face-api/dist/face-api.min.js

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Buffer shape=${this.shape}`;throw new Error(i)}t++}let s=e[e.length-1];for(let n=0;n<e.length-1;++n)s+=this.strides[n]*e[n];return this.values[s]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let s=0;s<e.length-1;++s)t+=this.strides[s]*e[s];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let s=0;s<t.length-1;++s)t[s]=Math.floor(e/this.strides[s]),e-=t[s]*this.strides[s];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Kn().makeTensor(this.values,this.shape,this.dtype)}}let Kn=null,aa=null,L1=null;function oS(e){Kn=e}function aS(e){aa=e}function lS(e){L1=e}class ue{constructor(e,t,s,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=We(e),this.strides=qn(e),this.dataId=s,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return aa.buffer(this.shape,this.dtype,e)}bufferSync(){return aa.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Br(this.shape,e)}arraySync(){return Br(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Kn().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(s=>Oh(s))}catch(s){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Kn().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Oh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Kn().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Kn().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return aa.print(this,e)}clone(){return this.throwIfDisposed(),aa.clone(this)}toString(e=!1){const t=this.dataSync();return iS(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),aa.cast(this,e)}variable(e=!0,t,s){return this.throwIfDisposed(),Kn().makeVariable(this,e,t,s)}}Object.defineProperty(ue,Symbol.hasInstance,{value:e=>!!e&&e.dataId!=null&&e.shape!=null&&e.dtype!=null});class Xn extends ue{constructor(e,t,s,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=s}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!It(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Kn().disposeTensor(this),this.dataId=e.dataId,Kn().incRef(this,null)}dispose(){Kn().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Xn,Symbol.hasInstance,{value:e=>e instanceof ue&&e.assign!=null&&e.assign instanceof Function});var uy;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(uy||(uy={}));var hy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(hy||(hy={}));var dy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(dy||(dy={}));var my;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(my||(my={}));var fy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(fy||(fy={}));const S1={float32:my,int32:hy,bool:dy,complex64:fy};function wt(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return S1[e][t]}function tp(e){return wt(e,"int32")}const rn={};Re(rn,{assertTypesMatch:()=>gy,getTensorsInContainer:()=>sp,isTensorInList:()=>v1,makeTypesMatch:()=>Ne});function Ne(e,t){if(e.dtype===t.dtype)return[e,t];const s=wt(e.dtype,t.dtype);return[e.cast(s),t.cast(s)]}function gy(e,t){v(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function v1(e,t){return t.some(s=>s.id===e.id)}function sp(e){const t=[],s=new Set();return cS(e,t,s),t}function cS(e,t,s){if(e==null)return;if(e instanceof ue){t.push(e);return}if(!T1(e))return;const n=e;for(const i in n){const r=n[i];s.has(r)||(s.add(r),cS(r,t,s))}}function T1(e){return Array.isArray(e)||typeof e=="object"}class pS{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap(),this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class np{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new pS()}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const s=e[t],n=await this.initializeBackend(s).success;if(n){await this.setBackend(s);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,s=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:s},!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;const{success:t,asyncInit:s}=this.initializeBackend(e),n=s?await t:t;if(!n)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new eS(this.backendInstance),!0}setupRegisteredKernels(){const e=Ch(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Ch(e);t.forEach(s=>{s.disposeFunc!=null&&s.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const s=t.factory();if(Promise.resolve(s)===s){const n=++this.pendingBackendInitId,i=s.then(r=>n<this.pendingBackendInitId?!1:(this.registry[e]=r,this.pendingBackendInit=null,!0)).catch(r=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(r.stack||r.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=s,{success:!0,asyncInit:!1}}catch(s){return console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const s=e[t],{success:n,asyncInit:i}=this.initializeBackend(s);if(i||n)return{name:s,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const s=this.state.tensorInfo.get(t),n=s.backend,i=this.readSync(t);n.disposeData(t),s.backend=e,e.move(t,i,s.shape,s.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let s=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");s=e}let n;return this.scopedRun(()=>this.startScope(s),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,s){e();try{const n=s();return t(),n}catch(n){throw t(),n}}nextTensorId(){return np.nextTensorId++}nextVariableId(){return np.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),s={x:e},n=r=>({x:()=>{const o="float32",a={x:r},l={dtype:o};return T.runKernelFunc(c=>c.cast(r,o),a,null,kr,l)}}),i=[];return this.addTapeNode(this.state.activeScope.name,s,[t],n,i,{}),t}runKernel(e,t,s,n,i){const r=null,o=null;return this.runKernelFunc(r,t,o,e,s,n,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,s){const n=this.backend.numDataIds();let i=0;s.forEach(a=>{i+=a.dtype==="complex64"?3:1});const r=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=n-t-i-r;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e,t,s,n,i,r,o){let a,l=[];const c=this.isTapeOn();n==null&&(n=this.state.activeScope!=null?this.state.activeScope.name:"");const p=this.state.numBytes,u=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let h;const d=qc(n,this.backendName);let m;if(d!=null)h=()=>{const y=this.backend.numDataIds();m=d.kernelFunc({inputs:t,attrs:i,backend:this.backend});const b=Array.isArray(m)?m:[m];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,y,b);const S=b.map(({dataId:x,shape:I,dtype:A})=>this.makeTensorFromDataId(x,I,A));if(c){let x=this.getTensorsForGradient(n,t,S);if(x==null){o==null&&(o=[]);const I=S.filter((A,k)=>o[k]);x=(r||[]).slice().concat(I)}l=this.saveTensorsForBackwardMode(x)}return S};else{const y=b=>{if(!c)return;l=b.map(S=>this.keep(this.clone(S)))};h=()=>{const b=this.backend.numDataIds();m=this.tidy(()=>e(this.backend,y));const S=Array.isArray(m)?m:[m];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,b,S),S}}let f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?a=h():(f=this.profiler.profileKernel(n,t,()=>h()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),a=f.outputs)}),c&&this.addTapeNode(n,t,a,s,l,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:n,bytesAdded:this.state.numBytes-p,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-u,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(y=>t[y]!=null?t[y].shape:null),outputShapes:a.map(y=>y.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(m)?a:a[0]}saveTensorsForBackwardMode(e){const t=e.map(s=>this.keep(this.clone(s)));return t}getTensorsForGradient(e,t,s){const n=Nh(e);if(n!=null){const i=n.inputsToSave||[],r=n.outputsToSave||[];let o;n.saveAllInputs?(v(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=i.map(l=>t[l]);const a=s.filter((l,c)=>r[c]);return o.concat(a)}return null}makeTensor(e,t,s,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");s=s||"float32",n=n||this.backend;let i=e;s==="string"&&vn(e[0])&&(i=e.map(a=>cy(a)));const r=n.write(i,t,s),o=new ue(t,s,r,this.nextTensorId());if(this.incRef(o,n),s==="string"){const a=this.state.tensorInfo.get(r),l=ly(i);this.state.numBytes+=l-a.bytes,a.bytes=l}return o}makeTensorFromDataId(e,t,s,n){s=s||"float32";const i=new ue(t,s,e,this.nextTensorId());return this.incRef(i,n),i}makeVariable(e,t=!0,s,n){s=s||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));const i=new Xn(e,t,s,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const s=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,s===0){this.state.numDataBuffers++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*ay(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.state.numBytes+=n}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Xn||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),s=t.refCount;s<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const 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(){const 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;const t=this.state.numBytes,s=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-s;for(const 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,s,n,i,r){const o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:s,saved:i},a=Nh(e);a!=null&&(n=a.gradFunc),n!