,e){super(t,e);this.kernel=null,jc.verifyArgs(e),this.filters=e.filters,$n(this.filters,"filters"),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Tn(e.kernelConstraint),this.kernelRegularizer=Ke(e.kernelRegularizer)}build(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[e]}`);let r=t[e],o=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:r}}],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r,o=this.bias==null?null:this.bias.read(),s=FC(this.activation.getClassName());if(s!=null&&this.rank===2)r=t0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)r=VS(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=t0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=GS(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ut("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Re(t);let e=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<r.length;++s){let c=To(r[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(c)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:rn(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:vn(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new Y(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class Kc extends jc{constructor(t){super(2,t);Kc.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,2))throw new Y(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}Kc.className="Conv2D",vt(Kc);class Bp extends jc{constructor(t){super(3,t);Bp.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new Y(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}Bp.className="Conv3D",vt(Bp);class e0 extends Kc{constructor(t){super(t);if(this.inputSpec=[new In({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Y(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Re(t),t.length!==4)throw new Y("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y("The channel dimension of the inputs should be defined. Found `None`.");let r=t[e],o=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new In({ndim:4,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{let r=Qt(t);if(r.shape.length!==4)throw new Y(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let o=r.shape,s=o[0],c,l;this.dataFormat==="channelsFirst"?(c=2,l=3):(c=1,l=2);let p=o[c],f=o[l],m=this.kernelSize[0],y=this.kernelSize[1],b=this.strides[0],v=this.strides[1],T=dm(p,b,m,this.padding),N=dm(f,v,y,this.padding),S=[s,T,N,this.filters];this.dataFormat!=="channelsLast"&&(r=Kt(r,[0,2,3,1]));let D=ld(r,this.kernel.read(),S,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(D=Kt(D,[0,3,1,2])),this.bias!=null&&(D=qo(D,this.bias.read(),this.dataFormat)),this.activation!=null&&(D=this.activation.apply(D)),D})}computeOutputShape(t){t=Re(t);let e=t.slice(),r,o,s;this.dataFormat==="channelsFirst"?(r=1,o=2,s=3):(r=3,o=1,s=2);let c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],f=this.strides[1];return e[r]=this.filters,e[o]=dm(e[o],p,c,this.padding),e[s]=dm(e[s],f,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}}e0.className="Conv2DTranspose",vt(e0);class US extends jc{constructor(t,e){super(t,e);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new Y("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new Y("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new Y(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ke(e.depthwiseRegularizer),this.depthwiseConstraint=Tn(e.depthwiseConstraint),this.pointwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ke(e.pointwiseRegularizer),this.pointwiseConstraint=Tn(e.pointwiseConstraint)}build(t){if(t=Re(t),t.length<this.rank+2)throw new Y(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let r=t[e],o=this.kernelSize.concat([r,this.depthMultiplier]),s=[];for(let l=0;l<this.rank;++l)s.push(1);s.push(r*this.depthMultiplier,this.filters);let c=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,c,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,c,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,c,this.biasConstraint):this.bias=null,this.inputSpec=[new In({ndim:this.rank+2,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r;if(this.rank===1)throw new Ut("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Kt(t,[0,2,3,1])),r=Rw(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=qo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=Kt(r,[0,3,1,2])),r})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.pointwiseInitializer=rn(this.pointwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.pointwiseRegularizer=Pe(this.pointwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseConstraint),t.pointwiseConstraint=vn(this.pointwiseConstraint),t}}US.className="SeparableConv";class n0 extends US{constructor(t){super(2,t)}}n0.className="SeparableConv2D",vt(n0);class zp extends jc{constructor(t){super(1,t);zp.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,1))throw new Y(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}zp.className="Conv1D",vt(zp);class r0 extends ye{constructor(t){super(t);typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return rt(()=>{if(t=Qt(t),this.dataFormat==="channelsLast"){let r=qd(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return qd(r,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=qd(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return qd(r,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}r0.className="Cropping2D",vt(r0);class o0 extends ye{constructor(t){super(t);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],r=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,r]}else{let e=t[1]==null?null:this.size[0]*t[1],r=t[2]==null?null:this.size[1]*t[2];return[t[0],e,r,t[3]]}}call(t,e){return rt(()=>{let r=Qt(t),o=r.shape;if(this.dataFormat==="channelsFirst"){r=Kt(r,[0,2,3,1]);let s=this.size[0]*o[2],c=this.size[1]*o[3],l=r.resizeNearestNeighbor([s,c]);return Kt(l,[0,3,1,2])}else{let s=this.size[0]*o[1],c=this.size[1]*o[2];return r.resizeNearestNeighbor([s,c])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}o0.className="UpSampling2D",vt(o0);function wU(n,t,e=[1,1],r="valid",o,s){return rt(()=>{o==null&&(o=go()),nn(o);let c=Qv(n,o);if(n.rank!==4)throw new Y(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(t.rank!==4)throw new Y(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return c=na(c,t,e,r==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}class s0 extends mm{constructor(t){super(2,t);this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Tn(t.depthwiseConstraint),this.depthwiseRegularizer=Ke(t.depthwiseRegularizer)}build(t){if(t=Re(t),t.length<4)throw new Y(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let r=t[e],o=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r=wU(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=qo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=To(e,this.kernelSize[0],this.padding,this.strides[0]),c=To(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,c]:[t[0],s,c,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseRegularizer),t}}s0.className="DepthwiseConv2D",vt(s0);function qS(n,t,e,r){if(Array.isArray(n)){if(t!=null||e!=null)throw new Y("When inputs is an array, neither initialState or constants should be provided");r!=null&&(e=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(t=n.slice(1,n.length)),n=n[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:n,initialState:t,constants:e}}function HS(n,t,e,r=!1,o,s,c=!1,l=!1){return rt(()=>{let p=t.shape.length;if(p<3)throw new Y(`Input should be at least 3D, but is ${p}D.`);let f=[1,0].concat(yo(2,p));if(t=Kt(t,f),s!=null)throw new Ut("The rnn() functoin of the deeplearn.js backend does not support constants yet.");c&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===p-1&&(o=cr(o,-1)),o=Kt(o,f)),r&&(t=Rr(t,0),o!=null&&(o=Rr(o,0)));let m=[],y,b=e,v=t.shape[0],T=mo(t),N;o!=null&&(N=mo(o));for(let D=0;D<v;++D){let I=T[D],P=rt(()=>n(I,b));if(o==null)y=P[0],b=P[1];else{let E=rt(()=>{let L=N[D],B=qn(L).sub(L),q=P[0].mul(L).add(b[0].mul(B)),H=b.map((Z,J)=>P[1][J].mul(L).add(Z.mul(B)));return{output:q,newStates:H}});y=E.output,b=E.newStates}l&&m.push(y)}let S;if(l){let D=1;S=ur(m,D)}return[y,S,b]})}class ko extends ye{constructor(t){super(t);let e;if(t.cell==null)throw new Y("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new bm({cells:t.cell}):e=t.cell,e.stateSize==null)throw new Y("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new In({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return yo(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){$v(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let r=e[0],o;if(this.returnSequences?o=[t[0],t[1],r]:o=[t[0],r],this.returnState){let s=[];for(let c of e)s.push([t[0],c]);return[o].concat(s)}else return o}computeMask(t,e){return rt(()=>{Array.isArray(e)&&(e=e[0]);let r=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[r].concat(o)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let r=0;r<t;++r)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){let e=null;if(this.numConstants!=null)throw new Ut("Constants support is not implemented in RNN yet.");$v(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new In({shape:[r,null,...o]});let s=[t[0]].concat(t.slice(2));if(e!=null)throw new Ut("Constants support is not implemented in RNN yet.");this.cell.build(s);let c;if(Array.isArray(this.cell.stateSize)?c=this.cell.stateSize:c=[this.cell.stateSize],this.stateSpec!=null){if(!lt(this.stateSpec.map(l=>l.shape[l.shape.length-1]),c))throw new Y(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=c.map(l=>new In({shape:[null,l]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new Go("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new Y("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=>xe([r,o])):this.states_=[xe([r,this.cell.stateSize])];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>xe([r,o])):this.states_[0]=xe([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],c=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,l=[r,c];if(!lt(s.shape,l))throw new Y(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Sn(o.clone()))})}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=qS(t,r,o,this.numConstants);t=s.inputs,r=s.initialState,o=s.constants;let c=[],l=[];if(r!=null){e.initialState=r,c=c.concat(r),this.stateSpec=[];for(let f of r)this.stateSpec.push(new In({shape:f.shape}));l=l.concat(this.stateSpec)}o!=null&&(e.constants=o,c=c.concat(o),this.numConstants=o.length);let p=c[0]instanceof xo;if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Qt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let c=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==c)throw new Y(`RNN Layer has ${c} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let l={training:o},p=(T,N)=>{let S=this.cell.call([T].concat(N),l);return[S[0],S.slice(1)]},f=HS(p,t,s,this.goBackwards,r,null,this.unroll,this.returnSequences),m=f[0],y=f[1],b=f[2];this.stateful&&this.resetStates(b,o);let v=this.returnSequences?y:m;return this.returnState?[v].concat(b):v})}getInitialState(t){return rt(()=>{let e=xe(t.shape);return e=zt(e,[1,2]),e=Dp(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?xv(e,[1,r]):e):this.cell.stateSize>1?[xv(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===ko.className&&(e.cell={className:this.cell.getClassName(),config:r}),Object.assign({},r,t,e)}static fromConfig(t,e,r={}){let o=e.cell,s=wo(o,r);return new t(Object.assign(e,{cell:s}))}}ko.className="RNN",vt(ko);class Xc extends ye{}class gm extends Xc{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let r=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(r),rate:this.recurrentDropout,training:o}));let s,c=this.dropoutMask,l=this.recurrentDropoutMask;c!=null?s=Uo(nt(t,c),this.kernel.read()):s=Uo(t,this.kernel.read()),this.bias!=null&&(s=qo(s,this.bias.read())),l!=null&&(r=nt(r,l));let p=Tt(s,Uo(r,this.recurrentKernel.read()));return this.activation!=null&&(p=this.activation.apply(p)),[p,p]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}gm.className="SimpleRNNCell",vt(gm);class i0 extends ko{constructor(t){t.cell=new gm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}}i0.className="SimpleRNN",vt(i0);class ym extends Xc{constructor(t){super(t);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",t.resetAfter)throw new Y("GRUCell does not support reset_after parameter set to true.");this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:r,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:3}));let s=this.dropoutMask,c=this.recurrentDropoutMask,l,p,f;0<this.dropout&&this.dropout<1&&(t=nt(t,s[0]));let m=Uo(t,this.kernel.read());this.useBias&&(m=qo(m,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,c[0]));let y=this.recurrentKernel.read(),[b,v]=Tr(y,[2*this.units,this.units],y.rank-1),T=Uo(o,b),[N,S,D]=Tr(m,3,m.rank-1),[I,P]=Tr(T,2,T.rank-1);l=this.recurrentActivation.apply(Tt(N,I)),p=this.recurrentActivation.apply(Tt(S,P));let E=Uo(nt(p,o),v);f=this.activation.apply(Tt(D,E));let L=Tt(nt(l,o),nt(Tt(1,tn(l)),f));return[L,L]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),recurrentActivation:bi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}ym.className="GRUCell",vt(ym);class a0 extends ko{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new ym(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}a0.className="GRU",vt(a0);class Wp extends Xc{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Re(t);let r=t[t.length-1];this.kernel=this.addWeight("kernel",[r,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,c=this.units;o=new(e=class extends Qr{apply(p,f){let m=s.apply([c]),y=new jd().apply([c]),b=s.apply([c*2]);return VC(VC(m,y),b)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new Y(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:r,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:4}));let c=this.dropoutMask,l=this.recurrentDropoutMask,p,f,m,y;0<this.dropout&&this.dropout<1&&(t=nt(t,c[0]));let b=Uo(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,l[0])),b=Tt(b,Uo(o,this.recurrentKernel.read())),this.useBias&&(b=qo(b,this.bias.read()));let[v,T,N,S]=Tr(b,4,b.rank-1);p=this.recurrentActivation.apply(v),f=this.recurrentActivation.apply(T),m=Tt(nt(f,s),nt(p,this.activation.apply(N))),y=this.recurrentActivation.apply(S);let D=nt(y,this.activation.apply(m));return[D,D,m]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),recurrentActivation:bi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}Wp.className="LSTMCell",vt(Wp);class c0 extends ko{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Wp(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}c0.className="LSTM",vt(c0);class bm extends Xc{constructor(t){super(t);this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return rt(()=>{t=t;let r=t.slice(1),o=[];for(let l of this.cells.slice().reverse())Array.isArray(l.stateSize)?o.push(r.splice(0,l.stateSize.length)):o.push(r.splice(0,1));o.reverse();let s=[],c;for(let l=0;l<this.cells.length;++l){let p=this.cells[l];r=o[l],l===0?c=[t[0]].concat(r):c=[c[0]].concat(r),c=p.call(c,e),s.push(c.slice(1))}r=[];for(let l of s.slice().reverse())r.push(...l);return[c[0]].concat(r)})}build(t){$v(t)&&(t=t[0]),t=t;let e;this.cells.forEach((r,o)=>{fa(`RNNCell_${o}`,()=>{r.build(t),Array.isArray(r.stateSize)?e=r.stateSize[0]:e=r.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),r=this.cells.map(e),o={cells:r};return Object.assign({},t,o)}static fromConfig(t,e,r={}){let o=[];for(let s of e.cells)o.push(wo(s,r));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.cells)e.push(...r.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Iv(t)}setWeights(t){let e=[];for(let r of this.cells){let o=r.weights.length,s=t.splice(o);for(let c=0;c<r.weights.length;++c)e.push([r.weights[c],s[c]])}Ev(e)}}bm.className="StackedRNNCells",vt(bm);function wi(n){let{ones:t,rate:e,training:r=!1,count:o=1}=n,s=()=>UC(t(),e),c=()=>Fp(s,t,r);if(!o||o<=1)return Sn(c().clone());let l=Array(o).fill(void 0).map(c);return l.map(p=>Sn(p.clone()))}var vU=function(n,t){var e={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&t.indexOf(r)<0&&(e[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,r=Object.getOwnPropertySymbols(n);o<r.length;o++)t.indexOf(r[o])<0&&Object.prototype.propertyIsEnumerable.call(n,r[o])&&(e[r[o]]=n[r[o]]);return e};class fst extends Xc{}class jS extends ko{constructor(t){if(t.unroll)throw new Ut("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Ut("It is not possible at the moment to stack convolutional cells.");super(t);this.inputSpec=[new In({ndim:5})]}call(t,e){return rt(()=>{if(this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new Y("ConvRNN2D cell does not support constants");let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return rt(()=>{let{stateSize:e}=this.cell,r=t.shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=xe(s);return Array.isArray(e)?Array(e.length).fill(c):[c]})}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new Go("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=r[0];if(c==null)throw new Y("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(()=>xe(s)):this.states_=[xe(s)];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xe(s)):this.states_[0]=xe(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let l=0;l<this.states_.length;++l){let p=t[l],f=s;if(!lt(p.shape,f))throw new Y(`State ${l} is incompatible with layer ${this.name}: expected shape=${f}, received shape=${p.shape}`);this.states_[l]=p}}this.states_=this.states_.map(l=>Sn(l.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:r,kernelSize:o,padding:s,strides:c,dilationRate:l}=this.cell,p=e==="channelsFirst",f=t[p?3:2],m=t[p?4:3],y=To(f,o[0],s,c[0],l[0]),b=To(m,o[1],s,c[1],l[1]),v=[...t.slice(0,2),...p?[r,y,b]:[y,b,r]];return v}}jS.className="ConvRNN2D";class xm extends Wp{constructor(t){let{filters:e,kernelSize:r,strides:o,padding:s,dataFormat:c,dilationRate:l}=t;super(Object.assign({},t,{units:e}));this.filters=e,$n(this.filters,"filters"),this.kernelSize=Hc(r,2,"kernelSize"),this.kernelSize.forEach(p=>$n(p,"kernelSize")),this.strides=Hc(o||1,2,"strides"),this.strides.forEach(p=>$n(p,"strides")),this.padding=s||"valid",Or(this.padding),this.dataFormat=c||"channelsLast",nn(this.dataFormat),this.dilationRate=Hc(l||1,2,"dilationRate"),this.dilationRate.forEach(p=>$n(p,"dilationRate"))}build(t){var e;t=Re(t);let r=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[r]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[r]}`);let o=t[r],s=4,c=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",c,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let l=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",l,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let p;if(this.unitForgetBias){let f=this.biasInitializer,m=this.filters;p=new(e=class extends Qr{apply(b,v){let T=f.apply([m]),N=ho([m]),S=f.apply([m*2]);return bv([T,N,S])}},e.className="CustomInit",e)}else p=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,p,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return rt(()=>{if(t.length!==3)throw new Y(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training||!1,o=t[0],s=t[1],c=t[2],l=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(o),rate:this.dropout,training:r,count:l}));let p=this.dropoutMask,f=(kt,Nt,At)=>!Nt||!Nt[At]?kt:nt(Nt[At],kt),m=f(o,p,0),y=f(o,p,1),b=f(o,p,2),v=f(o,p,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(s),rate:this.recurrentDropout,training:r,count:l}));let T=this.recurrentDropoutMask,N=f(s,T,0),S=f(s,T,1),D=f(s,T,2),I=f(s,T,3),P=3,[E,L,B,q]=Tr(this.kernel.read(),l,P),[H,Z,J,it]=this.useBias?Tr(this.bias.read(),l):[null,null,null,null];m=this.inputConv(m,E,H,this.padding),y=this.inputConv(y,L,Z,this.padding),b=this.inputConv(b,B,J,this.padding),v=this.inputConv(v,q,it,this.padding);let[pt,ht,dt,ft]=Tr(this.recurrentKernel.read(),l,P);N=this.recurrentConv(N,pt),S=this.recurrentConv(S,ht),D=this.recurrentConv(D,dt),I=this.recurrentConv(I,ft);let ut=this.recurrentActivation.apply(Tt(m,N)),bt=this.recurrentActivation.apply(Tt(y,S)),yt=Tt(nt(bt,c),nt(ut,this.activation.apply(Tt(b,D)))),xt=nt(this.recurrentActivation.apply(Tt(v,I)),this.activation.apply(yt));return[xt,xt,yt]})}getConfig(){let t=super.getConfig(),{units:e}=t,r=vU(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},r,o)}inputConv(t,e,r,o){let s=fs(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?qo(s,r,this.dataFormat):s}recurrentConv(t,e){let r=1;return fs(t,e,r,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}xm.className="ConvLSTM2DCell",vt(xm);class l0 extends jS{constructor(t){let e=new xm(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}l0.className="ConvLSTM2D",vt(l0);class wm extends ye{constructor(t){super(t);this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,r=[];for(let o=0;o<this.noiseShape.length;++o)r.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return r}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(r),c=Fp(()=>UC(r,this.rate,s,this.seed),()=>r,o);return c}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}wm.className="Dropout",vt(wm);class u0 extends wm{constructor(t){super(t);this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}}u0.className="SpatialDropout1D",vt(u0);class p0 extends ye{constructor(t){super(t);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Tn(t.kernelConstraint),this.biasConstraint=Tn(t.biasConstraint),this.kernelRegularizer=Ke(t.kernelRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Re(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Re(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=FC(this.activation.getClassName()),s;return o!=null?s=Uo(r,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Uo(r,this.kernel.read()),this.bias!=null&&(s=qo(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:bi(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}p0.className="Dense",vt(p0);class h0 extends ye{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Re(t);for(let e of t.slice(1))if(e==null)throw new Y(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],fi(t,1)]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.dataFormat==="channelsFirst"&&r.rank>1){let o=[0];for(let s=2;s<r.rank;++s)o.push(s);o.push(1),r=r.transpose(o)}return UV(r)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}}h0.className="Flatten",vt(h0);class f0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.activation=xi(t.activation)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);return this.activation.apply(r)})}getConfig(){let t={activation:bi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}f0.className="Activation",vt(f0);class d0 extends ye{constructor(t){super(t);this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return rt(()=>(t=Qt(t),VV(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}d0.className="RepeatVector",vt(d0);class m0 extends ye{constructor(t){super(t);this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let r="Total size of new array must be unchanged.",o=e.slice(),s=1,c=null;for(let p=0;p<o.length;++p){let f=o[p];if(this.isUnknown(f))if(c===null)c=p;else throw new Y("Can only specifiy one unknown dimension.");else s*=f}let l=fi(t);if(c!==null){if(s===0||l%s!==0)throw new Y(r);o[c]=l/s}else if(l!==s)throw new Y(r);return o}computeOutputShape(t){let e=!1;for(let r=0;r<t.length;++r)if(this.isUnknown(t[r])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=r.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return r.reshape(s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}m0.className="Reshape",vt(m0);class g0 extends ye{constructor(t){super(t);if(t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=yo(1,t.dims.length+1);if(!lt(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new In({ndim:this.dims.length+1})]}computeOutputShape(t){t=Re(t);let e=t.slice();return this.dims.forEach((r,o)=>{e[o+1]=t[r]}),e}call(t,e){return Kt(Qt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}g0.className="Permute",vt(g0);class y0 extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let r=Qt(t),o=-1;return Zu(ci(r,this.maskValue),o)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=-1,s=!0,c=Zu(ci(r,this.maskValue),o,s),l=r.mul(c.asType(r.dtype));return l})}}y0.className="Masking",vt(y0);class b0 extends ye{constructor(t){super(t);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ze(t.inputLength))}this.inputDim=t.inputDim,$n(this.inputDim,"inputDim"),this.outputDim=t.outputDim,$n(this.outputDim,"outputDim"),this.embeddingsInitializer=je(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ke(t.embeddingsRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.embeddingsConstraint=Tn(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return rt(()=>this.maskZero?(t=Qt(t),ci(t,re(t))):null)}computeOutputShape(t){if(t=Re(t),this.inputLength==null)return[...t,this.outputDim];let e=ze(this.inputLength);if(e.length!==t.length-1)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let r=0;for(let o=0;o<e.length;++o){let s=e[o],c=t[o+1];if(s!=null&&c!=null&&s!==c)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[r]=c),r++}}return[t[0],...e,this.outputDim]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);r.dtype!=="int32"&&(r=Ep(r,"int32"));let o=GC(this.embeddings.read(),r.as1D());return o.reshape(Re(this.computeOutputShape(r.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:rn(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:vn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}b0.className="Embedding",vt(b0);class ya extends ye{constructor(t){super(t||{});this.supportsMasking=!0}mergeFunction(t){throw new Ut}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let r=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],c=e[o];if(s==null||c==null||s<0||c<0)r.push(null);else if(s===1)r.push(c);else if(c===1)r.push(s);else{if(s!==c)throw new Y("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));r.push(s)}}return r}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Re(t)]),t=t,t.length<2)throw new Y(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=hi(e),e.length>1)throw new Y(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let r=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let c=t[s]==null?null:t[s].slice(1);r=this.computeElementwiseOpOutputShape(r,c)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&hi(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return rt(()=>{if(t=t,this.reshapeRequired){let r=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=di(o);for(let c of t){let l=c.rank;for(let p=0;p<s-l;++p)c=Dp(c,1);r.push(c)}return this.mergeFunction(r)}else{let s=!1;for(let p of t){let f=p.rank;if(f==null){let m=p.shape,y=m[0],b=m.slice(1).concat([y]),v=p.reshape([y].concat(fi(m.slice(1))));v=Kt(v,[1,0]),v=v.reshape(b),r.push(v),s=!0}else if(f>1){let m=yo(1,f).concat([0]);r.push(Kt(p,m)),s=!0}else r.push(p)}let c=this.mergeFunction(r),l=c.rank;if(s){if(l==null){let p=c.shape,f=p.length,m=p[f-1],y=[m].concat(p.slice(0,p.length-1));c=Kt(c.reshape([-1,m]),[1,0]).reshape(y)}else if(l>1){let p=[l-1].concat(yo(0,l-1));c=Kt(c,p)}}return c}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let r=[];for(let o of t)o!=null&&o[0]!==null&&r.push(o[0]);return r=hi(r),r.length===1?e=r.concat(e):e=[null].concat(e),e}computeMask(t,e){return rt(()=>{if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an Array");if(!Array.isArray(t))throw new Y("`inputs` should be an Array");if(e.length!==t.length)throw new Y(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:cr(o,0));let r=e[0];for(let o=1;o<e.length-1;++o)r=Yr(r,e[o]);return r})}}class Vp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return e})}}Vp.className="Add",vt(Vp);function dst(n){if(Array.isArray(n)){let t=new Vp({});return t.apply(n)}else return new Vp(n)}class Gp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=nt(e,t[r]);return e})}}Gp.className="Multiply",vt(Gp);function mst(n){if(Array.isArray(n)){let t=new Gp({});return t.apply(n)}else return new Gp(n)}class Up extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return nt(1/t.length,e)})}}Up.className="Average",vt(Up);function gst(n){if(Array.isArray(n)){let t=new Up({});return t.apply(n)}else return new Up(n)}class qp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=Xr(e,t[r]);return e})}}qp.className="Maximum",vt(qp);function yst(n){if(Array.isArray(n)){let t=new qp({});return t.apply(n)}else return new qp(n)}class Hp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=oa(e,t[r]);return e})}}Hp.className="Minimum",vt(Hp);function bst(n){if(Array.isArray(n)){let t=new Hp({});return t.apply(n)}else return new Hp(n)}class jp extends ya{constructor(t){super(t);this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new Y("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let r=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let c=!1;for(let l of r)if(lt(l,s)){c=!0;break}c||r.push(s)}if(r.length>1)throw new Y("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return rt(()=>bv(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new Y("A `Concatenate` layer should be called on a list of inputs.");let e=t,r=e[0].slice(),o=this.axis<0?r.length+this.axis:this.axis;for(let s of e.slice(1)){if(r[o]==null||s[o]==null){r[o]=null;break}r[o]+=s[o]}return r}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new Y("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new Y(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return rt(()=>{let r=!0;if(e.forEach(c=>{if(c!=null){r=!1;return}}),r)return null;let o=[];for(let c=0;c<t.length;++c)e[c]==null?o.push(qn(t[c]).asType("bool")):e[c].rank<t[c].rank?o.push(cr(e[c],-1)):o.push(e[c]);let s=Qe(o,this.axis);return rd(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}jp.className="Concatenate",vt(jp);function xst(n){if(Array.isArray(n)){let t=new jp({});return t.apply(n)}else return new jp(n)}function Kp(n,t){for(;n<0;)n+=t;return n}function TU(n,t,e){if(n.shape.length>3||t.shape.length>3)throw new Ut("batchDot is not implemented for tensors of 4D or higher rank yet");if(_(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),_(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Ut("batchDot is not implemented for complex64-type Tensors yet.");let r=n.shape.length,o=t.shape.length;e==null&&(e=[r-1,o-2]);let s=e;return rt(()=>{let c;if(r>o){c=r-o;let p=[];for(let f=0;f<c;++f)p.push(1);t=t.reshape(t.shape.concat(p))}else if(o>r){c=o-r;let p=[];for(let f=0;f<c;++f)p.push(1);n=n.reshape(n.shape.concat(p))}else c=0;let l;if(n.shape.length===2&&t.shape.length===2)s[0]===s[1]?l=n.mul(t).sum(s[0]):l=n.transpose([1,0]).mul(t).sum(s[1]);else{let p=s[0]!==n.shape.length-1,f=s[1]===t.shape.length-1;l=n.matMul(t,p,f)}if(c>0){let p;r>o?p=r+o-3:p=r-1;let f=[];for(let m=p;m<p+c;++m)f.push(m);l=l.squeeze(f)}return l.shape.length===1&&(l=l.expandDims(1)),l})}class x0 extends ya{constructor(t){super(t);this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],r=t[1];if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);if(e[o[0]]!==r[o[1]])throw new Y(`Dimension incompatibility: ${e[o[0]]} !== ${r[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new Y(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],r=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,c)=>Kp(s,t[c].shape.length)):o=[Kp(this.axes,e.shape.length),Kp(this.axes,r.shape.length)],this.normalize&&(e=sm(e,o[0]),r=sm(r,o[1])),TU(e,r,o)}interpretAxes(t,e){let r;return Array.isArray(this.axes)?r=this.axes:r=[Kp(this.axes,t.length),Kp(this.axes,e.length)],r}computeOutputShape(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),r=t[1].slice();if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);e.splice(o[0],1),r.splice(o[1],1),r.splice(0,1);let s=e.concat(r);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}x0.className="Dot",vt(x0);class w0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=()=>Hd(r.shape,0,this.stddev).add(r),s=Fp(o,()=>r,e.training||!1);return s})}}w0.className="GaussianNoise",vt(w0);class v0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.rate>0&&this.rate<1){let o=()=>{let s=Math.sqrt(this.rate/(1-this.rate));return r.mul(Hd(r.shape,1,s))};return Fp(o,()=>r,e.training||!1)}return r})}}v0.className="GaussianDropout",vt(v0);class T0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Qt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(t),o=()=>{let s=Qt(t),c=1.6732632423543772,l=1.0507009873554805,p=-c*l,f=ds(aa(r),this.rate);f=Ep(f,"float32");let m=((1-this.rate)*(1+this.rate*p**2))**-.5,y=-m*p*this.rate,b=s.mul(f).add(f.add(-1).mul(p));return b.mul(m).add(y)};return Fp(o,()=>Qt(t),e.training||!1)}return t})}}T0.className="AlphaDropout",vt(T0);function Xp(n,t,e,r,o,s=.001){let c;if(n.rank===2)c=b_(n,t,e,r,o,s);else if(n.rank===3)c=x_(n,t,e,r,o,s);else if(n.rank===4)c=w_(n,t,e,r,o,s);else throw new Ut(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return c}function kU(n,t,e,r,o=.001){return rt(()=>{let s=xd(n,r),c=s.mean,l=s.variance,p=Xp(n,c,l,e,t,o);return[p,c,l]})}function NU(n,t,e,r,o=.001){return rt(()=>{let s=xd(n,r),c=s.mean,l=s.variance,p=[];for(let T of yo(0,n.rank))r.indexOf(T)!==-1?p.push(1):p.push(n.shape[T]);let f=c.reshape(p),m=l.reshape(p),y=t==null?null:t.reshape(p),b=e==null?null:e.reshape(p),v=Xp(n,f,m,b,y,o);return[v,c,l]})}function _U(n,t,e,r,o=.001){return lt(r.slice().sort(),yo(0,n.rank-1))?kU(n,t,e,r,o):NU(n,t,e,r,o)}class k0 extends ye{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.movingMeanInitializer=je(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(t.movingVarianceInitializer||"ones"),this.betaConstraint=Tn(t.betaConstraint),this.gammaConstraint=Tn(t.gammaConstraint),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer)}build(t){t=Re(t);let e=this.axis>=0?this.axis:this.axis+t.length,r=t[e];if(r==null)throw new Y(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new In({ndim:t.length,axes:{[e]:r}})];let o=[r];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training,o=Qt(t),s=o.shape,c=s.length,l=yo(0,c),p=this.axis>=0?this.axis:this.axis+c;l.splice(p,1);let f=pa(1,c);f[p]=s[p];let m=l.slice();m.sort();let y=!lt(m,yo(0,c).slice(0,c-1)),b=()=>{if(y){let I=this.movingMean.read().reshape(f),P=this.movingVariance.read().reshape(f),E=this.center?this.beta.read().reshape(f):null,L=this.scale?this.gamma.read().reshape(f):null;return Xp(o,I,P,E,L,this.epsilon)}else return Xp(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return b();let[v,T,N]=_U(o,this.gamma.read(),this.beta.read(),l,this.epsilon),S=(I,P,E)=>{rt(()=>{let L=1-E,B=I.read(),q=B.sub(P).mul(L);I.write(B.sub(q))})},D=()=>{S(this.movingMean,T,this.momentum),S(this.movingVariance,N,this.momentum)};return D(),v})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),movingMeanInitializer:rn(this.movingMeanInitializer),movingVarianceInitializer:rn(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:vn(this.betaConstraint),gammaConstraint:vn(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}k0.className="BatchNormalization",vt(k0);class N0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Re(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==hi(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let r=Qt(t),o=r.shape,s=o.length;return rt(()=>{let c=!0,{mean:l,variance:p}=xd(r,this.axis,c),f=pa(1,s);for(let N of this.axis)f[N]=o[N];let m=N=>N!=null&&N.shape.length!==s&&this.axis!==[s-1]?N.reshape(f):N,y=m(this.gamma.read()),b=m(this.beta.read()),v=[],T=[];for(let N=0;N<s;++N)this.axis.indexOf(N)!==-1?(v.push(o[N]),T.push(1)):(v.push(1),T.push(o[N]));return l=l.tile(v),p=p.tile(v),y=y.tile(T),b=b.tile(T),Xp(r,l,p,b,y,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}N0.className="LayerNormalization",vt(N0);function wst(n,t){return rt(()=>{if(n.rank!==3)throw new Y(`temporalPadding expects input tensor to be 3-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new Y(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);let e=[[0,0],t,[0,0]];return Wo(n,e)})}function CU(n,t,e){return rt(()=>{if(n.rank!==4)throw new Y(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new Y("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=go()),e!=="channelsLast"&&e!=="channelsFirst")throw new Y(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return e==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Wo(n,r)})}class _0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?go():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new Y(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,r;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],r=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new Y(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new Y(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);r=t.padding[1]}this.padding=[e,r]}this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e,r;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?r=t[3]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],t[1],e,r]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?r=t[2]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],e,r,t[3]])}call(t,e){return rt(()=>CU(Qt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}_0.className="ZeroPadding2D",vt(_0);function vm(n,t,e,r,o,s){return rt(()=>{nn(o),OC(s),Or(r),e==null&&(e=[1,1]),r==null&&(r="valid"),o==null&&(o=go()),s==null&&(s="max"),n=Qv(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=lp(n,t,e,l):c=ep(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}function KS(n,t,e,r,o,s){return rt(()=>{nn(o),OC(s),Or(r),e==null&&(e=[1,1,1]),r==null&&(r="valid"),o==null&&(o=go()),s==null&&(s="max"),n=WS(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=Cw(n,t,e,l):c=fw(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,4,1,2,3])),c})}class XS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new Y(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if($n(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new Y(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,Or(this.padding),this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){t=Re(t);let e=To(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return rt(()=>{this.invokeCallHook(t,e),t=Dp(Qt(t),2);let r=this.poolingFunction(Qt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return li(r,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class C0 extends XS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"max")}}C0.className="MaxPooling1D",vt(C0);class S0 extends XS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"avg")}}S0.className="AveragePooling1D",vt(S0);class YS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new Y(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Or(this.padding),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=To(e,this.poolSize[0],this.padding,this.strides[0]),r=To(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r]:[t[0],e,r,t[3]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class $0 extends YS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"max")}}$0.className="MaxPooling2D",vt($0);class I0 extends YS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"avg")}}I0.className="AveragePooling2D",vt(I0);class JS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new Y(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Or(this.padding),this.inputSpec=[new In({ndim:5})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=To(e,this.poolSize[0],this.padding,this.strides[0]),r=To(r,this.poolSize[1],this.padding,this.strides[1]),o=To(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r,o]:[t[0],e,r,o,t[4]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class E0 extends JS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),KS(t,e,r,o,s,"max")}}E0.className="MaxPooling3D",vt(E0);class D0 extends JS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),KS(t,e,r,o,s,"avg")}}D0.className="AveragePooling3D",vt(D0);class ZS extends ye{constructor(t){super(t);this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Ut}}class A0 extends ZS{constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return en(r,1)})}}A0.className="GlobalAveragePooling1D",vt(A0);class F0 extends ZS{constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return lr(r,1)})}}F0.className="GlobalMaxPooling1D",vt(F0);class QS extends ye{constructor(t){super(t);this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Ut}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class R0 extends QS{call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?en(r,[1,2]):en(r,[2,3])})}}R0.className="GlobalAveragePooling2D",vt(R0);class P0 extends QS{call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?lr(r,[1,2]):lr(r,[2,3])})}}P0.className="GlobalMaxPooling2D",vt(P0);class t$ extends ye{constructor(t){super(t);this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,r={}){let o=e.layer,s=wo(o,r);delete e.layer;let c={layer:s};return Object.assign(c,e),new t(c)}}class O0 extends t${constructor(t){super(t);this.supportsMasking=!0}build(t){if(t=Re(t),t.length<3)throw new Y(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Re(t);let e=[t[0]].concat(t.slice(2)),r=this.layer.computeOutputShape(e),o=t[1];return[r[0],o].concat(r.slice(1))}call(t,e){return rt(()=>{t=Qt(t);let r=(c,l)=>{let p=Qt(this.layer.call(c,e));return[p,[]]},o=HS(r,t,[],!1,null,null,!1,!0),s=o[1];return s})}}O0.className="TimeDistributed",vt(O0);function SU(n){zc(OV,"BidirectionalMergeMode",n)}let $U="concat";class L0 extends t${constructor(t){super(t);let e=t.layer.getConfig(),r={};r.className=t.layer.getClassName(),r.config=e,this.forwardLayer=wo(r),e.goBackwards=!