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

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:o},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:o}=this.initializeBackend(e);if(!(o?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Tb(this.backendInstance),!0}setupRegisteredKernels(){dm(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){dm(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Ws)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=o,{success:!0,asyncInit:!1}}catch(o){return console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t),a=n.refCount(t);n.disposeData(t,!0),o.backend=e,e.move(t,s,o.shape,o.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");o=e}let n;return this.scopedRun(()=>this.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return du.nextTensorId++}nextVariableId(){return du.nextVariableId++}clone(e){let t=D.runKernel(Ro,{x:e}),o={x:e},n=a=>({x:()=>{let i="float32",l={x:a},u={dtype:i};return D.runKernel(Do,l,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(!(Lc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=Pb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Pb(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Lc(d,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let w=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let b=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,w,b);let _=b.map(k=>{if(k.rank!=null)return k;let{dataId:A,shape:N,dtype:$}=k;return this.makeTensorFromDataId(A,N,$)});if(n){let k=this.getTensorsForGradient(d,h,_);o=this.saveTensorsForBackwardMode(k)}return _}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let x=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:p}=e,m=Pb(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Lh(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let l=o.filter((u,c)=>a[c]);return i.concat(l)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=e;o==="string"&&is(e[0])&&(s=e.map(l=>Qa(l)));let a=n.write(s,t,o),i=new Ve(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o==="string"){let l=this.state.tensorInfo.get(a),u=wb(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s=new Ve(t,o,e,this.nextTensorId());return this.trackTensor(s,n),s}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new el(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Ph(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof el||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let o=e.size*Ph(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},l=Lh(e);l!=null&&(n=l.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,p)=>{if(c==null){let m=o[p],f=Rc(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=ym(e),o=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!o.has(a.id)&&a.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));E(s instanceof Ve,()=>"The result y returned by f() must be a tensor.");let a=G0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return D.runKernel(Vl,s,a)}var fw=T({bincount_:ZG});function JG(r,e){let t=v(r,"broadcastTo","x"),o=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);if(e.length<t.rank)throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t.rank}.`);if(e.length>t.rank){let u=t.shape.slice();for(;u.length<e.length;)u.unshift(1);t=L(t,u)}let n=t.shape,s=Array.from(e);for(let u=e.length-1;u>=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Oo(t);let i={x:t},l={reps:s};return D.runKernel(_o,i,l)}var sl=T({broadcastTo_:JG});function QG(r){let t={x:v(r,"x","ceil")};return D.runKernel(Yo,t)}var 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of input (${p}) must match input depth for filter ${l.shape[2]}.`),E(_r(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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c?L(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Is=T({depthwiseConv2d_:yW});function bW(r){let t={x:v(r,"x","diag")};return D.runKernel(Xl,t)}var wW=T({diag_:bW});function _W(r,e,t,o,n=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=L(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:o,dilations:n},m=D.runKernel(sa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var $m=T({dilation2d_:_W});function kW(r,e){let t=r.length,o=[];for(let n=0;n<t;n++){let s=t-1-n,a=r[s]||1;(e[e.length-1-n]||1)>1&&a===1&&o.unshift(s)}return o}function kt(r,e){let t=[];for(let o=0;o<e.length;o++){let n=r[r.length-o-1],s=e.length-o-1,a=e[s];(n==null||n===1&&a>1)&&t.unshift(s)}return t}function Be(r,e){let t=[],o=Math.max(r.length,e.length);for(let n=0;n<o;n++){let s=r[r.length-n-1];s==null&&(s=1);let a=e[e.length-n-1];if(a==null&&(a=1),s===1)t.unshift(a);else if(a===1)t.unshift(s);else if(s!==a){let i=`Operands could not be broadcast together with shapes ${r} and ${e}.`;throw Error(i)}else t.unshift(s)}return t}function vW(r,e){let t=v(r,"a","equal"),o=v(e,"b","equal");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return D.runKernel(ni,n)}var vo=T({equal_:vW});function CW(r,e,t){let o=v(e,"a","where"),n=v(t,"b","where"),s=v(r,"condition","where","bool"),a=Be(o.shape,n.shape),i=sl(o,a),l=sl(n,a);s.rank===1&&E(s.shape[0]===o.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&Ct(s.shape,l.shape,"Error in where: ");let u={condition:s,t:i,e:l};return D.runKernel(hs,u)}var $t=T({where_:CW});function IW(r){let t={x:v(r,"x","zerosLike")};return D.runKernel(bs,t)}var Ce=T({zerosLike_:IW});function NW(r,e){let t=v(r,"a","div"),o=v(e,"b","div");[t,o]=Ge(t,o);let n=me(t,o),s=Ce(n),a=vo(o,s);return $t(a,s,n)}var Rm=T({divNoNan_:NW});function SW(r,e){let t=v(r,"t1","dot"),o=v(e,"t2","dot");E((t.rank===1||t.rank===2)&&(o.rank===1||o.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${o.rank}.`);let n=t.rank===1?t.size:t.shape[1],s=o.rank===1?o.size:o.shape[0];if(E(n===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`),t.rank===1&&o.rank===1){let a=L(t,[1,-1]),i=L(o,[-1,1]),l=We(a,i);return L(l,[])}else if(t.rank===1&&o.rank===2){let a=L(t,[1,-1]),i=L(o,[o.shape[0],o.shape[1]]),l=We(a,i);return L(l,[l.size])}else if(t.rank===2&&o.rank===1){let a=L(o,[-1,1]),i=We(t,a);return L(i,[i.size])}else{let a=L(o,[o.shape[0],o.shape[1]]);return We(t,a)}}var bw=T({dot_:SW});function TW(r){let t={x:v(r,"x","elu")};return D.runKernel(ri,t)}var Ns=T({elu_:TW});function 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n.data(),[l,u]=[a.length/s,s],c=gb("bool",l);for(let p=0;p<l;p++){let m=p*u,f=a.subarray(m,m+u),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<t;h++)if(d[h].index===i[p]){c[p]=1;break}}return r!==o&&o.dispose(),e!==n&&n.dispose(),Rr(c,n.shape,"bool")}var lj=aj;var Gn={};Je(Gn,{conv2d:()=>pj,depthwiseConv2d:()=>hj,matMul:()=>xj});function uj(r,e,t,o,n,s="NHWC",a){let i=r;r.rank===3&&(i=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]])),E(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),E(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),E(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s==="NHWC"?i.shape[3]:i.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];E(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth 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cj({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",ju(D.state.gradientDepth,l)===!1){let k=Kr(r,e,t,o,n,s,a);return i!=null&&(k=ee(k,i)),Uu(k,l,u,c)}let p=v(r,"x","conv2d"),m=v(e,"filter","conv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=L(p,[1,p.shape[0],p.shape[1],p.shape[2]])),E(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&E(ot(o),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),E(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),E(_r(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},p=D.runKernel(wi,u,c);return{selectedIndices:p[0],selectedScores:p[1]}}var IN=T({nonMaxSuppressionWithScore_:$j});async function Rj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,"boxes","nonMaxSuppressionAsync"),i=v(e,"scores","nonMaxSuppressionAsync"),l=Wn(a,i,t,o,n,s);t=l.maxOutputSize,o=l.iouThreshold,n=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],p=u[1],{selectedIndices:m,selectedScores:f}=Yw(c,p,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Gt(m,"int32"),selectedScores:Gt(f)}}var NN=Rj;function Fj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,"boxes","nonMaxSuppression"),i=v(e,"scores","nonMaxSuppression"),l=Wn(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,m={boxes:a,scores:i},f={maxOutputSize:u,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=D.runKernel(bi,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var SN=T({nonMaxSuppressionPadded_:Fj});async function Oj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,"boxes","nonMaxSuppressionAsync"),i=v(e,"scores","nonMaxSuppressionAsync"),l=Wn(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,[m,f]=await Promise.all([a.data(),i.data()]),{selectedIndices:d,validOutputs:h}=Xw(m,f,u,c,p,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Gt(d,"int32"),validOutputs:le(h,"int32")}}var TN=Oj;function Pj(r,e,t=!1,o=!1){let n=v(r,"images","resizeBilinear");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=n,a=!1;n.rank===3&&(a=!0,s=L(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:o,size:e},u=D.runKernel(vn,i,l);return a?L(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var fg=T({resizeBilinear_:Pj});function Mj(r,e,t=!1,o=!1){let n=v(r,"images","resizeNearestNeighbor");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype==="float32"||n.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),E(o===!1||t===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=n,a=!1;n.rank===3&&(a=!0,s=L(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:o,size:e},u=D.runKernel(pa,i,l);return a?L(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var dg=T({resizeNearestNeighbor_:Mj});function Lj(r,e,t="nearest",o="constant",n=0,s){let a=v(r,"image","transform","float32"),i=v(e,"transforms","transform","float32");E(a.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${a.rank}.`),E(i.rank===2&&(i.shape[0]===a.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),E(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return D.runKernel(cu,l,u)}var AN=T({transform_:Lj});function zj(r,e,t){E(e%1==0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1==0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=v(r,"a","bandPart");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=L(Jc(0,s,1,"int32"),[-1,1]),l=Jc(0,a,1,"int32"),u=ce(i,l),c=hr(zo(u,le(+e,"int32")),io(u,le(-t,"int32"))),p=gt([s,a],o.dtype);return L(Vt(pr(L(o,[-1,s,a])).map(m=>$t(c,m,p))),n)}var EN=T({bandPart_:zj});function Bj(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let n=r[0].shape[0];for(let s=1;s<r.length;++s)E(r[s].shape[0]===n,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=cr(r,r.shape[0],0).map(n=>Co(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: 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n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),Ct(n.shape,s.shape,"Error in hingeLoss: ");let i=le(1);n=ce(P(le(2),n),i);let l=Sr(ce(i,P(n,s)));return Tr(l,a,o)}var PN=T({hingeLoss_:jj});function Hj(r,e,t,o=1,n=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),Ct(s.shape,a.shape,"Error in huberLoss: ");let l=le(o),u=Nt(ce(a,s)),c=As(u,l),p=ce(u,c),m=ee(P(le(.5),Oe(c)),P(l,p));return Tr(m,i,n)}var MN=T({huberLoss_:Hj});function qj(r,e,t,o=1e-7,n=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),Ct(s.shape,a.shape,"Error in logLoss: ");let l=le(1),u=le(o),c=He(P(s,lr(ee(a,u)))),p=P(ce(l,s),lr(ee(ce(l,a),u))),m=ce(c,p);return Tr(m,i,n)}var LN=T({logLoss_:qj});function Kj(r,e,t,o=Wt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),Ct(n.shape,s.shape,"Error in meanSquaredError: ");let i=zu(n,s);return Tr(i,a,o)}var zN=T({meanSquaredError_:Kj});function Xj(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");Ct(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Sr(o),s=P(o,t),a=Tu(Zt(He(Nt(o))));return ee(ce(n,s),a)}function Yj(r,e,t,o=0,n=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),Ct(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(.5);s=ee(P(s,ce(c,u)),P(p,u))}let l=Xj(s,a);return Tr(l,i,n)}var BN=T({sigmoidCrossEntropy_:Yj});function Zj(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Xr((n,s,a)=>{let l=Lm(s,[t],!0),u=ce(ne(s,"float32"),l);a([n,u]);let c=He(P(u,n));return{value:ge(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Bn(f.shape,[t]);return[P(L(f,x),ce(ne(h,"float32"),Zt(g))),P(L(f,x),ce(Zt(g),ne(h,"float32")))]}}})(r,e)}function Jj(r,e,t,o=0,n=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),Ct(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(s.shape[1]);s=ee(P(s,ce(c,u)),me(u,p))}let l=Zj(s,a);return Tr(l,i,n)}var VN=T({softmaxCrossEntropy_:Jj});var Qj={fft:Ea,ifft:Pi,rfft:Da,irfft:Lu},eH={hammingWindow:gN,hannWindow:pg,frame:mg,stft:xN},$s={flipLeftRight:bN,resizeNearestNeighbor:dg,resizeBilinear:fg,rotateWithOffset:wN,cropAndResize:yN,nonMaxSuppression:_N,nonMaxSuppressionAsync:CN,nonMaxSuppressionWithScore:IN,nonMaxSuppressionWithScoreAsync:NN,nonMaxSuppressionPadded:SN,nonMaxSuppressionPaddedAsync:TN,transform:AN},Zw={bandPart:EN,gramSchmidt:DN,qr:RN},tH={absoluteDifference:FN,computeWeightedLoss:Tr,cosineDistance:ON,hingeLoss:PN,huberLoss:MN,logLoss:LN,meanSquaredError:zN,sigmoidCrossEntropy:BN,softmaxCrossEntropy:VN};var Pr=class extends Jh{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return og(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Pr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Pr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=D.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:G(()=>Ce(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:G(()=>Ce(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;G(()=>{let c=ee(P(l,this.rho),P(Oe(i),1-this.rho)),p=P(me(xt(ee(u,this.epsilon)),xt(ee(l,this.epsilon))),i),m=ee(P(u,this.rho),P(Oe(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(P(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";so(rp);var op=class extends Pr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=D.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:G(()=>va(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;G(()=>{let l=ee(i,Oe(a));i.assign(l);let u=ee(P(me(a,xt(ee(l,D.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";so(op);var np=class extends Pr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],G(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(o).variable()}),n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);G(()=>{let o=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:G(()=>Ce(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:G(()=>Ce(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=ee(P(p,this.beta2),P(Oe(u),1-this.beta2)),d=me(m,o),h=me(f,n);c.assign(m),p.assign(f);let g=ee(P(me(d,ee(xt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),G(()=>{this.accBeta1.assign(Or(this.beta1,this.iterations_+1)),this.accBeta2.assign(Or(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};np.className="Adam";so(np);var sp=class extends Pr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],G(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);G(()=>{let o=ce(1,this.accBeta1),n=me(-this.learningRate,ee(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sp.className="Adamax";so(sp);var al=class extends Pr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=D.registeredVariables[o];G(()=>{let i=ee(P(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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f=new Os(e);for(let d=0;d<p.length;++d){if(o!=null){let F=Hc().numTensors;F>o.maxNumTensors&&(o.maxNumTensors=F),F<o.minNumTensors&&(o.minNumTensors=F)}let h=p[d],g=h.sourceLayer;if(g instanceof Bi)continue;let x=[],w=[],b=[],_=!1;for(let F of h.inputs){let M=f.getValue(F),V=f.getMask(F);x.push(M),w.push(V),V!=null&&(_=!0),n||(m[F.name]--,m[F.name]===0&&!e.hasKey(F)&&i.indexOf(F.name)===-1&&!M.isDisposed&&F.sourceLayer.stateful!==!0&&b.push(M))}_&&(t=t||{},t.mask=w[0]);let k=yt(g.apply(x,t)),A=null;g.supportsMasking&&(A=g.computeMask(x,w));let N=Wq(h),$=Array.isArray(N)?N:[N];for(let F=0;F<$.length;++F){f.hasKey($[F])||f.add($[F],k[F],Array.isArray(A)?A[0]:A);let M=i.indexOf($[F].name);M!==-1&&(l[M]=k[F])}n||Te(b)}return f.disposeMasks(),s?l:l[0]}function Gq(r,e){y.assert(r!=null&&r.length>0,()=>"Expected at least one fetch, got none");let t=[],o={};if(r.length===1){let n=w1(r[0],e);t=n.sorted,o=n.recipientMap}else{let n=new Set;for(let s of r){let{sorted:a,recipientMap:i}=w1(s,e);for(let l of a)n.has(l.name)||(t.push(l),n.add(l.name));for(let l in i)o[l]==null&&(o[l]=new Set),i[l].forEach(u=>o[l].add(u))}}return{sorted:t,recipientCounts:Uq(o)}}function Uq(r){let e={};for(let t in r)e[t]=r[t].size;return e}function w1(r,e){let t=new Set,o=[],n={};for(let i of e.names())t.add(i);let s=[],a=[];for(s.push(r);s.length>0;){let i=s[s.length-1];if(t.has(i.name)){s.pop();continue}let l=a[a.length-1]===s.length-1;if(i.inputs.length===0||l)s.pop(),o.push(i),t.add(i.name),l&&a.pop();else{a.push(s.length-1);for(let u of i.inputs)n[u.name]==null&&(n[u.name]=new Set),n[u.name].add(i.name),!t.has(u.name)&&s.push(u)}}return{sorted:o,recipientMap:n}}function Wq(r){let e;if(r.sourceLayer.inboundNodes.length===1)e=r.sourceLayer.output;else{let t=null;for(let o=0;o<r.sourceLayer.inboundNodes.length;++o)for(let n of r.sourceLayer.inboundNodes[o].outputTensors)if(n.id===r.id){t=o;break}e=r.sourceLayer.getOutputAt(t)}return e}var Vo=class extends Pe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let w=this.getClassName().toLowerCase();this.name=cl(w)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],qn(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new z(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new z(`${a.length} of ${n} weights are not set: ${a}`)}_p(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${dl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Bg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return G(()=>{e=yt(e);let o=new Os;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Zu(this.outputs,o,t)})}computeMask(e,t){return G(()=>{e=yt(e);let o;return t==null?o=Un(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=bp(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(of);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],w=u.tensorIndices[h],b=`${g.name}_${x}_${w}`,_=o[b];p.push(_)}let m=c.computeOutputShape(gr(p)),f=bp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Bo(l in o),s.push(o[l])}return gr(s)}runInternalGraph(e,t){t==null&&(t=Un(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(of);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,w,b;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,k]=d[0];h.mask==null&&(h.mask=k),w=yt(p.call(_,h)),b=yt(p.computeMask(_,k)),g=[_],x=[k]}else g=d.map(_=>_[0]),x=d.map(_=>_[1]),h.mask==null&&(h.mask=x),w=yt(p.call(g,h)),b=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Ne("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let k=f[_],A=w[_],N=b[_];o[k.id]=[A,N]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Bo(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Vo?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Vo.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new z(`No such layer: ${e}`)}calculateLosses(){return G(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Vo.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Vo.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],w=m.nodeIndices[g],b=m.tensorIndices[g],_=Vo.nodeKey(x,w),k=t[_];k==null&&(k=0),h.push([x.name,k,b,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,o.push(c)}e.layers=o;let n=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Vo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];n.push([i.name,c,p])}e.inputLayers=n;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Vo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,o={},n=!1){let s={},a={};function i(g,x){g.name in a?a[g.name].push(x):a[g.name]=[x]}function l(g,x){let w=[],b;for(let _ of x){let k=_[0],A=_[1],N=_[2];if(b=_[3]==null?{}:_[3],!(k in s)){i(g,x);return}let $=s[k];if($.inboundNodes.length<=A){i(g,x);return}let F=$.inboundNodes[A];w.push(F.outputTensors[N])}w.length>0&&g.apply(gr(w),b)}function u(g){let x=g.name,w=Qr(g,t.customObjects!=null?t.customObjects:{});w.setFastWeightInitDuringBuild(n),s[x]=w,g.inboundNodes.forEach(_=>{if(!