=null&&(o.gradient=l=>(l=l.map((c,p)=>{if(c==null){const u=s[p],h=Yn(u.size,u.dtype);return this.makeTensor(h,u.shape,u.dtype)}return c}),n(l.length>1?l:l[0],i,r))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=sp(e),s=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const r=this.state.activeScope.track[i];!r.kept&&!s.has(r.id)&&r.dispose()}const n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===n.id&&this.track(i)})}gradients(e,t,s,n=!1){if(v(t.length>0,()=>"gradients() received an empty list of xs."),s!=null&&s.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${s.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));v(i instanceof ue,()=>"The result y returned by f() must be a tensor.");const r=tS(this.state.activeTape,t,i);if(!n&&r.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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VS(e,t){if(t==null)return e.shape.slice();if(It(e.shape,t))return t;if(e.shape.length===t.length){const s=[];for(let n=0;n<e.shape.length;n++)t[n]==null&&e.shape[n]!=null?s.push(e.shape[n]):s.push(t[n]);return s}return t}function FD(e,t,s,n){const i=g(e,"x","dropout");if(v(i.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${i.dtype} tensor instead.`),v(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof ue?i.clone():i;const r=VS(i,s),o=1-t,a=J(Nn(W(Gs(r,0,1,"float32",n),o)),o);return C(i,a)}const jh=L({dropout_:FD});function Vh(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function ga(e,t,s){const n=1-e%2,i=new Float32Array(e);for(let r=0;r<e;++r){const o=2*Math.PI*r/(e+n-1);i[r]=t-s*Math.cos(o)}return Fe(i,"float32")}async function MD(e,t,s=1){const n=g(e,"predictions","inTopK"),i=g(t,"targets","inTopK");v(n.rank>1,()=>`inTopK() expects the predictions to be of 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l=t;l.rank===3&&(l=O(t,[1,t.shape[0],t.shape[1],t.shape[2]])),v(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),v(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),v(s.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${s}.`);const c=r==="NHWC"?a.shape[3]:a.shape[1],p=r==="NHWC"?l.shape[3]:l.shape[1];v(c===s[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${s[2]}.`),v(p===s[3],()=>`Error in conv2dDerFilter: depth of dy (${p}) must match output depth for filter (${s[3]}).`),o!=null&&v(De(i),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${i}.`);const u=m=>{const f=1,y=ir(r),b=is(a.shape,s,n,f,i,o,!1,y);return m.conv2dDerFilter(a,l,b)},h={x:a,dy:l},d={strides:n,pad:i,dataFormat:r,dimRoundingMode:o};return T.runKernelFunc(u,h,null,wg,d)}const ya=L({conv2DBackpropFilter_:UD});function 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k=(M,B)=>{const[V,Y,G,Z]=B,q=ho(O(M,G.shape),G,r);let ee,Q;if(!s&&!n?(ee=Le(q,Y,!1,!0),Q=Le(V,q,!0,!1)):!s&&n?(ee=Le(q,Y,!1,!1),Q=Le(q,V,!0,!1)):s&&!n?(ee=Le(Y,q,!1,!0),Q=Le(V,q,!1,!1)):(ee=Le(Y,q,!0,!0),Q=Le(q,V,!0,!0)),i!=null){const ie=mo(Z,q);return[ee,Q,ie]}else return[ee,Q]},R=M=>{const B=M.fusedBatchMatMul({a:S,b:x,transposeA:s,transposeB:n,bias:I,activation:r,preluActivationWeights:A});return B},D={a:S,b:x,bias:I,preluActivationWeights:A},F={transposeA:s,transposeB:n,activation:r};if(i==null){const M=ms((B,V,Y)=>{const G=T.runKernelFunc(R,D,null,Th,F);return Y([B,V,G]),{value:O(G,b),gradFunc:k}});return M(S,x)}else{const M=ms((B,V,Y,G)=>{const Z=T.runKernelFunc(R,D,null,Th,F);return G([B,V,Z,Y]),{value:O(Z,b),gradFunc:k}});return M(S,x,I)}}const GD=L({fusedMatMul_:VD});const Hs={};Re(Hs,{conv2d:()=>WD,depthwiseConv2d:()=>jD,matMul:()=>GD});function HD(e){return ga(e,.54,.46)}const HS=L({hammingWindow_:HD});function qD(e){return ga(e,.5,.5)}const qh=L({hannWindow_:qD});function YD(e,t,s,n=!1,i=0){let r=0;const o=[];for(;r+t<=e.size;)o.push(he(e,r,t)),r+=s;if(n)for(;r<e.size;){const a=r+t-e.size,l=ge([he(e,r,t-a),Ft([a],i)]);o.push(l),r+=s}return o.length===0?ts([],[0,t]):O(ge(o),[o.length,t])}const Yh=L({frame_:YD});function KD(e,t,s,n,i=qh){n==null&&(n=Vh(t));const r=Yh(e,t,s),o=C(r,i(t)),a=[];for(let l=0;l<r.shape[0];l++)a.push(Ai(he(o,[l,0],[1,t]),n));return ge(a)}const qS=L({stft_:KD});function XD(e,t,s,n,i,r){const o=g(e,"image","cropAndResize"),a=g(t,"boxes","cropAndResize","float32"),l=g(s,"boxInd","cropAndResize","int32");i=i||"bilinear",r=r||0;const c=a.shape[0];v(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),v(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${a.shape}.`),v(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${a.shape}.`),v(n.length===2,()=>`Error in cropAndResize: cropSize must be of 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Rt.getHandlers(e,"load",t)}static getHandlers(e,t,s){const n=[],i=t==="load"?Rt.getInstance().loadRouters:Rt.getInstance().saveRouters;return i.forEach(r=>{const o=r(e,s);o!==null&&n.push(o)}),n}}const _F=e=>Rt.registerSaveRouter(e),DF=e=>Rt.registerLoadRouter(e),FF=e=>Rt.getSaveHandlers(e),MF=(e,t)=>Rt.getLoadHandlers(e,t);const ba="://";class _n{constructor(){this.managers={}}static getInstance(){return _n.instance==null&&(_n.instance=new _n()),_n.instance}static registerManager(e,t){v(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(ba)&&(e=e.slice(0,e.indexOf(ba))),v(e.length>0,()=>"scheme must not be an empty string.");const s=_n.getInstance();v(s.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),s.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function ed(e){if(e.indexOf(ba)===-1)throw new Error(`The url string provided does not contain a scheme. 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BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const s=[],n=t.map(r=>db(r.name)),i={};for(const r of e)r.paths.forEach(o=>{const a=db(o);if(s.indexOf(a)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${a}'`);if(s.push(a),n.indexOf(a)===-1)throw new Error(`Weight file with basename '${a}' is not provided.`);i[o]=t[n.indexOf(a)]});if(s.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${s.length}) and the number of weight files provided (${t.length}).`);return i}}const PF=e=>$().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(wa.URL_SCHEME))?BF(e.slice(wa.URL_SCHEME.length)):null;Rt.registerSaveRouter(PF);function BF(e="model"){return new wa(e)}function jF(e){return new zF(e)}function mb(e,t,s,n){o(e),s=s==null?0:s,n=n==null?1:n,a(s,n);let i=0;const r=l=>(l.then(c=>{const p=s+ ++i/e.length*(n-s);return t(p),c}),l);function o(l){v(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function a(l,c){v(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),v(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),v(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(r))}async function fb(e,t){t==null&&(t={});const s=t.fetchFunc==null?$().platform.fetch:t.fetchFunc,n=e.map(u=>s(u,t.requestInit,{isBinary:!0})),i=0,r=.5,o=t.onProgress==null?await Promise.all(n):await mb(n,t.onProgress,i,r),a=o.map(u=>u.arrayBuffer()),l=.5,c=1,p=t.onProgress==null?await Promise.all(a):await mb(a,t.onProgress,l,c);return p}async function VF(e,t="",s,n){const i=o=>fb(o,{requestInit:n}),r=Rv(i);return r(e,t,s)}function Rv(e){return async(t,s="",n)=>{const i=t.map(()=>!1),r={},o=n!=null?n.map(()=>!1):[],a=[];if(t.forEach((d,m)=>{let f=0;d.weights.forEach(y=>{const b="quantization"in y?y.quantization.dtype:y.dtype,S=Hp[b]*We(y.shape),x=()=>{i[m]=!0,r[m]==null&&(r[m]=[]),r[m].push({manifestEntry:y,groupOffset:f,sizeBytes:S})};n!=null?n.forEach((I,A)=>{I===y.name&&(x(),o[A]=!0)}):x(),a.push(y.name),f+=S})}),!o.every(d=>d)){const d=n.filter((m,f)=>!o[f]);throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}. 
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Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(p){let u=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?u+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. 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The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new wb({modelTopology:e,weightSpecs:t,weightData:s,trainingConfig:n})}function JF(e){return new KF(e)}const Ot={};Re(Ot,{browserFiles:()=>jF,browserHTTPRequest:()=>YF,concatenateArrayBuffers:()=>qp,copyModel:()=>Av,decodeWeights:()=>hb,encodeWeights:()=>NF,fromMemory:()=>XF,getLoadHandlers:()=>MF,getModelArtifactsInfoForJSON:()=>Yp,getSaveHandlers:()=>FF,http:()=>bb,isHTTPScheme:()=>yb,listModels:()=>Tv,loadWeights:()=>VF,moveModel:()=>Nv,registerLoadRouter:()=>DF,registerSaveRouter:()=>_F,removeModel:()=>Iv,weightsLoaderFactory:()=>Rv,withSaveHandler:()=>JF});function ZF(e,t,s){const n=g(e,"labels","confusionMatrix"),i=g(t,"predictions","confusionMatrix");v(s==null||s>0&&Number.isInteger(s),()=>`If provided, numClasses must be a positive integer, but got ${s}`),v(n.rank===1,()=>`Expected the rank of labels to be 1, but got ${n.rank}`),v(i.rank===1,()=>`Expected the rank of predictions to be 1, but got ${i.rank}`),v(n.shape[0]===i.shape[0],()=>`Mismatch in the number of examples: ${n.shape[0]} vs. ${i.shape[0]}. Labels and predictions should have the same number of elements.