(e.goBackwards===!0);let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wo(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?$U:t.mergeMode,SU(this.mergeMode),t.weights)throw new Ut("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,r=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,r)),this.backwardLayer.setWeights(t.slice(r))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let r,o,s;return this.returnState&&(s=e.slice(1)),r=e[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,o=[r]):this.mergeMode==null?o=[r,r.slice()]:o=[r],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[r].concat(s).concat(s.slice()):pr(o)}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=qS(t,r,o,this.numConstants);if(t=s.inputs,r=s.initialState,o=s.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(r==null||r.length===0)&&o==null)return super.apply(t,e);let c=[],l=[];if(r!=null){let f=r.length;if(f%2>0)throw new Y("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=r,c.push(...r);let m=r.map(y=>new In({shape:y.shape}));this.forwardLayer.stateSpec=m.slice(0,f/2),this.backwardLayer.stateSpec=m.slice(f/2),l.push(...m)}if(o!=null)throw new Ut("Support for constants in Bidirectional layers is not implemented yet.");let p=c[0]instanceof xo;for(let f of c)if(f instanceof xo!==p)throw new Y("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e.initialState,o,s;if(r==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let p=r.slice(0,r.length/2),f=r.slice(r.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:p})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:f}))}let c;this.returnState&&(Array.isArray(o)&&(c=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Rr(s,1));let l;return this.mergeMode==="concat"?l=bv([o,s]):this.mergeMode==="sum"?l=Tt(o,s):this.mergeMode==="ave"?l=nt(.5,Tt(o,s)):this.mergeMode==="mul"?l=nt(o,s):this.mergeMode==null&&(l=[o,s]),this.returnState?this.mergeMode==null?l.concat(c):[l].concat(c):l})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fa(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fa(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[e,e]:r=e:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let o=this.forwardLayer.states,s=o.map(c=>null);return Array.isArray(r)?r.concat(s).concat(s):[r].concat(s).concat(s)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let r=wo(e.layer);if(delete e.layer,e.numConstants!=null)throw new Ut("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=r,new t(o)}}L0.className="Bidirectional",vt(L0);function IU(n){return new Gc(n)}function EU(n){return new Yv(n)}function DU(n){return new jv(n)}function AU(n){return new Kv(n)}function FU(n){return new Xv(n)}function RU(n){return new Zv(n)}function PU(n){return new Jv(n)}function OU(n){return new zp(n)}function LU(n){return new Kc(n)}function MU(n){return new e0(n)}function BU(n){return 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gH=Object.freeze({__proto__:null,json:mH});let yH=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"F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bH=Object.freeze({__proto__:null,json:yH});let 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wH=Object.freeze({__proto__:null,json:xH});let vH=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var TH=Object.freeze({__proto__:null,json:vH});let kH=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}];var NH=Object.freeze({__proto__:null,json:kH});let _H=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];var CH=Object.freeze({__proto__:null,json:_H});let SH=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]}];var $H=Object.freeze({__proto__:null,json:SH});let IH=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var EH=Object.freeze({__proto__:null,json:IH});let DH=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var AH=Object.freeze({__proto__:null,json:DH});let FH=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var RH=Object.freeze({__proto__:null,json:FH});let PH=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var OH=Object.freeze({__proto__:null,json:PH});let LH=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];var MH=Object.freeze({__proto__:null,json:LH});let BH=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var zH=Object.freeze({__proto__:null,json:BH});let WH=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var VH=Object.freeze({__proto__:null,json:WH});let GH=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var UH=Object.freeze({__proto__:null,json:GH});class u${static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[hH,dH,gH,bH,wH,TH,NH,AH,EH,CH,RH,OH,MH,zH,VH,UH,$H],e=[].concat(...t.map(r=>r.json));this.opMappers=e.reduce((r,o)=>(r[o.tfOpName]=o,r),{})}transformGraph(t,e={}){let r=t.node,o=[],s=[],c=[],l=r.reduce((N,S)=>(N[S.name]=this.mapNode(S),S.op.startsWith("Placeholder")?o.push(N[S.name]):S.op==="Const"?s.push(N[S.name]):(S.input==null||S.input.length===0)&&c.push(N[S.name]),N),{}),p=[],f=[],m={},y={};e!=null&&(m=this.mapSignatureEntries(e.inputs),y=this.mapSignatureEntries(e.outputs));let b=Object.keys(l);b.forEach(N=>{let S=l[N];S.inputNames.forEach(D=>{let[I]=vs(D);S.inputs.push(l[I]),l[I].children.push(S)})}),Object.keys(y).length===0?b.forEach(N=>{let S=l[N];S.children.length===0&&f.push(S)}):Object.keys(y).forEach(N=>{let[S]=vs(N),D=l[S];D!=null&&(D.signatureKey=y[N],f.push(D))}),Object.keys(m).length>0?Object.keys(m).forEach(N=>{let[S]=vs(N),D=l[S];D&&(D.signatureKey=m[N],p.push(D))}):p=o;let v={};t.library!=null&&t.library.function!=null&&(v=t.library.function.reduce((N,S)=>(N[S.signature.name]=this.mapFunction(S),N),{}));let T={nodes:l,inputs:p,outputs:f,weights:s,placeholders:o,signature:e,functions:v};return c.length>0&&(T.initNodes=c),T}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,r)=>(e[t[r].name]=r,e),{})}mapNode(t){let e=l$(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let r={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(o=>o.startsWith("^")?o.substr(1):o),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr};return e.inputs!=null&&(r.inputParams=e.inputs.reduce((o,s)=>(o[s.name]={type:s.type,inputIndexStart:s.start,inputIndexEnd:s.end},o),{})),e.attrs!=null&&(r.attrParams=e.attrs.reduce((o,s)=>{let c=s.type,l;switch(s.type){case"string":l=V0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=V0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":l=Y0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=Y0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":l=U0(t.attr,s.tfName,s.defaultValue||0),l===void 0&&!!s.tfDeprecatedName&&(l=U0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":l=X0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=X0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":l=G0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=G0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":l=Z0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=Z0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":l=K0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=K0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":l=J0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=J0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":l=H0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=H0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":l=j0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=j0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":l=h$(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=h$(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${t.op}`)}return o[s.name]={value:l,type:c},o},{})),r}mapFunction(t){let e=t.nodeDef,r=[],o=[],s={};e!=null&&(s=e.reduce((y,b)=>(y[b.name]=this.mapNode(b),b.op==="Const"&&o.push(y[b.name]),y),{}));let c=[],l=[];t.signature.inputArg.forEach(y=>{let[b]=vs(y.name),v={name:b,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:q0(y.type),type:"dtype"}},children:[]};v.signatureKey=y.name,c.push(v),s[b]=v});let p=Object.keys(s);p.forEach(y=>{let b=s[y];b.inputNames.forEach(v=>{let[T]=vs(v);b.inputs.push(s[T]),s[T].children.push(b)})});let f=t.ret;t.signature.outputArg.forEach(y=>{let[b,v]=vs(f[y.name]),T=s[b];T!=null&&(T.defaultOutput=v,l.push(T))});let m=this.mapArgsToSignature(t);return{nodes:s,inputs:c,outputs:l,weights:o,placeholders:r,signature:m}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r),e),{}),outputs:t.signature.outputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r,t.ret),e),{})}}mapArgToTensorInfo(t,e){let r=t.name;return e!=null&&(r=e[r]),{name:r,dtype:t.type}}}function qH(n){let t=ct().global;if(typeof t.atob!="undefined")return t.atob(n);if(typeof Buffer!="undefined")return new Buffer(n,"base64").toString();throw new Error("Unable to decode base64 in this environment. 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jH=(n,t,e)=>{switch(n.op){case"BiasAdd":case"AddV2":case"Add":return[Tt(A("a",n,t,e),A("b",n,t,e))];case"AddN":return[g_(A("tensors",n,t,e))];case"FloorMod":case"Mod":return[bd(A("a",n,t,e),A("b",n,t,e))];case"Mul":return[nt(A("a",n,t,e),A("b",n,t,e))];case"RealDiv":case"Div":return[Bt(A("a",n,t,e),A("b",n,t,e))];case"DivNoNan":return[xw(A("a",n,t,e),A("b",n,t,e))];case"FloorDiv":return[nd(A("a",n,t,e),A("b",n,t,e))];case"Sub":return[Dt(A("a",n,t,e),A("b",n,t,e))];case"Minimum":return[oa(A("a",n,t,e),A("b",n,t,e))];case"Maximum":return[Xr(A("a",n,t,e),A("b",n,t,e))];case"Pow":return[fo(A("a",n,t,e),A("b",n,t,e))];case"SquaredDifference":return[gp(A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Tst="arithmetic";let 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TypeError(`Node type ${n.op} is not implemented`)}},kst="basic_math";function to(n,t,e=""){_(XH(n,t),()=>e+` Shapes ${n} and ${t} must match`)}function XH(n,t){if(n.length!==t.length)return!1;for(let e=0;e<n.length;e++)if(n[e]!==-1&&t[e]!==-1&&n[e]!==t[e])return!1;return!0}class YH{constructor(t,e,r,o,s,c,l){this.name=t,this.dtype=e,this.maxSize=r,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=c,this.clearAfterRead=l,this.tensors=[],this.closed_=!1,this.idTensor=Et(0),Sn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let 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p=r[l],f=o[l];Sn(f),this.tensorMap.set(p,f)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return rt(()=>{let o=[];for(let s=0;s<r.length;s++){let c=r[s],l=this.findWithDefault(c,e);o.push(l)}return ur(o)})}findWithDefault(t,e){let r=this.tensorMap.get(t);return r!=null?r:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}}let cj=async(n,t,e,r)=>{switch(n.op){case"HashTable":case"HashTableV2":{let o=A("keyDType",n,t,e),s=A("valueDType",n,t,e),c=new aj(o,s);return r.addHashTable(n.name,c),[c.handle]}case"LookupTableImport":case"LookupTableImportV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("values",n,t,e),l=r.getHashTableById(o.id);return[await l.import(s,c)]}case"LookupTableFind":case"LookupTableFindV2":{let 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r=A("axis",n,t,e),o=A("x",n,t,e),s=A("indices",n,t,e);return[$c(o,$t(s,"int32"),r)]}case"ReverseV2":case"Reverse":{let r=A("axis",n,t,e),o=A("x",n,t,e);return[Rr(o,r)]}case"Slice":{let r=A("begin",n,t,e),o=A("size",n,t,e);return[ce(A("x",n,t,e),r,o)]}case"StridedSlice":{let r=A("begin",n,t,e),o=A("end",n,t,e),s=A("strides",n,t,e),c=A("beginMask",n,t,e),l=A("endMask",n,t,e),p=A("ellipsisMask",n,t,e),f=A("newAxisMask",n,t,e),m=A("shrinkAxisMask",n,t,e),y=A("x",n,t,e);return[Lw(y,r,o,s,c,l,p,f,m)]}case"Pack":return rt(()=>{let r=A("axis",n,t,e),o=A("tensors",n,t,e),s=o[0].shape,c=li(o[0]).shape,l=o.map(p=>{let f=lt(p.shape,s);if(!f&&!lt(li(p).shape,c))throw new Error("the input tensors shape does not match");return f?p:Q(p,s)});return[ur(l,r)]});case"Unpack":{let r=A("axis",n,t,e),o=A("tensor",n,t,e);return mo(o,r)}case"Tile":{let r=A("reps",n,t,e);return[ii(A("x",n,t,e),r)]}case"Split":case"SplitV":{let r=A("axis",n,t,e),o=A("numOrSizeSplits",n,t,e),s=A("x",n,t,e);return Tr(s,o,r)}case"ScatterNd":{let r=A("indices",n,t,e),o=A("values",n,t,e),s=A("shape",n,t,e);return[nC(r,o,s)]}case"GatherNd":{let r=A("x",n,t,e),o=A("indices",n,t,e);return[rC(r,o)]}case"SparseToDense":{let r=A("sparseIndices",n,t,e),o=A("outputShape",n,t,e),s=A("sparseValues",n,t,e),c=A("defaultValue",n,t,e);return[Vw(r,s,o,s.dtype===c.dtype?c:$t(c,s.dtype))]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ost="slice_join";let mj=(n,t,e)=>{switch(n.op){case"FFT":return[dp(A("x",n,t,e))];case"IFFT":return[Rc(A("x",n,t,e))];case"RFFT":return[mp(A("x",n,t,e))];case"IRFFT":return[Sd(A("x",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Lst="spectral";let gj=(n,t,e)=>{switch(n.op){case"Cast":return[$t(A("x",n,t,e),A("dtype",n,t,e))];case"ExpandDims":{let r=A("axis",n,t,e);return[cr(A("x",n,t,e),r)]}case"Squeeze":{let r=A("axis",n,t,e);return[li(A("x",n,t,e),r)]}case"Reshape":return[Q(A("x",n,t,e),A("shape",n,t,e))];case"MirrorPad":return[Sw(A("x",n,t,e),A("padding",n,t,e),A("mode",n,t,e))];case"PadV2":case"Pad":return[Wo(A("x",n,t,e),A("padding",n,t,e),A("constantValue",n,t,e))];case"SpaceToBatchND":{let r=A("blockShape",n,t,e),o=A("paddings",n,t,e);return[up(A("x",n,t,e),r,o)]}case"BatchToSpaceND":{let r=A("blockShape",n,t,e),o=A("crops",n,t,e);return[np(A("x",n,t,e),r,o)]}case"DepthToSpace":{let r=A("blockSize",n,t,e),o=A("dataFormat",n,t,e).toUpperCase();return[yw(A("x",n,t,e),r,o)]}case"BroadcastTo":return[rp(A("x",n,t,e),A("shape",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Mst="transformation";function m$(n,t,e,r){let o=((s,c,l)=>{switch(s.category){case"arithmetic":return rt(()=>jH(s,c,l));case"basic_math":return rt(()=>KH(s,c,l));case"control":return ej(s,c,l);case"convolution":return rt(()=>nj(s,c,l));case"creation":return rt(()=>rj(s,c,l));case"dynamic":return oj(s,c,l);case"evaluation":return rt(()=>sj(s,c,l));case"image":return rt(()=>lj(s,c,l));case"graph":return rt(()=>ij(s,c,l));case"logical":return rt(()=>uj(s,c,l));case"matrices":return rt(()=>pj(s,c,l));case"normalization":return rt(()=>hj(s,c,l));case"reduction":return rt(()=>fj(s,c,l));case"slice_join":return rt(()=>dj(s,c,l));case"spectral":return rt(()=>mj(s,c,l));case"transformation":return rt(()=>gj(s,c,l));case"hash_table":return cj(s,c,l,r);case"custom":let p=l$(s.op);if(p&&p.customExecutor)return p.customExecutor(new HH(s,c,l));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(n,t,e);return qi(o)?o.then(s=>[].concat(s)):[].concat(o)}class g${constructor(t={},e={},r={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=r,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let r=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(r))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}}function y$(n,t,e,r){let o=new Set,s=[],c=null,l=null,p=new Set,f=Object.keys(n).map(b=>Nr(b)[0]),m=[];r!=null&&(m=r.map(b=>Nr(b.name)[0]));let y=[...t];for(;y.length>0;){let b=y.pop();if((b$(b)||vj(b)||Tj(b))&&(c==null&&(c=b,l=c.children.map(v=>v.name).filter(v=>o.has(v)))),o.add(b.name),e[b.name]!=null)continue;if(f.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){s.push(b.name);continue}b.inputs.forEach(v=>{if(p.has(v.name))return;p.add(v.name),y.push(v)})}return{inputs:n,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:c,syncInputs:l}}function yj(n,t,e){let{usedNodes:r,inputs:o}=e,s=[],c=Object.keys(o).map(m=>Nr(m)[0]).map(m=>n.nodes[m]),l=n.initNodes;c.forEach(m=>{r.has(m.name)&&s.push(m)}),n.weights.forEach(m=>{r.has(m.name)&&s.push(m)}),l!=null&&l.forEach(m=>{r.has(m.name)&&s.push(m)});let p=new Set,f=[];for(;s.length>0;){let m=s.pop();p.add(m.name),t[m.name]||f.push(m),m.children.forEach(y=>{!p.has(y.name)&&r.has(y.name)&&y.inputs.every(b=>p.has(b.name))&&s.push(y)})}return f}let bj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],xj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],wj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function b$(n){return bj.indexOf(n.op)>=0}function vj(n){return xj.indexOf(n.op)>=0}function Tj(n){return wj.indexOf(n.op)>=0}class _m{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new _m(t.functions[r],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(r=>t[r].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let r=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return r.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let r=y$(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:c}=r;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${c}]`);if(o.length>0){let l=e.map(f=>f.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${p}]. Missing the following inputs: [${o}]`)}return yj(this.graph,this.weightMap,r)}execute(t,e){t=this.mapInputs(t);let r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=r.map(y=>this.graph.nodes[Nr(y)[0]]),s=e.map(y=>Nr(y)[0]),c=s.map(y=>this.graph.nodes[y]);c.length===0&&(c=this._outputs);let l=this.getCompilationKey(o,c),p=this.compiledMap.get(l);p==null&&(p=this.compile(t,c),this.compiledMap.set(l,p));let f={},m={};return rt(()=>{let y=new g$(this.weightMap,f,m,this.functionExecutorMap),b=Object.assign({},this.weightMap);Object.keys(t).forEach(N=>{let[S,D]=Nr(N),I=[];I[D]=t[N],b[S]=I});let v=this.getFrozenTensorIds(b),T={};for(let N=0;N<p.length;N++){let S=p[N];if(!b[S.name]){let D=m$(S,b,y,this._resourceManager);if(qi(D))throw new Error(`The execution of the op '${S.op}' returned a promise. Please use model.executeAsync() instead.`);b[S.name]=D,this.checkTensorForDisposal(S.name,S,b,y,v,s,T)}}return this.parent==null&&y.dispose(v),e.map(N=>fr(N,b,y))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(r=>t[r]).map(r=>r.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,r,o,s,c,l){if(e.category==="control"||c.indexOf(t)!==-1)return;r[t].forEach(p=>{p!=null&&(l[p.id]=(l[p.id]||0)+e.children.length)}),e.inputs.forEach(p=>{if(p.category!=="control"){let f=uH(p.name,r,o);f!=null&&f.forEach(m=>{if(m&&!s.has(m.id)){let y=l[m.id];y===1?(m.dispose(),delete l[m.id]):y!=null&&l[m.id]--}})}})}async executeAsync(t,e){return this._executeAsync(t,e)}async _executeAsync(t,e,r=!1,o={},s={}){r||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));let c=new g$(this.weightMap,o,s,this.functionExecutorMap),l=await this.executeWithControlFlow(t,c,e,r),p=e.map(b=>fr(b,l,c)),f=p.map(b=>b.id),m=Object.keys(t).map(b=>t[b].id),y=new Set([...f,...m,...this.weightIds]);return Object.keys(l).forEach(b=>{let v=l[b];v.forEach(T=>{T&&!T.isDisposed&&!y.has(T.id)&&T.dispose()})}),this.parent==null&&c.dispose(y),p}async executeFunctionAsync(t,e,r){let o=t.reduce((s,c,l)=>(s[this.inputs[l].name]=c,s),{});return this._executeAsync(o,this.outputNodes,!0,e,r)}async executeWithControlFlow(t,e,r,o){let s=Object.keys(t),c=s.map(P=>this.graph.nodes[Nr(P)[0]]),l=r.map(P=>Nr(P)[0]),p=l.map(P=>this.graph.nodes[P]);p.length===0&&(p=this._outputs);let{usedNodes:f,missingInputs:m,dynamicNode:y,syncInputs:b}=y$(t,p,this.weightMap,this._initNodes),v=[...c,...this.graph.weights,...this._initNodes||[]].map(P=>({node:P,contexts:e.currentContext})),T=Object.assign({},this.weightMap);Object.keys(t).forEach(P=>{let[E,L]=Nr(P),B=[];B[L]=t[P],T[E]=B});let N={},S=this.getFrozenTensorIds(T),D={};for(;v.length>0;){let P=this.processStack(c,v,e,T,D,S,l,N,f);await Promise.all(P)}y==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let I=p.filter(P=>!b$(P)&&!fr(P.name,T,e)).map(P=>P.name);if(I.length>0){let P="";throw y!=null&&(P=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${b}]`),new Error(`Cannot compute the outputs [${I}] from the provided inputs [${s}]. Consider providing the following inputs: [${m}]. ${P}`)}return T}processStack(t,e,r,o,s,c,l,p,f){let m=[];for(;e.length>0;){let y=e.pop();r.currentContext=y.contexts;let b="";if(y.node.op==="Enter"&&A("isConstant",y.node,o,r)&&([b]=vs(y.node.name,r)),o[y.node.name]==null){let v=m$(y.node,o,r,this._resourceManager);b||([b]=vs(y.node.name,r));let T=r.currentContext;qi(v)?m.push(v.then(N=>(o[b]=N,r.currentContext=T,this.checkTensorForDisposal(b,y.node,o,r,c,l,p),this.processChildNodes(y.node,e,r,o,s,f),N))):(o[b]=v,this.checkTensorForDisposal(b,y.node,o,r,c,l,p),this.processChildNodes(y.node,e,r,o,s,f))}else this.processChildNodes(y.node,e,r,o,s,f)}return m}processChildNodes(t,e,r,o,s,c){t.children.forEach(l=>{let[p]=vs(l.name,r);if(s[p]||!c.has(l.name))return;l.op==="Merge"?l.inputNames.some(f=>!!fr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l})):l.inputNames.every(f=>!!fr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l}))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let r=t[e],[o]=Nr(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let c=s.attrParams.shape.value,l=c.length===r.shape.length&&r.shape.every((p,f)=>c[f]===-1||c[f]===p);_(l,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${c}], but was [${r.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&_(r.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){let e={};for(let r in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let o=this._signature.inputs[r];e[o.name]=t[r]}else e[r]=t[r];return e}checkInputs(t){let e=Object.keys(t).filter(r=>{let[o]=Nr(r);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null){let r=this._signature.outputs[e];return r.name}return e},{})}checkOutputs(t){t.forEach(e=>{let[r]=Nr(e);if(!this.graph.nodes[r])throw new Error(`The output '${e}' is not found in the graph`)})}}class kj{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}}let Nj="?tfjs-format=file",_j="model.json";class x${constructor(t,e={}){this.modelUrl=t,this.loadOptions=e,this.version="n/a",e==null&&(this.loadOptions={}),this.resourceManager=new kj}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=Xf(t,this.loadOptions);else{let e=Bx(t,this.loadOptions);if(e.length===0)e.push(Xf(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=await this.handler.load();return this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,r={};this.artifacts.userDefinedMetadata!=null&&(r=this.artifacts.userDefinedMetadata.signature),this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=qf(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new _m(u$.Instance.transformGraph(e,r)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null){let s=u$.Instance.transformGraph(t.modelInitializer);this.initializer=new _m(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(t,e){if(typeof t=="string"){let r=Mx(t);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${t}'`);t=r[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof ot)&&!Array.isArray(t))return t;if(t=Array.isArray(t)?t:[t],t.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${t.length} input tensors.`);return this.inputNodes.reduce((e,r,o)=>(e[r]=t[o],e),{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}execute(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=this.executor.execute(t,e);return r.length>1?r:r[0]}async executeAsync(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=await this.executor.executeAsync(t,e);return r.length>1?r:r[0]}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,r)=>(e[r]=[t[r]],e),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function Cj(n,t={}){if(n==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(n.load==null&&(n.endsWith("/")||(n=n+"/"),n=`${n}${_j}${Nj}`));let e=new x$(n,t);return await e.load(),e}let w$="2.7.0";function Sj(n,t){return Cm(n,t)}function Cm(n,t,e=new Map,r=new Set){if(n==null)return null;if(r.has(n))throw new Error("Circular references are not supported.");if(e.has(n))return e.get(n);let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(Jc(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let c in n){let l=n[c],p=Cm(l,t,e,r);s[c]=p}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return e.set(n,o.value),o.value}function $j(n,t=T$){return v$(n,t)}function v$(n,t,e=new Set){let r=n[0];if(e.has(r))throw new Error("Circular references are not supported.");let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(Jc(r)){let s=Array.isArray(r)?[]:{};e.add(r);for(let c in r){let l=n.map(f=>f[c]),p=v$(l,t,e);s[c]=p}return e.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return o.value}function T$(n){return n===null?null:Jc(n[0])?{value:null,recurse:!0}:{value:n,recurse:!1}}async function k$(n,t){let e=new Map;Cm(n,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(qi(s)){let c=await s;e.set(o,c)}}let r=Cm(n,t,e);return r}function Jc(n){return n!=null&&!ArrayBuffer.isView(n)&&(Array.isArray(n)||typeof n=="object"&&!(n instanceof ot))}function Ij(n){return n==null||Ej(n)||Array.isArray(n)||typeof n=="object"&&n instanceof ot||gn(n)}function Ej(n){return n===null||typeof n!="object"&&typeof n!="function"}function Dj(n){return Sj(n,Aj)}function Aj(n){return n instanceof ot?{value:n.clone(),recurse:!1}:Jc(n)?{value:null,recurse:!0}:{value:n,recurse:!1}}class N${constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),r=this.get(e);return this.set(e,this.pop()),r}}class Sm extends N${constructor(){super(Sm.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),r=this.length();for(let o=0;o<r;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}}Sm.INITIAL_CAPACITY=32;function _$(n){return new Rj(n)}function Bst(n){let t=n;return Yp(()=>({value:t++,done:!1}))}function Yp(n){return new Pj(n)}function C$(n,t){return new $$(n,t)}function zst(n,t,e){return C$(Yp(n).take(t),e)}function Fj(n,t=vi.FAIL){return new Uj(n,t)}class En{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],r=await t.next();for(;!r.done;)e.push(r.value),r=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),r=t(e.value);for(;!e.done&&r;)e=await this.next(),r=t(e.value)}handleErrors(t){return new Vj(this,t)}filter(t){return new zj(this,t)}map(t){return new Wj(this,t)}mapAsync(t){return new S$(this,t)}serialMapAsync(t){return new S$(this,t).serial()}flatmap(t){return new Gj(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new Bj(this,t,e)}columnMajorBatch(t,e=!0,r=T$){let o=this.rowMajorBatch(t,e);return o.map(s=>$j(s,r))}concatenate(t,e){return new $$(_$([this,t]),e)}take(t){return t<0||t==null?this:new Mj(this,t)}skip(t){return t<0||t==null?this:new Lj(this,t)}prefetch(t){return new I$(this,t)}shuffle(t,e){return new qj(this,t,e)}serial(){return new Oj(this)}}class Rj extends En{constructor(t){super();this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:Dj(t),done:!1}}}class Pj extends En{constructor(t){super();this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}}class Oj extends En{constructor(t){super();this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class Lj extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Xt(t.value)}return this.upstream.next()}}class Mj extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class Bj extends En{constructor(t,e,r=!0){super();this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}}class zj extends En{constructor(t,e){super();this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Xt(t.value)}}}class Wj extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=cs(t.value),r=this.transform(t.value),o=cs(r);for(let s of e)Gf(s,o)||s.dispose();return{value:r,done:!1}}}class Vj extends En{constructor(t,e){super();this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}}class S$ extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=cs(t.value),r=await this.transform(t.value),o=cs(r);for(let s of e)Gf(s,o)||s.dispose();return{value:r,done:!1}}}class t1 extends En{constructor(){super();this.outputQueue=new Sm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class Gj extends t1{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=cs(t.value),r=this.transform(t.value),o=cs(r);this.outputQueue.pushAll(r);for(let s of e)Gf(s,o)||s.dispose();return!0}}class $$ extends En{constructor(t,e){super();this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){let t="TODO: fill in upstream of chained summaries";return`${t} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}}var vi;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(vi||(vi={}));class Uj extends En{constructor(t,e=vi.FAIL){super();this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){let t="TODO: fill in upstream of zip summaries";return`{${t}} -> Zip`}async nextState(t){await t;let e=0,r=0;function o(c){if(c instanceof En){let l=c.next();return{value:l.then(p=>(e++,p.done&&r++,p.value)),recurse:!1}}else return{value:null,recurse:!0}}let s=await k$(this.iterators,o);if(e===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case vi.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case vi.SHORTEST:return{value:null,done:!0};case vi.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class I$ extends En{constructor(t,e){super();this.upstream=t,this.bufferSize=e,this.buffer=new N$(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}}class qj extends I${constructor(t,e,r){super(t,e);this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=Fc(r||or().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}}class Zc{constructor(){this.size=null}batch(t,e=!0){let r=this;_(t>0,()=>`batchSize needs to be positive, but it is
${t}`);let o;return this.size===Infinity||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),_r(async()=>(await r.iterator()).columnMajorBatch(t,e,Kj),o)}concatenate(t){let e=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,_r(async()=>(await e.iterator()).concatenate(await t.iterator()),r)}filter(t){let e=this,r;return this.size===Infinity?r=Infinity:r=null,_r(async()=>(await e.iterator()).filter(o=>rt(()=>t(o))),r)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return _r(async()=>(await e.iterator()).map(r=>rt(()=>t(r))),this.size)}mapAsync(t){let e=this;return _r(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return _r(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,_r(async()=>{let o=Yp(async()=>({value:await e.iterator(),done:!1}));return C$(o.take(t))},r)}skip(t){let e=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?r=0:r=null,_r(async()=>(await e.iterator()).skip(t),r)}shuffle(t,e,r=!0){if(t==null||t<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 (${this.size} elements)`);let o=this,s=Fc(e||or().toString());return _r(async()=>{let c=s.int32();return r&&(c+=s.int32()),(await o.iterator()).shuffle(t,c.toString())},this.size)}take(t){let e=this,r;return this.size!=null&&this.size>t?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,_r(async()=>(await e.iterator()).take(t),r)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Zc.MAX_BUFFER_SIZE=1e4;function _r(n,t=null){return new class extends Zc{constructor(){super(...arguments);this.size=t}async iterator(){return n()}}}function Hj(n){return _r(async()=>_$(n),n.length)}function jj(n){if(!Jc(n))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(n))for(let e=0;e<n.length;e++)t=t==null?n[e].size:Math.min(t,n[e].size);else if(n instanceof Object)for(let e in n)t=t==null?n[e].size:Math.min(t,n[e].size);return _r(async()=>{let e=await k$(n,r=>{if(r instanceof Zc)return{value:r.iterator(),recurse:!1};if(Jc(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Fj(e,vi.SHORTEST)},t)}function Kj(n){if(n===null)return null;let t=n[0];if(Ij(t)){let e=Xj(n);return{value:e,recurse:!1}}return{value:null,recurse:!0}}function Xj(n){if(n.length===0)throw new Error("Can't make a batch of zero elements.");return n[0]instanceof ot?ur(n):un(n)}class E$ extends Zc{constructor(t){super();this.input=t}async iterator(){let t=await this.input.iterator(),e=t.decodeUTF8(),r=e.split(`
`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o));return r}}let $m='"',Jp=Symbol("out"),D$=Symbol("field"),Im=Symbol("quote"),e1=Symbol("quoteafterquote"),A$=Symbol("quoteinquote");class F$ extends Zc{constructor(t,e){super();this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new E$(t),e||(e={}),this.hasHeader=!(e.hasHeader===!1),this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(_(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&_(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),r=Object.keys(e).filter(o=>e[o]>1);if(_(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs)for(let o of Object.keys(this.columnConfigs)){let s=this.fullColumnNames.indexOf(o);if(s===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await this.base.iterator(),e=await t.next();if(e.done)throw new Error("No data was found for CSV parsing.");let r=e.value,o=this.parseRow(r,!1);return o}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),r={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let c=this.fullColumnNames[s],l=this.columnConfigs?this.columnConfigs[c]:null;if(this.configuredColumnsOnly&&!l)continue;{let p=e[s],f=null;if(p==="")if(l&&l.default!==void 0)f=l.default;else{if(l&&(l.required||l.isLabel))throw new Error(`Required column ${c} is empty in this line: ${t}`);f=void 0}else{let m=Number(p);if(isNaN(m))l&&l.dtype==="bool"?f=this.getBoolean(p):f=p;else if(!l||!l.dtype)f=m;else switch(l.dtype){case"float32":f=m;break;case"int32":f=Math.floor(m);break;case"bool":f=this.getBoolean(p);break;default:f=m}}l&&l.isLabel?o[c]=f:r[c]=f}}return Object.keys(o).length===0?r:{xs:r,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let r=[],o=0,s=t.length,c=Jp;for(let l=0;l<s;l++)switch(c){case Jp:switch(t.charAt(l)){case $m:o=l+1,c=Im;break;case this.delimiter:if(o=l+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),c=Jp;break;default:c=D$,o=l;break}break;case D$:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l)),c=Jp,o=l+1;break;default:}break;case Im:switch(t.charAt(l)){case $m:c=e1;break;default:}break;case e1:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l-1)),c=Jp,o=l+1;break;case $m:c=Im;break;default:c=A$;break}break;case A$:switch(t.charAt(l)){case $m:c=Im;break;default:}break;default:}if(c===e1?r.push(t.substring(o,s-1)):r.push(t.substring(o)),e&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}}class n1 extends En{constructor(t){super();this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=!(t.includeSpectrogram===!1),this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(ct().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let e=new n1(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,r=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(r.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],r=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,r=new Float32Array(t.length*e);return t.forEach((o,s)=>r.set(o,s*e)),r}getTensorFromAudioDataArray(t,e){let r=new Float32Array(G(e));return r.set(t,r.length-t.length),un(r,e)}}class r1 extends En{constructor(t,e){super();if(this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=vr([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-r)/2,c=(1-o)/2,l=s+r,p=o+c;this.cropBox=ui([c,s,p,l],[1,4])}else this.cropBox=ui([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(ct().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let r=new r1(t,e);return await r.start(),r}async start(){this.webcamConfig.facingMode&&_(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=e_(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return rt(()=>{let e=t.toFloat().expandDims(0),r;r=pi.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=r.shape;return r.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){let t=this.stream.getTracks();t.forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class R${}class P$ extends En{split(t){return new Yj(this,t)}}class Yj extends P${constructor(t,e){super();this.upstream=t,this.impl=new Jj(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Jj extends t1{constructor(t,e){super();this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let r of e.slice(0,-1))this.outputQueue.push(r);return this.carryover=e[e.length-1],!0}}class Zj extends En{decodeUTF8(){return new Qj(this)}}class Qj extends P${constructor(t){super();this.upstream=t,this.impl=new t6(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class t6 extends t1{constructor(t){super();if(this.upstream=t,ct().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let r;return ct().get("IS_BROWSER")?r=this.decoder.decode(e,{stream:!0}):r=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(r),!0}}class O$ extends Zj{constructor(t,e={}){super();this.file=t,this.options=e,_(t instanceof Uint8Array||(ct().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};let t=new Promise((e,r)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=l=>{let p=s.result;if(p instanceof ArrayBuffer&&(p=new Uint8Array(p)),!(p instanceof Uint8Array))return r(new TypeError("FileReader returned unknown type."));e(p)},s.onabort=l=>r(new Error("Aborted")),s.onerror=l=>r(new Error(l.type));let c=this.file.slice(this.offset,o);s.readAsArrayBuffer(c)}this.offset=o});return{value:await t,done:!1}}}async function e6(n,t={}){let e,r;typeof n=="string"?e=n:(e=n.url,r=n6(n));let o=await SN(e,r);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new O$(s,t)}else throw new Error(o.statusText)}let n6=n=>{let t={method:n.method,headers:n.headers,body:n.body,mode:n.mode,credentials:n.credentials,cache:n.cache,redirect:n.redirect,referrer:n.referrer,integrity:n.integrity};return t};function L$(n){return typeof n=="string"&&n.substr(0,7)==="file://"}class M$ extends R${constructor(t,e={}){super();this.input=t,this.options=e}async iterator(){if(L$(this.input)&&ct().get("IS_NODE")){let t=require("fs");this.input=t.readFileSync(this.input.substr(7))}return new O$(this.input,this.options)}}class B$ extends R${constructor(t,e={}){super();this.url=t,this.fileOptions=e}async iterator(){return L$(this.url)?new M$(this.url,this.fileOptions).iterator():e6(this.url,this.fileOptions)}}function r6(n,t={}){return new F$(new B$(n),t)}function o6(n){let t=Yp(n);return _r(async()=>t)}function s6(n){return _r(async()=>{let t=await n();return Yp(()=>t.next())})}async function i6(n,t){return r1.create(n,t)}async function a6(n){return n1.create(n)}let z$="2.7.0";var c6=Object.freeze({__proto__:null,array:Hj,Dataset:Zc,zip:jj,CSVDataset:F$,TextLineDataset:E$,csv:r6,func:o6,generator:s6,microphone:a6,webcam:i6,FileDataSource:M$,URLDataSource:B$,version_data:z$});function Ct(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&_(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}let l6=Od,u6=sv,p6=iv,h6=av,f6=Id;class d6 extends d{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new h(this,ps())}write(t,e,r){this.firstUse&&(this.firstUse=!1,ct().get("IS_NODE")&&Bc(`
============================
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.