(_ instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${_}`);i(w,_)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!LT(a);)for(let g of p){let x=s[g.name];if(x.name in a){let w=a[x.name];delete a[x.name];for(let b of w)l(x,b)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],w=g[1],b=g[2];Bo(x in s);let k=s[x].inboundNodes[w].outputTensors;m.push(k[b])}let h=t.outputLayers;for(let g of h){let x=g[0],w=g[1],b=g[2];Bo(x in s);let k=s[x].inboundNodes[w].outputTensors;f.push(k[b])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. 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e.className=r,e.config={},O_(e)}else return r instanceof co?r:O_(r)}function P_(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var M_=class extends Q.Serializable{},Ju=class extends M_{constructor(e){super();P_(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return G(()=>{let t=gt([1]);return this.hasL1&&(t=ee(t,ge(P(this.l1,Nt(e))))),this.hasL2&&(t=ee(t,ge(P(this.l2,qu(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ju.className="L1L2";Q.registerClass(Ju);function M1(r){return P_(r),new Ju({l1:r!=null?r.l1:null,l2:0})}function L1(r){return P_(r),new Ju({l2:r!=null?r.l2:null,l1:0})}var z1={l1l2:"L1L2"};function st(r){return lp(r)}function B1(r,e={}){return Li(r,Q.SerializationMap.getMap().classNameMap,e,"regularizer")}function bt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in z1?z1[r]:r,config:{}};return B1(t)}else return r instanceof M_?r:B1(r)}var Tf=class extends Pe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Fe(e);let o=Sr(e);return this.maxValue!=null&&(o=ir(o,0,this.maxValue)),o}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Tf.className="ReLU";Q.registerClass(Tf);var Af=class extends Pe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Fe(e);return Ca(o,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Af.className="LeakyReLU";Q.registerClass(Af);var Ef=class extends Pe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=bt(e.alphaRegularizer),this.alphaConstraint=Ot(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ze(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let o={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)o[n]=e[n];this.inputSpec=[new St({ndim:e.length,axes:o})],this.built=!0}call(e,t){return e=Fe(e),Ta(e,this.alpha.read())}getConfig(){let e={alphaInitializer:vt(this.alphaInitializer),alphaRegularizer:st(this.alphaRegularizer),alphaConstraint:Ft(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Ef.className="PReLU";Q.registerClass(Ef);var Df=class extends Pe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ne(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Fe(e);return Ns(o)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Df.className="ELU";Q.registerClass(Df);var $f=class extends Pe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let o=Fe(e);return o.mul(Fa(o.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};$f.className="ThresholdedReLU";Q.registerClass($f);var Rf=class extends Pe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Sf().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let o=Fe(e);return this.softmax(o,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Rf.className="Softmax";Q.registerClass(Rf);function gl(r,e,t){if(typeof r=="number")return Un(r,e);if(r.length!==e)throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. 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instead`);if(s==="channelsFirst"&&(r=je(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=ku(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ao(i,t)),i})}function V1(r,e,t,o=[1,1],n="valid",s,a,i=null){return G(()=>{if(s==null&&(s=Zr()),Rt(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=Of(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Gn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=je(l,[0,3,1,2])),l})}function sK(r,e,t,o=[1,1,1],n="valid",s,a){return G(()=>{if(s==null&&(s=Zr()),Rt(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=L_(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Em(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ao(i,t)),s==="channelsFirst"&&(i=je(i,[0,4,1,2,3])),i})}var Np=class extends Pe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Np.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gl(t.kernelSize,e,"kernelSize"),this.strides=gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Ms(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ot(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Bo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!yg(e.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ps(this.activation),useBias:this.useBias,biasInitializer:vt(this.biasInitializer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Qu=class extends Np{constructor(e,t){super(e,t);this.kernel=null,Qu.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ot(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. 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Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new St({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return G(()=>{let o=Fe(e);if(o.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Ff(l,m,c,this.padding),h=Ff(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=je(o,[0,2,3,1]));let x=vu(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=je(x,[0,3,1,2])),this.bias!=null&&(x=ao(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Ze(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Ff(t[n],l,a,this.padding),t[s]=Ff(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Pf.className="Conv2DTranspose";Q.registerClass(Pf);var z_=class extends Qu{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Ot(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Ot(t.pointwiseConstraint)}build(e){if(e=Ze(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new St({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return G(()=>{e=Fe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=je(e,[0,2,3,1])),o=Um(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ao(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.pointwiseInitializer=vt(this.pointwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.pointwiseRegularizer=st(this.pointwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseConstraint),e.pointwiseConstraint=Ft(this.pointwiseConstraint),e}};z_.className="SeparableConv";var Mf=class extends z_{constructor(e){super(2,e)}};Mf.className="SeparableConv2D";Q.registerClass(Mf);var tc=class extends Qu{constructor(e){super(1,e);tc.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!yg(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};tc.className="Conv1D";Q.registerClass(tc);var Lf=class extends Pe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return G(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let o=cf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return cf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=cf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return cf(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Lf.className="Cropping2D";Q.registerClass(Lf);var zf=class extends Pe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,KT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return G(()=>{let o=Fe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=je(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return je(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};zf.className="UpSampling2D";Q.registerClass(zf);function iK(r,e,t=[1,1],o="valid",n,s){return G(()=>{n==null&&(n=Zr()),Rt(n);let a=Of(r,n);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Is(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=je(a,[0,3,1,2])),a})}var Bf=class extends Np{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ot(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Ze(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return G(()=>{e=Fe(e);let o=iK(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ao(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=po(t,this.kernelSize[0],this.padding,this.strides[0]),a=po(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseRegularizer),e}};Bf.className="DepthwiseConv2D";Q.registerClass(Bf);function B_(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function V_(r,e,t,o=!1,n,s,a=!1,i=!1){return G(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Lr(2,l));if(e=je(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=ar(n,-1)),n=je(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=pr(e),h;n!=null&&(h=pr(n));for(let x=0;x<f;++x){let w=d[x],b=G(()=>r(w,m));if(n==null)p=b[0],m=b[1];else{let _=G(()=>{let k=h[x],A=tr(k).sub(k),N=b[0].mul(k).add(m[0].mul(A)),$=m.map((F,M)=>b[1][M].mul(k).add(F.mul(A)));return{output:N,newStates:$}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Vt(c,1)),[p,g,m]})}var mo=class extends Pe{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Sp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new St({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Lr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Sg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return G(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Ne("Constants support is not implemented in RNN yet.");Sg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new St({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new St({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Io("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_=[gt([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_[0]=gt([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new z(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Dt(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=B_(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new St({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Br){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return G(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Fe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=V_((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return G(()=>{let t=gt(e.shape);return t=ge(t,[1,2]),t=Oa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?kg(t,[1,o]):t):this.cell.stateSize>1?[kg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let o=this.cell.getConfig();return this.getClassName()===mo.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Qr(n,o);return new e(Object.assign(t,{cell:s}))}};mo.className="RNN";Q.registerClass(mo);var yl=class extends Pe{},Tp=class extends yl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Ms(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Hu([1,Fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return G(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>tr(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>tr(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Xn(P(e,a),this.kernel.read()):s=Xn(e,this.kernel.read()),this.bias!=null&&(s=ao(s,this.bias.read())),i!=null&&(o=P(o,i));let l=ee(s,Xn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ps(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Tp.className="SimpleRNNCell";Q.registerClass(Tp);var Vf=class extends mo{constructor(e){e.cell=new Tp(e),super(e)}call(e,t){return G(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Vf.className="SimpleRNN";Q.registerClass(Vf);var Ap=class extends yl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Hu([1,Fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return G(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>tr(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>tr(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let c=Xn(e,this.kernel.read());this.useBias&&(c=ao(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=cr(p,[2*this.units,this.units],p.rank-1),d=Xn(n,m),[h,g,x]=cr(c,3,c.rank-1),[w,b]=cr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,w)),l=this.recurrentActivation.apply(ee(g,b));let _=Xn(P(l,n),f);u=this.activation.apply(ee(x,_));let k=ee(P(i,n),P(ee(1,He(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ps(this.activation),recurrentActivation:Ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Ap.className="GRUCell";Q.registerClass(Ap);var Gf=class extends mo{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Ap(e),super(e)}call(e,t){return G(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Gf.className="GRU";Q.registerClass(Gf);var bl=class extends yl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Hu([1,Fs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hu([1,Fs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Ze(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends lo{apply(l,u){let c=s.apply([a]),p=new Ku().apply([a]),m=s.apply([a*2]);return p_(p_(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return G(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>tr(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>tr(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let m=Xn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,i[0])),m=ee(m,Xn(n,this.recurrentKernel.read())),this.useBias&&(m=ao(m,this.bias.read()));let[f,d,h,g]=cr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(P(u,s),P(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=P(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ps(this.activation),recurrentActivation:Ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};bl.className="LSTMCell";Q.registerClass(bl);var Wf=class extends mo{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new bl(e),super(e)}call(e,t){return G(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Wf.className="LSTM";Q.registerClass(Wf);var Sp=class extends yl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return G(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Sg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{Rs(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Qr(s,o));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return bf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}_p(t)}};Sp.className="StackedRNNCells";Q.registerClass(Sp);function Ma(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Cg(e(),t),a=()=>ul(s,e,o);return!n||n<=1?Dt(a().clone()):Array(n).fill(void 0).map(a).map(l=>Dt(l.clone()))}var aK=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var G_=class extends mo{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new St({ndim:5})]}call(e,t){return G(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return G(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Io("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_=[gt(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_[0]=gt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=po(u,n[0],s,a[0],i[0]),m=po(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};G_.className="ConvRNN2D";var Ep=class extends bl{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=gl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Ut(l,"kernelSize")),this.strides=gl(n||1,2,"strides"),this.strides.forEach(l=>Ut(l,"strides")),this.padding=s||"valid",Jr(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=gl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Ut(l,"dilationRate"))}build(e){var t;e=Ze(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends lo{apply(m,f){let d=u.apply([c]),h=Nr([c]),g=u.apply([c*2]);return pp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return G(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>tr(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(J,ie,ue)=>!ie||!ie[ue]?J:P(ie[ue],J),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>tr(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),w=u(s,d,3),b=3,[_,k,A,N]=cr(this.kernel.read(),i,b),[$,F,M,V]=this.useBias?cr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,$,this.padding),p=this.inputConv(p,k,F,this.padding),m=this.inputConv(m,A,M,this.padding),f=this.inputConv(f,N,V,this.padding);let[W,U,H,q]=cr(this.recurrentKernel.read(),i,b);h=this.recurrentConv(h,W),g=this.recurrentConv(g,U),x=this.recurrentConv(x,H),w=this.recurrentConv(w,q);let X=this.recurrentActivation.apply(ee(c,h)),oe=this.recurrentActivation.apply(ee(p,g)),Y=ee(P(oe,a),P(X,this.activation.apply(ee(m,x)))),re=P(this.recurrentActivation.apply(ee(f,w)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=aK(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=Kr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ao(s,o,this.dataFormat):s}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ep.className="ConvLSTM2DCell";Q.registerClass(Ep);var Uf=class extends G_{constructor(e){let t=new Ep(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Uf.className="ConvLSTM2D";Q.registerClass(Uf);var Dp=class extends Pe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return ul(()=>Cg(o,this.rate,s,this.seed),()=>o,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Dp.className="Dropout";Q.registerClass(Dp);var jf=class extends Dp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};jf.className="SpatialDropout1D";Q.registerClass(jf);var Hf=class extends Pe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=Ms(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ot(e.kernelConstraint),this.biasConstraint=Ot(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ze(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Ze(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=bg(this.activation.getClassName()),s;return n!=null?s=Xn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Xn(o,this.kernel.read()),this.bias!=null&&(s=ao(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ps(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Hf.className="Dense";Q.registerClass(Hf);var qf=class extends Pe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ze(e);for(let t of e.slice(1))if(t==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Kn(e,1)]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return t1(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};qf.className="Flatten";Q.registerClass(qf);var Kf=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ms(e.activation)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Ps(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Kf.className="Activation";Q.registerClass(Kf);var Xf=class extends Pe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return G(()=>(e=Fe(e),QT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Xf.className="RepeatVector";Q.registerClass(Xf);var Yf=class extends Pe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=Kn(e);if(a!==null){if(s===0||i%s!=0)throw new z(o);n[a]=i/s}else if(i!==s)throw new z(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Yf.className="Reshape";Q.registerClass(Yf);var Zf=class extends Pe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Lr(1,e.dims.length+1);if(!y.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new St({ndim:this.dims.length+1})]}computeOutputShape(e){e=Ze(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return je(Fe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Zf.className="Permute";Q.registerClass(Zf);var Jf=class extends Pe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let o=Fe(e),n=-1;return ol(Vn(o,this.maskValue),n)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=-1,s=!0,a=ol(Vn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};Jf.className="Masking";Q.registerClass(Jf);var Qf=class extends Pe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Ot(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return G(()=>this.maskZero?(e=Fe(e),Vn(e,Ce(e))):null)}computeOutputShape(e){if(e=Ze(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);return o.dtype!