`),v(s>0&&Number.isInteger(s),()=>`numClasses is required to be a positive integer, but got ${s}`);const r=On(K(n,"int32"),s),o=On(K(i,"int32"),s),a=se(r);return K(Le(a,o),"int32")}const QF=L({confusionMatrix_:ZF});const Ev={};Re(Ev,{confusionMatrix:()=>QF});const pr={};Re(pr,{fromPixels:()=>sM,toPixels:()=>tM});let xa;function eM(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let s=!1,n=!1,i=!1,r=!1,o=!1;if(e.data instanceof Uint8Array)s=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)n=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)i=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)r=!0;else if(e.getContext!=null)o=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(i){const d=2;if(i&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}const a=qc(ta,T.backendName);if(a!=null){const d={pixels:e},m={numChannels:t};return T.runKernel(ta,d,m)}const[l,c]=i?[e.videoWidth,e.videoHeight]:[e.width,e.height];let p;o?p=e.getContext("2d").getImageData(0,0,l,c).data:n||s?p=e.data:(r||i)&&(xa==null&&(xa=document.createElement("canvas").getContext("2d")),xa.canvas.width=l,xa.canvas.height=c,xa.drawImage(e,0,0,l,c),p=xa.getImageData(0,0,l,c).data);let u;if(t===4)u=new Int32Array(p);else{const d=l*c;u=new Int32Array(d*t);for(let m=0;m<d;m++)for(let f=0;f<t;++f)u[m*t+f]=p[m*4+f]}const h=[c,l,t];return Pp(u,h,"int32")}async function tM(e,t){let s=g(e,"img","toPixels");if(!(e instanceof ue)){const y=s;s=K(y,"int32"),y.dispose()}if(s.rank!==2&&s.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${s.rank}.`);const[n,i]=s.shape.slice(0,2),r=s.rank===2?1:s.shape[2];if(r>4||r===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${r}`);const o=await s.data(),a=an(s),l=st(s),c=await Promise.all([a.data(),l.data()]),p=c[0],u=c[1],h=p[0],d=u[0];if(a.dispose(),l.dispose(),s.dtype==="float32"){if(h<0||d>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but got range [${h} - ${d}].`)}else if(s.dtype==="int32"){if(h<0||d>255)throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but got range [${h} - ${d}].`)}else throw new Error(`Unsupported type for toPixels: ${s.dtype}. Please use float32 or int32 tensors.`);const m=s.dtype==="float32"?255:1,f=new Uint8ClampedArray(i*n*4);for(let y=0;y<n*i;++y){let b,S,x,I;r===1?(b=o[y]*m,S=o[y]*m,x=o[y]*m,I=255):r===3?(b=o[y*3]*m,S=o[y*3+1]*m,x=o[y*3+2]*m,I=255):r===4&&(b=o[y*4]*m,S=o[y*4+1]*m,x=o[y*4+2]*m,I=o[y*4+3]*m);const A=y*4;f[A+0]=Math.round(b),f[A+1]=Math.round(S),f[A+2]=Math.round(x),f[A+3]=Math.round(I)}if(t!=null){t.width=i,t.height=n;const y=t.getContext("2d"),b=new ImageData(f,i,n);y.putImageData(b,0,0)}return s!==e&&s.dispose(),f}const sM=L({fromPixels_:eM});const kv={};Re(kv,{prepareAndValidate:()=>_v});function _v(e,t){if(e.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[t.rank-1]>e.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[t.rank-1]} vs. ${e.rank}`);if(e.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);const s=t.shape,n=s[s.length-1];let i=1;for(let c=0;c<s.length-1;++c)i*=s[c];const r=e.shape,o=s.slice();o.pop();let a=1;for(let c=n;c<e.rank;++c)a*=r[c],o.push(r[c]);const l=[...qn(e.shape).map(c=>c/a),1].slice(0,n);return[o,i,a,l]}const P={};Re(P,{Serializable:()=>xb,SerializationMap:()=>yo,registerClass:()=>Ts});class xb{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class yo{constructor(){this.classNameMap={}}static getMap(){return yo.instance==null&&(yo.instance=new yo()),yo.instance}static register(e){yo.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function Ts(e){v(e.className!=null,()=>"Class being registered does not have the static className property defined."),v(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),v(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),yo.register(e)}const Lb="2.3.0";class fs extends xb{minimize(e,t=!1,s){const{value:n,grads:i}=this.computeGradients(e,s);if(s!=null){const r=s.map(o=>({name:o.name,tensor:i[o.name]}));this.applyGradients(r)}else this.applyGradients(i);return ce(i),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 Uh(e,t)}dispose(){this.iterations_!=null&&ce(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:j(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(fs,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class bo extends fs{constructor(e,t,s=null){super();this.learningRate=e,this.rho=t,this.epsilon=s,this.accumulatedGrads=[],this.accumulatedUpdates=[],s==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=T.registeredVariables[s],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${s}/accum_grad`,variable:N(()=>ne(i).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${s}/accum_var`,variable:N(()=>ne(i).variable(r))});const o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;const a=this.accumulatedGrads[n].variable,l=this.accumulatedUpdates[n].variable;N(()=>{const c=W(C(a,this.rho),C(ye(o),1-this.rho)),p=C(J(He(W(l,this.epsilon)),He(W(a,this.epsilon))),o),u=W(C(l,this.rho),C(ye(p),1-this.rho));a.assign(c),l.assign(u);const h=W(C(p,-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ce(this.accumulatedGrads.map(e=>e.variable)),ce(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,s=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}bo.className="Adadelta";Ts(bo);class wo extends fs{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=T.registeredVariables[s];if(this.accumulatedGrads[n]==null){const a=!1;this.accumulatedGrads[n]={originalName:`${s}/accumulator`,variable:N(()=>Ft(i.shape,this.initialAccumulatorValue).variable(a))}}const r=Array.isArray(e)?e[n].tensor:e[s];if(r==null)return;const o=this.accumulatedGrads[n].variable;N(()=>{const a=W(o,ye(r));o.assign(a);const l=W(C(J(r,He(W(a,T.backend.epsilon()))),-this.learningRate),i);i.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ce(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);const t=!1;this.accumulatedGrads=e.map(s=>({originalName:s.name,variable:s.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}wo.className="Adagrad";Ts(wo);class xo extends fs{constructor(e,t,s,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=s,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],N(()=>{this.accBeta1=j(t).variable(),this.accBeta2=j(s).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);N(()=>{const s=X(1,this.accBeta1),n=X(1,this.accBeta2);t.forEach((i,r)=>{const o=T.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:N(()=>ne(o).variable(a))}),this.accumulatedSecondMoment[r]==null&&(this.accumulatedSecondMoment[r]={originalName:`${i}/v`,variable:N(()=>ne(o).variable(a))});const l=Array.isArray(e)?e[r].tensor:e[i];if(l==null)return;const c=this.accumulatedFirstMoment[r].variable,p=this.accumulatedSecondMoment[r].variable,u=W(C(c,this.beta1),C(l,1-this.beta1)),h=W(C(p,this.beta2),C(ye(l),1-this.beta2)),d=J(u,s),m=J(h,n);c.assign(u),p.assign(h);const f=W(C(J(d,W(He(m),this.epsilon)),-this.learningRate),o);o.assign(f)}),this.accBeta1.assign(C(this.accBeta1,this.beta1)),this.accBeta2.assign(C(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ce(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ce(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const 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),N(()=>{this.accBeta1.assign(qt(this.beta1,this.iterations_+1)),this.accBeta2.assign(qt(this.beta2,this.iterations_+1))});const t=e.length/2,s=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)}))}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)}}xo.className="Adam";Ts(xo);class Lo extends fs{constructor(e,t,s,n=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=s,this.epsilon=n,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],N(()=>{this.iteration=j(0).variable(),this.accBeta1=j(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);N(()=>{const s=X(1,this.accBeta1),n=J(-this.learningRate,W(C(this.iteration,this.decay),1));t.forEach((i,r)=>{const o=T.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:ne(o).variable(a)}),this.accumulatedWeightedInfNorm[r]==null&&(this.accumulatedWeightedInfNorm[r]={originalName:`${i}/v`,variable:ne(o).variable(a)});const l=Array.isArray(e)?e[r].tensor:e[i];if(l==null)return;const c=this.accumulatedFirstMoment[r].variable,p=this.accumulatedWeightedInfNorm[r].variable,u=W(C(c,this.beta1),C(l,1-this.beta1)),h=C(p,this.beta2),d=Je(l),m=Wt(h,d);c.assign(u),p.assign(m);const f=W(C(J(n,s),J(u,W(m,this.epsilon))),o);o.assign(f)}),this.iteration.assign(W(this.iteration,1)),this.accBeta1.assign(C(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ce(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ce(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new 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)}}Lo.className="Adamax";Ts(Lo);class Ei extends fs{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=Array.isArray(e)?e[n].tensor:e[s];if(i==null)return;const r=T.registeredVariables[s];N(()=>{const o=W(C(this.c,i),r);r.