============================`));let o={};return this.data.set(o,{values:t,dtype:r,refCount:1}),o}makeTensorInfo(t,e,r){let o;if(e==="string"&&r!=null&&r.length>0&&as(r[0])){let s=r.map(c=>zf(c));o=this.write(s,t,e)}else o=this.write(r,t,e);return{dataId:o,shape:t,dtype:e}}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,r,o){this.data.set(t,{values:e,dtype:o,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:r}=this.data.get(t);if(e==="complex64"){let o=this.readSync(r.real.dataId),s=this.readSync(r.imag.dataId);return ys(o,s)}return this.data.get(t).values}bufferSync(t){let e=this.readSync(t.dataId),r=e;if(t.dtype==="string")try{r=e.map(o=>Uu(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Se(t.shape,t.dtype,r)}makeOutput(t,e,r){let o=this.write(t,e,r);return ps().makeTensorFromDataId(o,e,r,this)}disposeData(t){if(this.data.has(t)){let{complexTensorInfos:e}=this.data.get(t);e!=null&&(this.disposeData(e.real.dataId),this.disposeData(e.imag.dataId)),this.data.delete(t)}}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.data.has(e)){let r=this.data.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}async time(t){let e=or();t();let r=or()-e;return{kernelMs:r}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}stridedSlice(t,e,r,o){Ct(t,"stridedSlice");let s=Zf(e,r,o);if(s.some(p=>p===0))return un([],s);let c=Se(s,t.dtype),l=this.bufferSync(t);for(let p=0;p<c.size;p++){let f=c.indexToLoc(p),m=new Array(f.length);for(let y=0;y<m.length;y++)m[y]=f[y]*o[y]+e[y];c.set(l.get(...m),...f)}return c.toTensor()}diag(t){let e=this.readSync(t.dataId),r=Se([t.size,t.size],t.dtype),o=r.values;for(let s=0;s<e.length;s++)o[s*t.size+s]=e[s];return r.toTensor()}unstack(t,e){let r=t.shape[e],o=new Array(t.rank-1),s=0;for(let f=0;f<t.rank;f++)f!==e&&(o[s++]=t.shape[f]);let c=new Array(t.rank).fill(0),l=t.shape.slice();l[e]=1;let p=new Array(r);for(let f=0;f<p.length;f++)c[e]=f,p[f]=ce(t,c,l).reshape(o);return p}reverse(t,e){Ct(t,"reverse");let r=Se(t.shape,t.dtype),o=this.bufferSync(t);for(let s=0;s<r.size;s++){let c=r.indexToLoc(s),l=c.slice();e.forEach(p=>l[p]=t.shape[p]-1-l[p]),r.set(o.get(...l),...c)}return r.toTensor()}neg(t){return Ct(t,"neg"),nt(Et(-1),t)}addN(t){Ct(t,"addN");let e=t.map(s=>this.readSync(s.dataId)),r=Se(t[0].shape,t[0].dtype),o=r.values;for(let s=0;s<t.length;s++){let c=e[s];for(let l=0;l<o.length;l++)o[l]+=c[l]}return r.toTensor()}softmax(t,e){let r=Vt([e],t.shape),o=lr(t,r),s=Rn(o.shape,r),c=Dt(t,o.reshape(s)),l=Ar(c),p=this.sum(l,r).reshape(s);return Bt(l,p)}pow(t,e){return Ct([t,e],"pow"),this.broadcastedBinaryOp(t,e,t.dtype,(r,o)=>Math.pow(r,o))}floorDiv(t,e){Ct([t,e],"floorDiv");let r=(s,c)=>Math.floor(s/c),o="int32";return this.broadcastedBinaryOp(t,e,o,r)}sum(t,e){Ct(t,"sum"),sr("sum",e,t.rank);let[r,o]=Fn(t.shape,e),s=jn(t.dtype,"int32"),c=xe(r,s),l=G(o),p=this.readSync(c.dataId),f=this.readSync(t.dataId);for(let m=0;m<p.length;++m){let y=m*l,b=0;for(let v=0;v<l;++v)b+=f[y+v];p[m]=b}return c}prod(t,e){Ct(t,"sum");let[r,o]=Fn(t.shape,e),s=jn(t.dtype,"int32"),c=xe(r,s),l=G(o),p=this.readSync(c.dataId),f=this.readSync(t.dataId);for(let m=0;m<p.length;++m){let 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yt=f[ht+bt],xt=f[dt+bt],kt=f[ft+bt],Nt=f[ut+bt],At=yt+(kt-yt)*it,It=xt+(Nt-xt)*it,St=At+(It-At)*E;m[v++]=St}}}return un(m,[s,e,r,p])}resizeBilinearBackprop(t,e,r){Ct([t,e],"resizeBilinearBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=[r&&p>1?s-1:s,r&&f>1?c-1:c],b=[r&&p>1?p-1:p,r&&f>1?f-1:f],v=y[0]/b[0],T=y[1]/b[1],N=this.readSync(t.dataId),S=0;for(let D=0;D<o;D++){let I=D*e.strides[0];for(let P=0;P<p;P++){let E=P*v,L=Math.floor(E),B=Math.min(Math.ceil(E),s-1),q=I+L*e.strides[1],H=I+B*e.strides[1],Z=E-L,J=1-Z;for(let it=0;it<f;it++){let pt=it*T,ht=Math.floor(pt),dt=Math.min(Math.ceil(pt),c-1),ft=pt-ht,ut=1-ft,bt=q+ht*e.strides[2],yt=q+dt*e.strides[2],xt=H+ht*e.strides[2],kt=H+dt*e.strides[2],Nt=J*ut,At=J*ft,It=Z*ut,St=Z*ft;for(let Lt=0;Lt<l;Lt++){let Ht=N[S++];m[bt+Lt]+=Ht*Nt,m[yt+Lt]+=Ht*At,m[xt+Lt]+=Ht*It,m[kt+Lt]+=Ht*St}}}}return Oc(m,[o,c,s,l],e.dtype)}resizeNearestNeighbor(t,e,r,o){Ct(t,"resizeNearestNeighbor");let[s,c,l,p]=t.shape,f=this.readSync(t.dataId),m=new Float32Array(s*e*r*p),y=[o&&e>1?c-1:c,o&&r>1?l-1:l],b=[o&&e>1?e-1:e,o&&r>1?r-1:r],v=y[0]/b[0],T=y[1]/b[1],N=0;for(let S=0;S<s;S++){let D=S*t.strides[0];for(let I=0;I<e;I++){let P=v*I,E=Math.min(c-1,o?Math.round(P):Math.floor(P)),L=D+E*t.strides[1];for(let B=0;B<r;B++){let q=T*B,H=Math.min(l-1,o?Math.round(q):Math.floor(q)),Z=L+H*t.strides[2];for(let J=0;J<p;J++){let it=f[Z+J];m[N++]=it}}}}return un(m,[s,e,r,p],t.dtype)}resizeNearestNeighborBackprop(t,e,r){Ct([t,e],"resizeNearestNeighborBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=this.readSync(t.dataId),b=[r&&p>1?s-1:s,r&&f>1?c-1:c],v=[r&&p>1?p-1:p,r&&f>1?f-1:f],T=b[0]/v[0],N=b[1]/v[1],S=1/T,D=1/N,I=Math.ceil(S)*2+2,P=Math.ceil(D)*2+2;for(let E=0;E<o;E++){let L=E*e.strides[0];for(let B=0;B<s;B++){let q=L+B*e.strides[1],H=Math.floor(B*S),Z=Math.floor(H-I/2);for(let J=0;J<c;J++){let it=q+J*e.strides[2],pt=Math.floor(J*D),ht=Math.floor(pt-P/2);for(let dt=0;dt<l;dt++){let ft=0;for(let ut=0;ut<I;ut++){let bt=ut+Z;if(bt<0||bt>=p)continue;let yt=L+bt*t.strides[1],xt=bt*T,kt=Math.min(s-1,r?Math.round(xt):Math.floor(xt));if(B!==kt)continue;for(let Nt=0;Nt<P;Nt++){let At=Nt+ht;if(At<0||At>=f)continue;let It=yt+At*t.strides[2],St=At*N,Lt=Math.min(c-1,r?Math.round(St):Math.floor(St));J===Lt&&(ft+=y[It+dt])}}m[it+dt]=ft}}}}return Oc(m,e.shape,e.dtype)}localResponseNormalization4D(t,e,r,o,s){Ct(t,"localResponseNormalization4D");let c=t.shape[3],l=c-1,p=this.readSync(t.dataId),f=t.size,m=new Float32Array(f);function y(b){let v=b%c,T=b-v+Math.max(0,v-e),N=b-v+Math.min(v+e,l),S=0;for(;T<=N;T++){let D=p[T];S+=D*D}return S}for(let b=0;b<f;b++){let v=y(b),T=p[b]*Math.pow(r+o*v,-s);m[b]=T}return Oc(m,t.shape)}LRNGrad(t,e,r,o,s,c,l){Ct(t,"LRNGrad");let p=t.shape[3],f=this.readSync(t.dataId),m=this.readSync(e.dataId),y=this.readSync(r.dataId),b=new Float32Array(t.size),v=t.size;for(let T=0;T<v;T++){let N=T%p,S=T-N+Math.max(0,N-o),D=T-N+Math.min(p,N+o+1),I=0;for(let P=S;P<D;P++)I+=Math.pow(m[P],2);I=c*I+s;for(let P=S;P<D;P++){let E=-2*c*l*m[P]*y[T]/I;T===P&&(E+=Math.pow(I,-l)),E*=f[T],b[P]+=E}}return Oc(b,t.shape)}multinomial(t,e,r,o){Ct(t,"multinomial");let s=e?t:ca(t),c=s.shape[0],l=s.shape[1],p=xe([c,r],"int32"),f=this.readSync(p.dataId),m=this.readSync(s.dataId);for(let y=0;y<c;++y){let b=y*l,v=new Float32Array(l-1);v[0]=m[b];for(let S=1;S<v.length;++S)v[S]=v[S-1]+m[b+S];let T=Fc(o.toString()),N=y*r;for(let S=0;S<r;++S){let D=T();f[N+S]=v.length;for(let I=0;I<v.length;I++)if(D<v[I]){f[N+S]=I;break}}}return p}oneHot(t,e,r,o){Ct(t,"oneHot");let s=new Float32Array(t.size*e);s.fill(o);let c=this.readSync(t.dataId);for(let l=0;l<t.size;++l)c[l]>=0&&c[l]<e&&(s[l*e+c[l]]=r);return ui(s,[t.size,e],"int32")}nonMaxSuppression(t,e,r,o,s){Ct(t,"nonMaxSuppression");let c=this.readSync(t.dataId),l=this.readSync(e.dataId);return 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iI=Ee(Iu,n=>Math.max(0,n)),X6={kernelName:Iu,backendName:"cpu",kernelFunc:iI};let aI=Ee(Du,n=>Math.min(Math.max(0,n),6)),Y6={kernelName:Du,backendName:"cpu",kernelFunc:aI};function a1(n,t,e,r){if(e==="linear")return ba({inputs:{x:t},backend:n});if(e==="relu")return iI({inputs:{x:t},backend:n});if(e==="elu")return oI({inputs:{x:t},backend:n});if(e==="relu6")return aI({inputs:{x:t},backend:n});if(e==="prelu")return sI({inputs:{x:t,alpha:r},backend:n});throw new Error(`Activation ${e} has not been implemented for the CPU backend.`)}function Ho(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=G(o.shape),l=Ge(s,c),p=G(l);_(c===p,()=>`The new shape (${l}) has ${p} elements and the old shape (${o.shape}) has ${c} elements. 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Got input batch dimensions of (${T}) and (${N}).`);let P=S>D?o.shape.slice(0,-2):s.shape.slice(0,-2),E=P.concat([b,v]);_(m===y,()=>`Error in matMul: inner shapes (${m}) and (${y}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${c} and transposeB=${l} must match.`);let L=c?[S,m,b]:[S,b,m],B=l?[D,v,y]:[D,y,v],q=Ho({inputs:{x:o},backend:e,attrs:{shape:L}}),H=Ho({inputs:{x:s},backend:e,attrs:{shape:B}}),Z=c?q.shape[1]:q.shape[2],J=c?q.shape[2]:q.shape[1],it=l?H.shape[1]:H.shape[2],pt=Math.max(S,D),ht=e.data.get(q.dataId).values,dt=e.data.get(H.dataId).values,ft=Yt(q.shape),ut=Yt(H.shape),[bt,yt,xt]=c?[ft[0],1,ft[1]]:[ft[0],ft[1],1],[kt,Nt,At]=l?[1,ut[1],ut[0]]:[ut[1],1,ut[0]],It=J*it,St=Se([pt,J,it],q.dtype),Lt=St.values,Ht=e.blockSize;for(let oe=0;oe<pt;oe++)for(let de=0;de<J;de+=Ht)for(let ie=0;ie<it;ie+=Ht)for(let we=0;we<Z;we+=Ht){let pe=Math.min(de+Ht,J),Xe=Math.min(ie+Ht,it),on=Math.min(we+Ht,Z);for(let sn=de;sn<pe;sn++)for(let Ae=ie;Ae<Xe;Ae++){let Ye=0;for(let Le=we;Le<on;Le++){let Ln=Math.min(oe,S-1)*bt,$s=Math.min(oe,D-1)*At,Mn=ht[Ln+sn*yt+Le*xt],Co=dt[Le*kt+Ae*Nt+$s];Ye+=Mn*Co}Lt[oe*It+(sn*it+Ae)]+=Ye}}return e.disposeIntermediateTensorInfo(q),e.disposeIntermediateTensorInfo(H),e.makeTensorInfo(E,St.dtype,St.values)}let Z6={kernelName:cf,backendName:"cpu",kernelFunc:cI};function Q6(n){let{inputs:t,backend:e,attrs:r}=n,{a:o,b:s,bias:c,preluActivationWeights:l}=t,{transposeA:p,transposeB:f,activation:m}=r,y,b,v,T=[],N=cI({inputs:{a:o,b:s},attrs:{transposeA:p,transposeB:f},backend:e});y=N,c&&(b=th({inputs:{a:y,b:c},backend:e}),T.push(y),y=b),m&&(v=a1(e,y,m,l),T.push(y),y=v);for(let S of T)e.disposeIntermediateTensorInfo(S);return y}let tK={kernelName:Pf,backendName:"cpu",kernelFunc:Q6};let eK=Ee(eu,n=>Math.acos(n)),nK={kernelName:eu,backendName:"cpu",kernelFunc:eK};let rK=Ee(nu,n=>Math.acosh(n)),oK={kernelName:nu,backendName:"cpu",kernelFunc:rK};let sK=Ee(ru,n=>Math.asin(n)),iK={kernelName:ru,backendName:"cpu",kernelFunc:sK};let aK=Ee(ou,n=>Math.asinh(n)),cK={kernelName:ou,backendName:"cpu",kernelFunc:aK};let lK=Ee(su,n=>Math.atan(n)),uK={kernelName:su,backendName:"cpu",kernelFunc:lK};let pK=Ee(iu,n=>Math.atanh(n)),hK={kernelName:iu,backendName:"cpu",kernelFunc:pK};function c1(n,t,e,r,o,s){let c=o.strideHeight,l=o.strideWidth,p=o.dilationHeight,f=o.dilationWidth,m=o.effectiveFilterHeight,y=o.effectiveFilterWidth,b=o.padInfo.top,v=o.padInfo.left,T=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,N=Se(o.outShape,e),S=N.values,D=o.outShape[1]*o.outShape[2]*o.outShape[3],I=o.outShape[2]*o.outShape[3],P=o.outShape[3];for(let E=0;E<o.batchSize;++E){let L=E*D,B=E*r[0];for(let q=0;q<o.inChannels;++q)for(let H=0;H<o.outHeight;++H){let Z=H*c-b,J=Math.max(0,Z),it=Math.min(o.inHeight,m+Z),pt=L+H*I;for(let ht=0;ht<o.outWidth;++ht){let dt=ht*l-v,ft=Math.max(0,dt),ut=Math.min(o.inWidth,y+dt),bt=T,yt=0,xt=0;for(let Nt=J;Nt<it;Nt+=p){let At=B+Nt*r[1];for(let It=ft;It<ut;It+=f){let St=At+It*r[2],Lt=n[St+q];s==="max"&&Lt>bt?bt=Lt:s==="avg"&&(yt+=Lt,xt++)}if(isNaN(bt))break}let kt=pt+ht*P+q;S[kt]=s==="avg"?yt/xt:bt}}}return N}function lI(n,t,e,r,o=!1,s=!1){let c=Se(r.outShape,"int32"),l=r.strideHeight,p=r.strideWidth,f=r.dilationHeight,m=r.dilationWidth,y=r.effectiveFilterHeight,b=r.effectiveFilterWidth,v=r.padInfo.top,T=r.padInfo.left,N=Se(t,e,n);for(let S=0;S<r.batchSize;++S)for(let D=0;D<r.inChannels;++D)for(let I=0;I<r.outHeight;++I){let P=I*l-v,E=P;for(;E<0;)E+=f;let L=Math.min(r.inHeight,y+P);for(let B=0;B<r.outWidth;++B){let q=B*p-T,H=q;for(;H<0;)H+=m;let Z=Math.min(r.inWidth,b+q),J=Number.NEGATIVE_INFINITY,it=-1;for(let pt=E;pt<L;pt+=f){let ht=pt-P;for(let dt=H;dt<Z;dt+=m){let ft=dt-q,ut=N.get(S,pt,dt,D);ut>J&&(J=ut,o?it=s?((S*r.inHeight+pt)*r.inWidth+dt)*r.inChannels+D:(pt*r.inWidth+dt)*r.inChannels+D:it=ht*b+ft)}}c.set(it,S,I,B,D)}}return c}function fK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;Ct(o,"avgPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Kn(o.shape,s,c,f,l,p),y;if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))y=ba({inputs:{x:o},backend:e});else{let b=e.data.get(o.dataId).values,v=Yt(o.shape),T=c1(b,o.shape,o.dtype,v,m,"avg");y=e.makeTensorInfo(m.outShape,o.dtype,T.values)}return y}let dK={kernelName:au,backendName:"cpu",kernelFunc:fK};function mK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s}=t,c=s;Ct([o,s],"avgPoolBackprop");let{filterSize:l,strides:p,pad:f}=r,m=Kn(c.shape,l,p,1,f),y=m.strideHeight,b=m.strideWidth,v=m.filterHeight,T=m.filterWidth,N=m.dilationHeight,S=m.dilationWidth,D=m.effectiveFilterHeight,I=m.effectiveFilterWidth,P=I-1-m.padInfo.left,E=D-1-m.padInfo.top,L=Se(c.shape,"float32"),B=1/(v*T),q=e.data.get(o.dataId).values,H=Se(o.shape,"float32",q);for(let Z=0;Z<m.batchSize;++Z)for(let J=0;J<m.inChannels;++J)for(let it=0;it<m.inHeight;++it)for(let pt=0;pt<m.inWidth;++pt){let ht=it-E,dt=pt-P,ft=0;for(let ut=0;ut<D;ut+=N){let bt=(ht+ut)/y;if(bt<0||bt>=m.outHeight||Math.floor(bt)!==bt)continue;for(let yt=0;yt<I;yt+=S){let xt=(dt+yt)/b;if(xt<0||xt>=m.outWidth||Math.floor(xt)!==xt)continue;let kt=H.get(Z,bt,xt,J);ft+=kt}}L.set(ft*B,Z,it,pt,J)}return e.makeTensorInfo(L.shape,L.dtype,L.values)}let gK={kernelName:af,backendName:"cpu",kernelFunc:mK};function yK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,scale:s,offset:c,mean:l,variance:p}=t;_(l.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||l.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(s==null||l.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ct([o,l,p,s,c],"batchNorm");let{varianceEpsilon:f}=r;f==null&&(f=.001);let m=e.data.get(o.dataId).values,y=e.data.get(l.dataId).values,b=e.data.get(p.dataId).values,v=s?e.data.get(s.dataId).values:new Float32Array([1]),T=c?e.data.get(c.dataId).values:new Float32Array([0]),N=new Float32Array(m.length),S=T.length,D=v.length,I=b.length,P=y.length,E=0,L=0,B=0,q=0;for(let H=0;H<m.length;++H)N[H]=T[E++]+(m[H]-y[L++])*v[B++]/Math.sqrt(b[q++]+f),E>=S&&(E=0),L>=P&&(L=0),B>=D&&(B=0),q>=I&&(q=0);return e.makeTensorInfo(o.shape,o.dtype,N)}let bK={kernelName:yu,backendName:"cpu",kernelFunc:yK};let xK=Ee(lu,(n,t)=>{let e=t;return n>e.clipValueMax?e.clipValueMax:n<e.clipValueMin?e.clipValueMin:n}),wK={kernelName:lu,backendName:"cpu",kernelFunc:xK};function Em(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.data.get(r.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}let vK={kernelName:wf,backendName:"cpu",kernelFunc:Em};function eh(n){let{inputs:t,backend:e,attrs:r}=n,{axis:o}=r,s=Vt(o,t[0].shape)[0],c=hs(t.map(v=>v.shape),s);if(G(c)===0)return e.makeTensorInfo(c,t[0].dtype,[]);let l=t.filter(v=>G(v.shape)>0);if(l.length===1)return l[0];let p=l.map(v=>v.shape);if(id(p,s),l[0].dtype==="complex64"){let v=l.map(I=>Zp({inputs:{input:I},backend:e})),T=l.map(I=>Em({inputs:{input:I},backend:e})),N=eh({inputs:v,backend:e,attrs:{axis:s}}),S=eh({inputs:T,backend:e,attrs:{axis:s}}),D=_o({inputs:{real:N,imag:S},backend:e});return v.forEach(I=>e.disposeIntermediateTensorInfo(I)),T.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(N),e.disposeIntermediateTensorInfo(S),D}let f=l.map(v=>{let T=G(v.shape.slice(s)),N=[-1,T];return Ho({inputs:{x:v},backend:e,attrs:{shape:N}})});c=hs(f.map(v=>v.shape),1);let m=Ce(l[0].dtype,G(c));if(f[0].shape[0]===1){let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=G(T.shape);m.set(N,v),v+=S})}else{let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=0;for(let D=0;D<T.shape[0];++D){let I=D*c[1]+v;for(let P=0;P<T.shape[1];++P)m[I+P]=N[S++]}v+=T.shape[1]})}let y=hs(l.map(v=>v.shape),s),b=e.makeTensorInfo(y,t[0].dtype,m);return f.forEach(v=>e.disposeIntermediateTensorInfo(v)),b}let TK={kernelName:uu,backendName:"cpu",kernelFunc:eh};function uI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dataFormat:p,dilations:f,dimRoundingMode:m}=r;Ct([o,s],"conv2d");let y=si(p),b=Un(o.shape,s.shape,c,f,l,m,!1,y),v=b.filterHeight,T=b.filterWidth,N=b.dilationHeight,S=b.dilationWidth,D=b.padInfo.left,I=b.padInfo.top,P=b.dataFormat==="channelsLast",E=new hn(b.outShape,o.dtype),L=Yt(o.shape),B=Yt(s.shape),q=L[0],H=P?L[1]:L[2],Z=P?L[2]:1,J=P?1:L[1],it=E.strides[0],pt=P?E.strides[1]:E.strides[2],ht=P?E.strides[2]:1,dt=P?1:E.strides[1],ft=e.data.get(o.dataId).values,ut=e.data.get(s.dataId).values,bt=E.values;for(let yt=0;yt<b.batchSize;++yt){let xt=yt*q,kt=yt*it;for(let Nt=0;Nt<b.outHeight;++Nt){let At=kt+Nt*pt,It=Nt*b.strideHeight-I;for(let St=0;St<v;++St){let Lt=It+St*N;if(Lt<0||Lt>=b.inHeight)continue;let Ht=St*B[0],oe=xt+Lt*H;for(let de=0;de<b.outWidth;++de){let ie=At+de*ht,we=de*b.strideWidth-D;for(let pe=0;pe<T;++pe){let Xe=we+pe*S;if(Xe<0||Xe>=b.inWidth)continue;let on=Ht+pe*B[1],sn=oe+Xe*Z,Ae=on;for(let Ye=0;Ye<b.inChannels;++Ye){let Le=ft[sn+Ye*J];for(let Ln=0;Ln<b.outChannels;++Ln)bt[ie+Ln*dt]+=Le*ut[Ae+Ln];Ae+=b.outChannels}}}}}}return e.makeTensorInfo(E.shape,E.dtype,bt)}let kK={kernelName:uf,backendName:"cpu",kernelFunc:uI};function NK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,dataFormat:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"conv2dBackpropFilter");let y=si(p),b=Un(o.shape,m,c,1,l,f,!1,y),{strideHeight:v,strideWidth:T,filterHeight:N,filterWidth:S}=b,D=b.dataFormat==="channelsLast",I=new hn(b.filterShape,"float32"),P=b.padInfo.left,E=b.padInfo.top,L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=new hn(o.shape,o.dtype,L),H=new hn(s.shape,s.dtype,B);for(let Z=0;Z<N;++Z){let J=Math.max(0,Math.ceil((E-Z)/v)),it=Math.min(b.outHeight,(b.inHeight+E-Z)/v);for(let pt=0;pt<S;++pt){let ht=Math.max(0,Math.ceil((P-pt)/T)),dt=Math.min(b.outWidth,(b.inWidth+P-pt)/T);for(let ft=0;ft<b.inChannels;++ft)for(let ut=0;ut<b.outChannels;++ut){let bt=0;for(let yt=0;yt<b.batchSize;++yt)for(let xt=J;xt<it;++xt){let kt=Z+xt*v-E;for(let Nt=ht;Nt<dt;++Nt){let At=pt+Nt*T-P;D?bt+=q.get(yt,kt,At,ft)*H.get(yt,xt,Nt,ut):bt+=q.get(yt,ft,kt,At)*H.get(yt,ut,xt,Nt)}}I.set(bt,Z,pt,ft,ut)}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let _K={kernelName:Hb,backendName:"cpu",kernelFunc:NK};function CK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{inputShape:c,strides:l,pad:p,dataFormat:f,dimRoundingMode:m}=r;Ct([o,s],"conv2dBackpropInput");let y=Yt(s.shape),b=Yt(o.shape),v=si(f),T=Un(c,s.shape,l,1,p,m,!1,v),N=new hn(T.inShape,"float32"),S=N.values,D=e.data.get(o.dataId).values,I=e.data.get(s.dataId).values,[P,E,L]=y,{batchSize:B,filterHeight:q,filterWidth:H,inChannels:Z,inHeight:J,inWidth:it,outChannels:pt,outHeight:ht,outWidth:dt,strideHeight:ft,strideWidth:ut}=T;v=T.dataFormat;let bt=q-1-T.padInfo.top,yt=H-1-T.padInfo.left,xt=v==="channelsLast",kt=N.strides[0],Nt=xt?N.strides[1]:N.strides[2],At=xt?N.strides[2]:1,It=xt?1:N.strides[1],St=b[0],Lt=xt?b[1]:b[2],Ht=xt?b[2]:1,oe=xt?1:b[1];for(let de=0;de<B;++de)for(let ie=0;ie<Z;++ie)for(let we=0;we<J;++we){let pe=we-bt,Xe=Math.max(0,Math.ceil(pe/ft)),on=Math.min(ht,(q+pe)/ft);for(let sn=0;sn<it;++sn){let Ae=sn-yt,Ye=Math.max(0,Math.ceil(Ae/ut)),Le=Math.min(dt,(H+Ae)/ut),Ln=0;for(let Mn=Xe;Mn<on;++Mn){let Co=Mn*ft-pe;for(let Br=Ye;Br<Le;++Br){let va=Br*ut-Ae,So=St*de+Lt*Mn+Ht*Br,Ko=P*(q-1-Co)+E*(H-1-va)+L*ie;for(let Ni=0;Ni<pt;++Ni){let _i=D[So+oe*Ni],Ci=I[Ko+Ni];Ln+=_i*Ci}}}let $s=kt*de+Nt*we+At*sn+It*ie;S[$s]=Ln}}return e.makeTensorInfo(N.shape,N.dtype,N.values)}let SK={kernelName:pf,backendName:"cpu",kernelFunc:CK};function $K(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p}=r;Ct([o,s],"conv3d");let f=ri(o.shape,s.shape,c,p,l),{filterDepth:m,filterHeight:y,filterWidth:b,dilationDepth:v,dilationHeight:T,dilationWidth:N,padInfo:S}=f,D=S.front,I=S.left,P=S.top,E=new hn(f.outShape,o.dtype),L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=E.values,H=Yt(o.shape),Z=Yt(s.shape);for(let J=0;J<f.batchSize;++J){let it=J*H[0],pt=J*E.strides[0];for(let ht=0;ht<f.outDepth;++ht){let dt=pt+ht*E.strides[1],ft=ht*f.strideDepth-D;for(let ut=0;ut<m;++ut){let bt=ft+ut*v;if(bt<0||bt>=f.inDepth)continue;let yt=ut*Z[0],xt=it+bt*H[1];for(let kt=0;kt<f.outHeight;++kt){let Nt=dt+kt*E.strides[2],At=kt*f.strideHeight-P;for(let It=0;It<y;++It){let St=At+It*T;if(St<0||St>=f.inHeight)continue;let Lt=yt+It*Z[1],Ht=xt+St*H[2];for(let oe=0;oe<f.outWidth;++oe){let de=Nt+oe*f.outChannels,ie=oe*f.strideWidth-I;for(let we=0;we<b;++we){let pe=ie+we*N;if(pe<0||pe>=f.inWidth)continue;let Xe=Lt+we*Z[2],on=Ht+pe*f.inChannels,sn=Xe;for(let Ae=0;Ae<f.inChannels;++Ae){let Ye=L[on+Ae];for(let Le=0;Le<f.outChannels;++Le)q[de+Le]+=Ye*B[sn+Le];sn+=f.outChannels}}}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}let IK={kernelName:hf,backendName:"cpu",kernelFunc:$K};function EK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,filterShape:p}=r;Ct([o,s],"conv3dBackpropFilterV2");let f=Yt(o.shape),m=Yt(s.shape),y=ri(o.shape,p,c,1,l),b=y.strideDepth,v=y.strideHeight,T=y.strideWidth,N=y.filterDepth,S=y.filterHeight,D=y.filterWidth,I=new hn(y.filterShape,"float32"),P=I.values,[E,L,B,q]=I.strides,H=e.data.get(s.dataId).values,[Z,J,it,pt]=m,ht=e.data.get(o.dataId).values,[dt,ft,ut,bt]=f,yt=y.padInfo.front,xt=y.padInfo.left,kt=y.padInfo.top;for(let Nt=0;Nt<N;++Nt){let At=Math.max(0,Math.ceil((yt-Nt)/b)),It=Math.min(y.outDepth,(y.inDepth+yt-Nt)/b),St=Nt*E;for(let Lt=0;Lt<S;++Lt){let Ht=Math.max(0,Math.ceil((kt-Lt)/v)),oe=Math.min(y.outHeight,(y.inHeight+kt-Lt)/v),de=Lt*L+St;for(let ie=0;ie<D;++ie){let we=Math.max(0,Math.ceil((xt-ie)/T)),pe=Math.min(y.outWidth,(y.inWidth+xt-ie)/T),Xe=ie*B+de;for(let on=0;on<y.inChannels;++on){let sn=on*q+Xe;for(let Ae=0;Ae<y.outChannels;++Ae){let Ye=0;for(let Le=0;Le<y.batchSize;++Le){let Ln=Le*dt,$s=Le*Z;for(let Mn=At;Mn<It;++Mn){let Co=Nt+Mn*b-yt,Br=Co*ft+Ln,va=Mn*J+$s;for(let So=Ht;So<oe;++So){let Ko=Lt+So*v-kt,Ni=Ko*ut+Br,_i=So*it+va;for(let Ci=we;Ci<pe;++Ci){let fl=ie+Ci*T-xt,_1=fl*bt+Ni,C1=Ci*pt+_i;Ye+=ht[_1+on]*H[C1+Ae]}}}}P[sn+Ae]=Ye}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let DK={kernelName:jb,backendName:"cpu",kernelFunc:EK};function AK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{pad:c,strides:l,inputShape:p}=r;Ct([o],"conv3dBackpropInputV2");let f=Yt(o.shape),m=Yt(s.shape),y=ri(p,s.shape,l,1,c),b=new hn(y.inShape,"float32"),v=b.values,[T,N,S,D]=b.strides,I=e.data.get(o.dataId).values,[P,E,L,B]=f,q=e.data.get(s.dataId).values,[H,Z,J,it]=m,{batchSize:pt,filterDepth:ht,filterHeight:dt,filterWidth:ft,inChannels:ut,inDepth:bt,inHeight:yt,inWidth:xt,outChannels:kt,outDepth:Nt,outHeight:At,outWidth:It,strideDepth:St,strideHeight:Lt,strideWidth:Ht}=y,oe=ht-1-y.padInfo.front,de=dt-1-y.padInfo.top,ie=ft-1-y.padInfo.left;for(let we=0;we<pt;++we)for(let pe=0;pe<ut;++pe)for(let Xe=0;Xe<bt;++Xe){let on=Xe-oe,sn=Math.max(0,Math.ceil(on/St)),Ae=Math.min(Nt,(ht+on)/St);for(let Ye=0;Ye<yt;++Ye){let Le=Ye-de,Ln=Math.max(0,Math.ceil(Le/Lt)),$s=Math.min(At,(dt+Le)/Lt);for(let Mn=0;Mn<xt;++Mn){let Co=Mn-ie,Br=Math.max(0,Math.ceil(Co/Ht)),va=Math.min(It,(ft+Co)/Ht),So=0;for(let Ko=sn;Ko<Ae;++Ko){let Ni=Ko*St-on;for(let _i=Ln;_i<$s;++_i){let Ci=_i*Lt-Le;for(let fl=Br;fl<va;++fl){let 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BK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"depthwiseConv2dNativeBackpropFilter");let y=Un(o.shape,m,c,l,p,f,!0),{strideHeight:b,strideWidth:v,filterHeight:T,filterWidth:N}=y,S=new hn(y.filterShape,"float32"),D=y.padInfo.left,I=y.padInfo.top,P=y.outChannels/y.inChannels,E=e.data.get(o.dataId).values,L=new hn(o.shape,o.dtype,E),B=e.data.get(s.dataId).values,q=new hn(s.shape,s.dtype,B);for(let H=0;H<T;++H){let Z=Math.max(0,Math.ceil((I-H)/b)),J=Math.min(y.outHeight,(y.inHeight+I-H)/b);for(let it=0;it<N;++it){let pt=Math.max(0,Math.ceil((D-it)/v)),ht=Math.min(y.outWidth,(y.inWidth+D-it)/v);for(let dt=0;dt<y.outChannels;++dt){let ft=Math.trunc(dt/P),ut=dt%P,bt=0;for(let yt=0;yt<y.batchSize;++yt)for(let xt=Z;xt<J;++xt){let kt=H+xt*b-I;for(let Nt=pt;Nt<ht;++Nt){let At=it+Nt*v-D;bt+=L.get(yt,kt,At,ft)*q.get(yt,xt,Nt,dt)}}S.set(bt,H,it,ft,ut)}}}return e.makeTensorInfo(S.shape,S.dtype,S.values)}let 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r=n.shape,o=r[0],s=r[1],c=e.data.get(n.dataId),l=c.complexTensorInfos.real,p=c.complexTensorInfos.imag,f=[o,s],m=G(f),y=Ce("float32",m),b=Ce("float32",m);for(let S=0;S<o;S++){let D=s1({inputs:{x:l},backend:e,attrs:{begin:[S,0],size:[1,s]}}),I=s1({inputs:{x:p},backend:e,attrs:{begin:[S,0],size:[1,s]}}),P=_o({inputs:{real:D,imag:I},backend:e}),{real:E,imag:L}=n5(P,t,e),B=ys(E,L);for(let q=0;q<s;q++){let H=nv(B,q);y[S*s+q]=H.real,b[S*s+q]=H.imag}e.disposeIntermediateTensorInfo(D),e.disposeIntermediateTensorInfo(I),e.disposeIntermediateTensorInfo(P)}let v=e.makeTensorInfo(f,"float32",y),T=e.makeTensorInfo(f,"float32",b),N=_o({inputs:{real:v,imag:T},backend:e});return e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),N}function n5(n,t,e){let r=G(n.shape),o=e.data.get(n.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,c=e.data.get(o.complexTensorInfos.imag.dataId).values;if(r5(r)){let l=u1(s,c,r,t,e),p=[n.shape[0],n.shape[1]];if(t){let 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`))}function O8(n){return ks(n,()=>n.createProgram(),"Unable to create WebGLProgram.")}function L8(n,t){if(Rt(n,()=>n.linkProgram(t)),n.getProgramParameter(t,n.LINK_STATUS)===!1)throw console.log(n.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function f1(n,t){if(Rt(n,()=>n.validateProgram(t)),n.getProgramParameter(t,n.VALIDATE_STATUS)===!1)throw console.log(n.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function M8(n,t){let e=ks(n,()=>n.createBuffer(),"Unable to create WebGLBuffer");return Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,e)),Rt(n,()=>n.bufferData(n.ARRAY_BUFFER,t,n.STATIC_DRAW)),e}function B8(n,t){let e=ks(n,()=>n.createBuffer(),"Unable to create WebGLBuffer");return Rt(n,()=>n.bindBuffer(n.ELEMENT_ARRAY_BUFFER,e)),Rt(n,()=>n.bufferData(n.ELEMENT_ARRAY_BUFFER,t,n.STATIC_DRAW)),e}function qst(){return ct().getNumber("WEBGL_VERSION")===2?1:4}function z8(n){return ks(n,()=>n.createTexture(),"Unable to create WebGLTexture.")}function W8(n,t){let e=ct().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(n<=0||t<=0){let r=`[${n}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(n>e||t>e){let r=`[${n}x${t}]`,o=`[${e}x${e}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+o+".")}}function V8(n){return ks(n,()=>n.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function gI(n,t,e,r,o,s,c){let l=n.getAttribLocation(t,e);return l===-1?!1:(Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,r)),Rt(n,()=>n.vertexAttribPointer(l,o,n.FLOAT,!1,s,c)),Rt(n,()=>n.enableVertexAttribArray(l)),!0)}function G8(n,t,e){bI(n,e),Rt(n,()=>n.activeTexture(n.TEXTURE0+e)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t))}function Hst(n,t){bI(n,t),Rt(n,()=>n.activeTexture(n.TEXTURE0+t)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function U8(n,t,e){return ks(n,()=>n.getUniformLocation(t,e),'uniform "'+e+'" not present in program.')}function q8(n,t,e){return n.getUniformLocation(t,e)}function H8(n,t,e,r){Rt(n,()=>G8(n,t,r)),Rt(n,()=>n.uniform1i(e,r))}function jst(n){Rt(n,()=>n.bindFramebuffer(n.FRAMEBUFFER,null)),Rt(n,()=>n.viewport(0,0,n.canvas.width,n.canvas.height)),Rt(n,()=>n.scissor(0,0,n.canvas.width,n.canvas.height))}function d1(n,t,e){Rt(n,()=>n.bindFramebuffer(n.FRAMEBUFFER,e)),Rt(n,()=>n.framebufferTexture2D(n.FRAMEBUFFER,n.COLOR_ATTACHMENT0,n.TEXTURE_2D,t,0))}function yI(n,t){Rt(n,()=>n.bindFramebuffer(n.FRAMEBUFFER,t)),Rt(n,()=>n.framebufferTexture2D(n.FRAMEBUFFER,n.COLOR_ATTACHMENT0,n.TEXTURE_2D,null,0))}function Am(n){let t=n.checkFramebufferStatus(n.FRAMEBUFFER);if(t!==n.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+j8(n,t))}function j8(n,t){switch(t){case n.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case n.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case n.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case n.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function ks(n,t,e){let r=Rt(n,()=>t());if(r==null)throw new Error(e);return r}function bI(n,t){let e=n.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+n.TEXTURE0;if(r<n.TEXTURE0||r>e){let o=`[gl.TEXTURE0, gl.TEXTURE${e}]`;throw new Error(`textureUnit must be in ${o}.`)}}function rl(n,t=2){return G(n.slice(0,n.length-t))}function ol(n){if(n.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[n.length>1?n[n.length-2]:1,n[n.length-1]]}function m1(n){let t=[1,1,1],e=n.length===0||n.length===1&&n[0]===1;return e||(t=[rl(n),...ol(n)]),t}function K8(n,t=!1){let e=ct().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(e=e*2,n=n.map((o,s)=>s>=n.length-2?k(n[s]):n[s]),n.length===1&&(n=[2,n[0]])),n.length!==2){let o=ln(n);n=o.newShape}let 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t=jo(n);Om=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Om)}function J8(n){if(n===0)return 0;let t,e=jo(n);return eo(e,"EXT_disjoint_timer_query_webgl2")&&n===2?t=2:eo(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function eo(n,t){let e=n.getExtension(t);return e!=null}function xI(n){try{let t=jo(n);if(t!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Z8(n){if(n===0)return!1;let t=jo(n);if(n===1){if(!eo(t,"OES_texture_float"))return!1}else if(!eo(t,"EXT_color_buffer_float"))return!1;let e=g1(t);return e}function Q8(n){if(n===0)return!1;let t=jo(n);if(n===1){if(!eo(t,"OES_texture_float"))return!1;if(!eo(t,"WEBGL_color_buffer_float"))return!1}else{if(eo(t,"EXT_color_buffer_float"))return g1(t);let r="EXT_color_buffer_half_float";if(eo(t,r)){let o=t.getExtension(r);return tX(t,o)}return!1}let e=g1(t);return e}function g1(n){let t=h1(n),e=n.createTexture();n.bindTexture(n.TEXTURE_2D,e);let 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eX(n){if(n!==2)return!1;let t=jo(n),e=t.fenceSync!=null;return e}function sh(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&_(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}let qt=ct();qt.registerFlag("HAS_WEBGL",()=>qt.getNumber("WEBGL_VERSION")>0),qt.registerFlag("WEBGL_VERSION",()=>xI(2)?2:xI(1)?1:0),qt.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1),qt.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>qt.get("WEBGL_VERSION")===2),qt.registerFlag("WEBGL_CPU_FORWARD",()=>!0),qt.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1),qt.registerFlag("WEBGL_PACK",()=>qt.getBool("HAS_WEBGL")),qt.registerFlag("WEBGL_PACK_NORMALIZATION",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_CLIP",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1),qt.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_PACK_REDUCE",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_LAZILY_UNPACK",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_CONV_IM2COL",()=>qt.getBool("WEBGL_PACK")),qt.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>X8(qt.getNumber("WEBGL_VERSION"))),qt.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Y8(qt.getNumber("WEBGL_VERSION"))),qt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let n=qt.getNumber("WEBGL_VERSION");return n===0?0:J8(n)}),qt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>qt.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!PN()),qt.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Z8(qt.getNumber("WEBGL_VERSION"))),qt.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>qt.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:qt.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")),qt.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Q8(qt.getNumber("WEBGL_VERSION"))),qt.registerFlag("WEBGL_FENCE_API_ENABLED",()=>eX(qt.getNumber("WEBGL_VERSION"))),qt.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{let n=qt.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return n?4:0}),qt.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,n=>{if(n<0&&n!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${n}.`)});let{simpleAbsImpl:nX,addImpl:rX,ceilImpl:oX,expImpl:sX,expm1Impl:iX,floorImpl:aX,logImpl:cX,maxImpl:lX,multiplyImpl:uX,rsqrtImpl:pX,sliceImpl:hX,subImpl:fX,transposeImpl:y1,uniqueImpl:dX}=U6;class mX{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,c)=>`T${c}`);let r=[];this.variableNames.forEach(s=>{r.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
float result = ${o};
setOutput(result);
}