=="int32"&&(o=Fa(o,"int32")),vg(this.embeddings.read(),o.as1D()).reshape(Ze(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:vt(this.embeddingsInitializer),embeddingsRegularizer:st(this.embeddingsRegularizer),activityRegularizer:st(this.activityRegularizer),embeddingsConstraint:Ft(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="Embedding";Q.registerClass(Qf);var wl=class extends Pe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Ze(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=qn(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&qn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return G(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Fs(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Oa(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(Kn(c.slice(1))));f=je(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Lr(1,u).concat([0]);o.push(je(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=je(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Lr(0,i-1));a=je(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=qn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return G(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:ar(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=hr(o,t[n]);return o})}},ed=class extends wl{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return t})}};ed.className="Add";Q.registerClass(ed);var td=class extends wl{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=P(t,e[o]);return t})}};td.className="Multiply";Q.registerClass(td);var rd=class extends wl{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return P(1/e.length,t)})}};rd.className="Average";Q.registerClass(rd);var od=class extends wl{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Yr(t,e[o]);return t})}};od.className="Maximum";Q.registerClass(od);var nd=class extends wl{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=As(t,e[o]);return t})}};nd.className="Minimum";Q.registerClass(nd);var sd=class extends wl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return G(()=>pp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return G(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a<e.length;++a)t[a]==null?n.push(tr(e[a]).asType("bool")):t[a].rank<e[a].rank?n.push(ar(t[a],-1)):n.push(t[a]);let s=Ye(n,this.axis);return bu(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};sd.className="Concatenate";Q.registerClass(sd);function id(r,e){for(;r<0;)r+=e;return r}function lK(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return G(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var ad=class extends wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new z(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>id(s,e[a].shape.length)):n=[id(this.axes,t.shape.length),id(this.axes,o.shape.length)],this.normalize&&(t=wf(t,n[0]),o=wf(o,n[1])),lK(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[id(this.axes,e.length),id(this.axes,t.length)],o}computeOutputShape(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ad.className="Dot";Q.registerClass(ad);var ld=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);return ul(()=>mp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};ld.className="GaussianNoise";Q.registerClass(ld);var ud=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.rate>0&&this.rate<1?ul(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(mp(o.shape,1,s))},()=>o,t.training||!1):o})}};ud.className="GaussianDropout";Q.registerClass(ud);var cd=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Fe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return G(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return ul(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=io(Es(o),this.rate);u=Fa(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Fe(e),t.training||!1)}return e})}};cd.className="AlphaDropout";Q.registerClass(cd);function pd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=cw(r,e,t,o,n,s);else if(r.rank===3)a=pw(r,e,t,o,n,s);else if(r.rank===4)a=mw(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function uK(r,e,t,o,n=.001){return G(()=>{let s=Yc(r,o),a=s.mean,i=s.variance;return[pd(r,a,i,t,e,n),a,i]})}function cK(r,e,t,o,n=.001){return G(()=>{let s=Yc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Lr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[pd(r,u,c,m,p,n),a,i]})}function pK(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Lr(0,r.rank-1))?uK(r,e,t,o,n):cK(r,e,t,o,n)}var md=class extends Pe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ot(e.betaConstraint),this.gammaConstraint=Ot(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Ze(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new St({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return G(()=>{let o=t.training==null?!1:t.training,n=Fe(e),s=n.shape,a=s.length,i=Lr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Un(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Lr(0,a).slice(0,a-1)),m=()=>{if(p){let w=this.movingMean.read().reshape(u),b=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return pd(n,w,b,_,k,this.epsilon)}else return pd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=pK(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(w,b,_)=>{G(()=>{let k=1-_,A=w.read(),N=A.sub(b).mul(k);w.write(A.sub(N))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),movingMeanInitializer:vt(this.movingMeanInitializer),movingVarianceInitializer:vt(this.movingVarianceInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer),betaConstraint:Ft(this.betaConstraint),gammaConstraint:Ft(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};md.className="BatchNormalization";Q.registerClass(md);var fd=class extends Pe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Ze(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==qn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Fe(e),n=o.shape,s=n.length;return G(()=>{let a=!0,{mean:i,variance:l}=Yc(o,this.axis,a),u=Un(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),pd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};fd.className="LayerNormalization";Q.registerClass(fd);function mK(r,e,t){return G(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Zr()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],Fr(r,o)})}var dd=class extends Pe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Zr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new St({ndim:4})]}computeOutputShape(e){e=Ze(e);let t,o;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return G(()=>mK(Fe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};dd.className="ZeroPadding2D";Q.registerClass(dd);function qg(r,e,t,o,n,s){return G(()=>{Rt(n),u_(s),Jr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Zr()),s==null&&(s="max"),r=Of(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Na(r,e,t,i):a=wa(r,e,t,i),n==="channelsFirst"&&(a=je(a,[0,3,1,2])),a})}function G1(r,e,t,o,n,s){return G(()=>{Rt(n),u_(s),Jr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Zr()),s==null&&(s="max"),r=L_(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=zm(r,e,t,i):a=Tm(r,e,t,i),n==="channelsFirst"&&(a=je(a,[0,4,1,2,3])),a})}var W_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Jr(this.padding),this.inputSpec=[new St({ndim:3})]}computeOutputShape(e){e=Ze(e);let t=po(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return G(()=>{this.invokeCallHook(e,t),e=Oa(Fe(e),2);let o=this.poolingFunction(Fe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Co(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},hd=class extends W_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),qg(e,t,o,n,s,"max")}};hd.className="MaxPooling1D";Q.registerClass(hd);var gd=class extends W_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),qg(e,t,o,n,s,"avg")}};gd.className="AveragePooling1D";Q.registerClass(gd);var U_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Jr(this.padding),this.inputSpec=[new St({ndim:4})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=po(t,this.poolSize[0],this.padding,this.strides[0]),o=po(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return G(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},xd=class extends U_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),qg(e,t,o,n,s,"max")}};xd.className="MaxPooling2D";Q.registerClass(xd);var yd=class extends U_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),qg(e,t,o,n,s,"avg")}};yd.className="AveragePooling2D";Q.registerClass(yd);var j_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Jr(this.padding),this.inputSpec=[new St({ndim:5})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=po(t,this.poolSize[0],this.padding,this.strides[0]),o=po(o,this.poolSize[1],this.padding,this.strides[1]),n=po(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return G(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},bd=class extends j_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),G1(e,t,o,n,s,"max")}};bd.className="MaxPooling3D";Q.registerClass(bd);var wd=class extends j_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Jr(n),G1(e,t,o,n,s,"avg")}};wd.className="AveragePooling3D";Q.registerClass(wd);var H_=class extends Pe{constructor(e){super(e);this.inputSpec=[new St({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},_d=class extends H_{constructor(e){super(e||{})}call(e,t){return G(()=>{let o=Fe(e);return ht(o,1)})}};_d.className="GlobalAveragePooling1D";Q.registerClass(_d);var kd=class extends H_{constructor(e){super(e||{})}call(e,t){return G(()=>{let o=Fe(e);return ur(o,1)})}};kd.className="GlobalMaxPooling1D";Q.registerClass(kd);var q_=class extends Pe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new St({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vd=class extends q_{call(e,t){return G(()=>{let o=Fe(e);return this.dataFormat==="channelsLast"?ht(o,[1,2]):ht(o,[2,3])})}};vd.className="GlobalAveragePooling2D";Q.registerClass(vd);var Cd=class extends q_{call(e,t){return G(()=>{let o=Fe(e);return this.dataFormat==="channelsLast"?ur(o,[1,2]):ur(o,[2,3])})}};Cd.className="GlobalMaxPooling2D";Q.registerClass(Cd);var K_=class extends Pe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,o={}){let n=t.layer,s=Qr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Id=class extends K_{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Ze(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Ze(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return G(()=>(e=Fe(e),V_((a,i)=>[Fe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Id.className="TimeDistributed";Q.registerClass(Id);function fK(r){zi(qT,"BidirectionalMergeMode",r)}var dK="concat",Nd=class extends K_{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Qr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Qr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?dK:e.mergeMode,fK(this.mergeMode),e.weights)throw new Ne("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):gr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=B_(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=o,a.push(...o);let c=o.map(p=>new St({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Br;for(let u of a)if(u instanceof Br!==l)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return G(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=pp([n,s]):this.mergeMode==="sum"?i=ee(n,s):this.mergeMode==="ave"?i=P(.5,ee(n,s)):this.mergeMode==="mul"?i=P(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Rs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Rs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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bA=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[je(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[Gn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var wA=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ln(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"FusedBatchNormV3":return[Ln(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"LRN":return[Pm(C("x",r,e,t),C("radius",r,e,t),C("bias",r,e,t),C("alpha",r,e,t),C("beta",r,e,t))];case"Softmax":return[Aa(C("x",r,e,t))];case"LogSoftmax":return[Au(C("x",r,e,t))];case"SparseToDense":return[Qm(C("sparseIndices",r,e,t),C("outputShape",r,e,t),C("sparseValues",r,e,t),C("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _A=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ur(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ht(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Oi(C("x",r,e,t),a,i)]}case"Sum":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ge(C("x",r,e,t),a,i)]}case"All":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[bu(C("x",r,e,t),a,i)]}case"Any":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ol(C("x",r,e,t),a,i)]}case"ArgMax":{let a=C("axis",r,e,t);return[nl(C("x",r,e,t),a)]}case"ArgMin":{let a=C("axis",r,e,t);return[km(C("x",r,e,t),a)]}case"Prod":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Du(C("x",r,e,t),a,i)]}case"Cumsum":{let a=C("axis",r,e,t),i=C("exclusive",r,e,t),l=C("reverse",r,e,t);return[Iu(C("x",r,e,t),a,i,l)]}case"Bincount":let o=C("x",r,e,t),n=C("weights",r,e,t),s=C("size",r,e,t);return[fw(o,n,s)];case"DenseBincount":{let a=C("x",r,e,t),i=C("weights",r,e,t),l=C("size",r,e,t),u=C("binaryOutput",r,e,t);return[yw(a,i,l,u)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kA=(r,e,t)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=C("n",r,e,t),n=C("axis",r,e,t),s=C("tensors",r,e,t);return s=s.slice(0,o),[Ye(s,n)]}case"Gather":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[zn(o,ne(n,"int32"),0)]}case"GatherV2":{let o=C("axis",r,e,t),n=C("batchDims",r,e,t),s=C("x",r,e,t),a=C("indices",r,e,t);return[zn(s,ne(a,"int32"),o,n)]}case"Reverse":{let o=C("dims",r,e,t),n=[];for(let a=0;a<o.length;a++)o[a]&&n.push(a);let s=C("x",r,e,t);return[qt(s,n)]}case"ReverseV2":{let o=C("axis",r,e,t),n=C("x",r,e,t);return[qt(n,o)]}case"Slice":{let o=C("begin",r,e,t),n=C("size",r,e,t);return[Re(C("x",r,e,t),o,n)]}case"StridedSlice":{let o=C("begin",r,e,t),n=C("end",r,e,t),s=C("strides",r,e,t),a=C("beginMask",r,e,t),i=C("endMask",r,e,t),l=C("ellipsisMask",r,e,t),u=C("newAxisMask",r,e,t),c=C("shrinkAxisMask",r,e,t),p=C("x",r,e,t);return[Km(p,o,n,s,a,i,l,u,c)]}case"Pack":return G(()=>{let o=C("axis",r,e,t),n=C("tensors",r,e,t),s=n[0].shape,a=Co(n[0]).shape,i=n.map(l=>{let u=y.arraysEqual(l.shape,s);if(!u&&!y.arraysEqual(Co(l).shape,a))throw new Error("the input tensors shape does not match");return u?l:L(l,s)});return[Vt(i,o)]});case"Unpack":{let o=C("axis",r,e,t),n=C("tensor",r,e,t);return pr(n,o)}case"Tile":{let o=C("reps",r,e,t);return[Lo(C("x",r,e,t),o)]}case"Split":case"SplitV":{let o=C("axis",r,e,t),n=C("numOrSizeSplits",r,e,t),s=C("x",r,e,t);return cr(s,n,o)}case"ScatterNd":{let o=C("indices",r,e,t),n=C("values",r,e,t),s=C("shape",r,e,t);return[Ww(o,n,s)]}case"GatherNd":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[Uw(o,n)]}case"SparseToDense":{let o=C("sparseIndices",r,e,t),n=C("outputShape",r,e,t),s=C("sparseValues",r,e,t),a=C("defaultValue",r,e,t);return[Qm(o,s,n,s.dtype===a.dtype?a:ne(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vA=(r,e,t)=>{switch(r.op){case"FFT":return[Ea(C("x",r,e,t))];case"IFFT":return[Pi(C("x",r,e,t))];case"RFFT":return[Da(C("x",r,e,t))];case"IRFFT":return[Lu(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var CA=(r,e,t)=>{switch(r.op){case"Cast":return[ne(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[ar(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[Co(C("x",r,e,t),o)]}case"Reshape":return[L(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[Bm(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[Fr(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[Sa(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[_a(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[Dm(C("x",r,e,t),o,n)]}case"BroadcastTo":return[sl(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ik(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return G(()=>rA(s,a,i));case"basic_math":return G(()=>oA(s,a,i));case"control":return uA(s,a,i);case"convolution":return G(()=>pA(s,a,i));case"creation":return G(()=>mA(s,a,i));case"dynamic":return fA(s,a,i);case"evaluation":return G(()=>dA(s,a,i));case"image":return G(()=>xA(s,a,i));case"graph":return G(()=>hA(s,a,i));case"logical":return G(()=>yA(s,a,i));case"matrices":return G(()=>bA(s,a,i));case"normalization":return G(()=>wA(s,a,i));case"reduction":return G(()=>_A(s,a,i));case"slice_join":return G(()=>kA(s,a,i));case"spectral":return G(()=>vA(s,a,i));case"transformation":return G(()=>CA(s,a,i));case"hash_table":return gA(s,a,i,o);case"custom":let l=Zg(s.op);if(l&&l.customExecutor)return l.customExecutor(new _k(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)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 e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Sk(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>eo(m)[0]),c=[];o!=null&&(c=o.map(m=>eo(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((Nk(m)||a5(m)||l5(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function IA(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>eo(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var u5=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],c5=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],p5=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Nk(r){return u5.indexOf(r.op)>=0}function a5(r){return c5.indexOf(r.op)>=0}function l5(r){return p5.indexOf(r.op)>=0}var Rp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Rp(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=Sk(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;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 [${a}]`);if(n.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}return IA(this.graph,this.weightMap,o)}execute(e,t){e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(p=>this.graph.nodes[eo(p)[0]]),s=t.map(p=>eo(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return G(()=>{let p=new px(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,x]=eo(h),w=[];w[x]=e[h],m[g]=w});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<l.length;h++){let g=l[h];if(!m[g.name]){let x=Ik(g,m,p,this._resourceManager);if(y.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. 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You can use model.execute() instead.");let w=l.filter(b=>!Nk(b)&&!xr(b.name,d,t)).map(b=>b.name);if(w.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${b}`)}return d}processStack(e,t,o,n,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();o.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,n,o)&&([m]=Ls(p.node.name,o)),n[p.node.name]==null){let f=Ik(p.node,n,o,this._resourceManager);m||([m]=Ls(p.node.name,o));let d=o.currentContext;y.isPromise(f)?c.push(f.then(h=>(n[m]=h,o.currentContext=d,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u),h))):(n[m]=f,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u))}else this.processChildNodes(p.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Ls(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!xr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!xr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=eo(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=eo(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[o]=eo(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Tk=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?o=this.artifacts.userDefinedMetadata.signature:o=this.artifacts.signature,this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Rp(Qg.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Qg.Instance.transformGraph(e.modelInitializer);this.initializer=new Rp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let o=Ir.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ve)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,o,n)=>(t[o]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function NA(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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t.set(r,n.value),n.value}function WA(r,e=Pk){return GA(r,e)}function GA(r,e,t=new Set){let o=r[0];if(t.has(o))throw new Error("Circular references are not supported.");let n=e(r);if(n.recurse&&n.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(n.recurse)if(_l(o)){let s=Array.isArray(o)?[]:{};t.add(o);for(let a in o){let i=r.map(u=>u[a]),l=GA(i,e,t);s[a]=l}return t.delete(o),s}else throw new Error(`Can't recurse into non-iterable type: ${o}`);else return n.value}function Pk(r){return r===null?null:_l(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function gx(r,e){let t=new Map;hx(r,e,t);for(let n of Array.from(t.keys())){let s=t.get(n);if(y.isPromise(s)){let a=await s;t.set(n,a)}}return hx(r,e,t)}function _l(r){return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Ve))}function UA(r){return r==null||w5(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Ve||y.isTypedArray(r)}function w5(r){return r===null||typeof r!="object"&&typeof r!="function"}function jA(r){return VA(r,_5)}function _5(r){return r instanceof Ve?