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=gt(j(-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 not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Ei.className="SGD";Ts(Ei);class So extends Ei{constructor(e,t,s=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=s,this.accumulations=[],this.m=j(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=T.registeredVariables[s];if(this.accumulations[n]==null){const a=!1;this.accumulations[n]={originalName:`${s}/momentum`,variable:N(()=>ne(i).variable(a))}}const r=this.accumulations[n].variable,o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;N(()=>{let a;const l=W(C(this.m,r),o);this.useNesterov?a=W(C(this.c,W(o,C(l,this.m))),i):a=W(C(this.c,l),i),r.assign(l),i.assign(a)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ce(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(s=>({originalName:s.name,variable:s.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}So.className="Momentum";Ts(So);class vo extends fs{constructor(e,t=.9,s=0,n=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=s,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=T.registeredVariables[s],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${s}/rms`,variable:N(()=>ne(i).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${s}/momentum`,variable:N(()=>ne(i).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${s}/mg`,variable:N(()=>ne(i).variable(r))});const o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;const a=this.accumulatedMeanSquares[n].variable,l=this.accumulatedMoments[n].variable;N(()=>{const c=W(C(a,this.decay),C(ye(o),1-this.decay));if(this.centered){const p=this.accumulatedMeanGrads[n].variable,u=W(C(p,this.decay),C(o,1-this.decay)),h=J(C(o,this.learningRate),He(X(c,W(ye(u),this.epsilon)))),d=W(C(l,this.momentum),h);a.assign(c),p.assign(u),l.assign(d);const m=X(i,d);i.assign(m)}else{const p=W(C(a,this.decay),C(ye(o),1-this.decay)),u=W(C(l,this.momentum),J(C(o,this.learningRate),He(W(p,this.epsilon))));a.assign(p),l.assign(u);const h=X(i,u);i.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ce(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ce(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ce(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,s=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}vo.className="RMSProp";Ts(vo);class ur{static sgd(e){return new Ei(e)}static momentum(e,t,s=!1){return new So(e,t,s)}static rmsprop(e,t=.9,s=0,n=null,i=!1){return new vo(e,t,s,n,i)}static adam(e=.001,t=.9,s=.999,n=null){return new xo(e,t,s,n)}static adadelta(e=.001,t=.95,s=null){return new bo(e,t,s)}static adamax(e=.002,t=.9,s=.999,n=null,i=0){return new Lo(e,t,s,n,i)}static adagrad(e,t=.1){return new wo(e,t)}}So,Ei,bo,wo,vo,Lo,xo;const hr={sgd:ur.sgd,momentum:ur.momentum,adadelta:ur.adadelta,adagrad:ur.adagrad,rmsprop:ur.rmsprop,adamax:ur.adamax,adam:ur.adam};const nM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Kp(){return new Promise(e=>nM(()=>e()))}function iM(e,t,s){const n=s*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[n,i]}function rM(e,t,s,n=!0){let i=[];if(n)i=i.concat(t.slice(0)),i.push(e[0]/s),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const r=t.length;for(let o=0;o<r;++o)i=i.concat([e[o+1]/t[o],t[o]]);i=i.concat(e.slice(r+1))}return i}function oM(e,t,s=!0){const n=[];if(s){n.push(t);for(let i=t+1;i<e;++i)i<=2*t?(n.push(i),n.push(i-(t+1))):n.push(i)}else{const i=[],r=[];for(let o=1;o<e;++o)o>=t*2+1||o%2===1?r.push(o):i.push(o);n.push(...i),n.push(0),n.push(...r)}return n}function aM(e,t,s,n=!0){const i=[];n?i.push(e[0]/s):i.push(e[0]*s);for(let r=1;r<e.length;++r)r<=t.length?n?i.push(t[r-1]*e[r]):i.push(e[r]/t[r-1]):i.push(e[r]);return i}function lM(e,t){const s=[0];for(let n=0;n<t;++n)s.push(e[n][0]);return s}function cM(e,t,s){const n=e.slice(0,1);for(let i=0;i<s;++i)n.push(e[i+1]-t[i][0]-t[i][1]);return n}const Sb=1.7580993408473768,vb=1.0507009873554805;const pM=.3275911,uM=.254829592,hM=-.284496736,dM=1.421413741,mM=-1.453152027,fM=1.061405429;function gM(...e){$().getBool("IS_TEST")||console.warn(...e)}function yM(...e){$().getBool("IS_TEST")||console.log(...e)}function bM(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const s=new Float32Array(e.length*2);for(let n=0;n<s.length;n+=2)s[n]=e[n/2],s[n+1]=t[n/2];return s}function wM(e){const t=new Float32Array(e.length/2),s=new Float32Array(e.length/2);for(let n=0;n<e.length;n+=2)t[n/2]=e[n],s[n/2]=e[n+1];return{real:t,imag:s}}function xM(e){const t=Math.ceil(e.length/4),s=new Float32Array(t),n=new Float32Array(t);for(let i=0;i<e.length;i+=4)s[Math.floor(i/4)]=e[i],n[Math.floor(i/4)]=e[i+1];return{real:s,imag:n}}function LM(e){const t=Math.floor(e.length/4),s=new Float32Array(t),n=new Float32Array(t);for(let i=2;i<e.length;i+=4)s[Math.floor(i/4)]=e[i],n[Math.floor(i/4)]=e[i+1];return{real:s,imag:n}}function SM(e,t){const s=e[t*2],n=e[t*2+1];return{real:s,imag:n}}function vM(e,t,s,n){e[n*2]=t,e[n*2+1]=s}function TM(e,t){const s=new Float32Array(e/2),n=new Float32Array(e/2);for(let i=0;i<Math.ceil(e/2);i++){const r=(t?2:-2)*Math.PI*(i/e);s[i]=Math.cos(r),n[i]=Math.sin(r)}return{real:s,imag:n}}function IM(e,t,s){const n=(s?2:-2)*Math.PI*(e/t),i=Math.cos(n),r=Math.sin(n);return{real:i,imag:r}}const U={};Re(U,{ERF_A1:()=>uM,ERF_A2:()=>hM,ERF_A3:()=>dM,ERF_A4:()=>mM,ERF_A5:()=>fM,ERF_P:()=>pM,PARALLELIZE_THRESHOLD:()=>Fh,SELU_SCALE:()=>vb,SELU_SCALEALPHA:()=>Sb,applyActivation:()=>fo,assertAndGetBroadcastShape:()=>Se,assertAxesAreInnerMostDims:()=>F1,assertParamsConsistent:()=>vy,assignToTypedArray:()=>vM,axesAreInnerMostDims:()=>by,calculateShapes:()=>PS,castTensor:()=>AM,combineLocations:()=>dS,complexWithEvenIndex:()=>xM,complexWithOddIndex:()=>LM,computeConv2DInfo:()=>is,computeConv3DInfo:()=>rr,computeDefaultPad:()=>Ly,computeDilation2DInfo:()=>Y1,computeOptimalWindowSize:()=>dk,computeOutAndReduceShapes:()=>wy,computeOutShape:()=>Ty,computePool2DInfo:()=>Tn,computePool3DInfo:()=>bi,convertConv2DDataFormat:()=>ir,eitherStridesOrDilationsAreOne:()=>et,expandShapeToKeepDim:()=>ft,exponent:()=>IM,exponents:()=>TM,getAxesPermutation:()=>ut,getBroadcastDims:()=>ZE,getComplexWithIndex:()=>SM,getFusedBiasGradient:()=>mo,getFusedDyActivation:()=>ho,getImageCenter:()=>iM,getInnerMostAxes:()=>Gt,getPermuted:()=>oM,getReductionAxes:()=>Ke,getReshaped:()=>rM,getReshapedPermuted:()=>aM,getSliceBeginCoords:()=>lM,getSliceSize:()=>cM,getUndoAxesPermutation:()=>Pr,linspaceImpl:()=>CM,log:()=>yM,mergeRealAndImagArrays:()=>bM,prepareAndValidate:()=>_v,prepareSplitSize:()=>ob,reshapeTensor:()=>NM,segment_util:()=>bS,shouldFuse:()=>go,splitRealAndImagArrays:()=>wM,tupleValuesAreOne:()=>Bs,upcastType:()=>wt,validateInput:()=>zh,validateUpdateShape:()=>cb,warn:()=>gM});function AM(e,t,s){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const n=Ie(e.shape),i=K(e,"float32"),r=s.complex(i,n);return n.dispose(),i.dispose(),r}if(!oy(e.dtype,t))return T.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const n=s.real(e),i=K(n,t);return n.dispose(),i}if(t==="int32")return s.int(e);if(t==="bool"){const n=j(0,e.dtype),i=s.notEqual(e,n);return n.dispose(),i}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function NM(e,t){return T.makeTensorFromDataId(e.dataId,t,e.dtype)}function CM(e,t,s){const n=(t-e)/(s-1),i=Yn(s,"float32");i[0]=e;for(let r=1;r<i.length;r++)i[r]=i[r-1]+n;return Fe(i,"float32")}function Dv(e,t,s){const n=new Array(e.rank).fill(0),i=e.shape.slice();return t.map(r=>{const o=[...i];o[s]=r;const a=he(e,n,o);return n[s]+=r,a})}function Fv(e,t){const s=new Array(e.rank);for(let i=0;i<s.length;i++)s[i]=e.shape[i]*t[i];const n=le(s,e.dtype);for(let i=0;i<n.values.length;++i){const r=n.indexToLoc(i),o=new 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got ${e.length}`);t=e[0]}else t=e;return t}function Ue(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new _(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Aa(e){let t=0;for(const s of e)s.shape.length===0?t+=1:t+=s.shape.reduce((n,i)=>n*i);return t}const XI="Variable";class Td{constructor(e,t="float32",s=XI,n=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Ld(),s=s==null?XI:s,this.originalName=ld(s),this.name=cd(this.originalName),this.trainable_=n,this.constraint=i,this.val=Gp(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),hU(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function hU(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function lu(e){return e.map(t=>t.read())}function Na(e){e.forEach(t=>{const s=t[0];s.write(t[1])})}class rt{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class bs{constructor(e,t,s,n,i,r,o){this.dtype=e,this.shape=t,this.sourceLayer=s,this.inputs=n,this.callArgs=i,this.outputTensorIndex=o,this.id=Ld(),r!=null&&(this.originalName=ld(r),this.name=cd(this.originalName)),this.rank=t.length}}let dU=0;class Ao{constructor(e,t){this.callArgs=t,this.id=dU++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const s of e.inboundLayers)s!=null&&s.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let mU=0;class be extends P.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=mU++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const s=this.getClassName();t=pn(s)+"_"+dr(s)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let s;if(e.batchInputShape!