`}}class gX{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,c)=>`T${c}`);let r=[];this.variableNames.forEach(s=>{r.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
vec4 result = ${o};
setOutput(result);
}
`}}class yX{constructor(t,e,r){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:c}=t;r||this.variableNames.push("bestIndicesA"),this.outputShape=[s,c];let l=e==="max"?">":"<",p=r?"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 = ${p};
float candidate = getA(batch, inIdx);
if (candidate ${l} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}}function wI(n,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${n}.${e}`)}function Yn(n,t){return t===1?[n]:wI(n,t)}function bX(n,t){if(n===1)return"rc";let e="";for(let r=0;r<n;r++)e+=t[r],r<n-1&&(e+=",");return e}function Jn(){let n,t,e,r,o,s,c,l,p,f;return ct().getNumber("WEBGL_VERSION")===2?(n="#version 300 es",t="in",e="out",r="in",o="texture",s="outputColor",c="out vec4 outputColor;",l=`
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),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,p="",f=`
#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)));
}
`):(n="",t="attribute",e="varying",r="varying",o="texture2D",s="gl_FragColor",c="",l=`
#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));
}
`,p=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,f=`
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 * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}let vI=`
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;
}
`;let{getBroadcastDims:TI}=ov;function xX(n,t,e,r){let o=[];n.forEach(T=>{let N=G(T.shapeInfo.logicalShape);T.shapeInfo.isUniform?o.push(`uniform float ${T.name}${N>1?`[${N}]`:""};`):(o.push(`uniform sampler2D ${T.name};`),o.push(`uniform int offset${T.name};`))});let s=o.join(`
`),c=n.map(T=>wX(T,t,r)).join(`
`),l=t.texShape,p=Jn(),f=kX(p),m,y,b=CX(p);t.isPacked?(m=vX(t.logicalShape,l),y=_X(p)):(m=TX(t.logicalShape,l),y=NX(p)),r&&(b+=EX);let v=[b,f,y,s,m,c,e].join(`
`);return v}function sl(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return VX(n);case 1:return UX(n);case 2:return HX(n);case 3:return KX(n);case 4:return YX(n);case 5:return JX(n);case 6:return ZX(n);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function kI(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return WX(n);case 1:return GX(n);case 2:return qX(n);case 3:return jX(n);default:return XX(n)}}function wX(n,t,e=!1){let r="";e?r+=kI(n):r+=sl(n);let o=n.shapeInfo.logicalShape,s=t.logicalShape;return o.length<=s.length&&(e?r+=QX(n,t):r+=tY(n,t)),r}function vX(n,t){switch(n.length){case 0:return NI();case 1:return DX(n,t);case 2:return BX(n,t);case 3:return FX(n,t);default:return PX(n,t)}}function TX(n,t){switch(n.length){case 0:return NI();case 1:return AX(n,t);case 2:return zX(n,t);case 3:return RX(n,t);case 4:return OX(n,t);case 5:return LX(n,t);case 6:return MX(n,t);default:throw new Error(`${n.length}-D output sampling is not yet supported`)}}function kX(n){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${n.texture2D}(textureSampler, uv).r;
}
`}function NX(n){return`
void setOutput(float val) {
${n.output} = vec4(val, 0, 0, 0);
}
`}function _X(n){return`
void setOutput(vec4 val) {
${n.output} = val;
}
`}function CX(n){let t=`${n.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${n.varyingFs} vec2 resultUV;
${n.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;
${n.defineSpecialNaN}
${n.defineSpecialInf}
${n.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);
}
${SX}
${$X}
${IX}
`;return t}let SX=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,$X=`
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);
}
`,IX=`
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);
}
`,EX=`
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 NI(){return`
int getOutputCoords() {
return 0;
}
`}function DX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${e[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return 2 * (resTexRC.x * ${e[1]} + resTexRC.y);
}
`}function AX(n,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function FX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[2]/2),o=r*Math.ceil(n[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function RX(n,t){let e=xa(["r","c","d"],n);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
return ivec3(r, c, d);
}
`}function PX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[n.length-1]/2),o=r*Math.ceil(n[n.length-2]/2),s=o,c="",l="b, r, c";for(let p=2;p<n.length-1;p++)s*=n[n.length-p-1],c=`
int b${p} = index / ${s};
index -= b${p} * ${s};
`+c,l=`b${p}, `+l;return`
ivec${n.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${c}
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${n.length}(${l});
}
`}function OX(n,t){let e=xa(["r","c","d","d2"],n);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
return ivec4(r, c, d, d2);
}
`}function LX(n,t){let e=xa(["r","c","d","d2","d3"],n);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function MX(n,t){let e=xa(["r","c","d","d2","d3","d4"],n);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function BX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(lt(n,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`;let r=Math.ceil(n[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function zX(n,t){return lt(n,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:n[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:n[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${n[1]};
int c = index - r * ${n[1]};
return ivec2(r, c);
}
`}function wa(n){return`offset${n}`}function WX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Jn();return`
vec4 ${e}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function VX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${e}() {return ${t};}`;let[r,o]=n.shapeInfo.texShape;if(r===1&&o===1)return`
float ${e}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,c]=n.shapeInfo.texShape,l=wa(t);return`
float ${e}() {
vec2 uv = uvFromFlat(${s}, ${c}, ${l});
return sampleTexture(${t}, uv);
}
`}function GX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=n.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=Jn();return`
vec4 ${e}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function UX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
float ${e}(int index) {
${il(n)}
}
`;let r=n.shapeInfo.texShape,o=r[0],s=r[1];if(s===1&&o===1)return`
float ${e}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let c=wa(t);return s===1?`
float ${e}(int index) {
vec2 uv = vec2(0.5, (float(index + ${c}) + 0.5) / ${o}.0);
return sampleTexture(${t}, uv);
}
`:o===1?`
float ${e}(int index) {
vec2 uv = vec2((float(index + ${c}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${e}(int index) {
vec2 uv = uvFromFlat(${o}, ${s}, index + ${c});
return sampleTexture(${t}, uv);
}
`}function qX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=o[0],c=o[1],l=Jn();if(o!=null&&lt(t,o))return`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${s}.0);
return ${l.texture2D}(${e}, uv);
}
`;let p=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],f=Math.ceil(t[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${f}, ${p[0]}, ${p[1]}, row, col);
return ${l.texture2D}(${e}, uv);
}
`}function HX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape;if(o!=null&&lt(t,o)){let y=o[0],b=o[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
return sampleTexture(${e}, uv);
}
`}let{newShape:s,keptDims:c}=ln(t),l=s;if(l.length<t.length){let y=al(n,l),b=["row","col"];return`
${sl(y)}
float ${r}(int row, int col) {
return ${r}(${cl(b,c)});
}
`}if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${il(n)}
}
`;let p=o[0],f=o[1],m=wa(e);return f===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${p}.0);
return sampleTexture(${e}, uv);
}
`:p===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${f}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${m};
vec2 uv = uvFromFlat(${p}, ${f}, index);
return sampleTexture(${e}, uv);
}
`}function jX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(t[0]===1){let y=t.slice(1),b=[1,2],v=al(n,y),T=["b","row","col"];return`
${kI(v)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${cl(T,b)});
}
`}let c=s[0],l=s[1],p=Math.ceil(t[2]/2),f=p*Math.ceil(t[1]/2),m=Jn();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${c}, ${l}, ${f}, ${p}, b, row, col);
return ${m.texture2D}(${e}, uv);
}
`}function KX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[1]*t[2],s=t[2],{newShape:c,keptDims:l}=ln(t),p=c;if(p.length<t.length){let T=al(n,p),N=["row","col","depth"];return`
${sl(T)}
float ${r}(int row, int col, int depth) {
return ${r}(${cl(N,l)});
}
`}if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${o}, ${s}, 1)));
${il(n)}
}
`;let f=n.shapeInfo.texShape,m=f[0],y=f[1],b=n.shapeInfo.flatOffset;if(y===o&&b==null)return`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${y}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;if(y===s&&b==null)return`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;let v=wa(e);return`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${s} + depth + ${v};
vec2 uv = uvFromFlat(${m}, ${y}, index);
return sampleTexture(${e}, uv);
}
`}function XX(n){let t=n.shapeInfo.logicalShape,e=t.length,r=n.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n.shapeInfo.texShape,c=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],l=c[0],p=c[1],f=Math.ceil(t[e-1]/2),m=f*Math.ceil(t[e-2]/2),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${f} + (col / 2)`;for(let T=2;T<e-1;T++)y=`int b${T}, `+y,m*=t[e-T-1],b=`b${T} * ${m} + `+b;let v=Jn();return`
vec4 ${o}(${y}) {
int index = ${b};
int texR = index / ${p};
int texC = index - texR * ${p};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${l});
return ${v.texture2D}(${r}, uv);
}
`}function YX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[3],s=t[2]*o,c=t[1]*s,{newShape:l,keptDims:p}=ln(t);if(l.length<t.length){let T=al(n,l),N=["row","col","depth","depth2"];return`
${sl(T)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${cl(N,p)});
}
`}if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${c}, ${s}, ${o}, 1)));
${il(n)}
}
`;let f=n.shapeInfo.flatOffset,m=n.shapeInfo.texShape,y=m[0],b=m[1];if(b===c&&f==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${y}.0);
return sampleTexture(${e}, uv);
}
`;if(b===o&&f==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${y}.0);
return sampleTexture(${e}, uv);
}
`;let v=wa(e);return`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${s} +
depth * ${o} + depth2;
vec2 uv = uvFromFlat(${y}, ${b}, index + ${v});
return sampleTexture(${e}, uv);
}
`}function JX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,c=t[2]*s,l=t[1]*c,{newShape:p,keptDims:f}=ln(t);if(p.length<t.length){let N=al(n,p),S=["row","col","depth","depth2","depth3"];return`
${sl(N)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${cl(S,f)});
}
`}if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${l}, ${c}, ${s}, ${o})) +
depth3;
${il(n)}
}
`;let m=n.shapeInfo.flatOffset,y=n.shapeInfo.texShape,b=y[0],v=y[1];if(v===l&&m==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${v}.0, ${b}.0);
return sampleTexture(${e}, uv);
}
`;if(v===o&&m==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${v}.0, ${b}.0);
return sampleTexture(${e}, uv);
}
`;let T=wa(e);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${l} + col * ${c} + depth * ${s} +
depth2 * ${o} + depth3 + ${T};
vec2 uv = uvFromFlat(${b}, ${v}, index);
return sampleTexture(${e}, uv);
}
`}function ZX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=ln(t);if(o.length<t.length){let S=al(n,o),D=["row","col","depth","depth2","depth3","depth4"];return`
${sl(S)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${cl(D,s)});
}
`}let c=t[5],l=t[4]*c,p=t[3]*l,f=t[2]*p,m=t[1]*f;if(n.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${m}, ${f}, ${p}, ${l})) +
dot(
vec2(depth3, depth4),
vec2(${c}, 1)));
${il(n)}
}
`;let y=n.shapeInfo.flatOffset,b=n.shapeInfo.texShape,v=b[0],T=b[1];if(T===m&&y==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${f}, ${p}, ${l}, ${c})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${T}.0, ${v}.0);
return sampleTexture(${e}, uv);
}
`;if(T===c&&y==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${T}.0, ${v}.0);
return sampleTexture(${e}, uv);
}
`;let N=wa(e);return`
float ${r}(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 * ${m} + col * ${f} + depth * ${p} +
depth2 * ${l} + depth3 * ${c} + depth4 + ${N};
vec2 uv = uvFromFlat(${v}, ${T}, index);
return sampleTexture(${e}, uv);
}
`}function il(n){let t=n.name,e=G(n.shapeInfo.logicalShape);return e<2?`return ${t};`:`
for (int i = 0; i < ${e}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function QX(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=n.shapeInfo.logicalShape.length,c=t.logicalShape.length,l=TI(n.shapeInfo.logicalShape,t.logicalShape),p=Oe(c),f=c-s,m,y=["x","y","z","w","u","v"];s===0?m="":c<2&&l.length>=1?m="coords = 0;":m=l.map(I=>`coords.${y[I+f]} = 0;`).join(`
`);let b="";c<2&&s>0?b="coords":b=n.shapeInfo.logicalShape.map((I,P)=>`coords.${y[P+f]}`).join(", ");let v="return outputValue;",T=G(n.shapeInfo.logicalShape),N=T===1,S=G(t.logicalShape),D=S===1;if(s===1&&!N&&!D)v=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(N&&!D)c===1?v=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:v=`
return vec4(outputValue.x);
`;else if(l.length){let I=s-2,P=s-1;l.indexOf(I)>-1&&l.indexOf(P)>-1?v="return vec4(outputValue.x);":l.indexOf(I)>-1?v="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":l.indexOf(P)>-1&&(v="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${o}() {
${p} coords = getOutputCoords();
${m}
vec4 outputValue = get${r}(${b});
${v}
}
`}function tY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=t.texShape,c=n.shapeInfo.texShape,l=n.shapeInfo.logicalShape.length,p=t.logicalShape.length;if(!n.shapeInfo.isUniform&&l===p&&n.shapeInfo.flatOffset==null&&lt(c,s))return`
float ${o}() {
return sampleTexture(${e}, resultUV);
}
`;let f=Oe(p),m=TI(n.shapeInfo.logicalShape,t.logicalShape),y=p-l,b,v=["x","y","z","w","u","v"];l===0?b="":p<2&&m.length>=1?b="coords = 0;":b=m.map(N=>`coords.${v[N+y]} = 0;`).join(`
`);let T="";return p<2&&l>0?T="coords":T=n.shapeInfo.logicalShape.map((N,S)=>`coords.${v[S+y]}`).join(", "),`
float ${o}() {
${f} coords = getOutputCoords();
${b}
return get${r}(${T});
}
`}function Oe(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function al(n,t){let e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function cl(n,t){return t.map(e=>n[e]).join(", ")}class eY{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_(t.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],c=Math.ceil(s/e);this.outputShape=t.slice(0,-1),c>1&&this.outputShape.push(c),o||this.variableNames.push("bestIndicesA");let l=this.outputShape,p=l.length,f=Oe(p),m=Yn("coords",p),y,b;if(c===1){b=p+1;let H=Oe(b);y=`
${H} sourceLocR = ${H}(${m.join()}, 0);
++${m[p-1]};
${H} sourceLocG = ${H}(${m.join()}, 0);
++${m[p-2]};
${H} sourceLocA = ${H}(${m.join()}, 0);
--${m[p-1]};
${H} sourceLocB = ${H}(${m.join()}, 0);
--${m[p-2]};`}else b=p,y=`
${f} sourceLocR = coords;
++${m[p-1]};
${f} sourceLocG = coords;
++${m[p-2]};
${f} sourceLocA = coords;
--${m[p-1]};
${f} sourceLocB = coords;
--${m[p-2]};`;let v=["x","y","z","w","u","v"].slice(0,b),T="."+v[b-1],N=v.map(H=>"int "+H),S=Yn("sourceLocR",b-1).concat("inIdx.r"),D=Yn("sourceLocG",b-1).concat("inIdx.g"),I=Yn("sourceLocB",b-1).concat("inIdx.b"),P=Yn("sourceLocA",b-1).concat("inIdx.a"),E=r==="max"?"greaterThan":"lessThan",L=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${S.join()}),
getBestIndicesAChannel(${D.join()}),
getBestIndicesAChannel(${I.join()}),
getBestIndicesAChannel(${P.join()})));`,B=`vec4(
getAChannel(${S.join()}),
hasNextCol ? getAChannel(${D.join()}) : 0.,
hasNextRow ? getAChannel(${I.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${P.join()}) : 0.)`,q=o?"":`
float getBestIndicesAChannel(${N.join()}) {
return getChannel(getBestIndicesA(${v.join()}),
vec2(${v.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${N.join()}) {
return getChannel(getA(${v.join()}),
vec2(${v.slice(-2).join()}));
}
${q}
void main() {
${f} coords = getOutputCoords();
bool hasNextCol = ${m[p-1]} < ${l[p-1]-1};
bool hasNextRow = ${m[p-2]} < ${l[p-2]-1};
${y}
ivec4 srcIdx = ivec4(sourceLocR${T}, sourceLocG${T},
sourceLocB${T}, sourceLocA${T}) * ${e};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${B};
for (int i = 0; i < ${e}; i++) {
inIdx = srcIdx;
${L}
vec4 candidate = ${B};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${E}(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);
}
`}}class nY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterHeight,f=t.effectiveFilterWidth,m=p-1-t.padInfo.top,y=f-1-t.padInfo.left,b=1/(e*r);this.userCode=`
const ivec2 pads = ivec2(${m}, ${y});
const float avgMultiplier = float(${b});
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 < ${p};
wR += ${c}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC+= ${l}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class rY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterDepth,b=t.effectiveFilterHeight,v=t.effectiveFilterWidth,T=y-1-t.padInfo.front,N=b-1-t.padInfo.top,S=v-1-t.padInfo.left,D=1/(e*r*o);this.userCode=`
const ivec3 pads = ivec3(${T}, ${N}, ${S});
const float avgMultiplier = float(${D});
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 < ${y};
wD += ${p}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${b};
wR += ${f}) {
float dyR = float(dyRCorner + wR) / ${c}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${v};
wC += ${m}) {
float dyC = float(dyCCorner + wC) / ${l}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}}let _I=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,oY=`
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;
}
`,sY=`
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);
`,Jst="return (a - b) * (a - b);",iY="return float(a == b);",aY="return float(a < b);",cY="return float(a <= b);",lY="return float(a > b);",uY="return float(a >= b);",pY="return float(a >= 1.0 && b >= 1.0);",hY="return float(a >= 1.0 || b >= 1.0);",fY=_I+`
return max(a, b);
`,dY=_I+`
return min(a, b);
`,mY=`if (b == 0.0) return NAN;
return mod(a, b);`,gY="return (b >= 1.0) ? a : a * (b + 1.0);",CI="return (a < 0.) ? b * a : a;";class Hn{constructor(t,e,r){this.variableNames=["A","B"],this.outputShape=le(e,r),this.userCode=`
float binaryOperation(float a, float b) {
${t}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}}let Mm=`
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;
`,yY=`
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);
`,bY=`
// 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));
`+Mm+`
return result;
`,SI=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,xY=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,wY=`
return vec4(equal(a, b));
`,Zst=`
return vec4(notEqual(a, b));
`,vY=`
return vec4(lessThan(a, b));
`,TY=`
return vec4(lessThanEqual(a, b));
`,kY=`
return vec4(greaterThan(a, b));
`,NY=`
return vec4(greaterThanEqual(a, b));
`,_Y=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,CY=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,SY=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Mm+`
return result;
`,$Y=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Mm+`
return result;
`,IY=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Mm+`
return result;
`;class Ns{constructor(t,e,r,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=le(e,r);let s=this.outputShape.length,c="";if(o)if(s===0||G(this.outputShape)===1)c=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else{let l=Oe(s);if(c=`
${l} coords = getOutputCoords();
`,s===1)c+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let p=Yn("coords",s);c+=`
bool nextRowOutOfBounds =
(${p[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${p[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${t}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${c}
setOutput(result);
}
`}}class EY{constructor(t){this.variableNames=["A"],this.outputShape=t,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class DY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class AY{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}}class FY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
if (${c}) {
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);
}
`}}class RY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dataFormat==="channelsLast",l=e-1-t.padInfo.top,p=r-1-t.padInfo.left,f=c?1:2,m=c?2:3,y=c?3:1;this.userCode=`
const ivec2 pads = ivec2(${l}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${y}];
ivec2 dyCorner = ivec2(coords[${f}], coords[${m}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
if (${c}) {
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);
}
`}}class PY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,c=t.padInfo.top,l=t.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${t.batchSize}; b++) {
for (int yF = 0; yF < ${t.outDepth}; yF++) {
int xF = wF + yF * ${e} - ${s};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${r} - ${c};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${l};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class OY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=e-1-t.padInfo.front,f=r-1-t.padInfo.top,m=o-1-t.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${p}, ${f}, ${m});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${e}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${e} - 1 - wF;
for (int wR = 0; wR < ${r}; wR++) {
float dyR = float(dyRCorner + wR) / ${c}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${r} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${l}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}}class LY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.outChannels/t.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${c} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class MY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=e-1-t.padInfo.top,l=r-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`
const ivec2 pads = ivec2(${c}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${p}; dm++) {
int d2 = d1 * ${p} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}}class $I{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.padInfo.top,c=t.padInfo.left,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterHeight,b=t.filterWidth,v=Math.floor(t.inChannels/4)*4,T=t.inChannels%4,N=t.dataFormat==="channelsLast",S=N?1:2,D=N?2:3,I=N?3:1,P="",E="";r&&(o?P=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:P=`
float activation(float x) {
${r}
}
`,E="result = activation(result);");let L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${P}
const ivec2 strides = ivec2(${l}, ${p});
const ivec2 pads = ivec2(${s}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${I}];
ivec2 xRCCorner =
ivec2(coords[${S}], coords[${D}]) * 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 < ${y}; wR++) {
int xR = xRCorner + wR * ${f};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC++) {
int xC = xCCorner + wC * ${m};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${v}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${N}) {
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 (${T===1}) {
if (${N}) {
dotProd +=
getX(batch, xR, xC, ${v}) *
getW(wR, wC, ${v}, d2);
} else {
dotProd +=
getX(batch, ${v}, xR, xC) *
getW(wR, wC, ${v}, d2);
}
} else if (${T===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${v}, d2),
getW(wR, wC, ${v} + 1, d2)
);
if (${N}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${v}),
getX(batch, xR, xC, ${v} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${v}, xR, xC),
getX(batch, ${v} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${T===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${v}, d2),
getW(wR, wC, ${v} + 1, d2),
getW(wR, wC, ${v} + 2, d2)
);
if (${N}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${v}),
getX(batch, xR, xC, ${v} + 1),
getX(batch, xR, xC, ${v} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${v}, xR, xC),
getX(batch, ${v} + 1, xR, xC),
getX(batch, ${v} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${L}
${E}
setOutput(result);
}
`}}class BY{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,r=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterDepth,b=t.filterHeight,v=t.filterWidth,T=Math.floor(t.inChannels/4)*4,N=t.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${c}, ${l});
const ivec3 pads = ivec3(${e}, ${r}, ${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 < ${y}; wF++) {
int xF = xFCorner + wF * ${p};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${b}; wR++) {
int xR = xRCorner + wR * ${f};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; wC++) {
int xC = xCCorner + wC * ${m};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${T}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${N===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${T}) *
getW(wF, wR, wC, ${T}, d2);
} else if (${N===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${T}),
getX(batch, xF, xR, xC, ${T} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${T}, d2),
getW(wF, wR, wC, ${T} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${N===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${T}),
getX(batch, xF, xR, xC, ${T} + 1),
getX(batch, xF, xR, xC, ${T} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${T}, d2),
getW(wF, wR, wC, ${T} + 1, d2),
getW(wF, wR, wC, ${T} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}}class II{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=t.outChannels/t.inChannels,S="",D="";r&&(o?S=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:S=`
float activation(float x) {
${r}
}
`,D="result = activation(result);");let I=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${S}
const ivec2 strides = ivec2(${f}, ${m});
const ivec2 pads = ivec2(${l}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${N};
int q = d2 - d1 * ${N};
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 < ${v}; wR++) {
int xR = xRCorner + wR * ${y};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${T}; wC++) {
int xC = xCCorner + wC * ${b};
if (xC < 0 || xC >= ${c}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${I}
${D}
setOutput(result);
}
`}}class EI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=T,S="int xR; int xC; int xCOffset;";for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`
vec4 xTexelR${E}C${L*2} = vec4(0.);
vec4 wR${E}C${L} = vec4(0.);
vec4 xR${E}C${L} = vec4(0.);`;for(let E=0;E<v;E++)for(let L=0;L<N;L++){let B=L*2;if(S+=`
xR = xRCorner + ${E*y};
xC = xCCorner + ${B*b};
`,m===1){if(B<T&&(p%2===1?S+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${c}) {
xTexelR${E}C${B}.zw = vec2(0.);
}
} else {
xTexelR${E}C${B} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${c}) {
previous.zw = vec2(0.);
}
xR${E}C${B} = vec4(previous.zw, xTexelR${E}C${B}.xy);
} else {
xR${E}C${B} = vec4(0, 0, xTexelR${E}C${B}.xy);
}
`:S+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${c}) {
xTexelR${E}C${B} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${B} = vec4(0.);
}
xR${E}C${B} = xTexelR${E}C${B};
`,B+1<T)){let q=p%2===0?k(b):b;b%2===0&&p%2===1||b%2!==0&&p%2!==1?(S+=`
xCOffset = xC + ${p%2} + ${q};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
}
`,b>1&&(S+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${B} = vec4(0.);
}
`),S+=`
xR${E}C${B+1} = vec4(
xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.xy);
`):S+=`
xCOffset = xC + ${q};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
}
xR${E}C${B+1} = xTexelR${E}C${B+2};
`}}else B<T&&(S+=`
if(xR >= 0 && xR < ${s}) {
`,p%2===1?(S+=`
xCOffset = xC + 1 - ${m};
if(xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${B} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${c}) {
xTexelR${E}C${B+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${E}C${B+2} = vec4(0.);
}
xR${E}C${B} = vec4(
xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
`,B+1<T&&(S+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${m};
if(xCOffset >= 0 && xCOffset < ${c}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${E}C${B+1} = vec4(xTexelR${E}C${B+2}.xy, final.xy);
`)):(S+=`
if(xC >= 0 && xC < ${c}) {
xTexelR${E}C${B} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${B} = vec4(0.);
}
xCOffset = xC + ${m};
if(xCOffset >= 0 && xCOffset < ${c}) {
xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${B+2} = vec4(0.);
}
xR${E}C${B} = vec4(
xTexelR${E}C${B}.xy, xTexelR${E}C${B+2}.xy);
`,B+1<T&&(S+=`
xR${E}C${B+1} = vec4(
xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
`)),S+="}");B<T&&(S+=`
vec4 wTexelR${E}C${B} = getW(${E}, ${B}, d1, q);
wR${E}C${B} = vec4(wTexelR${E}C${B}.xz, wTexelR${E}C${B}.xz);
`,B+1<T&&(S+=`
vec4 wTexelR${E}C${B+1} = getW(${E}, ${B+1}, d1, q);
wR${E}C${B+1} =
vec4(wTexelR${E}C${B+1}.xz, wTexelR${E}C${B+1}.xz);`))}for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`dotProd += xR${E}C${L} * wR${E}C${L};`;let D="",I="";r&&(o?D=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${r}
}`:D=`vec4 activation(vec4 x) {
${r}
}`,I="result = activation(result);");let P=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${D}
const ivec2 strides = ivec2(${f}, ${m});
const ivec2 pads = ivec2(${l}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${S}
vec4 result = dotProd;
${P}
${I}
setOutput(result);
}
`}}class zY{constructor(t,e,r,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[c,l,p,f]=t,[m]=e,[y,b]=r;this.outputShape=[m,y,b,f];let v=o==="bilinear"?1:0,[T,N]=[`${l-1}.0`,`${p-1}.0`],[S,D,I]=y>1?[`${(l-1)/(y-1)}`,"(y2-y1) * height_ratio",`y1*${T} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${T}`],[P,E,L]=b>1?[`${(p-1)/(b-1)}`,"(x2-x1) * width_ratio",`x1*${N} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${N}`];this.userCode=`
const float height_ratio = float(${S});
const float width_ratio = float(${P});
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 >= ${c}) {
return;
}
float height_scale = ${D};
float width_scale = ${E};
float in_y = ${I};
if( in_y < 0.0 || in_y > ${T} ) {
setOutput(float(${s}));
return;
}
float in_x = ${L};
if( in_x < 0.0 || in_x > ${N} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${v} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}class DI{constructor(t,e,r){this.variableNames=["x"],this.outputShape=t;let o=t.length,s=e?"0.0":`getX(${AI(o,"coords")})`,c=t[t.length-1],l="",p="";e?(l=r?`end != ${c-1}`:"end != 0",p=r?"end + 1":"end - 1"):(l=r?`end + pow2 < ${c}`:"end >= pow2",p=r?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${Oe(o)} coords = getOutputCoords();
int end = ${FI(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${l}) {
int idx = ${p};
${FI(o,"coords")} = idx;
val += getX(${AI(o,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(t){return(e,r)=>{this.index==null&&(this.index=e.getUniformLocation(r,"index")),e.gl.uniform1f(this.index,t)}}}function AI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.x, ${t}.y`;if(n===3)return`${t}.x, ${t}.y, ${t}.z`;if(n===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}function FI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.y`;if(n===3)return`${t}.z`;if(n===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}class WY{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=nh.DENSE;let e=oh(t),r=Jn();this.outputShape=t,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${xa(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = 4 * (resTexRC.x * ${e[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);
}
${r.output} = result;
}
`}}class VY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=nh.DENSE;let e=oh(t),r=Jn();this.outputShape=t,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${xa(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = 4 * (resTexRC.x * ${e[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));