{value:r.clone(),recurse:!1}:_l(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var Td=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is 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n=0;n<o;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=o}};Fp.INITIAL_CAPACITY=32;function Mk(r){return new qA(r)}function Ad(r){return new KA(r)}function XA(r,e){return new Lk(r,e)}function ZA(r,e=La.FAIL){return new YA(r,e)}var Kt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],o=await e.next();for(;!o.done;)t.push(o.value),o=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),o=e(t.value);for(;!t.done&&o;)t=await this.next(),o=e(t.value)}handleErrors(e){return new nE(this,e)}filter(e){return new rE(this,e)}map(e){return new oE(this,e)}mapAsync(e){return new zk(this,e)}serialMapAsync(e){return new zk(this,e).serial()}flatmap(e){return new sE(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new tE(this,e,t)}columnMajorBatch(e,t=!0,o=Pk){return this.rowMajorBatch(e,t).map(s=>WA(s,o))}concatenate(e,t){return new Lk(Mk([this,e]),t)}take(e){return e<0||e==null?this:new eE(this,e)}skip(e){return e<0||e==null?this:new QA(this,e)}prefetch(e){return new Bk(this,e)}shuffle(e,t){return new iE(this,e,t)}serial(){return new JA(this)}},qA=class extends Kt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:jA(e),done:!1}}},KA=class extends Kt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},JA=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},QA=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},eE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},tE=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},rE=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},oE=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Mn.getTensorsInContainer(e.value),o=this.transform(e.value),n=Mn.getTensorsInContainer(o);for(let s of t)Mn.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},nE=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},zk=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Mn.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Mn.getTensorsInContainer(o);for(let s of t)Mn.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},Op=class extends Kt{constructor(){super();this.outputQueue=new Fp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},sE=class extends Op{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Mn.getTensorsInContainer(e.value),o=this.transform(e.value),n=Mn.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Mn.isTensorInList(s,n)||s.dispose();return!0}},Lk=class extends Kt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},La;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(La||(La={}));var YA=class extends Kt{constructor(e,t=La.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,o=0;function n(a){return a instanceof 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${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),fo(async()=>(await o.iterator()).columnMajorBatch(e,t,k5),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,fo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,fo(async()=>(await t.iterator()).filter(n=>G(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return fo(async()=>(await t.iterator()).map(o=>G(()=>e(o))),this.size)}mapAsync(e){let t=this;return fo(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return fo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,fo(async()=>{let n=Ad(async()=>({value:await t.iterator(),done:!1}));return XA(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?o=0:o=null,fo(async()=>(await t.iterator()).skip(e),o)}shuffle(e,t,o=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,s=aE.alea(t||y.now().toString());return fo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,fo(async()=>(await t.iterator()).take(e),o)}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()}};Ui.MAX_BUFFER_SIZE=1e4;function fo(r,e=null){return new class extends Ui{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function lE(r){return fo(async()=>Mk(r),r.length)}function uE(r){if(!_l(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return fo(async()=>{let t=await gx(r,o=>{if(o instanceof Ui)return{value:o.iterator(),recurse:!1};if(_l(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return ZA(t,La.SHORTEST)},e)}function k5(r){if(r===null)return null;let e=r[0];return UA(e)?{value:v5(r),recurse:!1}:{value:null,recurse:!0}}function v5(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Vt(r):Rr(r)}var Ed=class extends Ui{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var xx='"',Dd=Symbol("out"),cE=Symbol("field"),yx=Symbol("quote"),Vk=Symbol("quoteafterquote"),pE=Symbol("quoteinquote"),$d=class extends Ui{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Ed(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&y.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(y.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" 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(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let o=t.value;return this.parseRow(o,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),o={},n={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?n[a]=u:o[a]=u}}return Object.keys(n).length===0?o:{xs:o,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let o=[],n=0,s=e.length,a=Dd;for(let i=0;i<s;i++)switch(a){case Dd:switch(e.charAt(i)){case xx:n=i+1,a=yx;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;o.push(""),a=Dd;break;default:a=cE,n=i;break}break;case cE:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i)),a=Dd,n=i+1;break;default:}break;case yx:switch(e.charAt(i)){case xx:a=Vk;break;default:}break;case Vk:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i-1)),a=Dd,n=i+1;break;case xx:a=yx;break;default:a=pE;break}break;case pE:switch(e.charAt(i)){case xx:a=yx;break;default:}break;default:}if(a===Vk?o.push(e.substring(n,s-1)):o.push(e.substring(n)),t&&o.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Rd=class extends Kt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(j().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Rd(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(o){throw new Error(`Error thrown while initializing video stream: ${o.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Rr(o,t)}};var Fd=class extends Kt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Gt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Mi([a,s,l,i],[1,4])}else this.cropBox=Mi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(j().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let o=new Fd(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=qh.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return G(()=>{let t=ar(ne(e,"float32"),0),o;o=$s.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var Od=class{};var bx=class extends Kt{split(e){return new mE(this,e)}},mE=class extends bx{constructor(e,t){super();this.upstream=e,this.impl=new fE(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},fE=class extends Op{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Gk=class extends Kt{decodeUTF8(){return new hE(this)}},hE=class extends bx{constructor(e){super();this.upstream=e,this.impl=new gE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gE=class extends Op{constructor(e){super();if(this.upstream=e,j().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=dE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let o;return j().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Pd=class extends Gk{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(j().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>o(new Error("Aborted")),s.onerror=i=>o(new Error(i.type));let a=this.file.slice(this.offset,n);s.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function xE(r,e={}){let t,o;typeof r=="string"?t=r:(t=r.url,o=C5(r));let n=await y.fetch(t,o);if(n.ok){let s=new Uint8Array(await n.arrayBuffer());return new Pd(s,e)}else throw new Error(n.statusText)}var C5=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function wx(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var Md=class extends Od{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(wx(this.input)&&j().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Pd(this.input,this.options)}};var Ld=class extends Od{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return wx(this.url)?new Md(this.url,this.fileOptions).iterator():xE(this.url,this.fileOptions)}};function yE(r,e={}){return new $d(new Ld(r),e)}function bE(r){let e=Ad(r);return fo(async()=>e)}function wE(r){return fo(async()=>{let e=await r();return Ad(()=>e.next())})}async function _E(r,e){return Fd.create(r,e)}async function kE(r){return Rd.create(r)}var _x="3.3.0";function te(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var I5=Ar.whereImpl,Pp=class extends Ws{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Xa(this,Po())}nextDataId(){return Pp.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,j().get("IS_NODE")&&S.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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rD={kernelName:ql,backendName:"cpu",kernelFunc:CX};function IX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o;te([n,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(n.shape),m=y.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new lt(f.inShape,"float32"),h=d.values,[g,x,w]=d.strides,b=t.data.get(n.dataId).values,[_,k,A]=p,N=t.data.get(s.dataId).values,[$,F,M]=m,{batchSize:V,filterHeight:W,filterWidth:U,inChannels:H,inHeight:q,inWidth:X,outChannels:oe,outHeight:Y,outWidth:re,strideHeight:J,strideWidth:ie}=f,ue=W-1-f.padInfo.top,ae=U-1-f.padInfo.left,fe=oe/H;for(let de=0;de<V;++de)for(let xe=0;xe<H;++xe)for(let we=0;we<q;++we){let De=we-ue,Ie=Math.max(0,Math.ceil(De/J)),ze=Math.min(Y,(W+De)/J);for(let qe=0;qe<X;++qe){let it=qe-ae,At=Math.max(0,Math.ceil(it/ie)),Et=Math.min(re,(U+it)/ie),Ue=0;for(let ut=Ie;ut<ze;++ut){let mt=ut*J-De;for(let Mt=At;Mt<Et;++Mt){let 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bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",u=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(r="",e="attribute",t="varying",o="varying",n="texture2D",s="gl_FragColor",a="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Bs(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function Gp(r){let e=y.computeStrides(r).map(t=>t.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
}
`}var Rx=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;var Nv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=vl.DENSE;let t=Cl(e),o=Pt();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Bs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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);
}
${o.output} = result;
}
`}};var Sv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=vl.DENSE;let t=Cl(e),o=Pt();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Bs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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));
}
${o.output} = result;
}
`}};var Tv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Dr.DOWNLOAD;let t=Pt();this.outputShape=e,this.userCode=`
${Rx}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}};var Av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Dr.DOWNLOAD;let t=Pt();this.outputShape=e,this.userCode=`
${Rx}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}};var Ev=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Pt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Gp(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${a};
int c = imod(flatIndex, ${a});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${i}, 0., 0., 0.);
}
`}};var Dv=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Pt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${u} < ${e[1]}) {
localCoords[1] += ${u};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${a};
c = imod(flatIndex, ${a});
uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
values = ${n.texture2D}(A, uv);
if(offset == 0) {
result[${p}] = values[0];
} else if(offset == 1) {
result[${p}] = values[1];
} else if(offset == 2) {
result[${p}] = values[2];
} else {
result[${p}] = values[3];
}
}
}
`}this.userCode=`
${Gp(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${n.output} = ${l};
}
`}};function _R(r){let e=Pt(),t=`${e.version}
precision highp float;
${e.attribute} vec3 clipSpacePos;
${e.attribute} vec2 uv;
${e.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return rR(r,t)}function kR(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return iR(r,e)}function vR(r){let e=new Uint16Array([0,1,2,2,1,3]);return aR(r,e)}function eh(r,e,t,o,n,s){uR(e,t);let a=lR(r),i=r.TEXTURE_2D;return Ae(r,()=>r.bindTexture(i,a)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ae(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function $v(r){return r.internalFormatFloat}function CR(r,e,t,o){let[n,s]=ac(e,t);return eh(r,n,s,$v(o),o.textureFormatFloat,r.FLOAT)}function Rv(r){return r.internalFormatHalfFloat}function IR(r,e,t,o){let[n,s]=ac(e,t);return eh(r,n,s,Rv(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Fv(r){return r.downloadTextureFormat}function NR(r,e,t,o){let[n,s]=ac(e,t);return eh(r,n,s,Fv(o),r.RGBA,r.UNSIGNED_BYTE)}function Ov(r){return r.internalFormatPackedFloat}function SR(r,e,t,o){let[n,s]=Hi(e,t);return eh(r,n,s,Ov(o),r.RGBA,r.FLOAT)}function Pv(r){return r.internalFormatPackedHalfFloat}function TR(r,e,t,o){let[n,s]=Hi(e,t);return eh(r,n,s,Pv(o),r.RGBA,o.textureTypeHalfFloat)}function AR(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ae(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),wv(r,e,"clipSpacePos",t,3,s,o)&&wv(r,e,"uv",t,2,s,n)}function ER(r,e,t,o,n,s){Ae(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function DR(r,e,t){Ae(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function $R(r,e,t,o){let n=r.createBuffer();Ae(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ae(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ae(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ae(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function RR(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function FR(r,e,t,o){let[n,s]=ac(e,t),a=4,i=new Uint8Array(Q$(e*t,a));return Ae(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function OR(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(eR(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function PR(r,e,t){let o=new Float32Array(e*t*4);return Ae(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Mv=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=j().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,J$(t,e)):this.gl=Go(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(j().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Jd(this.gl,s),Wo(this.gl,a))this.textureHalfFloatExtension=Jd(this.gl,a);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Wo(this.gl,n))this.colorBufferHalfFloatExtension=Jd(this.gl,n);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(o="EXT_color_buffer_float",Wo(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wo(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=kR(this.gl),this.indexBuffer=vR(this.gl),this.framebuffer=cR(this.gl),this.textureConfig=Zd(this.gl,this.textureHalfFloatExtension)}get debug(){return j().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),TR(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),SR(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(_v(this.gl,this.framebuffer),this.outputTexture=null),Ae(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>FR(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return OR(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return RR(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=$R(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(j().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let 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this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),fR(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Hi(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Ax(this.gl,this.program),Qd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ae(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ae(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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n=this.gl;Ex(n,e,this.framebuffer),this.debug&&Qd(n),this.outputTexture=e,Ae(n,()=>n.viewport(0,0,t,o)),Ae(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ae(this.gl,()=>this.gl.scissor(e,t,o,n))}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 OY(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:MR}=S;function LR(r,e,t,o){let n=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(`
`),a=r.map(d=>PY(d,e,o)).join(`
`),i=e.texShape,l=Pt(),u=zY(l),c,p,m=GY(l);return e.isPacked?(c=MY(e.logicalShape,i),p=VY(l)):(c=LY(e.logicalShape,i),p=BY(l)),o&&(m+=WY),[m,u,p,s,c,a,t].join(`
`)}function Wp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return UY(r);case 1:return jY(r);case 2:return HY(r);case 3:return qY(r);case 4:return KY(r);case 5:return XY(r);case 6:return YY(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function zR(r){switch(r.shapeInfo.logicalShape.length){case 0:return ZY(r);case 1:return JY(r);case 2:return QY(r);case 3:return e7(r);default:return t7(r)}}function PY(r,e,t=!1){let o="";t?o+=zR(r):o+=Wp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=r7(r,e):o+=o7(r,e)),o}function MY(r,e){switch(r.length){case 0:return BR();case 1:return n7(r,e);case 2:return a7(r,e);case 3:return s7(r,e);default:return i7(r,e)}}function LY(r,e){switch(r.length){case 0:return BR();case 1:return l7(r,e);case 2:return f7(r,e);case 3:return u7(r,e);case 4:return c7(r,e);case 5:return p7(r,e);case 6:return m7(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function zY(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function BY(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function VY(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function GY(r){return`${r.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${r.varyingFs} vec2 resultUV;
${r.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${r.defineSpecialNaN}
${r.defineSpecialInf}
${r.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${d7}
${h7}
${g7}
`}var d7=`
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);
}
`,h7=`
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);
}
`,g7=`
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);
}
`,WY=`
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 BR(){return`
int getOutputCoords() {
return 0;
}
`}function n7(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
}
`}function l7(r,e){return e[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`}function s7(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int b = index / ${n};
index -= b * ${n};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec3(b, r, c);
}
`}function u7(r,e){let t=Bs(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
return ivec3(r, c, d);
}
`}function i7(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+a,i=`b${l}, `+i;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
int b = index / ${n};
index -= b * ${n};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec${r.length}(${i});
}
`}function c7(r,e){let t=Bs(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
return ivec4(r, c, d, d2);
}
`}function p7(r,e){let t=Bs(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function m7(r,e){let t=Bs(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function a7(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`;let o=Math.ceil(r[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function f7(r,e){return y.arraysEqual(r,e)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:r[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function uc(r){return`offset${r}`}function ZY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Pt();return`
vec4 ${t}() {
return ${o.texture2D}(${e}, halfCR);
}
`}function UY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
float ${t}() {
return sampleTexture(${e}, halfCR);
}
`;let[s,a]=r.shapeInfo.texShape,i=uc(e);return`
float ${t}() {
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
return sampleTexture(${e}, uv);
}
`}function JY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Pt();return`
vec4 ${t}(int index) {
vec2 uv = packedUVfrom1D(
${n[0]}, ${n[1]}, index);
return ${s.texture2D}(${e}, uv);