=null)s=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),s=[i].concat(e.inputShape)}this.batchInputShape=s;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Is(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new _(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Pt(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Pt(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ti(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new ti(`Layer ${this.name} is not connected, no input to return.`);return Pt(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ti(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ti(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Pt(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=qe(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=qe(this.inputSpec);if(e.length!==t.length)throw new _(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let s=0;s<e.length;s++){const n=e[s],i=t[s];if(i==null)continue;const r=n.rank;if(i.ndim!=null&&r!==i.ndim)throw new _(`Input ${s} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${r}`);if(i.maxNDim!=null&&r>i.maxNDim)throw new _(`Input ${s} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${r}`);if(i.minNDim!=null&&r<i.minNDim)throw new _(`Input ${s} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${r}.`);if(i.dtype!=null&&n.dtype!==i.dtype)throw new _(`Input ${s} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${n.dtype}.`);if(i.axes){const o=n.shape;for(const a in i.axes){const l=Number(a),c=i.axes[a],p=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(p)===-1)throw new _(`Input ${s} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(i.shape!=null)for(let o=0;o<i.shape.length;++o){const a=i.shape[o],l=n.shape[o];if(a!=null&&l!=null&&a!==l)throw new _(`Input ${s} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const s=qe(e);let n=!0;for(const r of s)if(!(r instanceof bs)){n=!1;break}let i=!0;for(const r of s)if(r instanceof bs){i=!1;break}if(n===i)throw new _("Arguments to apply() must be all SymbolicTensors or all Tensors");return Dn(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const r=[];for(const o of qe(e))r.push(o.shape);this.build(Pt(r)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let r=this.call(e,t);const o=qe(r),a=[];for(let l of o)s.indexOf(l)!==-1&&(l=l.clone()),a.push(l);if(r=Pt(a),this.activityRegularizer!=null)throw new ae("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}else{const r=fU(e),o=this.computeOutputShape(r);let a;const l=gU(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?r[0]:r),o!=null&&o.length>0&&Array.isArray(o[0])?a=o.map((c,p)=>new bs(l,c,this,qe(e),t,this.name,p)):a=new bs(l,o,this,qe(e),t,this.name),this.addInboundNode(e,a,null,null,r,o,t),this._refCount++,this.activityRegularizer!=null)throw new ae("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((s,n)=>{s!=null&&e[n]!=null&&e[n]!==s&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ti(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const s=JSON.stringify(t.outputShapes);e.indexOf(s)===-1&&e.push(s)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ti(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Is(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Aa(this.weights)}build(e){this.built=!0}getWeights(e=!1){return lu(e?this.trainableWeights:this.weights)}setWeights(e){N(()=>{const t=this.weights;if(t.length!==e.length)throw new _(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const s=[],n=lu(t);for(let i=0;i<n.length;++i){const r=n[i],o=t[i],a=e[i];if(!E.arraysEqual(r.shape,a.shape))throw new _(`Layer weight shape ${r.shape} not compatible with provided weight shape ${a.shape}`);s.push([o,a])}Na(s)})}addWeight(e,t,s,n,i,r,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new _(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),s==null&&(s="float32"),this.fastWeightInitDuringBuild&&(n=je("zeros"));const a=n.apply(t,s),l=new Td(a,s,e,r,o);return a.dispose(),i!=null&&this.addLoss(()=>i.apply(l.read())),r==null&&(r=!0),r?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=qe(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(s=>{if(s!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,s,n,i,r,o=null){const a=qe(e);t=qe(t),s=qe(s),n=qe(n),i=Ia(i),r=Ia(r);const l=[],c=[],p=[];for(const u of a)l.push(u.sourceLayer),c.push(u.nodeIndex),p.push(u.tensorIndex);new Ao({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:p,inputTensors:a,outputTensors:t,inputMasks:s,outputMasks:n,inputShapes:i,outputShapes:r},o);for(let u=0;u<t.length;u++)t[u].sourceLayer=this,t[u].nodeIndex=this.inboundNodes.length-1,t[u].tensorIndex=u}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function fU(e){e=qe(e);const t=[];for(const s of e)t.push(s.shape);return Pt(t)}function gU(e){return"float32"}function Wb(e,t,s){if((t==null||s!=null&&s>0)&&(t=e.sourceLayer,s=e.nodeIndex),t.inboundNodes.length===0)return[e];{const n=t.inboundNodes[s];if(n.inboundLayers.length===0)return n.inputTensors;{const i=[];for(let r=0;r<n.inboundLayers.length;r++){const o=n.inputTensors[r],a=n.inboundLayers[r],l=n.nodeIndices[r],c=Wb(o,a,l);for(const p of c)i.indexOf(p)===-1&&i.push(p)}return i}}}class $i extends be{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:dr("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new _("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new _("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new _("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const s=e.dtype||"float32";this.batchInputShape=t,this.dtype=s,this.inputSpec=[{shape:t}];const n=new bs(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Ao({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new _(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}$i.className="InputLayer";P.registerClass($i);function Id(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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params: ${l}`),n(`Non-trainable params: ${c}`),n("_".repeat(t))}function MU(e){let t;return e.collectedTrainableWeights!=null?t=Aa(e.collectedTrainableWeights):t=Aa(e.trainableWeights),t}function UU(e){let t=!0;const s=[],n=[];for(const i in e.nodesByDepth)s.push(e.nodesByDepth[i]);for(const i of s){if(i.length>1||i.length===1&&i[0].inboundLayers.length>1){t=!1;break}n.push(...i)}if(t)for(const i of e.layers){let r=!1;for(const o of i.inboundNodes)if(n.indexOf(o)!==-1)if(r){t=!1;break}else r=!0;if(!t)break}return t}function _d(e,t,s=console.log){let n="";for(let i=0;i<e.length;++i)i>0&&(n=n.slice(0,n.length-1)+" "),n+=e[i],n=n.slice(0,t[i]),n+=" ".repeat(t[i]-n.length);s(n)}function $U(e,t,s){let n;try{n=JSON.stringify(e.outputShape)}catch(a){n="multiple"}const i=e.name,r=e.getClassName(),o=[`${i} (${r})`,n,e.countParams().toString()];_d(o,t,s)}function WU(e,t,s,n){let i;try{i=JSON.stringify(e.outputShape)}catch(p){i="multiple"}const r=[];for(const p of e.inboundNodes){if(s!=null&&s.length>0&&s.indexOf(p)===-1)continue;for(let u=0;u<p.inboundLayers.length;++u){const h=p.inboundLayers[u].name,d=p.nodeIndices[u],m=p.tensorIndices[u];r.push(`${h}[${d}][${m}]`)}}const o=e.name,a=e.getClassName(),l=r.length===0?"":r[0],c=[`${o} (${a})`,i,e.countParams().toString(),l];_d(c,t,n);for(let p=1;p<r.length;++p)_d(["","","",r[p]],t,n)}function iA(e,t,s){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof s=="string"}function Ro(e,t){if(e===null)return null;if(typeof e=="string")return ki(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){const s=[],n=e.length;for(let i=0;i<n;++i){const r=e[i];iA(t,i,r)?s.push(r):s.push(Ro(r,t))}return s}else{const s={};for(const n of Object.keys(e)){const i=e[n];if(n==="name"&&typeof i=="string")s[n]=i;else{const r=ki(n);s[r]=Ro(i,r)}}return s}}function Dd(e,t){if(e==null)return null;if(typeof e=="string")return pn(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){const s=[],n=e.length;for(let i=0;i<n;++i){const r=e[i];iA(t,i,r)?s.push(r):s.push(Dd(r,t))}return s}else{const s={};for(const n of Object.keys(e)){const i=e[n],r=pn(n);(n==="name"||n==="className")&&typeof i=="string"?s[r]=i:s[r]=Dd(i,n)}return s}}const fr="2.3.0";function zU(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return K(t,e.dtype)}catch(s){throw new _(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}class Wi{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Wi)for(const t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(const t of e)this.add(t.key,t.value)}}add(e,t,s){if(this.id2Value[e.id]==null)this.id2Value[e.id]=zU(e,t),this.name2Id[e.name]=e.id,s!=null&&(this.id2Mask[e.id]=s);else throw new _(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof bs){if(this.id2Value[e.id]==null)throw new _(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{const t=this.name2Id[e];if(t==null)throw new _(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof bs){if(this.id2Value[e.id]==null)throw new _(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{const t=this.name2Id[e];if(t==null)throw new _(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&ce(this.id2Mask)}}const qb={},rA={};function Oo(e,t,s,n){const i=s==null?!1:s.training,r=Array.isArray(e),o=r?e:[e],a=o.map(m=>m.name),l=[],c=t.names();for(const m of a)c.indexOf(m)!