}
${r.output} = result;
}
`}}class GY{constructor(t,e,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${e};
int offset_h = imod(h, ${e});
int in_w = w / ${e};
int offset_w = imod(w, ${e});
int offset_d = (offset_h * ${e} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class UY{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}}class qY{constructor(t){this.variableNames=["A"],this.outTexUsage=Mr.DOWNLOAD;let e=Jn();this.outputShape=t,this.userCode=`
${vI}
void main() {
float x = getAAtOutCoords();
${e.output} = encode_float(x);
}
`}}class HY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Mr.DOWNLOAD;let e=Jn();this.outputShape=t,this.userCode=`
${vI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${e.output} = encode_float(x);
}
`}}class jY{constructor(t,e,r=!1){this.variableNames=["A"];let o=Jn(),[s,c]=e;this.outputShape=t;let l="result";r&&(l="floor(result * 255. + 0.5)"),this.userCode=`
${b1(t)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${c};
int c = imod(flatIndex, ${c});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
vec4 values = ${o.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];
}
${o.output} = vec4(${l}, 0., 0., 0.);
}
`}}class KY{constructor(t,e,r=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Jn(),[s,c]=e;this.outputShape=t;let l="",p="result";r&&(p="floor(result * 255. + 0.5)");for(let f=0;f<=1;f++)for(let m=0;m<=1;m++){let y=f*2+m;l+=`
localCoords = coords;
if(localCoords[2] + ${m} < ${t[2]}) {
localCoords[2] += ${m};
if(localCoords[1] + ${f} < ${t[1]}) {
localCoords[1] += ${f};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${c};
c = imod(flatIndex, ${c});
uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
values = ${o.texture2D}(A, uv);
if(offset == 0) {
result[${y}] = values[0];
} else if(offset == 1) {
result[${y}] = values[1];
} else if(offset == 2) {
result[${y}] = values[2];
} else {
result[${y}] = values[3];
}
}
}
`}this.userCode=`
${b1(t)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${l}
${o.output} = ${p};
}
`}}class XY{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(t){return(e,r)=>{this.valueLoc==null&&(this.valueLoc=e.getUniformLocationNoThrow(r,"value")),e.gl.uniform1f(this.valueLoc,t)}}}class YY{constructor(t,e,r){this.variableNames=["A","indices"];let o=t.slice();o[r]=e,this.outputShape=o,this.rank=o.length;let s=Oe(this.rank),c=JY(t,r);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${c}));
}
`}}function JY(n,t){let e=n.length;if(e>4)throw Error(`Gather for rank ${e} is not yet supported`);if(e===1)return"int(getIndices(resRC))";let r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let s=0;s<n.length;s++)s===t?o.push(`int(getIndices(${r[s]}))`):o.push(`${r[s]}`);return o.join()}class ZY{constructor(t,e,r){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=r;let o=Oe(e.length),s=Oe(r.length),c=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 * ${c};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}}function QY(n){let t=Jn(),e=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return A8(n,e)}function t7(n){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return M8(n,t)}function e7(n){let t=new Uint16Array([0,1,2,2,1,3]);return B8(n,t)}function ih(n,t,e,r,o,s){W8(t,e);let c=z8(n),l=n.TEXTURE_2D;return Rt(n,()=>n.bindTexture(l,c)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MIN_FILTER,n.NEAREST)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MAG_FILTER,n.NEAREST)),Rt(n,()=>n.texImage2D(l,0,r,t,e,0,o,s,null)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null)),c}function RI(n){return n.internalFormatFloat}function n7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,RI(r),r.textureFormatFloat,n.FLOAT)}function PI(n){return n.internalFormatHalfFloat}function r7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,PI(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function OI(n){return n.downloadTextureFormat}function o7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,OI(r),n.RGBA,n.UNSIGNED_BYTE)}function LI(n){return n.internalFormatPackedFloat}function s7(n,t,e,r){let[o,s]=nl(t,e);return ih(n,o,s,LI(r),n.RGBA,n.FLOAT)}function MI(n){return n.internalFormatPackedHalfFloat}function i7(n,t,e,r){let[o,s]=nl(t,e);return ih(n,o,s,MI(r),n.RGBA,r.textureTypeHalfFloat)}function a7(n,t,e){let r=0,o=3*4,s=3*4+2*4;Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,e));let c=gI(n,t,"clipSpacePos",e,3,s,r);return c&&gI(n,t,"uv",e,2,s,o)}function c7(n,t,e,r,o,s){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t));let c,l,p;o instanceof Uint8Array?(c=new Uint8Array(e*r*4),l=n.UNSIGNED_BYTE,p=n.RGBA):(c=new Float32Array(e*r*4),l=n.FLOAT,p=s.internalFormatPackedFloat),c.set(o),Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,p,e,r,0,n.RGBA,l,c)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function l7(n,t,e){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t)),e.data instanceof Uint8Array?Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,e.width,e.height,0,n.RGBA,n.UNSIGNED_BYTE,e.data)):Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,e)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function u7(n,t,e,r){let o=n.createBuffer();Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,o));let s=4,c=4,l=s*c*t*e;return Rt(n,()=>n.bufferData(n.PIXEL_PACK_BUFFER,l,n.STREAM_READ)),Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,0)),Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,null)),o}function p7(n,t,e){let r=n,o=new Float32Array(e);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,o),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),o}function h7(n,t,e,r){let[o,s]=rh(t,e),c=4,l=new Uint8Array(_8(t*e,c));return Rt(n,()=>n.readPixels(0,0,o,s,r.downloadTextureFormat,n.UNSIGNED_BYTE,l)),new Float32Array(l.buffer)}function f7(n,t,e,r,o,s,c,l){let p=n,f=new Float32Array(C8(s,c));return p.bindBuffer(p.PIXEL_PACK_BUFFER,t),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,f),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),f}function d7(n,t,e){let r=new Float32Array(t*e*4);return Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,r)),r}class m7{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let e=ct().getNumber("WEBGL_VERSION");t!=null?(this.gl=t,T8(e,t)):this.gl=jo(e);let r="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(ct().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",c="OES_texture_half_float";if(this.textureFloatExtension=Dm(this.gl,s),eo(this.gl,c))this.textureHalfFloatExtension=Dm(this.gl,c);else if(ct().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(r),eo(this.gl,o))this.colorBufferHalfFloatExtension=Dm(this.gl,o);else if(ct().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(r="EXT_color_buffer_float",eo(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(eo(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=t7(this.gl),this.indexBuffer=e7(this.gl),this.framebuffer=V8(this.gl),this.textureConfig=h1(this.gl,this.textureHalfFloatExtension)}get debug(){return ct().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. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;Rt(t,()=>t.finish()),Rt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),Rt(t,()=>t.deleteFramebuffer(this.framebuffer)),Rt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),Rt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),Rt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),n7(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),r7(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),o7(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),l7(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,r,o){this.throwIfDisposed(),c7(this.gl,t,e,r,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),i7(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),s7(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(yI(this.gl,this.framebuffer),this.outputTexture=null),Rt(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,r){return this.downloadMatrixDriver(t,()=>h7(this.gl,e,r,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,r,o,s,c){return f7(this.gl,t,e,r,o,s,c,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return p7(this.gl,t,e)}createBufferFromTexture(t,e,r){this.bindTextureToFrameBuffer(t);let o=u7(this.gl,e,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,r;if(ct().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),r=()=>{let c=o.clientWaitSync(s,0,0);return c===o.ALREADY_SIGNALED||c===o.CONDITION_SATISFIED},e=s}else ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(e,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:e,isFencePassed:r}}downloadMatrixFromPackedTexture(t,e,r){return this.downloadMatrixDriver(t,()=>d7(this.gl,e,r))}createProgram(t){this.throwIfDisposed();let e=this.gl,r=F8(e,t),o=QY(e),s=O8(e);return Rt(e,()=>e.attachShader(s,o)),Rt(e,()=>e.attachShader(s,r)),L8(e,s),this.debug&&f1(e,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=a7(e,this.program,this.vertexBuffer)),s}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&Rt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&f1(this.gl,this.program),Rt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,r=!0){return this.throwIfDisposed(),r?U8(this.gl,t,e):q8(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),Rt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,r){this.throwIfDisposed(),this.throwIfNoProgram(),H8(this.gl,t,e,r)}setOutputMatrixTexture(t,e,r){this.setOutputMatrixTextureDriver(t,r,e)}setOutputPackedMatrixTexture(t,e,r){this.throwIfDisposed();let[o,s]=nl(e,r);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,r,o){this.setOutputMatrixWriteRegionDriver(r,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,r,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&f1(this.gl,this.program),Am(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),Rt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Dm(this.gl,ct().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(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.createQuery();return r.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,r=this.getQueryTimerExtensionWebGL2();e.endQuery(r.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await _e(()=>this.disposed||this.isQueryAvailable(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let r=this.gl,o=r.getQueryParameter(t,r.QUERY_RESULT);return o/1e6}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT);return o/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.getQueryParameter(t,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=g7(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:r}=this.itemsToPoll[e];r()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;_e(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),d1(this.gl,t,this.framebuffer),this.debug&&Am(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(d1(this.gl,this.outputTexture,this.framebuffer),this.debug&&Am(this.gl)):yI(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let r=e();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(t,e,r){this.throwIfDisposed();let o=this.gl;d1(o,t,this.framebuffer),this.debug&&Am(o),this.outputTexture=t,Rt(o,()=>o.viewport(0,0,e,r)),Rt(o,()=>o.scissor(0,0,e,r))}setOutputMatrixWriteRegionDriver(t,e,r,o){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.scissor(t,e,r,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 g7(n){let t=0;for(;t<n.length;++t){let e=n[t]();if(!e)break}return t-1}function y7(n,t,e,r){let o=t.userCode,s=e.map((v,T)=>{let N={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(N.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[T],shapeInfo:N}}),c=s.map(v=>v.shapeInfo),l={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},p=xX(s,l,o,t.packedInputs),f=n.createProgram(p),m=null,y=n.getUniformLocation(f,"NAN",!1);ct().getNumber("WEBGL_VERSION")===1&&(m=n.getUniformLocation(f,"INFINITY",!1));let b={};for(let v=0;v<t.variableNames.length;v++){let T=t.variableNames[v],N=!1;b[T]=n.getUniformLocation(f,T,N),b[`offset${T}`]=n.getUniformLocation(f,`offset${T}`,N)}return{program:t,source:p,webGLProgram:f,uniformLocations:b,inShapeInfos:c,outShapeInfo:l,infLoc:m,nanLoc:y}}function BI(n,t){if(n.length!==t.length)throw Error(`Binary was compiled with ${n.length} inputs, but was executed with ${t.length} inputs`);n.forEach((e,r)=>{let o=e.logicalShape,s=t[r],c=s.shape;if(!lt(o,c))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${c} must match`);if(e.isUniform&&s.isUniform)return;let l=e.texShape,p=s.isUniform?null:s.texData.texShape;if(!lt(l,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${l} and ${p} must match`)})}function b7(n,t,e,r,o){BI(t.inShapeInfos,e),BI([t.outShapeInfo],[r]);let s=r.texData.texture,c=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(s,c[0],c[1]):n.setOutputMatrixTexture(s,c[0],c[1]),n.setProgram(t.webGLProgram),ct().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&n.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&n.gl.uniform1f(t.nanLoc,NaN),e.forEach((l,p)=>{let f=t.program.variableNames[p],m=t.uniformLocations[f],y=t.uniformLocations[`offset${f}`];if(m==null)return;if(l.isUniform){if(G(l.shape)<2)n.gl.uniform1f(m,l.uniformValues[0]);else{let b=l.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),n.gl.uniform1fv(m,b)}return}l.texData.slice!=null&&y!=null&&n.gl.uniform1i(y,l.texData.slice.flatOffset),n.setInputMatrixTexture(l.texData.texture,m,p)}),o!=null&&o(n,t.webGLProgram),n.executeProgram()}function x7(n,t,e){let r="";t.concat(e).forEach(c=>{let l=c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0,p=c.isUniform?"uniform":c.texData.texShape;r+=`${c.shape}_${p}_${l}`});let o=n.userCode,s=n.constructor.name;return s+="_"+r+"_"+o,s}class w7{constructor(t,e,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let{filterWidth:o,inChannels:s,strideWidth:c,strideHeight:l,padInfo:p,outWidth:f,dilationWidth:m,dilationHeight:y,dataFormat:b}=r,{left:v,top:T}=p,N=s*o,S=Jn(),D=b==="channelsLast",I=D?0:1,P=D?1:2,E="";for(let L=0;L<=1;L++)for(let B=0;B<=1;B++)E+=`
blockIndex = rc.y + ${B};
pos = rc.x + ${L};
if(blockIndex < ${t[1]} && pos < ${t[0]}) {
offsetY = int(blockIndex / (${f})) * ${l} - ${T};
d0 = offsetY + ${y} * (pos / ${N});
if(d0 < ${e[I]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${f}.) * ${c}. - ${v}.);
d1 = offsetX + ${m} * (int(mod(float(pos), ${N}.) / ${s}.));
if(d1 < ${e[P]} && d1 >= 0) {
ch = int(mod(float(pos), ${s}.));
if (${D}) {
innerDims = vec2(d1, ch);
result[${L*2+B}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${L*2+B}] = 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;
${E}
${S.output} = result;
}
`}}class v7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[];let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${c}; j <= ${c}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${l}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${p};
setOutput(val);
}
`}}class T7{constructor(t,e,r,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=r,this.alpha=o,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${e})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${e} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${o}) * norm + float(${r});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${o})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}class k7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * 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 - ${c};
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 = - ${c}; j <= ${c}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${l}));
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 * ${p};
setOutput(result);
}
`}}class N7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,r=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.top,p=c-1-t.padInfo.left,f=s*c-1;this.userCode=`
const ivec2 pads = ivec2(${l}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${e}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${f} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class _7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterDepth,f=t.effectiveFilterHeight,m=t.effectiveFilterWidth,y=p-1-t.padInfo.front,b=f-1-t.padInfo.top,v=m-1-t.padInfo.left,T=p*f*m-1;this.userCode=`
const ivec3 pads = ivec3(${y}, ${b}, ${v});
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 < ${p};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${e}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${f};
wR += ${c}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${m};
wC += ${l}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${T} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${f} * ${m} +
wR * ${m} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}}class x1{constructor(t,e,r,o=!1,s=!1,c=!1,l=null,p=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r;let f=o?t[1]:t[2],m=Math.ceil(f/2),y=o?"i * 2, rc.y":"rc.y, i * 2",b=s?"rc.z, i * 2":"i * 2, rc.z",v=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],T=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],N="",S="";l&&(p?N=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${l}
}`:N=`vec4 activation(vec4 x) {
${l}
}`,S="result = activation(result);");let D=c?"result += getBiasAtOutCoords();":"";c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights");let I="rc.x",P="rc.x";t[0]<e[0]?I=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(P=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
${N}
const float sharedDimension = ${m}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${m}; i++) {
int batchA = ${I};
int batchB = ${P};
vec4 a = getMatrixA(batchA, ${y});
vec4 b = getMatrixB(batchB, ${b});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${v[0]} * ${T[0]});
result += (${v[1]} * ${T[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${D}
${S}
setOutput(result);
}
`}}class C7{constructor(t,e,r){this.variableNames=["probs"],this.outputShape=[t,r],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${e-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${e-1}));
}
`}getCustomSetupFunc(t){return(e,r)=>{this.seedLoc==null&&(this.seedLoc=e.getUniformLocation(r,"seed")),e.gl.uniform1f(this.seedLoc,t)}}}class S7{constructor(t,e,r,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${r}),
float(index == coords.y)));
}
`}}class $7{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;let e=t.length;if(e===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let r=Yn("rc",e),o=Oe(e),s=E7(e,t,r),c=D7(e,t[t.length-1],t[t.length-2],r),l=A7(t,r);this.userCode=`
void main() {
${o} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${c}
setOutput(vec4(${l}));
}
}
`}}}function I7(n,t){let e=[];for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){let s=`${r===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let c=2;c<n;c++)s=`${t[t.length-1-c]},`+s;e.push(s)}return e}function E7(n,t,e){if(n===1)return`rc > ${t[0]}`;let r="";for(let o=n-2;o<n;o++)r+=`${e[o]} >= ${t[o]}`,o<n-1&&(r+="||");return r}function D7(n,t,e,r){if(n===1)return"";let o=r.slice(-2);return`
int r = ${o[0]};
int c = ${o[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${e};
`}function A7(n,t){let e=n.length,r=I7(e,t);return e===1?`getA(rc),
rc + 1 >= ${n[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${r[0]}),
cEdge ? 0. : getA(${r[1]}),
rEdge ? 0. : getA(${r[2]}),
rEdge || cEdge ? 0. : getA(${r[3]})`}class F7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((f,m)=>f[0]+t[m]+f[1]);let o=t.length,s=Oe(o),c=e.map(f=>f[0]).join(","),l=e.map((f,m)=>f[0]+t[m]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${c};
int end = ${l};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${r}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${c});
${s} end = ${s}(${l});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${r}));
} else {
${s} coords = outC - start;
setOutput(getX(${p}));
}
}
`}}class R7{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((N,S)=>N[0]+t[S]+N[1]);let o=t.length,s=Oe(o),c=e.map(N=>N[0]).join(","),l=e.map((N,S)=>N[0]+t[S]).join(","),p=Yn("rc",o),f=Yn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=[`${s} rc = outputLoc;`,`${p[o-1]} += 1;
if(${m}) {
`,o===1?"":`}
rc = outputLoc;
${p[o-2]} += 1;
if(${p[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${p[o-1]} += 1;
if(${m}) {`],v=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",T="";for(let N=0,S=o===1?2:4;N<S;N++)T+=`
${b[N]}
if (${v}) {
result[${N}] = float(${r});
} else {
${s} source = rc - start;
result[${N}] = getChannel(getX(${f.join()}), ${y});
}
`;T+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${c});
const ${s} end = ${s}(${l});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${T}
setOutput(result);
}
`}}class ah{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterHeight,b=t.effectiveFilterWidth,v=t.padInfo.top,T=t.padInfo.left;this.outputShape=t.outShape;let N=e==="avg",S=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,D=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,I="0.0";if(N||(I="-1.0 / 1e-20"),r){let H=">=";this.userCode=`
const ivec2 strides = ivec2(${l}, ${p});
const ivec2 pads = ivec2(${v}, ${T});
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 < ${y};
wR += ${f}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${H} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?S:D:`wR * ${b} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let P="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let L=Math.floor(c/4)*4,B=c%4,q=`
if (${N}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${P}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${l}, ${p});
const ivec2 pads = ivec2(${v}, ${T});
const float initializationValue = ${I};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${I});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${y};
wR += ${f}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${L}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${m}, d),
getValue(batch, xR, xC + 2 * ${m}, d),
getValue(batch, xR, xC + 3 * ${m}, d)
);
${q}
}
int xC = xCCorner + ${L};
if (${B===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${q}
} else if (${B===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${m}, d),
initializationValue,
initializationValue
);
${q}
} else if (${B===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${m}, d),
getValue(batch, xR, xC + 2 * ${m}, d),
initializationValue
);
${q}
}
}
setOutput(${E});
}
`}}class w1{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideDepth,p=t.strideHeight,f=t.strideWidth,m=t.dilationDepth,y=t.dilationHeight,b=t.dilationWidth,v=t.effectiveFilterDepth,T=t.effectiveFilterHeight,N=t.effectiveFilterWidth,S=t.padInfo.front,D=t.padInfo.top,I=t.padInfo.left;this.outputShape=t.outShape;let P=e==="avg",E="0.0";if(P||(E="-1.0 / 1e-20"),r){let J=">=";this.userCode=`
const ivec3 strides =
ivec3(${l}, ${p}, ${f});
const ivec3 pads = ivec3(${S}, ${D}, ${I});
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 < ${v};
wD += ${m}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${T};
wR += ${y}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${N};
wC += ${b}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${J} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${T} * ${N} +
wR * ${N} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let L="max",B=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(B="avgValue / count");let q=Math.floor(c/4)*4,H=c%4,Z=`
if (${P}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${L}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${l}, ${p}, ${f});
const ivec3 pads = ivec3(${S}, ${D}, ${I});
const float initializationValue = ${E};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${E});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${v};
wD += ${m}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${T};
wR += ${y}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${q}; wC += 4) {
int xC = xCCorner + wC * ${b};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${b}, ch),
getValue(batch, xD, xR, xC + 2 * ${b}, ch),
getValue(batch, xD, xR, xC + 3 * ${b}, ch)
);
${Z}
}
int xC = xCCorner + ${q};
if (${H===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${Z}
} else if (${H===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${b}, ch),
initializationValue,
initializationValue
);
${Z}
} else if (${H===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${b}, ch),
getValue(batch, xD, xR, xC + 2 * ${b}, ch),
initializationValue
);
${Z}
}
}
setOutput(${B});
}
}
`}}class zI{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l="0.0",p="";e==="prod"?l="1.0":e==="min"?(l="1.0 / 1e-20",p="min"):e==="max"&&(l="-1.0 / 1e-20",p="max");let f=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?f="sumValue":e==="prod"?f="prodValue":e==="all"?f="allValue":e==="any"&&(f="anyValue");let m=Math.floor(r/4)*4,y=r%4,b=`
if (${e==="sum"}) {
sumValue += dot(values, ones);
} else if (${e==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${p}(values, minMaxValue);
}
`,v="vec4";e==="all"?(l="1.0",b=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,v="bvec4"):e==="any"&&(l="0.0",b=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,v="bvec4");let T="";s%r>0&&(T=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${l};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${T}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
vec4 minMaxValue = vec4(${l});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${m}; i += 4) {
int inIdx = inOffset + i;
${v} values = ${v}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${b}
}
int inIdx = inOffset + ${m};
if (${y===1}) {
${v} values = ${v}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${b}
} else if (${y===2}) {
${v} values = ${v}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${b}
} else if (${y===3}) {
${v} values = ${v}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${b}
}
setOutput(${f});
}
`}}class WI{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let r="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),r+=`
${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=`
${P7(e)}
${b1(t)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${t[1]};
int cols = ${t[2]};
${r}
setOutput(result);
}
`}}function P7(n){let t=xa(["r","c","d"],n);return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t}
return ivec3(r, c, d);
}
`}class O7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${m});
const float widthScale = float(${y});
const float invHeightScale = float(${b});
const float invWidthScale = float(${v});
const int winHeight = int(${T});
const int winWidth = int(${N});
// 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 >= ${c}) {
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 >= ${l}) {
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);
}
`}}class L7{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${f[0]/m[0]},
${f[1]/m[1]});
const vec2 inputShapeRC = vec2(${c}.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 = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
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);
}
`}}class M7{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${f[0]/m[0]},
${f[1]/m[1]},
${f[1]/m[1]});
const vec3 inputShapeRC = vec3(${c}.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 = vec3(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${p-1};
bool hasNextRow = coords.z < ${r-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);
}
`}}class B7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*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(${m});
const float widthScale = float(${y});
const float invHeightScale = float(${b});
const float invWidthScale = float(${v});
const int winHeight = int(${T});
const int winWidth = int(${N});
// 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 >= ${c}) {
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 >= ${l}) {
continue;
}
float sourceFracRow =
float(${p[0]}) *
(float(dyR) / float(${f[0]}));
float sourceFracCol =
float(${p[1]}) *
(float(dyC) / float(${f[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${r} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${r} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}class z7{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r],y=o?"0.5":"0.0";this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${f[0]/m[0]},
${f[1]/m[1]});
const vec2 inputShapeRC = vec2(${c}.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 = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${y})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class W7{constructor(t,e){this.variableNames=["x"];let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=t,r===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${t[0]} - coord - 1));
}
`;return}let o=l=>e.indexOf(l)!==-1&&t[l]!==1?`${t[l]} - coords[${l}] - 1`:`coords[${l}]`,s=t.map((l,p)=>o(p)).join(","),c=Oe(r);this.userCode=`
void main() {
${c} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}}class V7{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=t;let o=Yn("rc",r),s=`${o[r-1]} + 1 < ${this.outputShape[r-1]}`,c=`${o[r-2]} + 1 < ${this.outputShape[r-2]}`,l=Oe(r);r===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${t[0]} - rc - 1),
${t[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),
${t[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${l} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${p(o.slice())};
if(${s}){
result.g = ${f(o.slice())};
}
if(${c}) {
result.b = ${m(o.slice())};
if(${s}) {
result.a = ${y(o.slice())};
}
}
setOutput(result);
}
`;function p(T){return b(T)}function f(T){return T[r-1]="("+T[r-1]+" + 1)",b(T)}function m(T){return T[r-2]="("+T[r-2]+" + 1)",b(T)}function y(T){return T[r-1]="("+T[r-1]+" + 1)",T[r-2]="("+T[r-2]+" + 1)",b(T)}function b(T){let N=t.map((I,P)=>v(P,T)),S=N.join(","),D=N.slice(-2).join(",");return`getChannel(getX(${S}), vec2(${D}))`}function v(T,N){return e.indexOf(T)!==-1&&t[T]!==1?`${t[T]} - ${N[T]} - 1`:`${N[T]}`}}}class VI{constructor(t,e,r,o,s,c,l=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=c;let p=Oe(s.length),f=Oe(c.length),m="";r===1?m="i":r===2&&(m="i, j");let y=`getIndices(${m})`,b="";o===1?b="i":o===2&&(b="i, coords[1]");let v=`getUpdates(${b})`,T=e>1?"strides[j]":"strides";this.userCode=`
${p} strides = ${p}(${s});
void main() {
${f} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${t}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${e}; j++) {
int index = round(${y});
flattenedIndex += index * ${T};
}
if (flattenedIndex == coords[0]) {
sum += ${v};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}}class G7{constructor(t,e){this.variableNames=["x","segmentIds"];let r=t.windowSize,o=t.batchSize,s=t.inSize,c=t.numSegments,l=c*Math.ceil(s/r);this.outputShape=[o,l];let p="0.0",f="sumValue",m=Math.floor(r/4)*4,y=r%4,b=`
sumValue += dot(values, segFilter);
`,v="";s%r>0&&(v=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let T="";s%r>0&&(T=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${p};
float getValue(int batch, int inIdx) {
${v}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${T}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${c})) * float(${r}));
int currentSeg = int(mod(float(outIdx), float(${c})));
float sumValue = 0.0;
for (int i = 0; i < ${m}; 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
);
${b}
}
int inIdx = inOffset + ${m};
if (${y===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
);
${b}
} else if (${y===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
);
${b}
} else if (${y===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
);
${b}
}
setOutput(${f});
}
`}}class U7{constructor(t,e,r){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)s="resRC",o="resRC";else{let l=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],f=[];for(let m=0;m<e.length;m++)f.push(`${l[m]}`),m<t&&p.push(`${l[m]}`);o=p.join(),s=f.join()}let c=Oe(r);this.userCode=`