}
`}function jY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
float ${t}(int index) {
${Up(r)}
}
`;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
float ${t}(int index) {
return sampleTexture(${e}, halfCR);
}
`;let a=uc(e);return s===1?`
float ${t}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
return sampleTexture(${e}, uv);
}
`:n===1?`
float ${t}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${t}(int index) {
vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
return sampleTexture(${e}, uv);
}
`}function QY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Pt();if(n!=null&&y.arraysEqual(e,n))return`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
return ${i.texture2D}(${t}, uv);
}
`;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
return ${i.texture2D}(${t}, uv);
}
`}function HY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=jp(r,i),m=["row","col"];return`
${Wp(p)}
float ${o}(int row, int col) {
return ${o}(${Hp(m,a)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
${Up(r)}
}
`;let l=n[0],u=n[1],c=uc(t);return u===1?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${t}, uv);
}
`:l===1?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${e[1]} + col + ${c};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${t}, uv);
}
`}function e7(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=jp(r,p),d=["b","row","col"];return`
${zR(f)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${Hp(d,m)});
}
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Pt();return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${i}, ${u}, ${l}, b, row, col);
return ${c.texture2D}(${t}, uv);
}
`}function qY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=jp(r,l),h=["row","col","depth"];return`
${Wp(d)}
float ${o}(int row, int col, int depth) {
return ${o}(${Hp(h,i)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${n}, ${s}, 1)));
${Up(r)}
}
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
float ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;if(p===s&&m==null)return`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;let f=uc(t);return`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n} + col * ${s} + depth + ${f};
vec2 uv = uvFromFlat(${c}, ${p}, index);
return sampleTexture(${t}, uv);
}
`}function t7(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Pt();return`
vec4 ${n}(${p}) {
int index = ${m};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
return ${f.texture2D}(${o}, uv);
}
`}function KY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=jp(r,i),h=["row","col","depth","depth2"];return`
${Wp(d)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${Hp(h,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${s}, ${n}, 1)));
${Up(r)}
}
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
float ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${n}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;if(m===n&&u==null)return`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${e[1]*e[2]}, ${e[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;let f=uc(t);return`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} +
depth * ${n} + depth2;
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
return sampleTexture(${t}, uv);
}
`}function XY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=jp(r,l),g=["row","col","depth","depth2","depth3"];return`
${Wp(h)}
float ${o}(int row, int col, int depth, int depth2, int depth3) {
return ${o}(${Hp(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${n})) +
depth3;
${Up(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${n}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(f===n&&c==null)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;let d=uc(t);return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${a} + depth * ${s} +
depth2 * ${n} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${t}, uv);
}
`}function YY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=jp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
${Wp(g)}
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${o}(${Hp(x,s)});
}
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${Up(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;if(d===a&&p==null)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;let h=uc(t);return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${t}, uv);
}
`}function Up(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function r7(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=MR(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(w=>`coords.${p[w+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((w,b)=>`coords.${p[b+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!x)a===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:f=`
return vec4(outputValue.x);
`;else if(i.length){let w=s-2,b=s-1;i.indexOf(w)>-1&&i.indexOf(b)>-1?f="return vec4(outputValue.x);":i.indexOf(w)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(b)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${n}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${o}(${m});
${f}
}
`}function o7(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
float ${n}() {
return sampleTexture(${t}, resultUV);
}
`;let u=Le(l),c=MR(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${n}() {
${u} coords = getOutputCoords();
${m}
return get${o}(${d});
}
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void main() {
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} else {
${a}
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}
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int cp1 = c + 1;
bool cEdge = cp1 >= ${e};
bool rEdge = rp1 >= ${t};
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${_7(t)}
${Gp(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${o}
setOutput(result);
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${Bs(["r","c","d"],r)}
return ivec3(r, c, d);
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float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},yr="if (isnan(x)) return x;",_F="return x;",Vv="return abs(x);";var kF="return (x >= 0.0) ? x : (exp(x) - 1.0);",vF=yr+`
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return (x < 0.0) ? 0.0 : min(6.0, x);
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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;
`,SF=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
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result.b = isNaN.b ? x.b : result.b;
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vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
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vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var Gv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=jt("rc",t),n=Le(t),s=xF(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${s});
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if (isnan(a)) return a;
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`;var rs=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var Sl=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;var Gs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
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result.z = 0.;
result.w = 0.;
`;else if(a=`
${Le(s)} coords = getOutputCoords();
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result.w = 0.;
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bool nextRowOutOfBounds =
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bool nextColOutOfBounds =
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result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Ht(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var EF={kernelName:Ro,backendName:"webgl",kernelFunc:Ht};function go(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=Ht({inputs:{x:o},backend:t}),l=Ht({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var DF={kernelName:Gl,backendName:"webgl",kernelFunc:go};var Wv="return (a < 0.) ? b * a : a;",Uv=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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`;function R7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gs(Uv,n.shape,a.shape):new rs(Wv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var $F={kernelName:un,backendName:"webgl",kernelFunc:R7};var jv="return (a < 0.) ? b * a : a;",Hv=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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result.r = isNaN.r > 0. ? NAN : result.r;
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result.a = isNaN.a > 0. ? NAN : result.a;
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vec4 b = getPreluActivationWeightsAtOutCoords();
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}`:g=`vec4 activation(vec4 x) {
${i}
}`,x="result = activation(result);");let w=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",_="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${g}
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${b};
int batchB = ${_};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${h[0]});
result += (${d[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${w}
${x}
setOutput(result);
}
`}};var qv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},zx=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var PF="return a * b;";function Kv(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=S.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new zx(qv.REAL,o.shape,n.shape),c=new zx(qv.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=go({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=iF(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Gs(PF,o.shape,n.shape):a=new rs(PF,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var MF={kernelName:xn,backendName:"webgl",kernelFunc:Kv};function LF(r,e,t){let o=[Il(r.shape),...Nl(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Il(e),...Nl(e)],a=new th(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!lc(n.shape,l)&&!(c.texture!==null&&lc(c.shape,l))?LF(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var zF={kernelName:ds,backendName:"webgl",kernelFunc:pe};var Bx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}};var Xv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
}
`,f="vec4";t==="all"?(i="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):t==="any"&&(i="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%o>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${u});
}
`}};function O7(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function To(r,e,t,o){let n=O7(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new Bx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Bx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Xv({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Yv=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=P7(t);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function P7(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Zv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=Lv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${l}) {
result[1] = ${u};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
result[2] = ${u};
if(${l}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Al(r,e,t){let o=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zv(r.shape,e):new Yv(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function BF(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=S.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Al(r,l,o),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=S.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=pe({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),w=fu(r.dtype),b=To(x,w,"sum",o),_=pe({inputs:{x:b},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(b),u&&o.disposeIntermediateTensorInfo(c),_}function nh(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return BF(n,s,a,t)}var VF={kernelName:Dn,backendName:"webgl",kernelFunc:nh};function Lt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=qp(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Al(n,s,a);return u}var GF={kernelName:Pn,backendName:"webgl",kernelFunc:Lt};var Jv=1e3;function cc({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),w=y.sizeFromShape(g),b=x===w||x===1||w===1;y.assert(u>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let k=(x>w?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let A=t?[x,p,f]:[x,f,p],N=o?[w,d,m]:[w,m,d],$=pe({inputs:{x:r},backend:n,attrs:{shape:A}}),F=pe({inputs:{x:e},backend:n,attrs:{shape:N}}),M=[$,F],V=Math.max(x,w),W=t?$.shape[1]:$.shape[2],U=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?Tl(l,!0):null,oe=U||H||q||X!=null,Y;if((f===1||d===1)&&W>Jv&&oe===!1){let J=$,ie=F;t&&(J=Lt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),M.push(J)),o&&(ie=Lt({inputs:{x:F},backend:n,attrs:{perm:[0,2,1]}}),M.push(ie));let ue=d!==1,ae=d===1,fe=J;ue&&(fe=pe({inputs:{x:J},backend:n,attrs:{shape:[V,W,1]}}),M.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=pe({inputs:{x:ie},backend:n,attrs:{shape:[V,1,W]}}),M.push(xe));let we=Kv({inputs:{a:fe,b:xe},backend:n});Y=nh({inputs:{x:we},backend:n,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let J=dr(r.dtype,e.dtype),ie=new oh(A,N,[V,f,d],t,o,U,X,H,q),ue=[$,F];if(s!=null&&ue.push(s),H&&ue.push(a),q){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));ue.push(ae),M.push(ae)}Y=n.runWebGLProgram(ie,ue,J)}let re=pe({inputs:{x:Y},backend:n,attrs:{shape:k}});M.push(Y);for(let J of M)n.disposeIntermediateTensorInfo(J);return re}function M7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return cc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var WF={kernelName:ws,backendName:"webgl",kernelFunc:M7};var UF="return abs(x);";function L7(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Ox(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Vs(o.shape,UF):n=new ho(o.shape,UF),t.runWebGLProgram(n,[o],o.dtype)}var jF={kernelName:as,backendName:"webgl",kernelFunc:L7};var z7=yr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,B7=_e({opSnippet:z7}),HF={kernelName:Hs,backendName:"webgl",kernelFunc:B7};var V7=yr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,G7=_e({opSnippet:V7}),qF={kernelName:qs,backendName:"webgl",kernelFunc:G7};var KF="return a + b;",W7=nt({opSnippet:KF,packedOpSnippet:KF,supportsComplex:!0,cpuKernelImpl:jR}),XF={kernelName:wo,backendName:"webgl",kernelFunc:W7};var Qv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${o.join(`
`)}
float result = ${n};
setOutput(result);
}
`}};var eC=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${o.join(`
`)}
vec4 result = ${n};
setOutput(result);
}
`}};function Vx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ht({inputs:{x:o[0]},backend:t});if(o.length>j().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Vx({inputs:o.slice(0,l),backend:t}),c=Vx({inputs:o.slice(l),backend:t});return Vx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>dr(l,u)),s=o.map(l=>l.shape),i=j().getBool("WEBGL_PACK")?new eC(o[0].shape,s):new Qv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var YF={kernelName:Ho,backendName:"webgl",kernelFunc:Vx};function U7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"all",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var ZF={kernelName:Ml,backendName:"webgl",kernelFunc:U7};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"any",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var JF={kernelName:Ll,backendName:"webgl",kernelFunc:j7};var tC=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${n}; i++) {
int inIdx = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var rC=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=jt("coords",l),p,m;if(a===1){m=l+1;let $=Le(m);p=`
${$} sourceLocR = ${$}(${c.join()}, 0);
++${c[l-1]};
${$} sourceLocG = ${$}(${c.join()}, 0);
++${c[l-2]};
${$} sourceLocA = ${$}(${c.join()}, 0);
--${c[l-1]};
${$} sourceLocB = ${$}(${c.join()}, 0);
--${c[l-2]};`}else m=l,p=`
${u} sourceLocR = coords;
++${c[l-1]};
${u} sourceLocG = coords;
++${c[l-2]};
${u} sourceLocA = coords;
--${c[l-1]};
${u} sourceLocB = coords;
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map($=>"int "+$),g=jt("sourceLocR",m-1).concat("inIdx.r"),x=jt("sourceLocG",m-1).concat("inIdx.g"),w=jt("sourceLocB",m-1).concat("inIdx.b"),b=jt("sourceLocA",m-1).concat("inIdx.a"),_=o==="max"?"greaterThan":"lessThan",k=n?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${w.join()}),
getBestIndicesAChannel(${b.join()})));`,A=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${w.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,N=n?"":`
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${f.join()}),
vec2(${f.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${h.join()}) {
return getChannel(getA(${f.join()}),
vec2(${f.slice(-2).join()}));
}
${N}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${A};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${A};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function QF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new tC(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=QF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function eO(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new rC(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=eO(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Gx(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!j().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=S.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=pe({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=QF(r,u,o);s.push(c);let p=pe({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return eO(r,e,o)}function H7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Gx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var tO={kernelName:qo,backendName:"webgl",kernelFunc:H7};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Gx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var rO={kernelName:ea,backendName:"webgl",kernelFunc:q7};var K7=yr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,X7=_e({opSnippet:K7}),oO={kernelName:Ks,backendName:"webgl",kernelFunc:X7};var Y7=yr+"return log(x + sqrt(x * x + 1.0));",Z7=_e({opSnippet:Y7}),nO={kernelName:Xs,backendName:"webgl",kernelFunc:Z7};var J7=yr+`
return atan(x);
`,Q7=_e({opSnippet:J7}),sO={kernelName:Ys,backendName:"webgl",kernelFunc:Q7};var e9=FF+`
return atan(a, b);
`,t9=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+OF+`
return result;
`,r9=nt({opSnippet:e9,packedOpSnippet:t9}),iO={kernelName:Js,backendName:"webgl",kernelFunc:r9};var o9=yr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,n9=_e({opSnippet:o9}),aO={kernelName:Zs,backendName:"webgl",kernelFunc:n9};var Ki=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,w="0.0";if(h||(w="-1.0 / 1e-20"),o){let $=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${$} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,A=a%4,N=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${w};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${w});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${N}
}
int xC = xCCorner + ${k};
if (${A===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${N}
} else if (${A===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${N}
} else if (${A===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${N}
}
}
setOutput(${_});
}
`}},pc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",_="0.0";if(b||(_="-1.0 / 1e-20"),o){let M=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${w});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${M} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let k="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let N=Math.floor(a/4)*4,$=a%4,F=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${w});
const float initializationValue = ${_};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${_});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${N}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${F}
}
int xC = xCCorner + ${N};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${F}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${F}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${F}
}
}
setOutput(${A});
}
}