==-1?l.push(t.getValue(m)):l.push(null);n!=null&&(n.maxNumTensors=-Infinity,n.minNumTensors=Infinity);const p=a.join(",")+"|"+t.names().join(",");let u,h;if(qb[p]==null){const m=BU(o,t);u=m.sorted,h=m.recipientCounts,qb[p]=u,rA[p]=h}u=qb[p],h={},i||Object.assign(h,rA[p]);const d=new Wi(t);for(let m=0;m<u.length;++m){if(n!=null){const F=pa().numTensors;F>n.maxNumTensors&&(n.maxNumTensors=F),F<n.minNumTensors&&(n.minNumTensors=F)}const f=u[m],y=f.sourceLayer;if(y instanceof $i)continue;const b=[],S=[],x=[];let I=!1;for(const F of f.inputs){const M=d.getValue(F),B=d.getMask(F);b.push(M),S.push(B),B!=null&&(I=!0),i||(h[F.name]--,h[F.name]===0&&!t.hasKey(F)&&a.indexOf(F.name)===-1&&!M.isDisposed&&F.sourceLayer.stateful!==!0&&x.push(M))}I&&(s=s||{},s.mask=S[0]);const A=qe(y.apply(b,s));let k=null;y.supportsMasking&&(k=y.computeMask(b,S));const R=PU(f),D=Array.isArray(R)?R:[R];for(let F=0;F<D.length;++F){d.hasKey(D[F])||d.add(D[F],A[F],Array.isArray(k)?k[0]:k);const M=a.indexOf(D[F].name);M!==-1&&(l[M]=A[F])}i||ce(x)}return d.disposeMasks(),r?l:l[0]}function BU(e,t){E.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let s=[],n={};if(e.length===1){const i=oA(e[0],t);s=i.sorted,n=i.recipientMap}else{const i=new Set();for(const r of e){const{sorted:o,recipientMap:a}=oA(r,t);for(const l of o)i.has(l.name)||(s.push(l),i.add(l.name));for(const l in a)n[l]==null&&(n[l]=new Set()),a[l].forEach(c=>n[l].add(c))}}return{sorted:s,recipientCounts:jU(n)}}function jU(e){const t={};for(const s in e)t[s]=e[s].size;return t}function oA(e,t){const s=new Set(),n=[],i={};for(const a of t.names())s.add(a);const r=[],o=[];for(r.push(e);r.length>0;){const a=r[r.length-1];if(s.has(a.name)){r.pop();continue}const l=o[o.length-1]===r.length-1;if(a.inputs.length===0||l)r.pop(),n.push(a),s.add(a.name),l&&o.pop();else{o.push(r.length-1);for(const c of a.inputs){if(i[c.name]==null&&(i[c.name]=new Set()),i[c.name].add(a.name),s.has(c.name))continue;r.push(c)}}}return{sorted:n,recipientMap:i}}function PU(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let s=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(const i of e.sourceLayer.inboundNodes[n].outputTensors)if(i.id===e.id){s=n;break}t=e.sourceLayer.getOutputAt(s)}return t}class Mn extends be{constructor(e){super({});if(this.containerNodes=new Set(),this.name=e.name,this.name==null){const b=this.getClassName().toLowerCase();this.name=dr(b)}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],hn(this.inputs).length!==this.inputs.length)throw new _(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);hn(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const b of this.outputs){const S=b.sourceLayer,x=b.nodeIndex,I=b.tensorIndex;this.outputLayers.push(S),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(I)}for(const b of this.inputs){const S=b.sourceLayer,x=b.nodeIndex,I=b.tensorIndex;qs(x===0,"input layer has >1 nodes"),qs(I===0,"input layer has >1 tensors"),this.inputLayers.push(S),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(I)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){const S=this.inputLayers[b];if(!(S instanceof $i))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${S.getClassName()}.`);this.inputNames.push(S.name),this.feedInputShapes.push(S.batchInputShape),this.feedInputNames.push(S.name)}for(const b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);const t={},s={},n={},i={},r={},o=[],a=(b,S,x,I,A,k)=>{(I==null||A==null||k==null)&&(I=b.sourceLayer,A=b.nodeIndex,k=b.tensorIndex);const R=I.inboundNodes[A];if(x.indexOf(R)!==-1)throw new Is(`The tensor ${b.name} at layer "${I.name}" is part of a cycle.`);if(S.indexOf(R)!==-1)return;this.containerNodes.add(Mn.nodeKey(I,A)),I.id in r||(r[I.id]=Object.keys(r).length),x.indexOf(R)===-1&&x.push(R);const D=R.inboundLayers.length;for(let F=0;F<D;F++){const M=R.inputTensors[F],B=R.inboundLayers[F],V=R.nodeIndices[F],Y=R.tensorIndices[F];a(M,S,x,B,V,Y)}for(S.push(R);x.indexOf(R)>=0;)x.splice(x.indexOf(R),1);o.push(R)},l=[],c=[];for(const b of this.outputs)a(b,l,c);const p=o.slice().reverse();for(const b of p){s[b.id]=b,b.id in t||(t[b.id]=0);let S=t[b.id];const x=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];S=Math.max(S,x),n[b.outboundLayer.id]=S,i[b.outboundLayer.id]=b.outboundLayer,t[b.id]=S;for(let I=0;I<b.inboundLayers.length;I++){const A=b.inboundLayers[I],k=b.nodeIndices[I],R=A.inboundNodes[k],D=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(S+1,D),s[R.id]=R}}const u={};for(const b in t){const S=t[b];S in u||(u[S]=[]),u[S].push(s[b])}const h={};for(const b in n){const S=n[b];S in h||(h[S]=[]),h[S].push(i[b])}let d=Object.keys(h).map(b=>parseInt(b,10)).sort(Zp);this.layers=[];for(const b of d){const S=h[b];S.sort((x,I)=>{const A=r[x.id],k=r[I.id];return A<k?-1:A>k?1:0});for(const x of S)x instanceof Mn&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=h,d=Object.keys(u).map(b=>parseInt(b,10)).sort(Zp);const m=this.inputs.slice(),f=[];for(const b of d)for(const S of u[b]){const x=S.outboundLayer;if(x!=null){for(const I of S.inputTensors)if(m.indexOf(I)===-1)throw new Is(`Graph disconnected: cannot obtain value for tensor ${I} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(const I of S.outputTensors)m.push(I);f.push(x.name)}}this.nodesByDepth=u;const y=this.layers.map(b=>b.name);for(const b of y){const S=y.filter(x=>x===b).length;if(S!==1)throw new Is(`The name "${b}" is used ${S} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new Ao({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(s=>s.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new _("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const s of this.layers)t.push(...s.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const s={};let n=0;for(const r of this.layers)for(const o of r.weights){if(s[o.originalName]!=null)throw new _(`Duplicate weight name: ${o.originalName}`);s[o.originalName]=o,n++}const i=[];for(const r in e){let o=r;if(s[r]==null){const a=r.split("/"),l=a.slice(0,-2).concat([a[a.length-1]]);o=l.join("/")}if(s[o]!=null)i.push([s[o],e[r]]);else if(t)throw new _(`Provided weight data has no target variable: ${r}`);delete s[o]}if(t){const r=[];for(const o in s)r.push(o);if(r.length>0)throw new _(`${r.length} of ${n} weights are not set: ${r}`)}Na(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${fr}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const s=Dd(this.updatedConfig());return t?JSON.stringify(s):s}call(e,t){return N(()=>{e=qe(e);const s=new Wi();for(let n=0;n<this.inputs.length;++n)s.add(this.inputs[n],e[n]);return Oo(this.outputs,s,t)})}computeMask(e,t){return N(()=>{e=qe(e);let s;return t==null?s=cn(null,e.length):s=qe(t),this.runInternalGraph(e,s)[1]})}computeOutputShape(e){const t=Ia(e);if(t.length!==this.inputLayers.length)throw new _(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const s={};for(let o=0;o<t.length;o++){const a=this.inputLayers[o],l=t[o],c=a.name+"_0_0";s[c]=l}const n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Zp);if(n.length>1)for(const o of n){const a=this.nodesByDepth[o];for(const l of a){const c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;const p=[];for(let m=0;m<l.inboundLayers.length;m++){const f=l.inboundLayers[m],y=l.nodeIndices[m],b=l.tensorIndices[m],S=`${f.name}_${y}_${b}`,x=s[S];p.push(x)}const u=c.computeOutputShape(Pt(p)),h=Ia(u),d=c.inboundNodes.indexOf(l);for(let m=0;m<h.length;m++){const f=`${c.name}_${d}_${m}`;s[f]=h[m]}}}const i=[],r=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],p=`${a.name}_${l}_${c}`;r.push(p)}for(let o=0;o<r.length;o++){const a=r[o];qs(a in s),i.push(s[a])}return Pt(i)}runInternalGraph(e,t){t==null&&(t=cn(null,e.length));const s={};for(let a=0;a<this.inputs.length;++a){const l=this.inputs[a],c=e[a],p=t[a];s[l.id]=[c,p]}const n=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Zp);for(const a of n){const l=this.nodesByDepth[a];for(const c of l){const p=c.outboundLayer,u=c.inputTensors,h=c.outputTensors,d=new Array();for(const m of u)m.id in s&&d.push(s[m.id]);if(d.length===u.length){let m={},f,y,b,S;if(c.callArgs!=null&&(m=c.callArgs),d.length===1){const[x,I]=d[0];m.mask==null&&(m.mask=I),b=qe(p.call(x,m)),S=qe(p.computeMask(x,I)),f=[x],y=[I]}else f=d.map(x=>x[0]),y=d.map(x=>x[1]),m.mask==null&&(m.mask=y),b=qe(p.call(f,m)),S=qe(p.computeMask(f,y));if(p.activityRegularizer)throw new ae("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<h.length;++x){const I=h[x],A=b[x],k=S[x];s[I.id]=[A,k]}}}}const i=[],r=[],o=[];for(const a of this.outputs){qs(a.id in s,`Could not compute output ${a.name} : ${a.id}`);const[l,c]=s[a.id];o.push(l.shape),i.push(l),r.push(c)}return[i,r,o]}buildNodeConversionMap(e){const t={};let s;for(const n of this.layers){s=n instanceof Mn?1:0;for(let i=0;i<n.inboundNodes.length;i++){const r=Mn.nodeKey(n,i);this.containerNodes.has(r)&&(t[r]=s,s+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new _(`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 _("Provide either a layer name or layer index");for(const s of this.layers)if(s.name===e)return s;throw new _(`No such layer: ${e}`)}calculateLosses(){return N(()=>{const e=[];for(const t of this.layers)for(let s=0;s<t.inboundNodes.length;++s){const n=Mn.nodeKey(t,s);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),s=[];for(const r of this.layers){const o=r.getClassName(),a=r.getConfig(),l=[];for(let p=0;p<r.inboundNodes.length;p++){const u=r.inboundNodes[p],h=Mn.nodeKey(r,p);let d={};if(this.containerNodes.has(h)){if(u.callArgs)try{JSON.stringify(u.callArgs),d=u.callArgs}catch(m){console.warn(`Layer ${r.name} was passed non-serializable keyword arguments: ${u.