void main() {
${c} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}}class q7{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=`uniform int start[${this.rank}];`,o=H7(this.rank),s,c=t.map((l,p)=>`sourceLoc.${v1[p]} = start[${p}] + coords.${v1[p]};`);s=`
${e} sourceLoc;
${e} coords = getOutputCoords();
${c.join(`
`)}
`,this.userCode=`
${r}
void main() {
${s}
setOutput(getSource(${o}));
}
`}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}let v1=["x","y","z","w","u","v"];function H7(n){if(n===1)return"sourceLoc";if(n<=6)return v1.slice(0,n).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class j7{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=Yn("coords",this.rank),o=Yn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,c=`getChannel(getSource(${o.join()}), ${s})`,l=`
result.x = ${c};
if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${c};
--${o[this.rank-1]};
}
`,p=this.rank===1?"":`
--${r[this.rank-1]};
if (++${r[this.rank-2]} < ${t[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${c};
if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${c};
}
}
`,f=this.rank<=4?`sourceLoc = coords +
${e}(${t.map((m,y)=>`start[${y}]`).join()});`:t.map((m,y)=>`${o[y]} = ${r[y]} + start[${y}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${e} coords = getOutputCoords();
${e} sourceLoc;
${f}
vec4 result = vec4(0.);
${l}
${p}
setOutput(result);
}
`}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}class K7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=r;let o=r.length,s=Oe(r.length),c=Oe(r.length),l="";if(o===1)l="coords * strides + begin";else{let p=0;l=r.map((f,m)=>(p++,r.length===1?`coords * strides[${m}] + begin[${m}]`:`coords[${p-1}] * strides[${m}] + begin[${m}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${t});
${s} strides = ${s}(${e});
void main() {
${c} coords = getOutputCoords();
setOutput(getX(${l}));
}
`}}class X7{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,r){let o=UI(e,r),s=qI(t,o,r);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let c=GI(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=c,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let l;return o===On.PACKED_2X2_FLOAT32?l=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===On.PACKED_2X2_FLOAT16?l=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===On.UNPACKED_FLOAT32?l=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===On.UNPACKED_FLOAT16?l=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===On.PACKED_4X1_UNSIGNED_BYTE&&(l=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(l),this.numUsedTextures++,this._numBytesAllocated+=c,this.log(),l}releaseTexture(t,e,r,o){if(this.freeTextures==null)return;let s=UI(r,o),c=qI(e,s,o);c in this.freeTextures||(this.freeTextures[c]=[]);let l=GI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),p=ct().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=l):(this.freeTextures[c].push(t),this.numFreeTextures++,this._numBytesFree+=l),this.numUsedTextures--;let f=this.usedTextures[c],m=f.indexOf(t);if(m<0)throw new Error("Cannot release a texture that was never provided by this texture manager");f.splice(m,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function Y7(n,t){let e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function GI(n,t,e,r,o){let s=J7(t,r),c;if(o){let[p,f]=nl(n[0],n[1]);c=p*f}else{let[p,f]=rh(n[0],n[1]);c=p*f}let l=Y7(e,s);return c*l}function J7(n,t){switch(n){case On.PACKED_2X2_FLOAT32:return LI(t);case On.PACKED_2X2_FLOAT16:return MI(t);case On.UNPACKED_FLOAT32:return RI(t);case On.UNPACKED_FLOAT16:return PI(t);case On.PACKED_4X1_UNSIGNED_BYTE:return OI(t);default:throw new Error(`Unknown physical texture type ${n}`)}}function Z7(n){return ct().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?On.PACKED_2X2_FLOAT32:On.UNPACKED_FLOAT32:n?On.PACKED_2X2_FLOAT16:On.UNPACKED_FLOAT16}function UI(n,t){if(n===Mr.UPLOAD)return On.PACKED_2X2_FLOAT32;if(n===Mr.RENDER||n==null)return Z7(t);if(n===Mr.DOWNLOAD||n===Mr.PIXELS)return On.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function qI(n,t,e){return`${n[0]}_${n[1]}_${t}_${e}`}class Q7{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[c]*e[c];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=tJ(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}}function tJ(n){let t=n.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${n[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let o=0;o<n.length;o++)r.push(`imod(${e[o]}, ${n[o]})`);return r.join()}class ue{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode=`
float unaryOperation(float x) {
${e}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}}let _s="if (isnan(x)) return x;",eJ="return x;",HI="return abs(x);",jI=_s+`
return (x < 0.0) ? 0.0 : x;
`,KI=_s+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,XI="return (x >= 0.0) ? x : (exp(x) - 1.0);",nJ=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${zd};
float scale = ${Wd};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function rJ(n=0){return _s+`
return x > 0.0 ? 1.0 : float(${n});
`}let YI="return -x;",JI="return ceil(x);",ZI="return floor(x);",oJ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,sJ="return float(isnan(x));",iJ="return float(isinf(x));",aJ="return float(!isnan(x) && !isinf(x));",cJ=`
// 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;
}
}
`,QI="return exp(x);",tE="return exp(x) - 1.0;",lJ=`if (x < 0.0) return NAN;
return log(x);`,uJ="return log(1.0 + x);",pJ="return sqrt(x);",hJ="return inversesqrt(x);",fJ="return 1.0 / (1.0 + exp(-1.0 * x));",dJ=`
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;
`,mJ=_s+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,gJ=_s+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,yJ=_s+`
return atan(x);
`,bJ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,xJ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,wJ=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,vJ=_s+"return log(x + sqrt(x * x + 1.0));",TJ=_s+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,kJ=_s+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NJ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${Yw};
float a1 = ${Jw};
float a2 = ${Zw};
float a3 = ${Qw};
float a4 = ${tv};
float a5 = ${ev};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,_J="return 1.0 / x;",CJ="return float(!(x >= 1.0));",Bm="return x;";let SJ="return x;",$J=`
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;
`,eE=`
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;
`,nE=`
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;
`,rE=`
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;
`;class ch{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
vec4 unaryOperation(vec4 x) {
${e}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class IJ{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;let e=t.length,r=Yn("rc",e),o=Oe(e),s=bX(e,r),c=r.slice(-2),l=e<=1?"rc":`vec2(${c.join(",")})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${l}));
}
`}}let{segment_util:oE}=ov,EJ=sv,DJ=iv,AJ=av,FJ=Id,RJ=1e-7,PJ=1e-4,zm={};function OJ(n){return n in zm||(zm[n]={}),zm[n]}function Wm(n,t=!1){if(n==="linear")return t?SJ:eJ;if(n==="relu")return t?eE:jI;if(n==="elu")return t?rE:XI;if(n==="relu6")return t?nE:KI;if(n==="prelu")return t?SI:CI;throw new Error(`Activation ${n} has not been implemented for the WebGL backend.`)}let LJ=128,MJ=600;function BJ(){return ct().global.screen==null?1024:ct().global.screen.height*ct().global.screen.width*window.devicePixelRatio*MJ/1024/1024}let sE=1e3;class zJ extends d{constructor(t){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ct().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(t==null){let e=jo(ct().getNumber("WEBGL_VERSION"));this.binaryCache=OJ(ct().getNumber("WEBGL_VERSION")),this.gpgpu=new m7(e),this.canvas=e.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=t,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;this.textureManager=new X7(this.gpgpu),this.numMBBeforeWarning=BJ(),this.texData=new h(this,ps())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(t,e,r){if((ct().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ct().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:e,dtype:r,values:t,usage:Mr.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,r,o){if(ct().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:o,values:e,usage:Mr.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.texData.has(e)){let r=this.texData.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}readSync(t){let e=this.texData.get(t),{values:r,dtype:o,complexTensorInfos:s,slice:c,shape:l,isPacked:p}=e;if(c!=null){let b;p?b=new ch(l,Bm):b=new ue(l,Bm);let v=this.runWebGLProgram(b,[{dataId:t,shape:l,dtype:o}],o),T=this.readSync(v.dataId);return this.disposeIntermediateTensorInfo(v),T}if(r!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return r;let f=this.activeTimers!=null,m;f&&(m=or());let y;if(o==="complex64"){let b=this.readSync(s.real.dataId),v=this.readSync(s.imag.dataId);y=ys(b,v)}else y=this.getValuesFromTexture(t);return f&&(this.downloadWaitMs+=or()-m),this.convertAndCacheOnCPU(t,y)}async read(t){if(this.pendingRead.has(t)){let T=this.pendingRead.get(t);return new Promise(N=>T.push(N))}let e=this.texData.get(t),{values:r,shape:o,slice:s,dtype:c,complexTensorInfos:l,isPacked:p}=e;if(s!=null){let T;p?T=new ch(o,Bm):T=new ue(o,Bm);let N=this.runWebGLProgram(T,[{dataId:t,shape:o,dtype:c}],c),S=this.read(N.dataId);return this.disposeIntermediateTensorInfo(N),S}if(r!=null)return this.convertAndCacheOnCPU(t);if(!ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ct().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let f=null,m;if(c!=="complex64"&&ct().get("WEBGL_BUFFER_SUPPORTED")){m=this.decode(t);let T=this.texData.get(m.dataId);f=this.gpgpu.createBufferFromTexture(T.texture,...oh(o))}this.pendingRead.set(t,[]),c!=="complex64"&&await this.gpgpu.createAndWaitForFence();let y;if(c==="complex64"){let T=await Promise.all([this.read(l.real.dataId),this.read(l.imag.dataId)]),N=T[0],S=T[1];y=ys(N,S)}else if(f==null)y=this.getValuesFromTexture(t);else{let T=G(o);y=this.gpgpu.downloadFloat32MatrixFromBuffer(f,T)}m!=null&&this.disposeIntermediateTensorInfo(m);let b=this.convertAndCacheOnCPU(t,y),v=this.pendingRead.get(t);return this.pendingRead.delete(t),v.forEach(T=>T(b)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t),this.pendingDeletes--),b}checkNumericalProblems(t){if(t==null)return;for(let e=0;e<t.length;e++){let r=t[e];if(!E8(r))throw ct().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:r,isPacked:o}=this.texData.get(t),s=G(e);if(ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let b=this.decode(t),v=this.texData.get(b.dataId),T=this.gpgpu.downloadMatrixFromPackedTexture(v.texture,...oh(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(b),T}let c=ct().getBool("WEBGL_PACK")&&o===!0,l=c?m1(e):e,p=c?new HY(l):new qY(l),f=this.runWebGLProgram(p,[{shape:l,dtype:r,dataId:t}],"float32"),m=this.texData.get(f.dataId),y=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(m.texture,m.texShape[0],m.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(f),y}async time(t){let e=this.activeTimers,r=[],o=!1;this.programTimersStack==null?(this.programTimersStack=r,o=!0):this.activeTimers.push(r),this.activeTimers=r,t();let s=tt(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),c=tt(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let l={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);l.kernelMs=C(p),l.getExtraProfileInfo=()=>p.map((f,m)=>({name:c[m],ms:f})).map(f=>`${f.name}: ${f.ms}`).join(", ")}else l.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,l}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:or(),endMs:null}}endTimer(t){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=or(),t)}async getQueryTime(t){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t){if(this.pendingDisposal.has(t))return;if(this.pendingRead.has(t)){this.pendingDisposal.add(t),this.pendingDeletes++;return}if(!this.texData.has(t))return;if(this.texData.get(t).complexParentRefCount>0){this.texData.get(t).refCount--;return}this.releaseGPUData(t);let{complexTensorInfos:e}=this.texData.get(t);e!=null&&(this.texData.get(e.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.real),this.texData.get(e.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.imag)),this.texData.delete(t)}releaseGPUData(t){let{texture:e,dtype:r,texShape:o,usage:s,isPacked:c,slice:l}=this.texData.get(t),p=l&&l.origDataId||t,f=this.dataRefCount.get(p);f>1?this.dataRefCount.set(p,f-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(e,o,s,c)));let m=this.texData.get(t);m.texture=null,m.texShape=null,m.isPacked=!1,m.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture}getDataInfo(t){return this.texData.get(t)}getCPUBackend(){return ct().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=ps().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(t,e=LJ){let r=this.getCPUBackend();return!this.warnedAboutCPUBackend&&r==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),r!=null&&t.every(o=>this.texData.get(o.dataId).texture==null&&G(o.shape)<e)}getGPGPUContext(){return this.gpgpu}slice(t,e,r){if(this.shouldExecuteOnCPU([t])){let c=hX(this.texData.get(t.dataId).values,e,r,t.shape,t.dtype);return this.makeOutput(r,t.dtype,c)}if(G(r)===0)return un([],r,t.dtype);let{isPacked:o}=this.texData.get(t.dataId),s=Yx(t.shape,e,r);if(o||!s){let c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new j7(r):new q7(r),l=c.getCustomSetupFunc(e);return this.compileAndRun(c,[t],null,l)}return this.uploadToGPU(t.dataId),this.shallowSlice(t,e,r)}shallowSlice(t,e,r){let o=this.texData.get(t.dataId),s=this.makeOutput(r,t.dtype),c=this.texData.get(s.dataId);Object.assign(c,o),c.shape=r,c.dtype=t.dtype;let l=Jx(e,t.strides);o.slice&&(l+=o.slice.flatOffset),c.slice={flatOffset:l,origDataId:o.slice&&o.slice.origDataId||t.dataId};let p=this.dataRefCount.get(c.slice.origDataId)||1;return this.dataRefCount.set(c.slice.origDataId,p+1),s}stridedSlice(t,e,r,o){let s=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.stridedSlice(t,e,r,o));if(s)return s;let c=Zf(e,r,o);if(c.some(p=>p===0))return un([],c);let l=new K7(e,o,c);return this.compileAndRun(l,[t])}reverse(t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V7(t.shape,e):new W7(t.shape,e);return this.compileAndRun(r,[t])}neg(t){let e=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.neg(t));if(e)return e;if(ct().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,YI,t.dtype);let r=new ue(t.shape,YI);return this.compileAndRun(r,[t])}batchMatMul(t,e,r,o){let s=r?t.shape[2]:t.shape[1],c=o?e.shape[1]:e.shape[2],l=r?t.shape[1]:t.shape[2],p=Math.max(t.shape[0],e.shape[0]);if((s===1||c===1)&&l>sE){r&&(t=Kt(t,[0,2,1])),o&&(e=Kt(e,[0,2,1]));let y=c===1?t:t.as3D(p,l,1),b=c===1?2:1,v=c===1?e.as3D(p,1,l):e,T=nt(y,v);return T.sum(b,!0)}let f=jn(t.dtype,e.dtype),m=new x1(t.shape,e.shape,[p,s,c],r,o);return this.compileAndRun(m,[t,e],f)}fusedBatchMatMul({a:t,b:e,transposeA:r,transposeB:o,bias:s,activation:c,preluActivationWeights:l}){let p=r?t.shape[2]:t.shape[1],f=o?e.shape[1]:e.shape[2],m=Math.max(t.shape[0],e.shape[0]),y=jn(t.dtype,e.dtype),b=s!=null,v=l!=null,T=c?Wm(c,!0):null,N=new x1(t.shape,e.shape,[m,p,f],r,o,b,T,v),S=[t,e];return s&&S.push(s),l&&S.push(l),this.compileAndRun(N,S,y)}localResponseNormalization4D(t,e,r,o,s){let c=ct().getBool("WEBGL_PACK_NORMALIZATION")?new k7(t.shape,e,r,o,s):new v7(t.shape,e,r,o,s);return this.compileAndRun(c,[t])}LRNGrad(t,e,r,o,s,c,l){let p=new T7(e.shape,o,s,c,l);return this.compileAndRun(p,[e,r,t])}tile(t,e){if(t.dtype==="string"){let o=this.readSync(t.dataId),s=o.map(l=>Uu(l)),c=Se(t.shape,t.dtype,s);return DJ(c,e)}let r=new Q7(t.shape,e);return this.compileAndRun(r,[t])}pad(t,e,r){let o=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new R7(t.shape,e,r):new F7(t.shape,e,r);return this.compileAndRun(o,[t])}gather(t,e,r){let o=this.tryRunOnCpuOrThrow([t,e],()=>this.cpuBackend.gather(t,e,r));if(o)return o;let s=new YY(t.shape,e.size,r);return this.compileAndRun(s,[t,e])}batchToSpaceND(t,e,r){_(t.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=e.reduce((m,y)=>m*y),s=Np(t.shape,e,o),c=_p(s.length,e.length),l=Cp(t.shape,e,o),p=Kw(r,e.length),f=Xw(l,r,e.length);return Kt(t.reshape(s),c).reshape(l).slice(p,f)}spaceToBatchND(t,e,r){_(t.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=e.reduce((y,b)=>y*b),s=[[0,0]];s.push(...r);for(let y=1+e.length;y<t.shape.length;++y)s.push([0,0]);let c=t.pad(s),l=Np(c.shape,e,o,!1),p=_p(l.length,e.length,!1),f=Cp(c.shape,e,o,!1),m=Kt(c.reshape(l),p);return Q(m,f)}reduce(t,e,r){let o=t.shape[0],s=t.shape[1],c=sp(s),l=Math.ceil(s/c),p={windowSize:c,inSize:s,batchSize:o,outSize:l},f=new zI(p,e),m=this.compileAndRun(f,[t],r);return m.shape[1]===1?m:this.reduce(m,e,r)}argReduce(t,e,r=null){let o=t.shape[0],s=t.shape[1];r!=null&&(o=r.shape[0],s=r.shape[1]);let c=sp(s),l={windowSize:c,inSize:s,batchSize:o,outSize:Math.ceil(s/c)},p=new yX(l,e,r==null),f=[t];r!=null&&f.push(r);let m=this.compileAndRun(p,f,"int32");return m.shape[1]===1?m:this.argReduce(t,e,m)}argReducePacked(t,e,r=null){let o=r!=null?r.shape:t.shape,s=o[o.length-1],c=sp(s),l=new eY(o,c,e,r==null),p=r==null?[t]:[t,r],f=this.compileAndRun(l,p,"int32");return f.rank===t.rank?this.argReducePacked(t,e,f):f}sum(t,e){sr("sum",e,t.rank);let[r,o]=Fn(t.shape,e),s=G(o),c=t.as2D(-1,s),l=Vf(t.dtype);return this.reduce(c,"sum",l).reshape(r)}prod(t,e){let r=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.prod(t,e));if(r)return r;let[o,s]=Fn(t.shape,e),c=G(s),l=t.as2D(-1,c),p=Vf(t.dtype);return this.reduce(l,"prod",p).reshape(o)}unsortedSegmentSum(t,e,r){let o=0,s=ir([o],t.rank),c=t;s!=null&&(c=Kt(t,s),o=xr(1,t.rank)[0]);let l=oE.computeOutShape(c.shape,o,r),p=G([c.shape[o]]),f=c.as2D(-1,p),m=Vf(t.dtype),y=this.segOpCompute(f,"unsortedSegmentSum",e,m,r).reshape(l);return s!=null&&(y=Kt(y,Ju(s))),y}segOpCompute(t,e,r,o,s){let c=t.shape[0],l=t.shape[1],p=oE.segOpComputeOptimalWindowSize(l,s),f={windowSize:p,inSize:l,batchSize:c,numSegments:s},m=new G7(f,e),y=this.compileAndRun(m,[t,r],o);return y.shape[1]===s?y:(r=hp(0,s).tile([l/p]),this.segOpCompute(y,e,r,o,s))}argMinMaxReduce(t,e,r){let o=[e];if(sr("arg"+r.charAt(0).toUpperCase()+r.slice(1),o,t.rank),!ct().getBool("WEBGL_PACK_REDUCE")||t.rank<=2){let[s,c]=Fn(t.shape,o),l=G(c),p=t.as2D(-1,l);return this.argReduce(p,r).reshape(s)}return this.argReducePacked(t,r)}argMin(t,e){return this.argMinMaxReduce(t,e,"min")}argMax(t,e){return this.argMinMaxReduce(t,e,"max")}cumsum(t,e,r,o){if(e!==t.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${t.rank-1} but got axis=${e}`);let s=t.shape[e],c=t;for(let l=0;l<=Math.ceil(Math.log2(s))-1;l++){let p=new DI(t.shape,!1,o),f=p.getCustomSetupFunc(l),m=c;c=this.compileAndRun(p,[c],c.dtype,f),m.dispose()}if(r){let l=new DI(t.shape,r,o),p=c;c=this.compileAndRun(l,[c]),p.dispose()}return c}equal(t,e){if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,wY,"bool");let r=new Hn(iY,t.shape,e.shape);return this.compileAndRun(r,[t,e],"bool")}less(t,e){let r=this.tryRunOnCpuOrThrow([t,e],()=>this.cpuBackend.less(t,e));if(r)return r;if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,vY,"bool");let o=new Hn(aY,t.shape,e.shape);return this.compileAndRun(o,[t,e],"bool")}lessEqual(t,e){if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,TY,"bool");let r=new Hn(cY,t.shape,e.shape);return this.compileAndRun(r,[t,e],"bool")}greater(t,e){let r=this.tryRunOnCpuOrThrow([t,e],()=>this.cpuBackend.greater(t,e));if(r)return r;if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,kY,"bool");let o=new Hn(lY,t.shape,e.shape);return this.compileAndRun(o,[t,e],"bool")}greaterEqual(t,e){if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,NY,"bool");let r=new Hn(uY,t.shape,e.shape);return this.compileAndRun(r,[t,e],"bool")}logicalNot(t){let e=new ue(t.shape,CJ);return this.compileAndRun(e,[t])}logicalAnd(t,e){if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,_Y,"bool");let r=new Hn(pY,t.shape,e.shape);return this.compileAndRun(r,[t,e],"bool")}logicalOr(t,e){if(ct().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,CY,"bool");let r=new Hn(hY,t.shape,e.shape);return this.compileAndRun(r,[t,e],"bool")}select(t,e,r){let o=new U7(t.rank,e.shape,e.rank);return this.compileAndRun(o,[t,e,r],jn(e.dtype,r.dtype))}where(t){Bc("tf.where() in webgl locks the UI thread. 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y.reshape(r)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=Yf(t,e),f=e.reshape([c,o]),m=t.reshape([t.size/l,l]),y=new ZY(o,p,[c,l]),b=this.compileAndRun(y,[m,f]);return b.reshape(s)}fill(t,e,r){if(r=r||ic(e),r==="string"){let o=rr(r,G(t));return o.fill(e),ps().makeTensor(o,t,r,this)}else{let o=new XY(t,e),s=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],r,s)}}onesLike(t){if(t.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)}zerosLike(t){return this.fill(t.shape,t.dtype==="string"?"":0,t.dtype)}linspace(t,e,r){return rv(t,e,r)}makeTensorInfo(t,e,r){let o=this.write(r,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,r){let{dataId:o}=this.makeTensorInfo(t,e,r);return ps().makeTensorFromDataId(o,t,e,this)}unpackTensor(t){let e=new IJ(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new $7(t.shape),r=!0;return this.runWebGLProgram(e,[t],t.dtype,null,r)}packedReshape(t,e){let r=[rl(t.shape),...ol(t.shape)],o={dtype:t.dtype,shape:r,dataId:t.dataId},s=[rl(e),...ol(e)],c=new WI(s,r),l=!0,p=this.runWebGLProgram(c,[o],t.dtype,null,l);return{dataId:p.dataId,shape:e,dtype:p.dtype}}decode(t){let e=this.texData.get(t),{isPacked:r,shape:o,dtype:s}=e,c=m1(o),l;r?l=new VY(c):l=new WY(c);let p=!0,f=this.runWebGLProgram(l,[{shape:c,dtype:s,dataId:t}],s,null,p);return{dtype:s,shape:o,dataId:f.dataId}}runWebGLProgram(t,e,r,o,s=!1){let c=this.makeTensorInfo(t.outputShape,r),l=this.texData.get(c.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===nh.DENSE){let N=oh(t.outputShape);l.texShape=N.map(S=>S*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),G(c.shape)===0)return l.values=Ce(c.dtype,0),c;let p=[],f=e.map(N=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let S=this.texData.get(N.dataId);if(S.texture==null){if(!t.packedInputs&&G(N.shape)<=ct().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:N.shape,texData:null,isUniform:!0,uniformValues:S.values};t.packedInputs&&(S.isPacked=!0,S.shape=N.shape)}else if(!!S.isPacked!==!!t.packedInputs)N=S.isPacked?this.unpackTensor(N):this.packTensor(N),p.push(N),S=this.texData.get(N.dataId);else if(S.isPacked&&!Rm(S.shape,N.shape)){let D=N,I=N.shape;N.shape=S.shape,N=this.packedReshape(N,I),p.push(N),S=this.texData.get(N.dataId),D.shape=I}return this.uploadToGPU(N.dataId),{shape:N.shape,texData:S,isUniform:!1}});this.uploadToGPU(c.dataId);let m={shape:c.shape,texData:l,isUniform:!1},y=x7(t,f,m),b=this.getAndSaveBinary(y,()=>y7(this.gpgpu,t,f,m)),v=this.activeTimers!=null,T;if(v&&(T=this.startTimer()),b7(this.gpgpu,b,f,m,o),p.forEach(N=>this.disposeIntermediateTensorInfo(N)),v&&(T=this.endTimer(T),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(T)})),!ct().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let N=this.unpackTensor(c);return this.disposeIntermediateTensorInfo(c),N}return c}compileAndRun(t,e,r,o,s=!1){r=r||e[0].dtype;let c=this.runWebGLProgram(t,e,r,o,s);return ps().makeTensorFromDataId(c.dataId,c.shape,c.dtype)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!ct().getBool("IS_TEST")){let t=Object.keys(this.binaryCache);t.forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=rt(()=>{if(!ct().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=ct().getBool("DEBUG");ct().set("DEBUG",!1);let e=this.abs(Et(1e-8)).dataSync()[0];if(ct().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?RJ:PJ}uploadToGPU(t){let e=this.texData.get(t),{shape:r,dtype:o,values:s,texture:c,usage:l,isPacked:p}=e;if(c!=null)return;let f=this.activeTimers!=null,m;f&&(m=or());let y=e.texShape;if(y==null&&(y=K8(r,p),e.texShape=y),s!=null){let b=m1(r),v,T=y[1],N=y[0],S=s instanceof Uint8Array;p?([T,N]=nl(y[0],y[1]),v=new KY(b,[N,T],S)):v=new jY(b,[N,T],S);let D=this.makeTensorInfo([N,T],o);S?this.texData.get(D.dataId).usage=Mr.PIXELS:this.texData.get(D.dataId).usage=Mr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(D.dataId),T,N,s);let I=!0,P=this.runWebGLProgram(v,[D],o,null,I),E=this.texData.get(P.dataId);e.texture=E.texture,e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,this.disposeIntermediateTensorInfo(D),this.texData.delete(P.dataId),e.values=null,f&&(this.uploadWaitMs+=or()-m)}else{let b=this.acquireTexture(y,l,o,p);e.texture=b}}convertAndCacheOnCPU(t,e){let r=this.texData.get(t),{dtype:o}=r;return this.releaseGPUData(t),e!=null&&(r.values=WJ(e,o)),r.values}acquireTexture(t,e,r,o){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*Pb(e)}tryRunOnCpuOrThrow(t,e){if(this.shouldExecuteOnCPU(t))try{return e()}catch(r){if(ct().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function WJ(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<e.length;++r)e[r]=Math.round(n[r]);return e}else throw new Error(`Unknown dtype ${t}`)}let VJ="2.7.0";function GJ(){ct().set("WEBGL_FORCE_F16_TEXTURES",!0)}Rx()&&ew("webgl",()=>new zJ,2);let Qst={forceHalfFloat:GJ};function Cs(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let UJ={kernelName:bu,backendName:"webgl",kernelFunc:Cs};function ll(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.makeTensorInfo(r.shape,"complex64"),c=e.texData.get(s.dataId),l=Cs({inputs:{x:r},backend:e}),p=e.texData.get(l.dataId);p.complexParentRefCount++;let f=Cs({inputs:{x:o},backend:e}),m=e.texData.get(f.dataId);return m.complexParentRefCount++,c.complexTensorInfos={real:l,imag:f},s}let qJ={kernelName:lf,backendName:"webgl",kernelFunc:ll};let iE="if (isnan(x)) return x;",HJ=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,jJ=`
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 Vm(n){return({inputs:t,backend:e})=>{let{x:r}=t,o=e,s=new ue(r.shape,n);return o.runWebGLProgram(s,[r],r.dtype)}}function ul({opSnippet:n,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:r=!1,cpuKernelImpl:o,dtype:s}){return({inputs:c,backend:l})=>{let{a:p,b:f}=c,m=l;if(r&&p.dtype==="complex64"){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=[[T.complexTensorInfos.real,N.complexTensorInfos.real],[T.complexTensorInfos.imag,N.complexTensorInfos.imag]].map(P=>{let[E,L]=P,B={dataId:E.dataId,dtype:E.dtype,shape:p.shape},q={dataId:L.dataId,dtype:L.dtype,shape:f.shape},H=new Hn(n,p.shape,f.shape);return m.runWebGLProgram(H,[B,q],jn(E.dtype,L.dtype))}),I=ll({inputs:{real:S,imag:D},backend:m});return m.disposeIntermediateTensorInfo(S),m.disposeIntermediateTensorInfo(D),I}let y=s||jn(p.dtype,f.dtype);if(m.shouldExecuteOnCPU([p,f])&&o!=null){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=o(p.shape,f.shape,T.values,N.values,y),I=m.makeTensorInfo(D,y),P=m.texData.get(I.dataId);return P.values=S,I}let b=ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,v;return b?v=new Ns(t,p.shape,f.shape,e):v=new Hn(n,p.shape,f.shape),m.runWebGLProgram(v,[p,f],y)}}let aE="return a + b;",KJ=ul({opSnippet:aE,packedOpSnippet:aE,supportsComplex:!0,cpuKernelImpl:rX}),XJ={kernelName:Hi,backendName:"webgl",kernelFunc:KJ};let YJ=HJ+`
return atan(a, b);
`,JJ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+jJ+`
return result;
`,ZJ=ul({opSnippet:YJ,packedOpSnippet:JJ}),QJ={kernelName:sf,backendName:"webgl",kernelFunc:ZJ};function tZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;sh(o,"avgPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Kn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Cs({inputs:{x:o},backend:e});let y=new ah(m,"avg",!1);return e.runWebGLProgram(y,[o],"float32")}let eZ={kernelName:au,backendName:"webgl",kernelFunc:tZ};function nZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s}=t,c=s;sh([o,s],"avgPoolBackprop");let{filterSize:l,strides:p,pad:f}=r,m=Kn(c.shape,l,p,1,f),y=new nY(m);return e.runWebGLProgram(y,[o],c.dtype)}let rZ={kernelName:af,backendName:"webgl",kernelFunc:nZ};class oZ{constructor(t,e,r,o,s,c){this.outputShape=[],this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="0.0";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="1.0";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${l};
float scale = ${p};
float inv = scale * inversesqrt(variance + float(${c}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}}class sZ{constructor(t,e,r,o,s,c){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="vec4(0.0)";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
vec4 offset = ${l};
vec4 scale = ${p};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${c}));
setOutput((x - mean) * inv + offset);
}
`}}let iZ=({inputs:n,backend:t,attrs:e})=>{let{x:r,mean:o,variance:s,offset:c,scale:l}=n;_(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||o.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let f=[r,o,s],m=null;c!=null&&(m=c.shape,f.push(c));let y=null;l!=null&&(y=l.shape,f.push(l));let b=ct().getBool("WEBGL_PACK_NORMALIZATION")?new sZ(r.shape,o.shape,s.shape,m,y,p):new oZ(r.shape,o.shape,s.shape,m,y,p),v=t.runWebGLProgram(b,f,f[0].dtype);return v},aZ={kernelName:yu,backendName:"webgl",kernelFunc:iZ};let cZ="return float(a != b);",cE=ul({opSnippet:cZ,dtype:"bool"}),lZ={kernelName:Su,backendName:"webgl",kernelFunc:cE};function T1(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Cs({inputs:{x:o.complexTensorInfos.real},backend:e})}let uZ={kernelName:$f,backendName:"webgl",kernelFunc:T1};let pZ="return float(int(x));";function hZ(n,t){let e=new ue(n.shape,pZ),r=t.runWebGLProgram(e,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function k1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Cs({inputs:{x:o},backend:e});let c=xe(o.shape),l=k1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),p=ll({inputs:{real:l,imag:c},backend:e});return c.dispose(),e.disposeIntermediateTensorInfo(l),p}if(o.dtype==="complex64"){let c=T1({inputs:{input:o},backend:e}),l=k1({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),l}if(!sc(o.dtype,s)){let c=Cs({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}if(s==="int32")return hZ(o,e);if(s==="bool"){let c=e.makeTensorInfo([],"bool",Ce("bool",1)),l={a:o,b:c},p=cE({inputs:l,backend:e});return e.disposeIntermediateTensorInfo(c),p}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}let fZ={kernelName:cc,backendName:"webgl",kernelFunc:k1};class dZ{constructor(t){this.outputShape=[],this.outputShape=hs(t,1),this.variableNames=t.map((c,l)=>`T${l}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let c=1;c<e.length;c++)e[c]=e[c-1]+t[c][1];let r=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let c=1;c<e.length;c++){let l=e[c-1];r.push(`else if (yC < ${e[c]}) setOutput(getT${c}(yR, yC-${l}));`)}let o=e.length,s=e[e.length-1];r.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${r.join(`
`)}
}
`}}class mZ{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=hs(t,e);let r=this.outputShape,o=r.length,s=Oe(o),c=Yn("coords",o),l=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((N,S)=>`T${S}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let N=1;N<p.length;N++)p[N]=p[N-1]+t[N][e];let f=l[e],m=l.slice(-2),y=l.join(),b=`if (${f} < ${p[0]}) {
return getChannel(
getT0(${y}), vec2(${m.join()}));
}`;for(let N=1;N<p.length;N++){let S=p[N-1];b+=`
if (${f} < ${p[N]} && ${f} >= ${p[N-1]}) {
return getChannel(
getT${N}(${Gm(l,f,S)}),
vec2(${Gm(m,f,S)}));
}`}let v=p.length,T=p[p.length-1];b+=`
return getChannel(
getT${v}(${Gm(l,f,T)}),
vec2(${Gm(m,f,T)}));`,this.userCode=`
float getValue(${l.map(N=>"int "+N)}) {
${b}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${c}), 0., 0., 0.);
${c[o-1]} = ${c[o-1]} + 1;
if (${c[o-1]} < ${r[o-1]}) {
result.g = getValue(${c});
}
${c[o-2]} = ${c[o-2]} + 1;
if (${c[o-2]} < ${r[o-2]}) {
result.a = getValue(${c});
}