`}};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;qi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:n},backend:t});let p=new Ki(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var lO={kernelName:Ko,backendName:"webgl",kernelFunc:s9};function i9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=S.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new pc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var uO={kernelName:ta,backendName:"webgl",kernelFunc:i9};var oC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},nC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${x});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${p};
wD += ${l}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function a9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new nC(m);return t.runWebGLProgram(f,[n],a.dtype)}var cO={kernelName:Bl,backendName:"webgl",kernelFunc:a9};function l9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;qi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=S.computePool2DInfo(a.shape,i,l,1,u),p=new oC(c);return t.runWebGLProgram(p,[n],a.dtype)}var pO={kernelName:zl,backendName:"webgl",kernelFunc:l9};function u9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return cc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var mO={kernelName:Xo,backendName:"webgl",kernelFunc:u9};var sC=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var iC=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}};var c9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=j().getBool("WEBGL_PACK_NORMALIZATION")?new iC(o.shape,n.shape,s.shape,c,p,l):new sC(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},fO={kernelName:an,backendName:"webgl",kernelFunc:c9};var aC=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=p9(this.rank),s,a=e.map((i,l)=>`sourceLoc.${lC[l]} = start[${l}] + coords.${lC[l]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
${o}
void main() {
${s}
setOutput(getSource(${n}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},lC=["x","y","z","w","u","v"];function p9(r){if(r===1)return"sourceLoc";if(r<=6)return lC.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var uC=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=jt("coords",this.rank),n=jt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
result.x = ${a};
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.y = ${a};
--${n[this.rank-1]};
}
`,l=this.rank===1?"":`
--${o[this.rank-1]};
if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
++${n[this.rank-2]};
result.z = ${a};
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${l}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function m9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=sr.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Va(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=sr.parseSliceParams(n,s,a);if(sr.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=pF(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=sr.isSliceContinous(n.shape,i,l);if(u||!c){let p=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uC(l):new aC(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),m9(n,i,l,t)}var dO={kernelName:gs,backendName:"webgl",kernelFunc:Va};var f9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((w,b)=>w*b),l=S.getReshaped(n.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=[],d=pe({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Va({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(w=>t.disposeIntermediateTensorInfo(w)),x},hO={kernelName:ra,backendName:"webgl",kernelFunc:f9};function d9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=Fx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var gO={kernelName:Vl,backendName:"webgl",kernelFunc:d9};var h9="return float(a != b);",cC=nt({opSnippet:h9,dtype:"bool"}),xO={kernelName:xi,backendName:"webgl",kernelFunc:cC};function Ga(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ht({inputs:{x:n.complexTensorInfos.real},backend:t})}var yO={kernelName:iu,backendName:"webgl",kernelFunc:Ga};var g9="return float(int(x));";function bO(r,e){let t=new ho(r.shape,g9),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function pC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ht({inputs:{x:n},backend:t});let a=gt(n.shape),i=pC({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=go({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Ga({inputs:{input:n},backend:t}),i=pC({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ht({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return bO(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=cC({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var wO={kernelName:Do,backendName:"webgl",kernelFunc:pC};var _O="return ceil(x);",x9=_e({opSnippet:_O,packedOpSnippet:_O,cpuKernelImpl:qR}),kO={kernelName:Yo,backendName:"webgl",kernelFunc:x9};var mC=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var fC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;j().getBool("WEBGL_PACK_CLIP")?i=new fC(n.shape):i=new mC(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var vO={kernelName:$o,backendName:"webgl",kernelFunc:y9};var dC=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function CO(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function b9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new dC(o.shape),a=[CO(o,n.complexTensorInfos.real),CO(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var IO={kernelName:oa,backendName:"webgl",kernelFunc:b9};var hC=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${o.join(`
`)}
}
`}};var gC=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=jt("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
return getChannel(
getT${h}(${Wx(i,u,g)}),
vec2(${Wx(c,u,g)}));
}`}let f=l.length,d=l[l.length-1];m+=`
return getChannel(
getT${f}(${Wx(i,u,d)}),
vec2(${Wx(c,u,d)}));`,this.userCode=`
float getValue(${i.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[n-1]} = ${a[n-1]} + 1;
if (${a[n-1]} < ${o[n-1]}) {
result.g = getValue(${a});
}
${a[n-2]} = ${a[n-2]} + 1;
if (${a[n-2]} < ${o[n-2]}) {
result.a = getValue(${a});
}
${a[n-1]} = ${a[n-1]} - 1;
if (${a[n-2]} < ${o[n-2]} &&
${a[n-1]} < ${o[n-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Wx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function mc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ht({inputs:{x:n.complexTensorInfos.imag},backend:t})}var NO={kernelName:Ql,backendName:"webgl",kernelFunc:mc};function fc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>Ga({inputs:{input:d},backend:t})),c=r.map(d=>mc({inputs:{input:d},backend:t})),p=fc(u,e,t),m=fc(c,e,t),f=go({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=SO(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=KR(p,c,o,m),d=S.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>j().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=fc(r.slice(0,u),e,t),p=fc(r.slice(u),e,t),m=fc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new gC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=SO(r,e,t),a=new hC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=pe({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function SO(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function xC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return Ht({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return S.assertParamsConsistent(l,s),fc(i,s,t)}var TO={kernelName:ls,backendName:"webgl",kernelFunc:xC};var sh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,w=g?2:3,b=g?3:1,_="",k="";o&&(n?_=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?_=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}`:_=`
float activation(float x) {
${o}
}
`,k="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${_}
const ivec2 strides = ivec2(${l}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${b}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${w}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${A}
${k}
setOutput(result);
}
`}},yC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${o}, ${n});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${p}; wF++) {
int xF = xFCorner + wF * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var bC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Pt(),x=m==="channelsLast",w=x?0:1,b=x?1:2,_="";for(let k=0;k<=1;k++)for(let A=0;A<=1;A++)_+=`
blockIndex = rc.y + ${A};
pos = rc.x + ${k};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
d0 = offsetY + ${p} * (pos / ${h});
if(d0 < ${t[w]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
if(d1 < ${t[b]} && d1 >= 0) {
ch = int(mod(float(pos), ${s}.));
if (${x}) {
innerDims = vec2(d1, ch);
result[${k*2+A}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${k*2+A}] = 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;
${_}
${g.output} = result;
}
`}};function Ux({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],w=(p===1||m===1)&&c>Jv,b=l[2]%2!=0&&!!u.isPacked;if(w||!j().getBool("WEBGL_LAZILY_UNPACK")||!j().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!b){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=pe({inputs:{x:r},backend:o,attrs:{shape:[1,_,t.inChannels]}}),A=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),N=cc({a:k,b:A,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=pe({inputs:{x:N},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(A),x.push(N)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},A=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(lc(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let N=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(N);let $=cc({a:k,b:N,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),F=o.texData.get($.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=A,F.shape=t.outShape,g=Ht({inputs:{x:$},backend:o}),g.shape=t.outShape,x.push($)}for(let _ of x)o.disposeIntermediateTensorInfo(_);return g}function jx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],w=!0,b=!1,_=[],k=pe({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),A=pe({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});_.push(k),_.push(A);let N=new bC(x,k.shape,t),$=o.runWebGLProgram(N,[k],"float32"),F=pe({inputs:{x:$},backend:o,attrs:{shape:[1,x[0],x[1]]}});_.push($),_.push(F);let M=n!=null,V=s!=null,W=i==="leakyrelu",U=i?Tl(i,!0):null,H=new oh(F.shape,A.shape,[1,g,t.outChannels],w,b,M,U,V,W),q=[F,A];if(n&&q.push(n),V&&q.push(s),W){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));q.push(re),_.push(re)}let X=o.runWebGLProgram(H,q,"float32"),oe=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=pe({inputs:{x:X},backend:o,attrs:{shape:oe}});_.push(X);for(let re of _)o.disposeIntermediateTensorInfo(re);return Y}function w9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Ux({x:n,filter:s,convInfo:m,backend:t});else if(j().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=jx({x:n,filter:s,convInfo:m,backend:t});else{let h=new sh(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=pe({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var AO={kernelName:Zo,backendName:"webgl",kernelFunc:w9};var wC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},_C=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},kC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${o} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},vC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${u}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${o}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${o} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new wC(m);return t.runWebGLProgram(f,[n,s],"float32")}var EO={kernelName:Wl,backendName:"webgl",kernelFunc:_9};function k9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new _C(m);return t.runWebGLProgram(f,[n,s],"float32")}var DO={kernelName:Jo,backendName:"webgl",kernelFunc:k9};function v9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new yC(u);return t.runWebGLProgram(c,[n,s],"float32")}var $O={kernelName:na,backendName:"webgl",kernelFunc:v9};function C9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=S.computeConv3DInfo(n.shape,l,a,1,i),c=new kC(u);return t.runWebGLProgram(c,[n,s],"float32")}var RO={kernelName:Ul,backendName:"webgl",kernelFunc:C9};function I9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=S.computeConv3DInfo(l,s.shape,i,1,a),c=new vC(u);return t.runWebGLProgram(c,[n,s],"float32")}var FO={kernelName:jl,backendName:"webgl",kernelFunc:I9};var N9=Lx+`
return cos(x);
`,S9=_e({opSnippet:N9}),OO={kernelName:Qo,backendName:"webgl",kernelFunc:S9};var T9=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,A9=_e({opSnippet:T9}),PO={kernelName:Qs,backendName:"webgl",kernelFunc:A9};var CC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,w]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[b,_,k]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${b});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${x};
float width_scale = ${_};
float in_y = ${w};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var E9=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new CC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},MO={kernelName:ei,backendName:"webgl",kernelFunc:E9};var Hx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${LO(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${Le(n)} coords = getOutputCoords();
int end = ${zO(n,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${l};
${zO(n,"coords")} = idx;
val += getX(${LO(n,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function LO(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function zO(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function D9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=S.getAxesPermutation([s],l),c=n;u!=null&&(c=Lt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=S.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Ht({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Hx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new Hx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=S.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var BO={kernelName:en,backendName:"webgl",kernelFunc:D9};function $9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Fx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=HR(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var VO={kernelName:Hl,backendName:"webgl",kernelFunc:$9};var IC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function R9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new IC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var GO={kernelName:ti,backendName:"webgl",kernelFunc:R9};var ih=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",w="";o&&(n?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}`:x=`
float activation(float x) {
${o}
}
`,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${g};
int q = d2 - d1 * ${g};
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 < ${d}; wR++) {
int xR = xRCorner + wR * ${m};
if (xR < 0 || xR >= ${a}) {
continue;
}
for (int wC = 0; wC < ${h}; wC++) {
int xC = xCCorner + wC * ${f};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${b}
${w}
setOutput(result);
}
`}};var ah=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let k=0;k<d;k++)for(let A=0;A<h;A++)x+=`
vec4 xTexelR${k}C${A*2} = vec4(0.);
vec4 wR${k}C${A} = vec4(0.);
vec4 xR${k}C${A} = vec4(0.);`;for(let k=0;k<d;k++)for(let A=0;A<g;A++){let N=A*2;if(x+=`
xR = xRCorner + ${k*m};
xC = xCCorner + ${N*f};
`,p===1){if(N<h&&(u%2==1?x+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${k}C${N}.zw = vec2(0.);
}
} else {
xTexelR${k}C${N} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${k}C${N} = vec4(previous.zw, xTexelR${k}C${N}.xy);
} else {
xR${k}C${N} = vec4(0, 0, xTexelR${k}C${N}.xy);
}
`:x+=`
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
xTexelR${k}C${N} = getX(batch, xR, xC, d1);
} else {
xTexelR${k}C${N} = vec4(0.);
}
xR${k}C${N} = xTexelR${k}C${N};
`,N+1<h)){let $=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
xCOffset = xC + ${u%2} + ${$};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
}
`,f>1&&(x+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${N} = vec4(0.);
}
`),x+=`
xR${k}C${N+1} = vec4(
xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.xy);
`):x+=`
xCOffset = xC + ${$};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
}
xR${k}C${N+1} = xTexelR${k}C${N+2};
`}}else N<h&&(x+=`
if(xR >= 0 && xR < ${a}) {
`,u%2==1?(x+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${N} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${k}C${N+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${k}C${N+2} = vec4(0.);
}
xR${k}C${N} = vec4(
xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.zw);
`,N+1<h&&(x+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${k}C${N+1} = vec4(xTexelR${k}C${N+2}.xy, final.xy);
`)):(x+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${k}C${N} = getX(batch, xR, xC, d1);
} else {
xTexelR${k}C${N} = vec4(0.);
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${N+2} = vec4(0.);
}
xR${k}C${N} = vec4(
xTexelR${k}C${N}.xy, xTexelR${k}C${N+2}.xy);
`,N+1<h&&(x+=`
xR${k}C${N+1} = vec4(
xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.zw);
`)),x+="}");N<h&&(x+=`
vec4 wTexelR${k}C${N} = getW(${k}, ${N}, d1, q);
wR${k}C${N} = vec4(wTexelR${k}C${N}.xz, wTexelR${k}C${N}.xz);
`,N+1<h&&(x+=`
vec4 wTexelR${k}C${N+1} = getW(${k}, ${N+1}, d1, q);
wR${k}C${N+1} =
vec4(wTexelR${k}C${N+1}.xz, wTexelR${k}C${N+1}.xz);`))}for(let k=0;k<d;k++)for(let A=0;A<h;A++)x+=`dotProd += xR${k}C${A} * wR${k}C${A};`;let w="",b="";o&&(n?w=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?w=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:w=`vec4 activation(vec4 x) {
${o}
}`,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${w}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
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.);
${x}
vec4 result = dotProd;
${_}
${b}
setOutput(result);
}
`}};function F9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return j().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new ah(p):m=new ih(p),t.runWebGLProgram(m,[n,s],"float32")}var WO={kernelName:tn,backendName:"webgl",kernelFunc:F9};var NC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},SC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function O9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=S.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new NC(p);return t.runWebGLProgram(m,[n,s],"float32")}var UO={kernelName:ql,backendName:"webgl",kernelFunc:O9};function P9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new SC(p);return t.runWebGLProgram(m,[n,s],"float32")}var jO={kernelName:Kl,backendName:"webgl",kernelFunc:P9};var TC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function M9(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new TC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=pe({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var HO={kernelName:Xl,backendName:"webgl",kernelFunc:M9};var AC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${o}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function L9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new AC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=pe({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var qO={kernelName:sa,backendName:"webgl",kernelFunc:L9};var z9="return (x >= 0.0) ? x : (exp(x) - 1.0);",B9=`
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;
`,V9=_e({opSnippet:z9,packedOpSnippet:B9}),KO={kernelName:ri,backendName:"webgl",kernelFunc:V9};var G9="return (b >= 1.0) ? a : a * (b + 1.0);",W9=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,U9=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gs(W9,o.shape,n.shape):new rs(G9,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},XO={kernelName:Yl,backendName:"webgl",kernelFunc:U9};var j9=`
return vec4(equal(a, b));
`,H9="return float(a == b);",q9=nt({opSnippet:H9,packedOpSnippet:j9,dtype:"bool"}),YO={kernelName:ni,backendName:"webgl",kernelFunc:q9};var K9=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${S.ERF_P};
float a1 = ${S.ERF_A1};
float a2 = ${S.ERF_A2};
float a3 = ${S.ERF_A3};
float a4 = ${S.ERF_A4};