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(u.inboundLayers.length>0){const m=[];for(let f=0;f<u.inboundLayers.length;f++){const y=u.inboundLayers[f],b=u.nodeIndices[f],S=u.tensorIndices[f],x=Mn.nodeKey(y,b);let I=t[x];I==null&&(I=0),m.push([y.name,I,S,d])}l.push(m)}}}const c={};c.name=r.name,c.className=o,c.config=a,c.inboundNodes=l,s.push(c)}e.layers=s;const n=[];for(let r=0;r<this.inputLayers.length;r++){const o=this.inputLayers[r],a=this.inputLayersNodeIndices[r],l=Mn.nodeKey(o,a);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);const p=this.inputLayersTensorIndices[r];n.push([o.name,c,p])}e.inputLayers=n;const i=[];for(let r=0;r<this.outputLayers.length;r++){const o=this.outputLayers[r],a=this.outputLayersNodeIndices[r],l=Mn.nodeKey(o,a);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);const p=this.outputLayersTensorIndices[r];i.push([o.name,c,p])}return e.outputLayers=i,e}static fromConfig(e,t,s={},n=!1){const i={},r={};function o(f,y){f.name in r?r[f.name].push(y):r[f.name]=[y]}function a(f,y){const b=[];let S;for(const x of y){const I=x[0],A=x[1],k=x[2];if(S=x[3]==null?{}:x[3],!(I in i)){o(f,y);return}const R=i[I];if(R.inboundNodes.length<=A){o(f,y);return}const D=R.inboundNodes[A];b.push(D.outputTensors[k])}b.length>0&&f.apply(Pt(b),S)}function l(f){const y=f.name,b=ws(f,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(n),i[y]=b;const S=f.inboundNodes;S.forEach(x=>{if(!(x instanceof Array))throw new _(`Corrupted configuration, expected array for nodeData: ${x}`);o(b,x)})}const c=t.name,p=t.layers;for(const f of p)l(f);for(;!NI(r);)for(const f of p){const y=i[f.name];if(y.name in r){const b=r[y.name];delete r[y.name];for(const S of b)a(y,S)}}const u=[],h=[],d=t.inputLayers;for(const f of d){const y=f[0],b=f[1],S=f[2];qs(y in i);const x=i[y],I=x.inboundNodes[b].outputTensors;u.push(I[S])}const m=t.outputLayers;for(const f of m){const y=f[0],b=f[1],S=f[2];qs(y in i);const x=i[y],I=x.inboundNodes[b].outputTensors;h.push(I[S])}return new e({inputs:u,outputs:h,name:c})}get stateful(){if(this._stateful)throw new _("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){N(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function VU(e,t,s){const n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${s} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(r=>{r in e?i.push(e[r]):i.push(null)}),i}else throw new Error(`The model has multiple (${n}) outputs, so ${s} must be either an array with ${n} elements or an object with ${t} keys. Provided ${s} not understood: ${JSON.stringify(e)}`)}function Fd(e,t){return VU(e,t,"classWeight")}async function Md(e,t,s,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(s!=null){const i=N(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const a=1;return e.argMax(a)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),r=Array.from(await i.data());ce(i);const o=[];return r.forEach(a=>{if(s[a]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${a} exists in the data but not in classWeight`);o.push(s[a])}),Fe(o,"float32")}else return null}function aA(e,t){return C(e,t)}const GU=32;function cA(e,t){let s,n;const i=t;s=i.xs,n=i.ys,E.assert(s!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const r=lA("input",e.inputNames,s),o=lA("output",e.outputNames,n),a=r[0].shape[0];E.assert(r.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${r.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),E.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<r.length;l++)E.assert(r[l].shape[0]===a,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${r[l].shape[0]}; expected  ${a} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)E.assert(o[l].shape[0]===a,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected  ${a} based on input ${e.inputNames[0]}.`);return{xs:r,ys:o}}function lA(e,t,s){if(s instanceof ue)return[s];if(Array.isArray(s))return E.assert(s.length===t.length,()=>`Received an array of ${s.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),s;{const n=[];for(const i of t){if(s[i]==null)throw new _(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);n.push(s[i])}return n}}function HU(e){if(e.length===3)throw new ae("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function uA(e,t,s){const n=s.batchesPerEpoch!=null;if(E.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),E.assert(s!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),E.assert(s.epochs!=null&&s.epochs>0&&Number.isInteger(s.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${s.epochs}`),E.assert(!n||s.batchesPerEpoch>0&&Number.isInteger(s.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${s.batchesPerEpoch}`),E.assert(s.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=s.validationData!=null;let r,o;if(i)if(pA(s.validationData))E.assert(s.validationBatches==null||s.validationBatches>0&&Number.isInteger(s.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${s.validationBatches}`);else{const y=HU(s.validationData);r=y.xs,o=y.ys}const a=e.makeTrainFunction(),l=e.getDedupedMetricsNames();let c;i?c=l.slice().concat(l.map(y=>"val_"+y)):c=l.slice();const p=Nd(s.callbacks,s.yieldEvery),u=s.verbose==null?1:s.verbose,{callbackList:h,history:d}=Cd(p,u,s.epochs,null,null,qU(t,s),null,i,c);h.setModel(e),e.history=d,await h.onTrainBegin(),e.stopTraining_=!1;let m=s.initialEpoch==null?0:s.initialEpoch,f=await t.iterator();for(;m<s.epochs;){const y={};await h.onEpochBegin(m);let b=0,S=0;for(n||(f=await t.iterator());n?b<s.batchesPerEpoch:!0;){const x=await f.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${s.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${s.batchesPerEpoch*s.epochs} batches). 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d=Ge(d),this.calculateLosses().forEach(m=>{d=W(d,m)}),d},a=this.collectedTrainableWeights.map(p=>p.read()),l=!0,c=this.optimizer_.minimize(o,l,a);return[c].concat(r)}}makeTestFunction(){this.testFunction=e=>N(()=>{const t=[];let s;const n=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=[];for(let l=0;l<this.inputs.length;++l)r.push({key:this.inputs[l],value:n[l]});const o=new Wi(r),a=Oo(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){const c=this.lossFunctions[l],p=Ge(c(i[l],a[l]));l===0?s=p:s=W(s,p),t.push(s)}for(let l=0;l<this.metricsTensors.length;++l){const c=this.metricsTensors[l][0],p=this.metricsTensors[l][1],u=Ge(c(i[p],a[p]));t.push(u)}return t})}async fit(e,t,s={}){return dA(this,e,t,s)}async fitDataset(e,t){return uA(this,e,t)}async trainOnBatch(e,t){const s=await this.standardizeUserData(e,t),n=s[0],i=s[1],r=this.makeTrainFunction(),o=r(n.concat(i)),a=[];for(const l of o){const c=await l.data();a.push(c[0])}return ce(o),Pt(a)}getNamedWeights(e){const t=[],s=e!=null&&e.trainableOnly,n=s?this.trainableWeights:this.weights,i=this.getWeights(s);for(let r=0;r<n.length;++r){if(s&&!n[r].trainable)continue;t.push({name:n[r].originalName,tensor:i[r]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=pa().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-pa().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=pn(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>pn(t))}else{const t=Object.keys(this.loss);e={};const s=this.loss;for(const n of t)if(typeof s[n]=="string")e[n]=pn(s[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[pn(fu(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>pn(fu(e)));{const e={};for(const t in this.metrics)e[t]=pn(fu(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Ro(e.optimizer_config),s=ws(t);let n;if(typeof e.loss=="string")n=ki(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(r=>ki(r));else if(e.loss!=null){n={};for(const r in e.loss)n[r]=ki(e.loss[r])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(r=>ki(r));else if(e.metrics!=null){i={};for(const r in e.metrics)i[r]=ki(e.metrics[r])}this.compile({loss:n,metrics:i,optimizer:s})}async save(e,t){if(typeof e=="string"){const l=Ot.getSaveHandlers(e);if(l.length===0)throw new _(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new _(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new _("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const s=await Ot.encodeWeights(this.getNamedWeights(t)),n=!1,i=null,r=this.toJSON(i,n),o={modelTopology:r,format:e$,generatedBy:`TensorFlow.js tfjs-layers v${fr}`,convertedBy:null},a=t==null?!1:t.includeOptimizer;if(a&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();const l="optimizer",{data:c,specs:p}=await Ot.encodeWeights(await this.optimizer.getWeights(),l);s.specs.push(...p),s.data=Ot.concatenateArrayBuffers([s.data,c])}if(this.userDefinedMetadata!