${c[o-1]} = ${c[o-1]} - 1;
if (${c[o-2]} < ${r[o-2]} &&
${c[o-1]} < ${r[o-1]}) {
result.b = getValue(${c});
}
setOutput(result);
}
`}}function Gm(n,t,e){let r=n.indexOf(t),o=n.map((s,c)=>c===r?`${s} - ${e}`:s);return o.join()}function lE(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Cs({inputs:{x:o.complexTensorInfos.imag},backend:e})}let gZ={kernelName:wf,backendName:"webgl",kernelFunc:lE};function yZ(n,t,e){let r=[rl(n.shape),...ol(n.shape)],o={dtype:n.dtype,shape:r,dataId:n.dataId},s=[rl(t),...ol(t)],c=new WI(s,r),l=!0,p=e.runWebGLProgram(c,[o],n.dtype,null,l);return{dataId:p.dataId,shape:t,dtype:p.dtype}}function Ss(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=e,l=G(o.shape),p=Ge(s,l),f=G(p);_(l===f,()=>`The new shape (${p}) has ${f} elements and the old shape (${o.shape}) has ${l} elements. The new shape and old shape must have the same number of elements.`);let m=c.texData.get(o.dataId);return m.isPacked&&!Rm(o.shape,p)&&!(m.texture!==null&&Rm(m.shape,p))?yZ(o,p,c):(c.incRef(o.dataId),{dataId:o.dataId,shape:p,dtype:o.dtype})}let bZ={kernelName:Eu,backendName:"webgl",kernelFunc:Ss};function pl(n,t,e){let r=n[0].dtype;if(r==="complex64"){let f=n.map(T=>T1({inputs:{input:T},backend:e})),m=n.map(T=>lE({inputs:{input:T},backend:e})),y=pl(f,t,e),b=pl(m,t,e),v=ll({inputs:{real:y,imag:b},backend:e});return f.forEach(T=>e.disposeIntermediateTensorInfo(T)),m.forEach(T=>e.disposeIntermediateTensorInfo(T)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),v}if(n.length>ct().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let f=Math.floor(n.length/2),m=pl(n.slice(0,f),t,e),y=pl(n.slice(f),t,e),b=pl([m,y],t,e);return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),b}if(ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&n[0].shape.length>1){let f=new mZ(n.map(m=>m.shape),t);return e.runWebGLProgram(f,n,r)}let o=hs(n.map(f=>f.shape),t),s=n.map(f=>Ss({inputs:{x:f},attrs:{shape:[-1,G(f.shape.slice(t))]},backend:e})),c=new dZ(s.map(f=>f.shape)),l=e.runWebGLProgram(c,s,r);s.forEach(f=>e.disposeIntermediateTensorInfo(f));let p=Ss({inputs:{x:l},attrs:{shape:o},backend:e});return e.disposeIntermediateTensorInfo(l),p}function xZ(n){let{inputs:t,backend:e,attrs:r}=n,{axis:o}=r,s=Vt(o,t[0].shape)[0],c=hs(t.map(f=>f.shape),s);if(G(c)===0)return e.makeTensorInfo(c,t[0].dtype,[]);let l=t.filter(f=>G(f.shape)>0);if(l.length===1)return l[0];let p=l.map(f=>f.shape);return id(p,s),pl(l,s,e)}let wZ={kernelName:uu,backendName:"webgl",kernelFunc:xZ};let vZ=iE+`
return cos(x);
`,TZ=Vm(vZ),kZ={kernelName:lc,backendName:"webgl",kernelFunc:TZ};let NZ=`
if (a == b) {
return 1.0;
};
return a / b;`,_Z=`
// 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;
`,CZ=ul({opSnippet:NZ,packedOpSnippet:_Z,checkOutOfBounds:!0}),SZ={kernelName:uc,backendName:"webgl",kernelFunc:CZ};class uE{constructor(t,e,r){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,c=r?`${o}.0`:"1.0",l;if(t==="real")l="return real * expR - imag * expI;";else if(t==="imag")l="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${l}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${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) / ${c};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}}function pE(n,t,e){let r=e.texData.get(n.dataId),o=G(n.shape),s=n.shape[n.shape.length-1],c=o/s,l=Ss({inputs:{x:n},backend:e,attrs:{shape:[c,s]}}),p=l.shape,f=new uE("real",p,t),m=new uE("imag",p,t),y=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:p},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:p}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=ll({inputs:{real:b,imag:v},backend:e});e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v);let N=Ss({inputs:{x:T},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(N),N}function $Z(n){let{inputs:t,backend:e}=n,{input:r}=t;return pE(r,!1,e)}let IZ={kernelName:yf,backendName:"webgl",kernelFunc:$Z};class EZ{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${e} - x;
float outputValue;
if(coordX >= 0 && coordX < ${e}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}let DZ={kernelName:bf,backendName:"webgl",kernelFunc:({inputs:n,backend:t})=>{let{image:e}=n,r=t,o=new EZ(e.shape),s=r.runWebGLProgram(o,[e],e.dtype);return s}};class AZ{constructor(t){this.variableNames=["A"];let e=Jn(),[r,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${r}.0);
vec4 values = ${e.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class FZ{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Jn(),[r,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.0, ${r}.0);
vec4 values = ${e.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${e.output} = result;
}
`}}let RZ={kernelName:Ff,backendName:"webgl",kernelFunc:PZ},hl;function PZ(n){let{inputs:t,backend:e,attrs:r}=n,{pixels:o}=t,{numChannels:s}=r,c=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,l=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[p,f]=c?[o.videoWidth,o.videoHeight]:[o.width,o.height],m=[f,p],y=[f,p,s];(l||c)&&(hl==null&&(hl=document.createElement("canvas").getContext("2d")),hl.canvas.width=p,hl.canvas.height=f,hl.drawImage(o,0,0,p,f),o=hl.canvas);let b=e.makeTensorInfo(m,"int32");e.texData.get(b.dataId).usage=Mr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(b.dataId),o);let v=ct().getBool("WEBGL_PACK")?new FZ(y):new AZ(y),T=e.runWebGLProgram(v,[b],"int32");return e.disposeData(b.dataId),T}function OZ(n){let{inputs:t,backend:e}=n,{input:r}=t;return pE(r,!0,e)}let LZ={kernelName:xf,backendName:"webgl",kernelFunc:OZ};class hE{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l=Math.floor(r/4)*4,p=r%4,f="sumValue += dot(values, ones);";if(e!=null){let y=1/e;f=`sumValue += dot(values * ${gt(y)?y.toPrecision(2):y}, ones);`}let m="";s%r>0&&(m=`
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) {
${m}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
float sumValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${f}
}
int inIdx = inOffset + ${l};
if (${p===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${f}
} else if (${p===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${f}
} else if (${p===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${f}
}
setOutput(sumValue);
}
`}}function MZ(n){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:n[1],r=sp(e);t.push({inSize:e,windowSize:r,outSize:Math.ceil(e/r)})}return t}function fE(n,t,e,r){let o=MZ(n.shape),s=n;for(let c=0;c<o.length;c++){let{inSize:l,windowSize:p,outSize:f}=o[c],m,y;e==="mean"?m=c===0?new hE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},l):new hE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f}):m=new zI({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},e),y=s,s=r.runWebGLProgram(m,[s],t),y.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(y)}return s}function BZ(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Ss({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=fE(l,n.dtype,"max",r),f=Ss({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}class zZ{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[e[c]];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=WZ(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}}function WZ(n){let t=n.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let o=0;o<n.length;o++)r[n[o]]=e[o];return r.join()}class VZ{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(t.length);for(let m=0;m<r.length;m++)r[m]=t[e[m]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Oe(this.rank),s=wI("rc",this.rank),c=new Array(this.rank);for(let m=0;m<e.length;m++)c[e[m]]=s[m];let l=`vec2(${c.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${r[this.rank-1]}`,f=`getChannel(getA(${c.join()}), ${l})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${f};
if(${p}) {
result[1] = ${f};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${r[this.rank-2]}) {
result[2] = ${f};
if(${p}) {
result[3] = ${f};
}
}
setOutput(result);
}
`}}function N1(n,t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VZ(n.shape,t):new zZ(n.shape,t);return e.runWebGLProgram(r,[n],n.dtype)}let GZ={kernelName:Nu,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{reductionIndices:o,keepDims:s}=t,c=e,l=r.shape.length,p=Vt(o,r.shape),f=p,m=ir(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=r;if(y){if(b){let I=c.texData.get(v.dataId),P=I.values,E=new Array(l);for(let q=0;q<E.length;q++)E[q]=r.shape[m[q]];let L=y1(P,r.shape,r.dtype,m,E);v=c.makeTensorInfo(E,r.dtype);let B=c.texData.get(v.dataId);B.values=L}else v=N1(r,m,c);f=xr(f.length,l)}sr("max",f,l);let[T,N]=Fn(v.shape,f),S=T;s&&(S=Rn(T,p));let D;if(b){let I=c.texData.get(v.dataId),P=I.values,E=lX(P,G(N),S,r.dtype);D=c.makeTensorInfo(S,r.dtype);let L=c.texData.get(D.dataId);L.values=E}else D=BZ(v,N,S,c);return y&&c.disposeIntermediateTensorInfo(v),D}};function UZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;sh(o,"maxPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Kn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Cs({inputs:{x:o},backend:e});let y=new ah(m,"max",!1);return e.runWebGLProgram(y,[o],o.dtype)}let qZ={kernelName:_u,backendName:"webgl",kernelFunc:UZ};function HZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s,output:c}=t,l=s;sh([s,c],"maxPoolBackprop");let{filterSize:p,strides:f,pad:m,dimRoundingMode:y}=r,b=Kn(l.shape,p,f,1,m,y),v=!0,T=new ah(b,"max",v),N=e.runWebGLProgram(T,[l],l.dtype),S=new N7(b),D=e.runWebGLProgram(S,[o,N],l.dtype);return e.disposeIntermediateTensorInfo(N),D}let jZ={kernelName:Tf,backendName:"webgl",kernelFunc:HZ};function KZ(n,t,e,r){let o=new ah(e,"max",!1),s=r.runWebGLProgram(o,[n],"float32");o=new ah(e,"max",!0,!0,t);let c=r.runWebGLProgram(o,[n],"float32");return[s,c]}let XZ={kernelName:kf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{filterSize:o,strides:s,pad:c,includeBatchInIndex:l}=t,p=e;_(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let f=[1,1];_(fn(s,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${f}'`);let m=Kn(r.shape,o,s,f,c),[y,b]=KZ(r,l,m,p);return[y,b]}};function YZ(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Ss({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=fE(l,"float32","mean",r),f=Ss({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}let JZ={kernelName:ix,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{keepDims:o,axis:s}=t,c=e,l=r.shape.length,p=Vt(s,r.shape),f=p,m=ir(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=[],T=r;if(y){if(b){let P=c.texData.get(T.dataId),E=P.values,L=new Array(l);for(let H=0;H<L.length;H++)L[H]=r.shape[m[H]];let B=y1(E,r.shape,r.dtype,m,L);T=c.makeTensorInfo(L,r.dtype);let q=c.texData.get(T.dataId);q.values=B}else T=N1(r,m,c);v.push(T),f=xr(f.length,l)}sr("sum",f,l);let[N,S]=Fn(T.shape,f),D=N;o&&(D=Rn(N,p));let I=YZ(T,S,D,c);for(let P of v)c.disposeIntermediateTensorInfo(P);return I}};class ZZ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((m,y)=>m[0]+t[y]+m[1]);let o=t.length,s=Oe(o),c=e.map(m=>m[0]).join(","),l=e.map((m,y)=>m[0]+t[y]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),f=r==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${c};
int end = ${l};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${f};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${f};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${c});
${s} end = ${s}(${l});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${f};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${f};
}
}
${s} coords = outC - start;
setOutput(getX(${p}));
}
`}}class QZ{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((T,N)=>T[0]+t[N]+T[1]);let o=t.length,s=Oe(o),c=e.map(T=>T[0]).join(","),l=e.map((T,N)=>T[0]+t[N]).join(","),p=Yn("rc",o),f=Yn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=r==="reflect"?0:1,v="";if(o===1){let T=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${b};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${b};
}
source -= start;
`;v=`
${s} rc = outputLoc;
${T}
result[0] = getChannel(getX(${f.join()}), ${y});
${p[o-1]} += 1;
if(${m}) {
${T}
result[1] = getChannel(getX(${f.join()}), ${y});
}
`}else{let T=`
${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 - ${b}) +
gte * ((end - 1) * 2 - source + ${b});
source -= start;
`;v=`
${s} rc = outputLoc;
${T}
result[0] = getChannel(getX(${f.join()}), ${y});
${p[o-1]} += 1;
if(${m}) {
${T}
result[1] = getChannel(getX(${f.join()}), ${y});
}
rc = outputLoc;
${p[o-2]} += 1;
if(${p[o-2]} < ${this.outputShape[o-2]}) {
${T}
result[2] = getChannel(getX(${f.join()}), ${y});
${p[o-1]} += 1;
if(${m}) {
${T}
result[3] = getChannel(getX(${f.join()}), ${y});
}
}
`}this.userCode=`
const ${s} start = ${s}(${c});
const ${s} end = ${s}(${l});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${v}
setOutput(result);
}
`}}let t9=({inputs:n,backend:t,attrs:e})=>{let{x:r}=n,{paddings:o,mode:s}=e,c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QZ(r.shape,o,s):new ZZ(r.shape,o,s),l=t.runWebGLProgram(c,[r],r.dtype);return l},e9={kernelName:Cu,backendName:"webgl",kernelFunc:t9};let dE={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class mE{constructor(t,e,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=le(e,r),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${t}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}let gE="return a * b;";function n9(n){let{inputs:t,backend:e}=n,{a:r,b:o}=t,s=jn(r.dtype,o.dtype);if(r.dtype==="complex64"){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),f=new mE(dE.REAL,r.shape,o.shape),m=new mE(dE.IMAG,r.shape,o.shape),y=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=ll({inputs:{real:b,imag:v},backend:e});return e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),T}if(e.shouldExecuteOnCPU([r,o])){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),[f,m]=uX(r.shape,o.shape,l.values,p.values,s),y=e.makeTensorInfo(m,s),b=e.texData.get(y.dataId);return b.values=f,y}let c;return ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")?c=new Ns(gE,r.shape,o.shape):c=new Hn(gE,r.shape,o.shape),e.runWebGLProgram(c,[r,o],s)}let r9={kernelName:pc,backendName:"webgl",kernelFunc:n9};let o9={kernelName:px,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=e,p=t,f=p.readSync(r.dataId),m=p.readSync(o.dataId),y=s,b=c,v=l;return Od(f,m,y,b,v)}};let s9=Ld,i9={kernelName:Nf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),{selectedIndices:b,validOutputs:v}=s9(m,y,s,c,l,p);return[b,v]}};let a9=Md,c9={kernelName:_f,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),b=s,v=c,T=l,N=p,{selectedIndices:S,selectedScores:D}=a9(m,y,b,v,T,N);return[S,D]}};class l9{constructor(t,e,r,o){this.variableNames=["Image"],this.outputShape=[];let s=t[1],c=t[2],l=Math.sin(e).toFixed(3),p=Math.cos(e).toFixed(3);this.outputShape=t;let[f,m]=jw(o,s,c),y=f.toFixed(3),b=m.toFixed(3),v="";typeof r=="number"?v=`float outputValue = ${r.toFixed(2)};`:v=`
vec3 fill = vec3(${r.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) - ${y}) * ${p} - (float(y) - ${b}) * ${l};
float coordYFloat = (float(x) - ${y}) * ${l} + (float(y) - ${b}) * ${p};
int coordX = int(round(coordXFloat + ${y}));
int coordY = int(round(coordYFloat + ${b}));
${v}
if(coordX >= 0 && coordX < ${c} && coordY >= 0 && coordY < ${s}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}}let u9={kernelName:Rf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{image:r}=n,{radians:o,fillValue:s,center:c}=t,l=e,p=new l9(r.shape,o,s,c),f=l.runWebGLProgram(p,[r],r.dtype);return f}};let p9=iE+`
return sin(x);
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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:a,asyncInit:u}=this.initializeBackendsAndReturnBest();if(u)throw new Error(`The highest priority backend '${a}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(a)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(a){if(!(a in this.registry))if(a in this.registryFactory){let{asyncInit:u}=this.initializeBackend(a);if(u)return null}else return null;return this.registry[a]}findBackendFactory(a){return a in this.registryFactory?this.registryFactory[a].factory:null}registerBackend(a,u,h=1){return a in this.registryFactory?(console.warn(`${a} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[a]={factory:u,priority:h},!0)}async setBackend(a){if(this.registryFactory[a]==null)throw new Error(`Backend name '${a}' not found in registry`);if(this.backendName=a,this.registry[a]==null){this.backendInstance=null;let{success:u,asyncInit:h}=this.initializeBackend(a),d=h?await u:u;if(!d)return!1}return this.backendInstance=this.registry[a],this.setupRegisteredKernels(),this.profiler=new oT(this.backendInstance),!0}setupRegisteredKernels(){let a=eT(this.backendName);a.forEach(u=>{u.setupFunc!=null&&u.setupFunc(this.backendInstance)})}disposeRegisteredKernels(a){let u=eT(a);u.forEach(h=>{h.disposeFunc!=null&&h.disposeFunc(this.registry[a])})}initializeBackend(a){let u=this.registryFactory[a];if(u==null)throw new Error(`Cannot initialize backend ${a}, no registration found.`);try{let h=u.factory();if(h&&!(h instanceof q1)&&typeof h.then=="function"){let d=++this.pendingBackendInitId,g=h.then(x=>d<this.pendingBackendInitId?!1:(this.registry[a]=x,this.pendingBackendInit=null,!0)).catch(x=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${a} failed`),console.warn(x.stack||x.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[a]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${a} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(a){if(!(a in this.registryFactory))throw new Error(`${a} backend not found in registry`);this.backendName===a&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,a in this.registry&&(this.disposeRegisteredKernels(a),this.registry[a].dispose(),delete this.registry[a]),delete this.registryFactory[a],this.backendName===a&&(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((a,u)=>this.registryFactory[u].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let a=this.getSortedBackends();for(let u=0;u<a.length;u++){let h=a[u],{success:d,asyncInit:g}=this.initializeBackend(h);if(g||d)return{name:h,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(a,u){let h=this.state.tensorInfo.get(u),d=h.backend,g=this.readSync(u);d.disposeData(u),h.backend=a,a.move(u,g,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(a,u){let h=null;if(u==null){if(typeof a!="function")throw new Error("Please provide a function to tidy()");u=a}else{if(typeof a!="string"&&!(a instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof u!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");h=a}let d;return this.scopedRun(()=>this.startScope(h),()=>this.endScope(d),()=>(d=u(),d instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),d))}scopedRun(a,u,h){a();try{let d=h();return u(),d}catch(d){throw u(),d}}nextTensorId(){return Ea.nextTensorId++}nextVariableId(){return Ea.nextVariableId++}clone(a){let u=this.makeTensorFromDataId(a.dataId,a.shape,a.dtype),h={x:a},d=x=>({x:()=>{let w="float32",k={x},C={dtype:w};return V.runKernelFunc($=>$.cast(x,w),k,null,kl,C)}}),g=[];return this.addTapeNode(this.state.activeScope.name,h,[u],d,g,{}),u}runKernel(a,u,h,d,g){let x=null,w=null;return this.runKernelFunc(x,u,w,a,h,d,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(a,u,h){let d=this.backend.numDataIds(),g=0;h.forEach(k=>{g+=k.dtype==="complex64"?3:1});let x=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],w=d-u-g-x;if(w>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${w} data ids) after running '${a}'`)}runKernelFunc(a,u,h,d,g,x,w){let k,C=[],$=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");let F=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let W,et=My(d,this.backendName),tt;if(et!=null)W=()=>{let mt=this.backend.numDataIds();tt=et.kernelFunc({inputs:u,attrs:g,backend:this.backend});let lt=Array.isArray(tt)?tt:[tt];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,mt,lt);let gt=lt.map(({dataId:_t,shape:Gt,dtype:se})=>this.makeTensorFromDataId(_t,Gt,se));if($){let _t=this.getTensorsForGradient(d,u,gt);if(_t==null){w==null&&(w=[]);let Gt=gt.filter((se,fe)=>w[fe]);_t=(x||[]).slice().concat(Gt)}C=this.saveTensorsForBackwardMode(_t)}return gt};else{let mt=lt=>{if(!$)return;C=lt.map(gt=>this.keep(this.clone(gt)))};W=()=>{let lt=this.backend.numDataIds();tt=this.tidy(()=>a(this.backend,mt));let gt=Array.isArray(tt)?tt:[tt];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,lt,gt),gt}}let G;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?k=W():(G=this.profiler.profileKernel(d,u,()=>W()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(G),k=G.outputs)}),$&&this.addTapeNode(d,u,k,h,C,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-F,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(mt=>u[mt]!=null?u[mt].shape:null),outputShapes:k.map(mt=>mt.shape),kernelTimeMs:G.timeMs,extraInfo:G.extraInfo}),Array.isArray(tt)?k:k[0]}saveTensorsForBackwardMode(a){let u=a.map(h=>this.keep(this.clone(h)));return u}getTensorsForGradient(a,u,h){let d=tT(a);if(d!=null){let g=d.inputsToSave||[],x=d.outputsToSave||[],w;d.saveAllInputs?(U(Array.isArray(u),()=>"saveAllInputs is true, expected inputs to be an array."),w=Object.keys(u).map(C=>u[C])):w=g.map(C=>u[C]);let k=h.filter((C,$)=>x[$]);return w.concat(k)}return null}makeTensor(a,u,h,d){if(a==null)throw new Error("Values passed to engine.makeTensor() are null");h=h||"float32",d=d||this.backend;let g=a;h==="string"&&gh(a[0])&&(g=a.map(k=>O2(k)));let x=d.write(g,u,h),w=new z(u,h,x,this.nextTensorId());if(this.incRef(w,d),h==="string"){let k=this.state.tensorInfo.get(x),C=RE(g);this.state.numBytes+=C-k.bytes,k.bytes=C}return w}makeTensorFromDataId(a,u,h,d){h=h||"float32";let g=new z(u,h,a,this.nextTensorId());return this.incRef(g,d),g}makeVariable(a,u=!0,h,d){h=h||this.nextVariableId().toString(),d!=null&&d!==a.dtype&&(a=a.cast(d));let g=new Th(a,u,h,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}incRef(a,u){let h=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(d=a.size*FE(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:u||this.backend,dtype:a.dtype,shape:a.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Th||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let u=this.state.tensorInfo.get(a.dataId),h=u.refCount;h<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=u.bytes),this.state.numDataBuffers--,u.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let a in this.state.registeredVariables){let u=this.state.registeredVariables[a];this.disposeVariable(u)}}disposeVariable(a){this.disposeTensor(a),this.state.registeredVariables[a.name]!=null&&delete this.state.registeredVariables[a.name]}memory(){let a=this.backend.memory();return a.numTensors=this.state.numTensors,a.numDataBuffers=this.state.numDataBuffers,a.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(a.unreliable=!0,a.reasons==null&&(a.reasons=[]),a.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),a}async profile(a){this.state.profiling=!0;let u=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await a(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-u,this.state.activeProfile.newTensors=this.state.numTensors-h;for(let d of this.state.activeProfile.kernels)d.kernelTimeMs=await d.kernelTimeMs,d.extraInfo=await d.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(a,u,h,d,g,x){let w={id:this.state.nextTapeNodeId++,kernelName:a,inputs:u,outputs:h,saved:g},k=tT(a);k!=null&&(d=k.gradFunc),d!=null&&(w.gradient=C=>(C=C.map(($,F)=>{if($==null){let _=h[F],W=Ia(_.size,_.dtype);return this.makeTensor(W,_.shape,_.dtype)}return $}),d(C.length>1?C:C[0],g,x))),this.state.activeTape.push(w)}keep(a){return a.kept=!0,a}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(a){let u={track:[],name:"unnamed scope",id:this.state.nextScopeId++};a&&(u.name=a),this.state.scopeStack.push(u),this.state.activeScope=u}endScope(a){let u=Wy(a),h=new Set(u.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){let x=this.state.activeScope.track[g];!x.kept&&!h.has(x.id)&&x.dispose()}let d=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],u.forEach(g=>{!g.kept&&g.scopeId===d.id&&this.track(g)})}gradients(a,u,h,d=!1){if(U(u.length>0,()=>"gradients() received an empty list of xs."),h!=null&&h.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${h.dtype}'`);let g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",a));U(g instanceof z,()=>"The result y returned by f() must be a tensor.");let x=M2(this.state.activeTape,u,g);if(!d&&x.length===0&&u.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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et=Gr(i,u.shape,h,g,d,x,!0);return W.depthwiseConv2DDerInput(w,u,et)},$={dy:w,filter:u},F={strides:h,pad:d,dimRoundingMode:x,dilations:g,inputShape:i},_=V.runKernelFunc(C,$,null,QE,F);return k?K(_,[_.shape[1],_.shape[2],_.shape[3]]):_}var UD=O({depthwiseConv2dNativeBackpropInput_:Snt});function $nt(i){return Zy(i,.54,.46)}var qD=O({hammingWindow_:$nt});function Int(i){return Zy(i,.5,.5)}var tb=O({hannWindow_:Int});function Ent(i,a,u,h=!1,d=0){let g=0,x=[];for(;g+a<=i.size;)x.push(ve(i,g,a)),g+=u;if(h)for(;g<i.size;){let w=g+a-i.size,k=mn([ve(i,g,a-w),JT([w],d)]);x.push(k),g+=u}return x.length===0?ja([],[0,a]):K(mn(x),[x.length,a])}var eb=O({frame_:Ent});function Dnt(i,a,u,h,d=tb){h==null&&(h=VD(a));let g=eb(i,a,u),x=at(g,d(a)),w=[];for(let k=0;k<g.shape[0];k++)w.push(qa(ve(x,[k,0],[1,a]),h));return mn(w)}var HD=O({stft_:Dnt});function Ant(i,a,u,h,d,g){let x=R(i,"image","cropAndResize"),w=R(a,"boxes","cropAndResize","float32"),k=R(u,"boxInd","cropAndResize","int32");d=d||"bilinear",g=g||0;let C=w.shape[0];U(x.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${x.rank}.`),U(w.rank===2&&w.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${C},4] but had shape ${w.shape}.`),U(k.rank===1&&k.shape[0]===C,()=>`Error in cropAndResize: boxInd must be have size [${C}] but had shape ${w.shape}.`),U(h.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${h.length}.`),U(h[0]>=1&&h[1]>=1,()=>`cropSize must be atleast [1,1], but was ${h}`),U(d==="bilinear"||d==="nearest",()=>`method must be bilinear or nearest, but was ${d}`);let $=et=>et.cropAndResize(x,w,k,h,d,g),F={image:x,boxes:w,boxInd:k},_={method:d,extrapolationValue:g,cropSize:h},W=V.runKernelFunc($,F,null,YE,_);return W}var jD=O({cropAndResize_:Ant});function Fnt(i){let 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u=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*u+h,g=a.slice(0,a.length-d),x=a.slice(a.length-d);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(x)}},Ik=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Mi=class{constructor(a){if(a.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${a.length}`);Ik.forEach((u,h)=>{this[u]=a[h]})}asSortedArray(){return Ik.map(a=>({expression:a,probability:this[a]})).sort((a,u)=>u.probability-a.probability)}},gb=class extends Xh{constructor(a=new jh){super("FaceExpressionNet",a)}forwardInput(a){return Wl.tidy(()=>Wl.softmax(this.runNet(a)))}async forward(a){return this.forwardInput(await Ve(a))}async predictExpressions(a){let u=await Ve(a),h=await this.forwardInput(u),d=await Promise.all(Wl.unstack(h).map(async x=>{let w=await x.data();return x.dispose(),w}));h.dispose();let g=d.map(x=>new Mi(x));return u.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ek(i){return i.expressions instanceof Mi}function yb(i,a){let u={expressions:a};return Object.assign({},i,u)}function xrt(i,a,u=.1,h){let d=Array.isArray(a)?a:[a];d.forEach(g=>{let x=g instanceof Mi?g:Ek(g)?g.expressions:void 0;if(!x)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let w=x.asSortedArray(),k=w.filter(F=>F.probability>u),C=Jo(g)?g.detection.box.bottomLeft:h||new Jt(0,0),$=new Ii(k.map(F=>`${F.expression} (${ka(F.probability)})`),C);$.draw(i)})}function Ja(i){return Jo(i)&&i.landmarks instanceof Wr&&i.unshiftedLandmarks instanceof Wr&&i.alignedRect instanceof Ue}function Vl(i,a){let{box:u}=i.detection,h=a.shiftBy(u.x,u.y),d=h.align(),{imageDims:g}=i.detection,x=new 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k=d(`${w}/separable_conv0`),C=d(`${w}/separable_conv1`),$=d(`${w}/separable_conv2`);return{separable_conv0:k,separable_conv1:C,separable_conv2:$}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}}function wR(i,a){let u=[],{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}=Trt(i,u),w=h("entry_flow/conv_in"),k=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),$={conv_in:w,reduction_block_0:k,reduction_block_1:C},F={};Xo(a,0,1).forEach(tt=>{F[`main_block_${tt}`]=x(`middle_flow/main_block_${tt}`)});let _=g("exit_flow/reduction_block"),W=d("exit_flow/separable_conv"),et={reduction_block:_,separable_conv:W};return tr(i,u),{params:{entry_flow:$,middle_flow:F,exit_flow:et},paramMappings:u}}function vR(i,a,u){return pn.add(pn.conv2d(i,a.filters,u,"same"),a.bias)}function Rk(i,a,u=!0){let h=u?pn.relu(i):i;return h=yr(h,a.separable_conv0,[1,1]),h=yr(pn.relu(h),a.separable_conv1,[1,1]),h=pn.maxPool(h,[3,3],[2,2],"same"),h=pn.add(h,vR(i,a.expansion_conv,[2,2])),h}function krt(i,a){let u=yr(pn.relu(i),a.separable_conv0,[1,1]);return u=yr(pn.relu(u),a.separable_conv1,[1,1]),u=yr(pn.relu(u),a.separable_conv2,[1,1]),u=pn.add(u,i),u}var Pk=class extends Gn{constructor(a){super("TinyXception");this._numMainBlocks=a}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyXception - load model before inference");return pn.tidy(()=>{let h=pn.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=Io(h,d).div(pn.scalar(256)),x=pn.relu(vR(g,u.entry_flow.conv_in,[2,2]));return x=Rk(x,u.entry_flow.reduction_block_0,!1),x=Rk(x,u.entry_flow.reduction_block_1),Xo(this._numMainBlocks,0,1).forEach(w=>{x=krt(x,u.middle_flow[`main_block_${w}`])}),x=Rk(x,u.exit_flow.reduction_block),x=pn.relu(yr(x,u.exit_flow.separable_conv,[1,1])),x})}async forward(a){return this.forwardInput(await Ve(a))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(a){return wR(a,this._numMainBlocks)}extractParams(a){return xR(a,this._numMainBlocks)}};function TR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),d=ub(u,a),g=d(512,1,"fc/age"),x=d(512,2,"fc/gender");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{fc:{age:g,gender:x}}}}function kR(i){let a=[],u=Sr(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:{age:h("fc/age"),gender:h("fc/gender")}};return tr(i,a),{params:d,paramMappings:a}}var Hs;(function(i){i.FEMALE="female",i.MALE="male"})(Hs||(Hs={}));var bb=class extends Gn{constructor(a=new Pk(2)){super("AgeGenderNet");this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return ss.tidy(()=>{let h=a instanceof Us?this.faceFeatureExtractor.forwardInput(a):a,d=ss.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),g=Kh(d,u.fc.age).as1D(),x=Kh(d,u.fc.gender);return{age:g,gender:x}})}forwardInput(a){return ss.tidy(()=>{let{age:u,gender:h}=this.runNet(a);return{age:u,gender:ss.softmax(h)}})}async forward(a){return this.forwardInput(await Ve(a))}async predictAgeAndGender(a){let u=await Ve(a),h=await this.forwardInput(u),d=ss.unstack(h.age),g=ss.unstack(h.gender),x=d.map((k,C)=>({ageTensor:k,genderTensor:g[C]})),w=await Promise.all(x.map(async({ageTensor:k,genderTensor:C})=>{let $=(await k.data())[0],F=(await C.data())[0],_=F>.5,W=_?Hs.MALE:Hs.FEMALE,et=_?F:1-F;return k.dispose(),C.dispose(),{age:$,gender:W,genderProbability:et}}));return h.age.dispose(),h.gender.dispose(),u.isBatchInput?w:w[0]}getDefaultModelName(){return"age_gender_model"}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return TR(a)}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=mb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),kR(h)}extractParams(a){let u=512*1+1+(512*2+2),h=a.slice(0,a.length-u),d=a.slice(a.length-u);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}};var $r=te(ee()),Yh=class extends Xh{postProcess(a,u,h){let d=h.map(({width:x,height:w})=>{let k=u/Math.max(w,x);return{width:x*k,height:w*k}}),g=d.length;return $r.tidy(()=>{let x=(F,_)=>$r.stack([$r.fill([68],F,"float32"),$r.fill([68],_,"float32")],1).as2D(1,136).as1D(),w=(F,_)=>{let{width:W,height:et}=d[F];return _(W,et)?Math.abs(W-et)/2:0},k=F=>w(F,(_,W)=>_<W),C=F=>w(F,(_,W)=>W<_),$=a.mul($r.fill([g,136],u,"float32")).sub($r.stack(Array.from(Array(g),(F,_)=>x(k(_),C(_))))).div($r.stack(Array.from(Array(g),(F,_)=>x(d[_].width,d[_].height))));return $})}forwardInput(a){return $r.tidy(()=>{let u=this.runNet(a);return this.postProcess(u,a.inputSize,a.