float a5 = ${S.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,X9=_e({opSnippet:K9}),ZO={kernelName:oi,backendName:"webgl",kernelFunc:X9};var JO="return exp(x);",EC=_e({opSnippet:JO,packedOpSnippet:JO,cpuKernelImpl:XR}),QO={kernelName:on,backendName:"webgl",kernelFunc:EC};function qx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var eP={kernelName:us,backendName:"webgl",kernelFunc:qx};var tP="return exp(x) - 1.0;",Y9=_e({opSnippet:tP,packedOpSnippet:tP,cpuKernelImpl:YR}),rP={kernelName:si,backendName:"webgl",kernelFunc:Y9};var Kx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${n});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${n}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function Xx(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new Kx("real",l,e),c=new Kx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=go({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=pe({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function Z9(r){let{inputs:e,backend:t}=r,{input:o}=e;return Xx(o,!1,t)}var oP={kernelName:Zl,backendName:"webgl",kernelFunc:Z9};var DC=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function lh(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new DC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var nP={kernelName:ia,backendName:"webgl",kernelFunc:lh};var $C=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var sP={kernelName:ii,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new $C(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var iP="return floor(x);",J9=_e({opSnippet:iP,packedOpSnippet:iP,cpuKernelImpl:ZR}),aP={kernelName:nn,backendName:"webgl",kernelFunc:J9};var Q9=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,eZ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,tZ=nt({opSnippet:Q9,packedOpSnippet:eZ,dtype:"int32"}),lP={kernelName:sn,backendName:"webgl",kernelFunc:tZ};var RC=class{constructor(e){this.variableNames=["A"];let t=Pt(),[o,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var FC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Pt(),[o,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${o}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}};var uP={kernelName:Pc,backendName:"webgl",kernelFunc:rZ},Xp;function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,[l,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,l],p=[u,l,s];(i||a)&&(Xp==null&&(Xp=document.createElement("canvas").getContext("2d")),Xp.canvas.width=l,Xp.canvas.height=u,Xp.drawImage(n,0,0,l,u),n=Xp.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Dr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let f=j().getBool("WEBGL_PACK")?new FC(p):new RC(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function oZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,w=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Ux({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(j().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=jx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,A=f==="leakyrelu",N=f?Tl(f,!1):null,$=new sh(g,_,N,k,A),F=[n,s];if(a&&F.push(a),i&&F.push(i),A){let M=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));F.push(M),w.push(M)}x=t.runWebGLProgram($,F,"float32")}let b=pe({inputs:{x},backend:t,attrs:{shape:g.outShape}});return w.push(x),w.forEach(_=>t.disposeIntermediateTensorInfo(_)),b}var cP={kernelName:_s,backendName:"webgl",kernelFunc:oZ};function nZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=S.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=j().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,w=m?Tl(m,x):null,b=[n,s],_=a!=null,k=i!=null,A=m==="leakyrelu";if(_&&b.push(a),k&&b.push(i),A){let F=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));b.push(F),d.push(F)}let N;x?N=new ah(g,_,w,k,A):N=new ih(g,_,w,k,A);let $=t.runWebGLProgram(N,b,"float32");return d.forEach(F=>t.disposeIntermediateTensorInfo(F)),$}var pP={kernelName:ks,backendName:"webgl",kernelFunc:nZ};var OC=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${n} strides = ${n}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function sZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=S.prepareAndValidate(o,n),p=pe({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new OC(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var mP={kernelName:ai,backendName:"webgl",kernelFunc:sZ};var PC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=iZ(e,2);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${n}));
}
`}};function iZ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function aZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=pe({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let w=t.bufferSync(f),b=t.bufferSync(m),_=JR(b,w,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new PC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(w=>t.disposeIntermediateTensorInfo(w)),x}var fP={kernelName:cs,backendName:"webgl",kernelFunc:aZ};var lZ="return float(a > b);",uZ=`
return vec4(greaterThan(a, b));
`,cZ=nt({opSnippet:lZ,packedOpSnippet:uZ,cpuKernelImpl:QR,dtype:"bool"}),dP={kernelName:li,backendName:"webgl",kernelFunc:cZ};var pZ="return float(a >= b);",mZ=`
return vec4(greaterThanEqual(a, b));
`,fZ=nt({opSnippet:pZ,packedOpSnippet:mZ,dtype:"bool"}),hP={kernelName:ln,backendName:"webgl",kernelFunc:fZ};function dZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Xx(o,!0,t)}var gP={kernelName:Jl,backendName:"webgl",kernelFunc:dZ};var hZ="return float(!isnan(x) && !isinf(x));",gZ=_e({opSnippet:hZ,dtype:"bool"}),xP={kernelName:ui,backendName:"webgl",kernelFunc:gZ};var xZ="return float(isinf(x));",yZ=_e({opSnippet:xZ,dtype:"bool"}),yP={kernelName:ci,backendName:"webgl",kernelFunc:yZ};var bZ="return float(isnan(x));",wZ=_e({opSnippet:bZ,dtype:"bool"}),bP={kernelName:pi,backendName:"webgl",kernelFunc:wZ};var _Z="return float(a < b);",kZ=`
return vec4(lessThan(a, b));
`,vZ=nt({opSnippet:_Z,packedOpSnippet:kZ,cpuKernelImpl:eF,dtype:"bool"}),wP={kernelName:mi,backendName:"webgl",kernelFunc:vZ};var CZ="return float(a <= b);",IZ=`
return vec4(lessThanEqual(a, b));
`,NZ=nt({opSnippet:CZ,packedOpSnippet:IZ,dtype:"bool"}),_P={kernelName:fi,backendName:"webgl",kernelFunc:NZ};function SZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=tF(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var kP={kernelName:eu,backendName:"webgl",kernelFunc:SZ};var TZ=`if (x < 0.0) return NAN;
return log(x);`,AZ=`
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;
`,EZ=_e({opSnippet:TZ,packedOpSnippet:AZ,cpuKernelImpl:rF}),vP={kernelName:cn,backendName:"webgl",kernelFunc:EZ};var DZ="return log(1.0 + x);",$Z=_e({opSnippet:DZ}),CP={kernelName:di,backendName:"webgl",kernelFunc:$Z};var RZ="return float(a >= 1.0 && b >= 1.0);",FZ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,OZ=nt({opSnippet:RZ,packedOpSnippet:FZ,dtype:"bool"}),IP={kernelName:hi,backendName:"webgl",kernelFunc:OZ};var PZ="return float(!(x >= 1.0));",MZ=_e({opSnippet:PZ}),NP={kernelName:Ya,backendName:"webgl",kernelFunc:MZ};var LZ="return float(a >= 1.0 || b >= 1.0);",zZ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,BZ=nt({opSnippet:LZ,packedOpSnippet:zZ,dtype:"bool"}),SP={kernelName:Za,backendName:"webgl",kernelFunc:BZ};var MC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}};var LC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${l};
setOutput(result);
}
`}};var VZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=j().getBool("WEBGL_PACK_NORMALIZATION")?new LC(n.shape,s,a,i,l):new MC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},TP={kernelName:aa,backendName:"webgl",kernelFunc:VZ};var zC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${n}) * norm + float(${o});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${n})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var GZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new zC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},AP={kernelName:tu,backendName:"webgl",kernelFunc:GZ};function EP(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,r.dtype,"max",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function BC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let b=t.texData.get(f.dataId).values,_=new Array(i);for(let N=0;N<_.length;N++)_[N]=n.shape[c[N]];let k=qp(b,n.shape,n.dtype,c,_);f=t.makeTensorInfo(_,n.dtype);let A=t.texData.get(f.dataId);A.values=k}else f=Al(n,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("max",u,i);let[d,h]=S.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=S.expandShapeToKeepDim(d,l));let x;if(m){let b=t.texData.get(f.dataId).values,_=oF(b,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(x.dataId);k.values=_}else x=EP(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var DP={kernelName:pn,backendName:"webgl",kernelFunc:BC};var WZ=Mx+`
return max(a, b);
`,UZ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Sl+`
return result;
`,jZ=nt({opSnippet:WZ,packedOpSnippet:UZ,cpuKernelImpl:nF}),$P={kernelName:mn,backendName:"webgl",kernelFunc:jZ};function HZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;qi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:n},backend:t});let p=new Ki(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var RP={kernelName:fn,backendName:"webgl",kernelFunc:HZ};function qZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=S.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new pc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var FP={kernelName:la,backendName:"webgl",kernelFunc:qZ};var VC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${n}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},GC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${l};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new pc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new GC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var OP={kernelName:ou,backendName:"webgl",kernelFunc:KZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;qi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=S.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Ki(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new VC(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var PP={kernelName:ru,backendName:"webgl",kernelFunc:XZ};function MP(r,e,t,o){let n=new Ki(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Ki(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var LP={kernelName:nu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,n,s,u,a),[p,m]=MP(o,i,c,l);return[p,m]}};function zP(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,"float32","mean",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var BP={kernelName:dn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let $=0;$<k.length;$++)k[$]=o.shape[c[$]];let A=qp(_,o.shape,o.dtype,c,k);d=a.makeTensorInfo(k,o.dtype);let N=a.texData.get(d.dataId);N.values=A}else d=Al(o,c,a);f.push(d),u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=S.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=S.expandShapeToKeepDim(h,l));let w=zP(d,g,x,a);for(let b of f)a.disposeIntermediateTensorInfo(b);return w}};function YZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,n.shape.length)),S.assertAxesAreInnerMostDims("min",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"min",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var VP={kernelName:hn,backendName:"webgl",kernelFunc:YZ};var ZZ=Mx+`
return min(a, b);
`,JZ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Sl+`
return result;
`,QZ=nt({opSnippet:ZZ,packedOpSnippet:JZ,cpuKernelImpl:sF}),GP={kernelName:gn,backendName:"webgl",kernelFunc:QZ};var WC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${n}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${s} coords = outC - start;
setOutput(getX(${l}));
}
`}};var UC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=jt("rc",n),u=jt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
rc = outputLoc;
${l[n-2]} += 1;
if(${l[n-2]} < ${this.outputShape[n-2]}) {
${d}
result[2] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${u.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var eJ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UC(o.shape,n,s):new WC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},WP={kernelName:ua,backendName:"webgl",kernelFunc:eJ};var tJ=`if (b == 0.0) return NAN;
return mod(a, b);`,rJ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Sl+`
return result;
`,oJ=nt({opSnippet:tJ,packedOpSnippet:rJ}),UP={kernelName:gi,backendName:"webgl",kernelFunc:oJ};var jC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],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 < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var nJ=`
if (a == b) {
return 1.0;
};
return a / b;`,sJ=`
// 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;
`,HC=nt({opSnippet:nJ,packedOpSnippet:sJ,checkOutOfBounds:!0}),jP={kernelName:rn,backendName:"webgl",kernelFunc:HC};var HP="return a - b;",qC=nt({opSnippet:HP,packedOpSnippet:HP,supportsComplex:!0,cpuKernelImpl:fF}),qP={kernelName:Fn,backendName:"webgl",kernelFunc:qC};function KC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=BC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=S.expandShapeToKeepDim(i.shape,a),u=pe({inputs:{x:i},backend:t,attrs:{shape:l}}),c=qC({inputs:{a:n,b:u},backend:t}),p=EC({inputs:{x:c},backend:t}),m=nh({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=pe({inputs:{x:m},backend:t,attrs:{shape:l}}),d=HC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var KP={kernelName:$n,backendName:"webgl",kernelFunc:KC};function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:KC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new jC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var XP={kernelName:su,backendName:"webgl",kernelFunc:iJ};var YP="return -x;";function aJ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=aF(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Vs(o.shape,YP):n=new ho(o.shape,YP),t.runWebGLProgram(n,[o],o.dtype)}var ZP={kernelName:ps,backendName:"webgl",kernelFunc:aJ};var lJ=Ar.nonMaxSuppressionV3Impl;function uJ(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=lJ(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var JP={kernelName:yi,backendName:"webgl",kernelFunc:uJ};var cJ=Ar.nonMaxSuppressionV4Impl;function pJ(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=cJ(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var QP={kernelName:bi,backendName:"webgl",kernelFunc:pJ};var mJ=Ar.nonMaxSuppressionV5Impl;function fJ(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=mJ(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var eM={kernelName:wi,backendName:"webgl",kernelFunc:fJ};var XC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${o}),
float(index == coords.y)));
}
`}};var dJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new XC(l,s,a,i),c=pe({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=pe({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},tM={kernelName:yn,backendName:"webgl",kernelFunc:dJ};function uh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ga({inputs:{input:o},backend:t}),s=uh({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=uh({inputs:{x:a},backend:t}),l=go({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return lh({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var rM={kernelName:bs,backendName:"webgl",kernelFunc:uh};function oM(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ga({inputs:{input:o},backend:t}),s=oM({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=uh({inputs:{x:a},backend:t}),l=go({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return lh({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var nM={kernelName:ms,backendName:"webgl",kernelFunc:oM};function hJ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return qx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=qx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=xC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var sM={kernelName:fs,backendName:"webgl",kernelFunc:hJ};var YC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
int start = ${a};
int end = ${i};
uniform float value;
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
uniform float value;
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${l}));
}
}
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var ZC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=jt("rc",n),u=jt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
if(${c}) {
`,n===1?"":`}
rc = outputLoc;
${l[n-2]} += 1;
if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${l[n-1]} += 1;
if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${p});
}
`;d+=n===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
uniform float value;
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var JC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ZC(n.shape,s,a):new YC(n.shape,s,a),l=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[n],n.dtype,l)},iM={kernelName:bn,backendName:"webgl",kernelFunc:JC};var gJ=`
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);
`,xJ=`
// 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));
`+Sl+`
return result;
`,yJ=nt({opSnippet:gJ,packedOpSnippet:xJ}),aM={kernelName:wn,backendName:"webgl",kernelFunc:yJ};function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=S.getAxesPermutation(c,i),m=n;p!=null&&(m=Lt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,i),l.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=lF(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=pe({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),w=fu(n.dtype),b=To(x,w,"prod",t);f=pe({inputs:{x:b},backend:t,attrs:{shape:d}}),l.push(x),l.push(b)}if(a){l.push(f);let d=S.expandShapeToKeepDim(f.shape,u);f=pe({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var lM={kernelName:_i,backendName:"webgl",kernelFunc:bJ};var QC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=uF(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},uM={kernelName:ca,backendName:"webgl",kernelFunc:QC};var wJ="return 1.0 / x;",_J=_e({opSnippet:wJ}),cM={kernelName:ki,backendName:"webgl",kernelFunc:_J};var kJ=yr+`
return (x < 0.0) ? 0.0 : x;
`,vJ=`
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;
`,CJ=_e({opSnippet:kJ,packedOpSnippet:vJ}),pM={kernelName:kn,backendName:"webgl",kernelFunc:CJ};var IJ=yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,NJ=`
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;
`,SJ=_e({opSnippet:IJ,packedOpSnippet:NJ}),mM={kernelName:Cn,backendName:"webgl",kernelFunc:SJ};var e0=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}};var t0=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${o-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=j().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new t0(n.shape,l,u,s,a):new e0(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var fM={kernelName:vn,backendName:"webgl",kernelFunc:TJ};var r0=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function AJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new r0(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var dM={kernelName:lu,backendName:"webgl",kernelFunc:AJ};var o0=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function EJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new o0(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var hM={kernelName:pa,backendName:"webgl",kernelFunc:EJ};var n0=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${n}) - 1),
${o} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${o} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function DJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new n0(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var gM={kernelName:au,backendName:"webgl",kernelFunc:DJ};var s0=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var i0=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=jt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${l(n.slice())};
if(${s}){
result.g = ${u(n.slice())};
}
if(${a}) {
result.b = ${c(n.slice())};
if(${s}) {
result.a = ${p(n.slice())};
}
}
setOutput(result);
}
`;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((w,b)=>f(b,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function $J(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Ht({inputs:{x:n},backend:t});let l=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new i0(n.shape,i):new s0(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var xM={kernelName:In,backendName:"webgl",kernelFunc:$J};var a0=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
uniform vec4 params;
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${s}