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(a){return this.forwardInput(await Ve(a))}async detectLandmarks(a){let u=await Ve(a),h=$r.tidy(()=>$r.unstack(this.forwardInput(u))),d=await Promise.all(h.map(async(g,x)=>{let w=Array.from(await g.data()),k=w.filter(($,F)=>Xm(F)),C=w.filter(($,F)=>!Xm(F));return new bl(Array(68).fill(0).map(($,F)=>new Jt(k[F],C[F])),{height:u.getInputHeight(x),width:u.getInputWidth(x)})}));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}},Gl=class extends Yh{constructor(a=new jh){super("FaceLandmark68Net",a)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Bi=te(ee());function NR(i){let a=[],{extractDenseBlock3Params:u}=db(i,a),h={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return tr(i,a),{params:h,paramMappings:a}}function _R(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),{extractDenseBlock3Params:d}=hb(u,a),g=d(3,32,"dense0",!0),x=d(32,64,"dense1"),w=d(64,128,"dense2");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{dense0:g,dense1:x,dense2:w}}}var Ok=class extends Gn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Bi.tidy(()=>{let h=Bi.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=Io(h,d).div(Bi.scalar(255)),x=ab(g,u.dense0,!0);return x=ab(x,u.dense1),x=ab(x,u.dense2),x=Bi.avgPool(x,[14,14],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await Ve(a))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(a){return NR(a)}extractParams(a){return _R(a)}},xb=class extends Yh{constructor(a=new Ok){super("FaceLandmark68TinyNet",a)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},CR=class extends Gl{};var jr=te(ee()),Ul=te(ee()),wb=te(ee());function SR(i,a){return wb.add(wb.mul(i,a.weights),a.biases)}function Lk(i,a,u,h,d="same"){let{filters:g,bias:x}=a.conv,w=Ul.conv2d(i,g,u,d);return w=Ul.add(w,x),w=SR(w,a.scale),h?Ul.relu(w):w}function $R(i,a){return Lk(i,a,[1,1],!0)}function Mk(i,a){return Lk(i,a,[1,1],!1)}function vb(i,a){return Lk(i,a,[2,2],!0,"valid")}var Ir=te(ee());function Nrt(i,a){function u(w,k,C){let $=i(w),F=$.length/(k*C*C);if(I1(F))throw new Error(`depth has to be an integer: ${F}, weights.length: ${$.length}, 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IR(i){let{extractWeights:a,getRemainingWeights:u}=er(i),h=[],{extractConvLayerParams:d,extractResidualLayerParams:g}=Nrt(a,h),x=d(4704,32,7,"conv32_down"),w=g(9216,32,3,"conv32_1"),k=g(9216,32,3,"conv32_2"),C=g(9216,32,3,"conv32_3"),$=g(36864,64,3,"conv64_down",!0),F=g(36864,64,3,"conv64_1"),_=g(36864,64,3,"conv64_2"),W=g(36864,64,3,"conv64_3"),et=g(147456,128,3,"conv128_down",!0),tt=g(147456,128,3,"conv128_1"),G=g(147456,128,3,"conv128_2"),mt=g(589824,256,3,"conv256_down",!0),lt=g(589824,256,3,"conv256_1"),gt=g(589824,256,3,"conv256_2"),_t=g(589824,256,3,"conv256_down_out"),Gt=Ir.tidy(()=>Ir.transpose(Ir.tensor2d(a(256*128),[128,256]),[1,0]));if(h.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);let se={conv32_down:x,conv32_1:w,conv32_2:k,conv32_3:C,conv64_down:$,conv64_1:F,conv64_2:_,conv64_3:W,conv128_down:et,conv128_1:tt,conv128_2:G,conv256_down:mt,conv256_1:lt,conv256_2:gt,conv256_down_out:_t,fc:Gt};return{params:se,paramMappings:h}}function _rt(i,a){let u=Sr(i,a);function h(x){let w=u(`${x}/scale/weights`,1),k=u(`${x}/scale/biases`,1);return{weights:w,biases:k}}function d(x){let w=u(`${x}/conv/filters`,4),k=u(`${x}/conv/bias`,1),C=h(x);return{conv:{filters:w,bias:k},scale:C}}function g(x){return{conv1:d(`${x}/conv1`),conv2:d(`${x}/conv2`)}}return{extractConvLayerParams:d,extractResidualLayerParams:g}}function ER(i){let 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w=x.mean([1,2]),k=jr.matMul(w,u.fc);return k})}async forward(a){return this.forwardInput(await Ve(a))}async computeFaceDescriptor(a){let u=await Ve(a),h=jr.tidy(()=>jr.unstack(this.forwardInput(u))),d=await Promise.all(h.map(g=>g.data()));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(a){return ER(a)}extractParams(a){return IR(a)}};function Crt(i){let a=new ql;return a.extractWeights(i),a}function Tb(i,a){let u={descriptor:a};return Object.assign({},i,u)}function Srt(i){return typeof i.age=="number"}function kb(i,a){let u={age:a};return Object.assign({},i,u)}function $rt(i){return(i.gender===Hs.MALE||i.gender===Hs.FEMALE)&&ml(i.genderProbability)}function Nb(i,a,u){let h={gender:a,genderProbability:u};return Object.assign({},i,h)}var Po=te(ee()),Ro=te(ee());function Irt(i,a){function u(k,C){let 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k=d(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),$=g(64,128,"mobilenetv1/conv_2"),F=g(128,128,"mobilenetv1/conv_3"),_=g(128,256,"mobilenetv1/conv_4"),W=g(256,256,"mobilenetv1/conv_5"),et=g(256,512,"mobilenetv1/conv_6"),tt=g(512,512,"mobilenetv1/conv_7"),G=g(512,512,"mobilenetv1/conv_8"),mt=g(512,512,"mobilenetv1/conv_9"),lt=g(512,512,"mobilenetv1/conv_10"),gt=g(512,512,"mobilenetv1/conv_11"),_t=g(512,1024,"mobilenetv1/conv_12"),Gt=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,conv_8:G,conv_9:mt,conv_10:lt,conv_11:gt,conv_12:_t,conv_13:Gt}}function w(){let 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DR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),{extractMobilenetV1Params:d,extractPredictionLayerParams:g}=Irt(u,a),x=d(),w=g(),k=Ro.tensor3d(u(5118*4),[1,5118,4]),C={extra_dim:k};if(a.push({paramPath:"output_layer/extra_dim"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{params:{mobilenetv1:x,prediction_layer:w,output_layer:C},paramMappings:a}}function Ert(i,a){let u=Sr(i,a);function h(C,$,F){let _=u(`${C}/Conv2d_${$}_pointwise/weights`,4,`${F}/filters`),W=u(`${C}/Conv2d_${$}_pointwise/convolution_bn_offset`,1,`${F}/batch_norm_offset`);return{filters:_,batch_norm_offset:W}}function d(C){let $=`mobilenetv1/conv_${C}`,F=`MobilenetV1/Conv2d_${C}_depthwise`,_=`${$}/depthwise_conv`,W=`${$}/pointwise_conv`,et=u(`${F}/depthwise_weights`,4,`${_}/filters`),tt=u(`${F}/BatchNorm/gamma`,1,`${_}/batch_norm_scale`),G=u(`${F}/BatchNorm/beta`,1,`${_}/batch_norm_offset`),mt=u(`${F}/BatchNorm/moving_mean`,1,`${_}/batch_norm_mean`),lt=u(`${F}/BatchNorm/moving_variance`,1,`${_}/batch_norm_variance`);return{depthwise_conv:{filters:et,batch_norm_scale:tt,batch_norm_offset:G,batch_norm_mean:mt,batch_norm_variance:lt},pointwise_conv:h("MobilenetV1",C,W)}}function g(){return{conv_0:h("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:d(1),conv_2:d(2),conv_3:d(3),conv_4:d(4),conv_5:d(5),conv_6:d(6),conv_7:d(7),conv_8:d(8),conv_9:d(9),conv_10:d(10),conv_11:d(11),conv_12:d(12),conv_13:d(13)}}function x(C,$){let F=u(`${C}/weights`,4,`${$}/filters`),_=u(`${C}/biases`,1,`${$}/bias`);return{filters:F,bias:_}}function w(C){let $=x(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),F=x(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:$,class_predictor:F}}function k(){return{conv_0:h("Prediction",0,"prediction_layer/conv_0"),conv_1:h("Prediction",1,"prediction_layer/conv_1"),conv_2:h("Prediction",2,"prediction_layer/conv_2"),conv_3:h("Prediction",3,"prediction_layer/conv_3"),conv_4:h("Prediction",4,"prediction_layer/conv_4"),conv_5:h("Prediction",5,"prediction_layer/conv_5"),conv_6:h("Prediction",6,"prediction_layer/conv_6"),conv_7:h("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:w(0),box_predictor_1:w(1),box_predictor_2:w(2),box_predictor_3:w(3),box_predictor_4:w(4),box_predictor_5:w(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:k}}function AR(i){let a=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:h}=Ert(i,a),d=i["Output/extra_dim"];if(a.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ds(d))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${d}`);let g={mobilenetv1:u(),prediction_layer:h(),output_layer:{extra_dim:d}};return tr(i,a),{params:g,paramMappings:a}}var js=te(ee()),zi=te(ee());function io(i,a,u){return zi.tidy(()=>{let h=zi.conv2d(i,a.filters,u,"same");return h=zi.add(h,a.batch_norm_offset),zi.clipByValue(h,0,6)})}var Drt=.0010000000474974513;function Art(i,a,u){return js.tidy(()=>{let h=js.depthwiseConv2d(i,a.filters,u,"same");return h=js.batchNorm(h,a.batch_norm_mean,a.batch_norm_variance,a.batch_norm_offset,a.batch_norm_scale,Drt),js.clipByValue(h,0,6)})}function Frt(i){return[2,4,6,12].some(a=>a===i)?[2,2]:[1,1]}function FR(i,a){return js.tidy(()=>{let u,h=io(i,a.conv_0,[2,2]),d=[a.conv_1,a.conv_2,a.conv_3,a.conv_4,a.conv_5,a.conv_6,a.conv_7,a.conv_8,a.conv_9,a.conv_10,a.conv_11,a.conv_12,a.conv_13];if(d.forEach((g,x)=>{let w=x+1,k=Frt(w);h=Art(h,g.depthwise_conv,k),h=io(h,g.pointwise_conv,[1,1]),w===11&&(u=h)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:h,conv11:u}})}function RR(i,a,u,h,d){let g=i.shape[0],x=Math.min(u,g),w=a.map(($,F)=>({score:$,boxIndex:F})).filter($=>$.score>d).sort(($,F)=>F.score-$.score),k=$=>$<=h?1:0,C=[];return w.forEach($=>{if(C.length>=x)return;let F=$.score;for(let _=C.length-1;_>=0;--_){let W=Rrt(i,$.boxIndex,C[_]);if(W===0)continue;if($.score*=k(W),$.score<=d)break}F===$.score&&C.push($.boxIndex)}),C}function Rrt(i,a,u){let h=i.arraySync(),d=Math.min(h[a][0],h[a][2]),g=Math.min(h[a][1],h[a][3]),x=Math.max(h[a][0],h[a][2]),w=Math.max(h[a][1],h[a][3]),k=Math.min(h[u][0],h[u][2]),C=Math.min(h[u][1],h[u][3]),$=Math.max(h[u][0],h[u][2]),F=Math.max(h[u][1],h[u][3]),_=(x-d)*(w-g),W=($-k)*(F-C);if(_<=0||W<=0)return 0;let et=Math.max(d,k),tt=Math.max(g,C),G=Math.min(x,$),mt=Math.min(w,F),lt=Math.max(G-et,0)*Math.max(mt-tt,0);return lt/(_+W-lt)}var Mt=te(ee());function Prt(i){let a=Mt.unstack(Mt.transpose(i,[1,0])),u=[Mt.sub(a[2],a[0]),Mt.sub(a[3],a[1])],h=[Mt.add(a[0],Mt.div(u[0],Mt.scalar(2))),Mt.add(a[1],Mt.div(u[1],Mt.scalar(2)))];return{sizes:u,centers:h}}function Ort(i,a){let{sizes:u,centers:h}=Prt(i),d=Mt.unstack(Mt.transpose(a,[1,0])),g=Mt.div(Mt.mul(Mt.exp(Mt.div(d[2],Mt.scalar(5))),u[0]),Mt.scalar(2)),x=Mt.add(Mt.mul(Mt.div(d[0],Mt.scalar(10)),u[0]),h[0]),w=Mt.div(Mt.mul(Mt.exp(Mt.div(d[3],Mt.scalar(5))),u[1]),Mt.scalar(2)),k=Mt.add(Mt.mul(Mt.div(d[1],Mt.scalar(10)),u[1]),h[1]);return Mt.transpose(Mt.stack([Mt.sub(x,g),Mt.sub(k,w),Mt.add(x,g),Mt.add(k,w)]),[1,0])}function PR(i,a,u){return Mt.tidy(()=>{let h=i.shape[0],d=Ort(Mt.reshape(Mt.tile(u.extra_dim,[h,1,1]),[-1,4]),Mt.reshape(i,[-1,4]));d=Mt.reshape(d,[h,d.shape[0]/h,4]);let g=Mt.sigmoid(Mt.slice(a,[0,0,1],[-1,-1,-1])),x=Mt.slice(g,[0,0,0],[-1,-1,1]);x=Mt.reshape(x,[h,x.shape[1]]);let w=Mt.unstack(d),k=Mt.unstack(x);return{boxes:w,scores:k}})}var Qh=te(ee()),Zh=te(ee());function Za(i,a){return Zh.tidy(()=>{let u=i.shape[0],h=Zh.reshape(Ya(i,a.box_encoding_predictor),[u,-1,1,4]),d=Zh.reshape(Ya(i,a.class_predictor),[u,-1,3]);return{boxPredictionEncoding:h,classPrediction:d}})}function OR(i,a,u){return Qh.tidy(()=>{let h=io(i,u.conv_0,[1,1]),d=io(h,u.conv_1,[2,2]),g=io(d,u.conv_2,[1,1]),x=io(g,u.conv_3,[2,2]),w=io(x,u.conv_4,[1,1]),k=io(w,u.conv_5,[2,2]),C=io(k,u.conv_6,[1,1]),$=io(C,u.conv_7,[2,2]),F=Za(a,u.box_predictor_0),_=Za(i,u.box_predictor_1),W=Za(d,u.box_predictor_2),et=Za(x,u.box_predictor_3),tt=Za(k,u.box_predictor_4),G=Za($,u.box_predictor_5),mt=Qh.concat([F.boxPredictionEncoding,_.boxPredictionEncoding,W.boxPredictionEncoding,et.boxPredictionEncoding,tt.boxPredictionEncoding,G.boxPredictionEncoding],1),lt=Qh.concat([F.classPrediction,_.classPrediction,W.classPrediction,et.classPrediction,tt.classPrediction,G.classPrediction],1);return{boxPredictions:mt,classPredictions:lt}})}var ao=class{constructor({minConfidence:a,maxResults:u}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=a||.5,this._maxResults=u||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},Qa=class extends Gn{constructor(){super("SsdMobilenetv1")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("SsdMobilenetv1 - load model before inference");return Po.tidy(()=>{let h=Po.cast(a.toBatchTensor(512,!1),"float32"),d=Po.sub(Po.mul(h,Po.scalar(.007843137718737125)),Po.scalar(1)),g=FR(d,u.mobilenetv1),{boxPredictions:x,classPredictions:w}=OR(g.out,g.conv11,u.prediction_layer);return PR(x,w,u.output_layer)})}async forward(a){return this.forwardInput(await Ve(a))}async locateFaces(a,u={}){let{maxResults:h,minConfidence:d}=new ao(u),g=await Ve(a),{boxes:x,scores:w}=this.forwardInput(g),k=x[0],C=w[0];for(let gt=1;gt<x.length;gt++)x[gt].dispose(),w[gt].dispose();let $=Array.from(await C.data()),F=.5,_=RR(k,$,h,F,d),W=g.getReshapedInputDimensions(0),et=g.inputSize,tt=et/W.width,G=et/W.height,mt=k.arraySync(),lt=_.map(gt=>{let[_t,Gt]=[Math.max(0,mt[gt][0]),Math.min(1,mt[gt][2])].map(_e=>_e*G),[se,fe]=[Math.max(0,mt[gt][1]),Math.min(1,mt[gt][3])].map(_e=>_e*tt);return new Ue($[gt],new yl(se,_t,fe-se,Gt-_t),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return k.dispose(),C.dispose(),lt}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(a){return AR(a)}extractParams(a){return DR(a)}};function LR(i){let a=new Qa;return a.extractWeights(i),a}function Lrt(i){return LR(i)}var MR=class extends Qa{},BR=.4,zR=[new Jt(.738768,.874946),new Jt(2.42204,2.65704),new Jt(4.30971,7.04493),new Jt(10.246,4.59428),new Jt(12.6868,11.8741)],WR=[new Jt(1.603231,2.094468),new Jt(6.041143,7.080126),new Jt(2.882459,3.518061),new Jt(4.266906,5.178857),new Jt(9.041765,10.66308)],VR=[117.001,114.697,97.404],GR="tiny_yolov2_model",UR="tiny_yolov2_separable_conv_model",He=te(ee()),_b=i=>typeof i=="number";function Bk(i){if(!i)throw new Error(`invalid config: ${i}`);if(typeof i.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${i.withSeparableConvs}`);if(!_b(i.iouThreshold)||i.iouThreshold<0||i.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${i.iouThreshold}`);if(!Array.isArray(i.classes)||!i.classes.length||!i.classes.every(a=>typeof a=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(i.classes)}`);if(!Array.isArray(i.anchors)||!i.anchors.length||!i.anchors.map(a=>a||{}).every(a=>_b(a.x)&&_b(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(i.anchors)}`);if(i.meanRgb&&(!Array.isArray(i.meanRgb)||i.meanRgb.length!==3||!i.meanRgb.every(_b)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(i.meanRgb)}`)}var lo=te(ee()),co=te(ee());function Hl(i){return co.tidy(()=>{let a=co.mul(i,co.scalar(.10000000149011612));return co.add(co.relu(co.sub(i,a)),a)})}function Ks(i,a){return lo.tidy(()=>{let u=lo.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=lo.conv2d(u,a.conv.filters,[1,1],"valid"),u=lo.sub(u,a.bn.sub),u=lo.mul(u,a.bn.truediv),u=lo.add(u,a.conv.bias),Hl(u)})}var Wi=te(ee());function Xs(i,a){return Wi.tidy(()=>{let u=Wi.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=Wi.separableConv2d(u,a.depthwise_filter,a.pointwise_filter,[1,1],"valid"),u=Wi.add(u,a.bias),Hl(u)})}var zk=te(ee());function Mrt(i,a){let u=Ll(i,a);function h(x,w){let k=zk.tensor1d(i(x)),C=zk.tensor1d(i(x));return a.push({paramPath:`${w}/sub`},{paramPath:`${w}/truediv`}),{sub:k,truediv:C}}function d(x,w,k){let C=u(x,w,3,`${k}/conv`),$=h(w,`${k}/bn`);return{conv:C,bn:$}}let g=Ml(i,a);return{extractConvParams:u,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}}function qR(i,a,u,h){let{extractWeights:d,getRemainingWeights:g}=er(i),x=[],{extractConvParams:w,extractConvWithBatchNormParams:k,extractSeparableConvParams:C}=Mrt(d,x),$;if(a.withSeparableConvs){let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=a.isFirstLayerConv2d?w(F,_,3,"conv0"):C(F,_,"conv0"),Gt=C(_,W,"conv1"),se=C(W,et,"conv2"),fe=C(et,tt,"conv3"),_e=C(tt,G,"conv4"),Ge=C(G,mt,"conv5"),Vt=lt?C(mt,lt,"conv6"):void 0,ln=gt?C(lt,gt,"conv7"):void 0,Ce=w(gt||lt||mt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ge,conv6:Vt,conv7:ln,conv8:Ce}}else{let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=k(F,_,"conv0"),Gt=k(_,W,"conv1"),se=k(W,et,"conv2"),fe=k(et,tt,"conv3"),_e=k(tt,G,"conv4"),Ge=k(G,mt,"conv5"),Vt=k(mt,lt,"conv6"),ln=k(lt,gt,"conv7"),Ce=w(gt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ge,conv6:Vt,conv7:ln,conv8:Ce}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:$,paramMappings:x}}function Brt(i,a){let u=Sr(i,a);function h(w){let k=u(`${w}/sub`,1),C=u(`${w}/truediv`,1);return{sub:k,truediv:C}}function d(w){let k=u(`${w}/filters`,4),C=u(`${w}/bias`,1);return{filters:k,bias:C}}function g(w){let k=d(`${w}/conv`),C=h(`${w}/bn`);return{conv:k,bn:C}}let x=Bl(u);return{extractConvParams:d,extractConvWithBatchNormParams:g,extractSeparableConvParams:x}}function HR(i,a){let u=[],{extractConvParams:h,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}=Brt(i,u),x;if(a.withSeparableConvs){let w=a.filterSizes&&a.filterSizes.length||9;x={conv0:a.isFirstLayerConv2d?h("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:w>7?g("conv6"):void 0,conv7:w>8?g("conv7"):void 0,conv8:h("conv8")}}else x={conv0:d("conv0"),conv1:d("conv1"),conv2:d("conv2"),conv3:d("conv3"),conv4:d("conv4"),conv5:d("conv5"),conv6:d("conv6"),conv7:d("conv7"),conv8:h("conv8")};return tr(i,u),{params:x,paramMappings:u}}var Wk;(function(i){i[i.XS=224]="XS",i[i.SM=320]="SM",i[i.MD=416]="MD",i[i.LG=608]="LG"})(Wk||(Wk={}));var is=class{constructor({inputSize:a,scoreThreshold:u}={}){this._name="TinyYolov2Options";if(this._inputSize=a||416,this._scoreThreshold=u||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}},Vk=class extends Gn{constructor(a){super("TinyYolov2");Bk(a),this._config=a}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(a,u){let h=Ks(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=Ks(h,u.conv6),h=Ks(h,u.conv7),Ya(h,u.conv8,"valid",!1)}runMobilenet(a,u){let h=this.config.isFirstLayerConv2d?Hl(Ya(a,u.conv0,"valid",!1)):Xs(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=u.conv6?Xs(h,u.conv6):h,h=u.conv7?Xs(h,u.conv7):h,Ya(h,u.conv8,"valid",!1)}forwardInput(a,u){let{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return He.tidy(()=>{let d=He.cast(a.toBatchTensor(u,!1),"float32");return d=this.config.meanRgb?Io(d,this.config.meanRgb):d,d=d.div(He.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(a,u){return await this.forwardInput(await Ve(a),u)}async detect(a,u={}){let{inputSize:h,scoreThreshold:d}=new is(u),g=await Ve(a),x=await this.forwardInput(g,h),w=He.tidy(()=>He.unstack(x)[0].expandDims()),k={width:g.getInputWidth(0),height:g.getInputHeight(0)},C=await this.extractBoxes(w,g.getReshapedInputDimensions(0),d);x.dispose(),w.dispose();let $=C.map(G=>G.box),F=C.map(G=>G.score),_=C.map(G=>G.classScore),W=C.map(G=>this.config.classes[G.label]),et=F1($.map(G=>G.rescale(h)),F,this.config.iouThreshold,!0),tt=et.map(G=>new Si(F[G],_[G],W[G],$[G],k));return tt}getDefaultModelName(){return""}extractParamsFromWeigthMap(a){return HR(a,this.config)}extractParams(a){let u=this.config.filterSizes||Vk.DEFAULT_FILTER_SIZES,h=u?u.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return qR(a,this.config,this.boxEncodingSize,u)}async extractBoxes(a,u,h){let{width:d,height:g}=u,x=Math.max(d,g),w=x/d,k=x/g,C=a.shape[1],$=this.config.anchors.length,[F,_,W]=He.tidy(()=>{let mt=a.reshape([C,C,$,this.boxEncodingSize]),lt=mt.slice([0,0,0,0],[C,C,$,4]),gt=mt.slice([0,0,0,4],[C,C,$,1]),_t=this.withClassScores?He.softmax(mt.slice([0,0,0,5],[C,C,$,this.config.classes.length]),3):He.scalar(0);return[lt,gt,_t]}),et=[],tt=await _.array(),G=await F.array();for(let mt=0;mt<C;mt++)for(let lt=0;lt<C;lt++)for(let gt=0;gt<$;gt++){let _t=lh(tt[mt][lt][gt][0]);if(!h||_t>h){let Gt=(lt+lh(G[mt][lt][gt][0]))/C*w,se=(mt+lh(G[mt][lt][gt][1]))/C*k,fe=Math.exp(G[mt][lt][gt][2])*this.config.anchors[gt].x/C*w,_e=Math.exp(G[mt][lt][gt][3])*this.config.anchors[gt].y/C*k,Ge=Gt-fe/2,Vt=se-_e/2,ln={row:mt,col:lt,anchor:gt},{classScore:Ce,label:rr}=this.withClassScores?await this.extractPredictedClass(W,ln):{classScore:1,label:0};et.push({box:new gl(Ge,Vt,Ge+fe,Vt+_e),score:_t,classScore:_t*Ce,label:rr,...ln})}}return F.dispose(),_.dispose(),W.dispose(),et}async extractPredictedClass(a,u){let{row:h,col:d,anchor:g}=u,x=await a.array();return Array(this.config.classes.length).fill(0).map((w,k)=>x[h][d][g][k]).map((w,k)=>({classScore:w,label:k})).reduce((w,k)=>w.classScore>k.classScore?w:k)}},jl=Vk;jl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Kl=class extends jl{constructor(a=!0){let u=Object.assign({},{withSeparableConvs:a,iouThreshold:BR,classes:["face"]},a?{anchors:WR,meanRgb:VR}:{anchors:zR,withClassScores:!0});super(u)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ue(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?UR:GR}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}};function zrt(i,a=!0){let u=new Kl(a);return u.extractWeights(i),u}var Cb=class extends is{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},uo=class{async then(a){return a(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf=te(ee()),Gk=te(ee());async function tc(i,a,u,h,d=({alignedRect:g})=>g){let g=i.map(k=>Ja(k)?d(k):k.detection),x=h||(a instanceof Gk.Tensor?await Pl(a,g):await Rl(a,g)),w=await u(x);return x.forEach(k=>k instanceof Gk.Tensor&&k.dispose()),w}async function Xl(i,a,u,h,d){return tc([i],a,async g=>u(g[0]),h,d)}var jR=.4,KR=[new Jt(1.603231,2.094468),new Jt(6.041143,7.080126),new Jt(2.882459,3.518061),new Jt(4.266906,5.178857),new Jt(9.041765,10.66308)],XR=[117.001,114.697,97.404],Yl=class extends jl{constructor(){let a={withSeparableConvs:!0,iouThreshold:jR,classes:["face"],anchors:KR,meanRgb:XR,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(a)}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ue(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}},Ne={ssdMobilenetv1:new Qa,tinyFaceDetector:new Yl,tinyYolov2:new Kl,faceLandmark68Net:new Gl,faceLandmark68TinyNet:new xb,faceRecognitionNet:new ql,faceExpressionNet:new gb,ageGenderNet:new bb},YR=(i,a)=>Ne.ssdMobilenetv1.locateFaces(i,a),Wrt=(i,a)=>Ne.tinyFaceDetector.locateFaces(i,a),Vrt=(i,a)=>Ne.tinyYolov2.locateFaces(i,a),JR=i=>Ne.faceLandmark68Net.detectLandmarks(i),Grt=i=>Ne.faceLandmark68TinyNet.detectLandmarks(i),Urt=i=>Ne.faceRecognitionNet.computeFaceDescriptor(i),qrt=i=>Ne.faceExpressionNet.predictExpressions(i),Hrt=i=>Ne.ageGenderNet.predictAgeAndGender(i),ZR=i=>Ne.ssdMobilenetv1.load(i),jrt=i=>Ne.tinyFaceDetector.load(i),Krt=i=>Ne.tinyYolov2.load(i),Xrt=i=>Ne.faceLandmark68Net.load(i),Yrt=i=>Ne.faceLandmark68TinyNet.load(i),Jrt=i=>Ne.faceRecognitionNet.load(i),Zrt=i=>Ne.faceExpressionNet.load(i),Qrt=i=>Ne.ageGenderNet.load(i),tot=ZR,eot=YR,not=JR,Uk=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},Ql=class extends Uk{async run(){let a=await this.parentTask,u=await tc(a,this.input,async h=>await Promise.all(h.map(d=>Ne.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return a.map((h,d)=>yb(h,u[d]))}withAgeAndGender(){return new Jl(this,this.input)}},tu=class extends Uk{async run(){let a=await this.parentTask;if(!a)return;let u=await Xl(a,this.input,h=>Ne.faceExpressionNet.predictExpressions(h),this.extractedFaces);return yb(a,u)}withAgeAndGender(){return new Zl(this,this.input)}},rc=class extends Ql{withAgeAndGender(){return new ec(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},oc=class extends tu{withAgeAndGender(){return new nc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},qk=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},Jl=class extends qk{async run(){let a=await this.parentTask,u=await tc(a,this.input,async h=>await Promise.all(h.map(d=>Ne.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return a.map((h,d)=>{let{age:g,gender:x,genderProbability:w}=u[d];return kb(Nb(h,x,w),g)})}withFaceExpressions(){return new Ql(this,this.input)}},Zl=class extends qk{async run(){let a=await this.parentTask;if(!a)return;let{age:u,gender:h,genderProbability:d}=await Xl(a,this.input,g=>Ne.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return kb(Nb(a,h,d),u)}withFaceExpressions(){return new tu(this,this.input)}},ec=class extends Jl{withFaceExpressions(){return new rc(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},nc=class extends Zl{withFaceExpressions(){return new oc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},Sb=class extends uo{constructor(a,u){super();this.parentTask=a;this.input=u}},Vi=class extends Sb{async run(){let a=await this.parentTask,u=await tc(a,this.input,h=>Promise.all(h.map(d=>Ne.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return u.map((h,d)=>Tb(a[d],h))}withFaceExpressions(){return new rc(this,this.input)}withAgeAndGender(){return new ec(this,this.input)}},Gi=class extends Sb{async run(){let a=await this.parentTask;if(!a)return;let u=await Xl(a,this.input,h=>Ne.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return Tb(a,u)}withFaceExpressions(){return new oc(this,this.input)}withAgeAndGender(){return new nc(this,this.input)}},$b=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?Ne.faceLandmark68TinyNet:Ne.faceLandmark68Net}},Ib=class extends $b{async run(){let a=await this.parentTask,u=a.map(g=>g.detection),h=this.input instanceof tf.Tensor?await Pl(this.input,u):await Rl(this.input,u),d=await Promise.all(h.map(g=>this.landmarkNet.detectLandmarks(g)));return h.forEach(g=>g instanceof tf.Tensor&&g.dispose()),a.map((g,x)=>Vl(g,d[x]))}withFaceExpressions(){return new rc(this,this.input)}withAgeAndGender(){return new ec(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},Eb=class extends $b{async run(){let a=await this.parentTask;if(!a)return;let{detection:u}=a,h=this.input instanceof tf.Tensor?await Pl(this.input,[u]):await Rl(this.input,[u]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(g=>g instanceof tf.Tensor&&g.dispose()),Vl(a,d)}withFaceExpressions(){return new oc(this,this.input)}withAgeAndGender(){return new nc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},Db=class extends uo{constructor(a,u=new ao){super();this.input=a;this.options=u}},ef=class extends Db{async run(){let{input:a,options:u}=this,h=u instanceof Cb?d=>Ne.tinyFaceDetector.locateFaces(d,u):u instanceof ao?d=>Ne.ssdMobilenetv1.locateFaces(d,u):u instanceof is?d=>Ne.tinyYolov2.locateFaces(d,u):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(a)}runAndExtendWithFaceDetections(){return new Promise(async a=>{let u=await this.run();return a(u.map(h=>_a({},h)))})}withFaceLandmarks(a=!1){return new Ib(this.runAndExtendWithFaceDetections(),this.input,a)}withFaceExpressions(){return new Ql(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Jl(this.runAndExtendWithFaceDetections(),this.input)}},Ab=class extends Db{async run(){let a=await new ef(this.input,this.options),u=a[0];return a.forEach(h=>{h.score>u.score&&(u=h)}),u}runAndExtendWithFaceDetection(){return new Promise(async a=>{let u=await this.run();return a(u?_a({},u):void 0)})}withFaceLandmarks(a=!1){return new Eb(this.runAndExtendWithFaceDetection(),this.input,a)}withFaceExpressions(){return new tu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Zl(this.runAndExtendWithFaceDetection(),this.input)}};function rot(i,a=new ao){return new Ab(i,a)}function Fb(i,a=new ao){return new ef(i,a)}async function QR(i,a){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Fb(i,new ao(a?{minConfidence:a}:{})).withFaceLandmarks().withFaceDescriptors()}async function oot(i,a={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Fb(i,new is(a)).withFaceLandmarks().withFaceDescriptors()}var sot=QR;function Hk(i,a){if(i.length!==a.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let u=Array.from(i),h=Array.from(a);return Math.sqrt(u.map((d,g)=>d-h[g]).reduce((d,g)=>d+Math.pow(g,2),0))}var Rb=class{constructor(a,u=.6){this._distanceThreshold=u;let h=Array.isArray(a)?a:[a];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1,g=()=>`person ${d++}`;this._labeledDescriptors=h.map(x=>{if(x instanceof As)return x;if(x instanceof Float32Array)return new As(g(),[x]);if(x.descriptor&&x.descriptor instanceof Float32Array)return new As(g(),[x.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(a,u){return u.map(h=>Hk(h,a)).reduce((h,d)=>h+d,0)/(u.length||1)}matchDescriptor(a){return this.labeledDescriptors.map(({descriptors:u,label:h})=>new uh(h,this.computeMeanDistance(a,u))).reduce((u,h)=>u.distance<h.distance?u:h)}findBestMatch(a){let u=this.matchDescriptor(a);return u.distance<this.distanceThreshold?u:new uh("unknown",u.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(a=>a.toJSON())}}static fromJSON(a){let u=a.labeledDescriptors.map(h=>As.fromJSON(h));return new Rb(u,a.distanceThreshold)}};function iot(i){let a=new Yl;return a.extractWeights(i),a}function tP(i,a){let{width:u,height:h}=new Zn(a.width,a.height);if(u<=0||h<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:h})}`);if(Array.isArray(i))return i.map(d=>tP(d,{width:u,height:h}));if(Ja(i)){let d=i.detection.forSize(u,h),g=i.unshiftedLandmarks.forSize(d.box.width,d.box.height);return Vl(_a(i,d),g)}return Jo(i)?_a(i,i.detection.forSize(u,h)):i instanceof Wr||i instanceof Ue?i.forSize(u,h):i}var eP="0.8.8",cot=typeof process!="undefined",lot=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",uot={faceapi:eP,node:cot,browser:lot};export{bb as AgeGenderNet,gl as BoundingBox,Fe as Box,uo as ComposableTask,Vi as ComputeAllFaceDescriptorsTask,Sb as ComputeFaceDescriptorsTaskBase,Gi as ComputeSingleFaceDescriptorTask,Ib as DetectAllFaceLandmarksTask,ef as DetectAllFacesTask,$b as DetectFaceLandmarksTaskBase,Db as DetectFacesTaskBase,Eb as DetectSingleFaceLandmarksTask,Ab as DetectSingleFaceTask,Zn as Dimensions,Ik as FACE_EXPRESSION_LABELS,Ue as FaceDetection,MR as FaceDetectionNet,gb as FaceExpressionNet,Mi as FaceExpressions,Gl as FaceLandmark68Net,xb as FaceLandmark68TinyNet,CR as FaceLandmarkNet,Wr as FaceLandmarks,NE as FaceLandmarks5,bl as FaceLandmarks68,uh as FaceMatch,Rb as FaceMatcher,ql as FaceRecognitionNet,Hs as Gender,ph as LabeledBox,As as LabeledFaceDescriptors,Us as NetInput,Gn as NeuralNetwork,Si as ObjectDetection,Jt as Point,_E as PredictedBox,yl as Rect,Qa as SsdMobilenetv1,ao as SsdMobilenetv1Options,Yl as TinyFaceDetector,Cb as TinyFaceDetectorOptions,Kl as TinyYolov2,is as TinyYolov2Options,Wk as TinyYolov2SizeType,sot as allFaces,QR as allFacesSsdMobilenetv1,oot as allFacesTinyYolov2,V1 as awaitMediaLoaded,G1 as bufferToImage,Urt as computeFaceDescriptor,xl as createCanvas,dh as createCanvasFromMedia,Lrt as createFaceDetectionNet,Crt as createFaceRecognitionNet,LR as createSsdMobilenetv1,iot as createTinyFaceDetector,zrt as createTinyYolov2,Fb as detectAllFaces,JR as detectFaceLandmarks,Grt as detectFaceLandmarksTiny,not as detectLandmarks,rot as detectSingleFace,Fk as draw,$e as env,Hk as euclideanDistance,kb as extendWithAge,Tb as extendWithFaceDescriptor,_a as extendWithFaceDetection,yb as extendWithFaceExpressions,Vl as extendWithFaceLandmarks,Nb as extendWithGender,Pl as extractFaceTensors,Rl as extractFaces,grt as fetchImage,Sk as fetchJson,yrt as fetchNetWeights,Xa as fetchOrThrow,dr as getContext2dOrThrow,Sa as getMediaDimensions,U1 as imageTensorToCanvas,Ck as imageToSquare,V9 as inverseSigmoid,D1 as iou,tg as isMediaElement,fh as isMediaLoaded,Srt as isWithAge,Jo as isWithFaceDetection,Ek as isWithFaceExpressions,Ja as isWithFaceLandmarks,$rt as isWithGender,Qrt as loadAgeGenderModel,tot as loadFaceDetectionModel,Zrt as loadFaceExpressionModel,Xrt as loadFaceLandmarkModel,Yrt as loadFaceLandmarkTinyModel,Jrt as loadFaceRecognitionModel,ZR as loadSsdMobilenetv1Model,jrt as loadTinyFaceDetectorModel,Krt as loadTinyYolov2Model,$k as loadWeightMap,eot as locateFaces,brt as matchDimensions,A1 as minBbox,Ne as nets,F1 as nonMaxSuppression,Io as normalize,R1 as padToSquare,Hrt as predictAgeAndGender,qrt as recognizeFaceExpressions,tP as resizeResults,Ca as resolveInput,W9 as shuffleArray,lh as sigmoid,YR as ssdMobilenetv1,aot as tf,Wrt as tinyFaceDetector,Vrt as tinyYolov2,Ve as toNetInput,S1 as utils,Bk as validateConfig,uot as version};
/**
* @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 See the LICENSE file. */
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