if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,o,n){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,o,n)}}};var yM={kernelName:Di,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new a0(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(n),Math.cos(n));return i.runWebGLProgram(l,[o],o.dtype,p)}};var RJ=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,FJ=_e({opSnippet:RJ}),bM={kernelName:Nn,backendName:"webgl",kernelFunc:FJ};var OJ="return inversesqrt(x);",PJ=_e({opSnippet:OJ,cpuKernelImpl:cF}),wM={kernelName:Sn,backendName:"webgl",kernelFunc:PJ};var ch=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
${l} strides = ${l}(${s});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function MJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=S.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=pe({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=pe({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new ch(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),w=pe({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),w}var _M={kernelName:vi,backendName:"webgl",kernelFunc:MJ};var l0=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function LJ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new l0(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dr(n.dtype,s.dtype))}var kM={kernelName:hs,backendName:"webgl",kernelFunc:LJ};var zJ=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${S.SELU_SCALEALPHA};
float scale = ${S.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,BJ=_e({opSnippet:zJ}),vM={kernelName:Ci,backendName:"webgl",kernelFunc:BJ};var VJ="return 1.0 / (1.0 + exp(-1.0 * x));",GJ=_e({opSnippet:VJ}),CM={kernelName:An,backendName:"webgl",kernelFunc:GJ};var WJ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,UJ=_e({opSnippet:WJ}),IM={kernelName:Ni,backendName:"webgl",kernelFunc:UJ};var jJ=Lx+`
return sin(x);
`,HJ=_e({opSnippet:jJ}),NM={kernelName:Tn,backendName:"webgl",kernelFunc:HJ};var qJ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,KJ=_e({opSnippet:qJ}),SM={kernelName:Ii,backendName:"webgl",kernelFunc:KJ};var XJ=`
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;
`,YJ=_e({opSnippet:XJ}),TM={kernelName:Si,backendName:"webgl",kernelFunc:YJ};var ZJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,w)=>x*w),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<n.shape.length;++x)l.push([0,0]);let u=[],c=JC({inputs:{x:n},backend:t,attrs:{paddings:l,constantValue:0}}),p=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,i,!1),d=pe({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},AM={kernelName:ma,backendName:"webgl",kernelFunc:ZJ};function JJ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=S.calculateShapes(s,n,i),m=!1,f=new ch(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var EM={kernelName:uu,backendName:"webgl",kernelFunc:JJ};function QJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],l=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Va({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var DM={kernelName:xs,backendName:"webgl",kernelFunc:QJ};var eQ="return sqrt(x);",tQ=_e({opSnippet:eQ}),$M={kernelName:En,backendName:"webgl",kernelFunc:tQ};var rQ="return x * x;",oQ=_e({opSnippet:rQ}),RM={kernelName:fa,backendName:"webgl",kernelFunc:oQ};var FM="return (a - b) * (a - b);",nQ=nt({opSnippet:FM,packedOpSnippet:FM}),OM={kernelName:Rn,backendName:"webgl",kernelFunc:nQ};function sQ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=yr+`
return x > 0.0 ? 1.0 : float(${e.alpha});
`,s=new ho(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var PM={kernelName:Fo,backendName:"webgl",kernelFunc:sQ};var u0=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function iQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:w}=sr.sliceInfo(n.shape,s,a,i,l,u,c,p,m),b=pe({inputs:{x:n},backend:t,attrs:{shape:x}}),_;if(f){let A=Va({inputs:{x:b},backend:t,attrs:{begin:d,size:g}});_=pe({inputs:{x:A},backend:t,attrs:{shape:w}}),t.disposeIntermediateTensorInfo(A)}else if(w.some(A=>A===0))_=t.makeTensorInfo(w,n.dtype,[]);else if(t.shouldExecuteOnCPU([b])){let $=t.texData.get(b.dataId).values,F=ve(b.shape,b.dtype,$),M=mF(w,F,h,d);_=t.makeTensorInfo(w,b.dtype,M.values)}else{let N=new u0(d,h,w);_=t.runWebGLProgram(N,[b],b.dtype)}let k=pe({inputs:{x:_},backend:t,attrs:{shape:w}});return t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(_),k}var MM={kernelName:Ti,backendName:"webgl",kernelFunc:iQ};var aQ="return tan(x);",lQ=_e({opSnippet:aQ}),LM={kernelName:Ai,backendName:"webgl",kernelFunc:lQ};var uQ=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,cQ=_e({opSnippet:uQ}),zM={kernelName:On,backendName:"webgl",kernelFunc:cQ};var c0=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=pQ(e);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function pQ(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function p0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype==="string"){let u=t.readSync(n.dataId).map(m=>y.decodeString(m)),c=ve(n.shape,n.dtype,u),p=dF(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new c0(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var BM={kernelName:_o,backendName:"webgl",kernelFunc:p0};function mQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=hF(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var VM={kernelName:Ei,backendName:"webgl",kernelFunc:mQ};var m0=class{constructor(e,t,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,l;switch(n){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${l} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function fQ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=o,[c,p,m,f]=n.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],x=new m0(p,m,a,i,l,g);return t.runWebGLProgram(x,[n,s],"float32")}var GM={kernelName:cu,backendName:"webgl",kernelFunc:fQ};function dQ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;qi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=gF(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var WM={kernelName:pu,backendName:"webgl",kernelFunc:dQ};function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Va({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),x=pe({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=x,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var UM={kernelName:ys,backendName:"webgl",kernelFunc:hQ};var f0=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=`
sumValue += dot(values, segFilter);
`,f="";s%o>0&&(f=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let d="";s%o>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${l};
float getValue(int batch, int inIdx) {
${f}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${o}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${m}
} else if (${p===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${m}
} else if (${p===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${m}
}
setOutput(${u});
}
`}};function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=S.getAxesPermutation([u],i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=S.getInnerMostAxes(1,i)[0]);let m=S.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=fu(n.dtype),g=(_,k,A,N,$)=>{let F=_.shape[0],M=_.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,$),W={windowSize:V,inSize:M,batchSize:F,numSegments:$},U=new f0(W,k),H=t.compileAndRun(U,[_,A],N);if(l.push(H),H.shape[1]===$)return H;let q=QC({backend:t,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),X=p0({inputs:{x:q},backend:t,attrs:{reps:[M/V]}});return l.push(q),l.push(X),g(H,k,X,N,$)},x=g(d,"unsortedSegmentSum",s,h,a),w=pe({inputs:{x},backend:t,attrs:{shape:m}}),b=w;if(c!=null){l.push(w);let _=S.getUndoAxesPermutation(c);b=Lt({inputs:{x:b},backend:t,attrs:{perm:_}})}return l.forEach(_=>t.disposeIntermediateTensorInfo(_)),b}var jM={kernelName:da,backendName:"webgl",kernelFunc:gQ};var xQ=[TP,AP,WF,jF,HF,qF,XF,YF,ZF,JF,tO,rO,oO,nO,iO,sO,aO,uO,lO,cO,pO,mO,fO,hO,gO,wO,kO,vO,IO,DF,TO,EO,DO,AO,RO,FO,$O,OO,PO,MO,BO,VO,GO,UO,jO,WO,HO,qO,KO,XO,YO,ZO,QO,eP,rP,oP,nP,sP,aP,lP,uP,cP,pP,mP,fP,dP,hP,EF,gP,NO,xP,yP,bP,$F,wP,_P,kP,CP,vP,IP,NP,SP,DP,FP,RP,OP,PP,LP,$P,BP,VP,GP,WP,UP,XP,MF,ZP,JP,QP,eM,xO,tM,nM,sM,iM,aM,RF,lM,uM,yO,jP,cM,mM,pM,zF,fM,dM,hM,gM,xM,yM,bM,wM,_M,kM,vM,CM,IM,NM,SM,dO,KP,TM,AM,EM,DM,$M,RM,OM,PM,MM,qP,VF,LM,zM,BM,VM,GM,GF,WM,UM,jM,rM];for(let r of xQ)Ja(r);var HM="3.3.0";var yQ={"tfjs-core":nw,"tfjs-backend-cpu":c2,"tfjs-backend-webgl":AF,"tfjs-data":_x,"tfjs-layers":dl,"tfjs-converter":fx,tfjs:HM};var zt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(zt||(zt={}));var El;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu"})(El||(El={}));var qM;function bQ(r){qM=r.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function wQ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m=t.dataIdMap.get(n.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let $=t.dataIdMap.get(a.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);d=$.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=El[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?n.shape[2]:n.shape[1],w=u?s.shape[1]:s.shape[2],b=n.shape[0],_=t.makeOutput([b,x,w],n.dtype),k=t.dataIdMap.get(_.dataId).id,A=new Uint8Array(new Int32Array(n.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return qM(m,A,n.shape.length,f,N,s.shape.length,l,u,g,d,h,p||0,k),_}var KM={kernelName:ws,backendName:"wasm",setupFunc:bQ,kernelFunc:wQ};function Tt(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:o}}var XM=Tt(as);function wt(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=S.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),w=i.dataIdMap.get(h.dataId).id,b=()=>o(p,g,u.shape.length,m,x,c.shape.length,zt[u.dtype],w);if(e&&u.dtype==="float32")return b(),h;let _=S.getBroadcastDims(u.shape,d),k=S.getBroadcastDims(c.shape,d),A=_.every(($,F)=>$===F),N=k.every(($,F)=>$===F);if(A&&N)return b(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var _Q=!0,YM=wt(wo,_Q);var ZM;function kQ(r){ZM=r.wasm.cwrap(Ho,null,["array","number","number","number"])}function vQ(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return ZM(s,n.length,zt[o.dtype],a),o}var JM={kernelName:Ho,backendName:"wasm",setupFunc:kQ,kernelFunc:vQ};function dc(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var QM={kernelName:Ro,backendName:"wasm",kernelFunc:dc};var eL;function CQ(r){eL=r.wasm.cwrap(Pn,null,["number","array","number","number","number","array","number"])}function Yp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=NQ(e.x.shape,o.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=IQ(e.x.shape,o.perm),l={dataId:e.x.dataId,shape:n,dtype:e.x.dtype};if(a){let d=dc({inputs:e,backend:t});return d.shape=i,d}let u=t.makeOutput(i,l.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(l.shape).buffer);return eL(c,f,l.shape.length,zt[l.dtype],p,m,s.length),u}function IQ(r,e){let t=new Array(r.length);for(let o=0;o<t.length;o++)t[o]=r[e[o]];return t}function NQ(r,e){let t=[],o=[];for(let n=0;n<r.length;++n)r[n]!==1&&t.push(r[n]),r[e[n]]!==1&&o.push(e[n]);for(let n=0;n<o.length;++n){let s=-1;for(let a=0;a<o.length;++a)o[a]>=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var tL={kernelName:Pn,backendName:"wasm",kernelFunc:Yp,setupFunc:CQ};function os(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=S.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f<c.length;f++)c[f]=o[i[f]];a=S.getInnerMostAxes(a.length,n),l=Yp({inputs:{x:r},attrs:{perm:i},backend:t});let p=t.dataIdMap.get(r.dataId).id;t.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:a,inputWasTransposed:u}}var rL;function SQ(r){rL=r.wasm.cwrap(qo,null,["number","number","number","number","number"])}function TQ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n}=o,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=os(s,n,e);if(p){let x=e.dataIdMap.get(u.dataId).id;x!==a&&(l=u,i=x)}let m=l.shape.slice(0,-1),f=e.makeOutput(m,"int32"),d=e.dataIdMap.get(f.dataId).id,h=y.sizeFromShape(f.shape),g=l.shape[c[0]];return rL(i,zt[l.dtype],h,g,d),p&&e.disposeData(u.dataId),f}var oL={kernelName:qo,backendName:"wasm",kernelFunc:TQ,setupFunc:SQ};var nL;function AQ(r){nL=r.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function EQ(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=S.computePool2DInfo(n.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,w=c.strideWidth,b=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let _=o.makeOutput(c.outShape,"float32"),k=o.dataIdMap.get(_.dataId).id;return nL(s,n.shape[0],n.shape[1],n.shape[2],p,m,f,d,h,g,x,w,b,k),_}var sL={kernelName:Ko,backendName:"wasm",setupFunc:AQ,kernelFunc:EQ};function Vr(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s);return y.assert(s===y.sizeFromShape(a),()=>`new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var iL={kernelName:ds,backendName:"wasm",kernelFunc:Vr};var aL;function DQ(r){aL=r.wasm.cwrap(Xo,null,["number","array","number","number","array","number","number","number","number"])}function $Q(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=n.shape.length,u=s.shape.length,c=a?n.shape[l-2]:n.shape[l-1],p=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[l-1]:n.shape[l-2],f=i?s.shape[u-2]:s.shape[u-1],d=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=g===x||g===1||x===1;y.assert(l>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}k0=e}var Ete="3.3.0";var Dte=2;xu("wasm",async()=>{let{wasm:r}=await kB();return new Qx(r)},Dte);export{as as Abs,Hs as Acos,qs as Acosh,rp as AdadeltaOptimizer,op as AdagradOptimizer,np as AdamOptimizer,sp as AdamaxOptimizer,wo as Add,Ho as AddN,Ml as All,Ll as Any,qo as ArgMax,ea as ArgMin,Ks as Asin,Xs as Asinh,Ys as Atan,Js as Atan2,Zs as Atanh,Ko as AvgPool,ta as AvgPool3D,Bl as AvgPool3DGrad,zl as AvgPoolGrad,Qx as BackendWasm,Xo as BatchMatMul,ra as BatchToSpaceND,Vl as Bincount,Cb as BroadcastTo,Kg as Callback,Eg as CallbackList,Do as Cast,Yo as Ceil,$o as ClipByValue,Gl as Complex,oa as ComplexAbs,ls as Concat,Zo as Conv2D,Wl as Conv2DBackpropFilter,Jo as Conv2DBackpropInput,na as Conv3D,Ul as Conv3DBackpropFilterV2,jl as Conv3DBackpropInputV2,Qo as Cos,Qs as Cosh,ei as CropAndResize,en as Cumsum,$g as CustomCallback,Xa as DataStorage,Hl as DenseBincount,ti as DepthToSpace,tn as DepthwiseConv2dNative,ql as DepthwiseConv2dNativeBackpropFilter,Kl as DepthwiseConv2dNativeBackpropInput,Xl as Diag,sa as Dilation2D,Oc as Dilation2DBackpropFilter,Fc as Dilation2DBackpropInput,_b as ENV,Yg as EarlyStopping,ri as Elu,Yl as EluGrad,Mh as Environment,ni as Equal,oi as Erf,on as Exp,us as ExpandDims,si as Expm1,Zl as FFT,ia as Fill,ii as FlipLeftRight,nn as Floor,sn as FloorDiv,Pc as FromPixels,an as FusedBatchNorm,_s as FusedConv2D,ks as FusedDepthwiseConv2D,ai as GatherNd,cs as GatherV2,mx as GraphModel,li as Greater,ln as GreaterEqual,Dg as History,Jl as IFFT,Ro as Identity,Ql as Imag,St as InputSpec,ui as IsFinite,ci as IsInf,pi as IsNan,Ws as KernelBackend,aa as LRN,tu as LRNGrad,yf as LayerVariable,No as LayersModel,un as LeakyRelu,mi as Less,fi as LessEqual,eu as LinSpace,cn as Log,di as Log1p,Ib as LogSoftmax,hi as LogicalAnd,Ya as LogicalNot,Za as LogicalOr,pn as Max,fn as MaxPool,la as MaxPool3D,ou as MaxPool3DGrad,ru as MaxPoolGrad,nu as MaxPoolWithArgmax,mn as Maximum,dn as Mean,hn as Min,gn as Minimum,ua as MirrorPad,gi as Mod,ip as MomentumOptimizer,su as Multinomial,xn as Multiply,ps as Neg,yi as NonMaxSuppressionV3,bi as NonMaxSuppressionV4,wi as NonMaxSuppressionV5,xi as NotEqual,Q0 as OP_SCOPE_SUFFIX,yn as OneHot,ms as OnesLike,Pr as Optimizer,fs as Pack,bn as PadV2,W3 as Pool,wn as Pow,_n as Prelu,_i as Prod,ap as RMSPropOptimizer,mo as RNN,ca as Range,Eb as Rank,iu as Real,rn as RealDiv,ki as Reciprocal,Wt as Reduction,kn as Relu,Cn as Relu6,ds as Reshape,vn as ResizeBilinear,lu as ResizeBilinearGrad,pa as ResizeNearestNeighbor,au as ResizeNearestNeighborGrad,In as Reverse,Di as RotateWithOffset,Nn as Round,Sn as Rsqrt,al as SGDOptimizer,vi as ScatterNd,hs as Select,Ci as Selu,Wi as Sequential,An as Sigmoid,Ni as Sign,Tn as Sin,Ii as Sinh,gs as Slice,$n as Softmax,Si as Softplus,ma as SpaceToBatchND,uu as SparseToDense,xs as SplitV,En as Sqrt,fa as Square,Rn as SquaredDifference,Fo as Step,Ti as StridedSlice,Fn as Sub,Dn as Sum,Br as SymbolicTensor,Ai as Tan,On as Tanh,Ve as Tensor,lt as TensorBuffer,_o as Tile,Ei as TopK,cu as Transform,Pn as Transpose,pu as Unique,ys as Unpack,da as UnsortedSegmentSum,el as Variable,bs as ZerosLike,ws as _FusedMatMul,Nt as abs,wm as acos,_m as acosh,ee as add,iw as addN,bu as all,ol as any,nl as argMax,km as argMin,vm as asin,Cm as asinh,Im as atan,Nm as atan2,Sm as atanh,wa as avgPool,Tm as avgPool3d,sw as backend,S as backend_util,jG as basicLSTMCell,Ln as batchNorm,cw as batchNorm2d,pw as batchNorm3d,mw as batchNorm4d,_a as batchToSpaceND,fw as bincount,QU as booleanMaskAsync,sl as broadcastTo,qh as browser,ve as buffer,K1 as callbacks,ne as cast,Am as ceil,ir as clipByValue,Oo as clone,ko as complex,Ye as concat,dw as concat1d,hw as concat2d,gw as concat3d,xw as concat4d,l_ as constraints,ku as conv1d,Kr as conv2d,vu as conv2dTranspose,Em as conv3d,mW as conv3dTranspose,H3 as copyRegisteredKernels,ka as cos,Cu as cosh,ef as cosineWindow,Iu as cumsum,Xr as customGrad,Wk as data,yw as denseBincount,Qh as deprecationWarn,Dm as depthToSpace,Is as depthwiseConv2d,Z1 as deregisterOp,hu as device_util,wW as diag,$m as dilation2d,nG as disableDeprecationWarnings,Te as dispose,sG as disposeVariables,me as div,Rm as divNoNan,bw as dot,jw as dropout,Ns as elu,oG as enableDebugMode,rG as enableProdMode,Hw as enclosingPowerOfTwo,Po as engine,j as env,vo as equal,Fm as erf,Zt as exp,ar as expandDims,Om as expm1,Xc as eye,Ea as fft,va as fill,pG as findBackend,mG as findBackendFactory,Ss as floor,yu as floorDiv,Gn as fused,zn as gather,Uw as gatherND,Kh as gather_util,uG as getBackend,Lh as getGradient,Lc as getKernel,dm as getKernelsForBackend,XW as grad,YW as grads,er as greater,io as greaterEqual,Pi as ifft,Nu as imag,$s as image,lj as inTopKAsync,f_ as initializers,Hg as input,Ir as io,Lu as irfft,ww as isFinite,_w as isInf,kw as isNaN,Dt as keep,Ar as kernel_impls,X_ as layers,Ca as leakyRelu,Su as less,zo as lessEqual,Zw as linalg,vw as linspace,NA as loadGraphModel,O1 as loadLayersModel,Pm as localResponseNormalization,lr as log,Tu as log1p,Cw as logSigmoid,Au as logSoftmax,Lm as logSumExp,hr as logicalAnd,Ia as logicalNot,Eu as logicalOr,Tw as logicalXor,tH as losses,We as matMul,EI as math,ur as max,Na as maxPool,zm as maxPool3d,Aw as maxPoolWithArgmax,Yr as maximum,ht as mean,Hc as memory,Q_ as metrics,Oi as min,As as minimum,Bm as mirrorPad,Vm as mod,R1 as model,ek as models,Yc as moments,rj as movingAverage,P as mul,I4 as multiRNNCell,Ew as multinomial,He as neg,tf as nextFrame,Vu as norm,Vn as notEqual,Cs as oneHot,Nr as ones,tr as onesLike,T as op,E4 as outerProduct,Fr as pad,R4 as pad1d,O4 as pad2d,M4 as pad3d,z4 as pad4d,Dw as pool,Or as pow,Ta as prelu,Kb as print,Du as prod,iG as profile,K4 as rand,rU as randomGamma,ag as randomNormal,Es as randomUniform,Jc as range,lG as ready,il as real,Gm as reciprocal,xu as registerBackend,P1 as registerCallbackConstructor,Sb as registerGradient,Ja as registerKernel,Y1 as registerOp,tk as regularizers,Sr as relu,Ru as relu6,cG as removeBackend,L as reshape,qt as reverse,pU as reverse1d,fU as reverse2d,hU as reverse3d,xU as reverse4d,Da as rfft,Wm as round,Fu as rsqrt,le as scalar,Ww as scatterND,Xh as scatter_util,Ou as selu,Um as separableConv2d,F1 as sequential,Q as serialization,qI as setBackend,fG as setPlatform,Tte as setWasmPath,Ate as setWasmPaths,Bw as setdiff1dAsync,qr as sigmoid,jm as sign,eH as signal,Pu as sin,Mu as sinh,Re as slice,Hm as slice1d,lg as slice2d,qm as slice3d,Qc as slice4d,sr as slice_util,Aa as softmax,Ts as softplus,Sa as spaceToBatchND,Qm as sparseToDense,Qj as spectral,cr as split,xt as sqrt,Oe as square,zu as squaredDifference,Co as squeeze,Vt as stack,Ds as step,Km as stridedSlice,ce as sub,ge as sum,fu as sumOutType,Xm as tan,Fi as tanh,Rr as tensor,Gt as tensor1d,Mi as tensor2d,Jb as tensor3d,WU as tensor4d,UU as tensor5d,jU as tensor6d,Mn as tensor_util,UI as test_util,G as tidy,Lo as tile,aG as time,Ym as topk,ll as train,je as transpose,Bu as truncatedNormal,ep as unique,j3 as unregisterGradient,U3 as unregisterKernel,Zm as unsortedSegmentSum,pr as unstack,dr as upcastType,y as util,ZW as valueAndGrad,JW as valueAndGrads,Vw as variable,og as variableGrads,yQ as version,fx as version_converter,nw as version_core,dl as version_layers,Ete as version_wasm,$t as where,Jm as whereAsync,gt as zeros,Ce as zerosLike};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=tfjs.esm.js.map
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