=null){const l=!0;Hb(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=s.data,o.weightSpecs=s.specs,e.save(o)}setUserDefinedMetadata(e){Hb(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}gn.className="Model";P.registerClass(gn);async function yA(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let s=e.modelTopology;s.model_config!=null&&(s=s.model_config);const n=Ro(s),i=ws(n,t);if(e.weightsManifest!=null){const r=await Ot.loadWeights(e.weightsManifest,e.pathPrefix,i.weights.map(a=>a.originalName)),o={};for(const a of i.weights)o[a.originalName]=r[a.originalName];i.loadWeights(o),ce(r)}return i}async function bA(e,t){if(t==null&&(t={}),typeof e=="string"){const s=Ot.getLoadHandlers(e,t);if(s.length===0)s.push(Ot.browserHTTPRequest(e,t));else if(s.length>1)throw new _(`Found more than one (${s.length}) load handlers for URL '${e}'`);e=s[0]}return t$(e,void 0,t)}async function t$(e,t,s){if(s==null&&(s={}),e.load==null)throw new _("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const n=await e.load();let i=n.modelTopology;i.model_config!=null&&(i=i.model_config);const r=s.strict==null?!0:s.strict,o=n.weightData!=null&&n.weightSpecs!=null&&r,a=ws(Ro(i),t,o),l=n.trainingConfig;if(l!=null&&a.loadTrainingConfig(l),n.userDefinedMetadata!=null&&a.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new _("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:c,optimizerWeights:p}=s$(n.weightData,n.weightSpecs);a.loadWeights(c,r),a.optimizer!=null&&p.length>0&&await a.optimizer.setWeights(p),ce(c),ce(p.map(u=>u.tensor))}return a}function s$(e,t){const s=Ot.decodeWeights(e,t),n={},i=[];return t.forEach(r=>{r.group==="optimizer"?i.push({name:r.name,tensor:s[r.name]}):n[r.name]=s[r.name]}),{modelWeights:n,optimizerWeights:i}}class yr extends gn{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:dr("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(s=>s<0))throw new _(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof yr||e instanceof gn;let s;if(t){if(s=e,s.outputs.length!==1)throw new _("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new gn({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,s=console.log){this.built||this.build(),super.summary(e,t,s)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,s={}){if(!this.built)throw new Is("The model needs to be compiled before being used.");return this.model.evaluate(e,t,s)}async evaluateDataset(e,t){if(!this.built)throw new Is("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,s={}){if(!this.built)throw new Is("The model needs to be compiled before being used.");return this.model.fit(e,t,s)}async fitDataset(e,t){if(!this.built)throw new Is("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,s={},n=!1){let i,r={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new _("Legacy serialization format not supported yet.");i=t}else E.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,r=t;const o=new e(r);if(!(o instanceof yr))throw new ae(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(const a of i){const l=void 0,c=ws(a,l,n);n&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new _("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new _("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const s={};s.className=t.getClassName(),s.config=t.getConfig(),e.push(s)}return{name:this.name,layers:e}}}yr.className="Sequential";P.registerClass(yr);function wA(e){return new gn(e)}function xA(e){return new yr(e)}function LA(e,t){return t==null&&(t={}),bA(e,t)}function zd(e){return Id(e)}function SA(e,t){Ks.registerCallbackConstructor(e,t)}class Xs extends P.Serializable{getConfig(){return{}}}class vA extends Xs{apply(e,t=1){return jI(e,t)}}vA.className="elu";P.registerClass(vA);class TA extends Xs{apply(e){return ao(e)}}TA.className="selu";P.registerClass(TA);class IA extends Xs{apply(e){return ke(e)}}IA.className="relu";P.registerClass(IA);class AA extends Xs{apply(e){return N(()=>Vs(6,ke(e)))}}AA.className="relu6";P.registerClass(AA);class NA extends Xs{apply(e){return e}}NA.className="linear";P.registerClass(NA);class CA extends Xs{apply(e){return Ss(e)}}CA.className="sigmoid";P.registerClass(CA);class RA extends Xs{apply(e){return GI(e)}}RA.className="hardSigmoid";P.registerClass(RA);class OA extends Xs{apply(e){return Rn(e)}}OA.className="softplus";P.registerClass(OA);class EA extends Xs{apply(e){return VI(e)}}EA.className="softsign";P.registerClass(EA);class kA extends Xs{apply(e){return Zn(e)}}kA.className="tanh";P.registerClass(kA);class Bd extends Xs{apply(e,t=-1){return Yt(e,t)}}Bd.className="softmax";P.registerClass(Bd);class _A extends Xs{apply(e,t=-1){return to(e,t)}}_A.className="logSoftmax";P.registerClass(_A);function xs(e){return e.getClassName()}function Xb(e,t={}){return si(e,P.SerializationMap.getMap().classNameMap,t,"activation")}function Un(e){if(e==null){const t={};return t.className="linear",t.config={},Xb(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Xb(t)}else return e instanceof Xs?e:Xb(e)}function Jb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class DA extends P.Serializable{}class ka extends DA{constructor(e){super();Jb(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 N(()=>{let t=Ie([1]);return this.hasL1&&(t=W(t,te(C(this.l1,Je(e))))),this.hasL2&&(t=W(t,te(C(this.l2,Io(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}ka.className="L1L2";P.registerClass(ka);function FA(e){return Jb(e),new ka({l1:e!=null?e.l1:null,l2:0})}function MA(e){return Jb(e),new ka({l2:e!=null?e.l2:null,l1:0})}const UA={l1l2:"L1L2"};function Ae(e){return La(e)}function $A(e,t={}){return si(e,P.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ye(e){if(e==null)return null;if(typeof e=="string"){const t=e in UA?UA[e]:e,s={className:t,config:{}};return $A(s)}else return e instanceof DA?e:$A(e)}class Pd extends be{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=fe(e);let s=ke(e);return this.maxValue!=null&&(s=Dt(s,0,this.maxValue)),s}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Pd.className="ReLU";P.registerClass(Pd);class jd extends be{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){const s=fe(e);return Qr(s,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}jd.className="LeakyReLU";P.registerClass(jd);class Vd extends be{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=je(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ye(e.alphaRegularizer),this.alphaConstraint=it(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 _(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ue(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const s={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)s[n]=e[n];this.inputSpec=[new rt({ndim:e.length,axes:s})],this.built=!0}call(e,t){return e=fe(e),Ii(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Me(this.alphaInitializer),alphaRegularizer:Ae(this.alphaRegularizer),alphaConstraint:Be(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Vd.className="PReLU";P.registerClass(Vd);class Gd extends be{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ae(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const s=fe(e);return An(s)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Gd.className="ELU";P.registerClass(Gd);class Hd extends be{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){const s=fe(e);return s.mul(Di(s.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Hd.className="ThresholdedReLU";P.registerClass(Hd);class qd extends be{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Bd().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const s=fe(e);return this.softmax(s,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}qd.className="Softmax";P.registerClass(qd);function Yd(e,t,s){if(typeof e=="number")return cn(e,t);if(e.length!==t)throw new _(`The ${s} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){const i=e[n];if(!$I(i))throw new _(`The ${s} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function yn(e,t,s,n,i=1){if(e==null)return e;const r=t+(t-1)*(i-1);let o;return s==="same"?o=e:o=e-r+1,Math.floor((o+n-1)/n)}function gu(e,t,s,n){if(e==null)return null;if(n==="valid")e=e*t+Fn([s-t,0]);else if(n==="same")e=e*t;else throw new _(`Unsupport padding mode: ${n}.`);return e}function yu(e,t){return N(()=>(dt(t),t==="channelsFirst"?se(e,[0,2,3,1]):e))}function Zb(e,t){return N(()=>(dt(t),t==="channelsFirst"?se(e,[0,2,3,4,1]):e))}function n$(e,t,s,n=1,i="valid",r,o=1){return N(()=>{if(r==null&&(r=gs()),dt(r),e.shape.length!==3)throw new _(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new _(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(s!=null&&s.shape.length!==1)throw new _(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(r==="channelsFirst"&&(e=se(e,[0,2,1])),i==="causal")throw new ae("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=Hr(e,t,n,i==="same"?"same":"valid","NWC",o);return s!=null&&(a=Ys(a,s)),a})}function WA(e,t,s,n=[1,1],i="valid",r,o,a=null){return N(()=>{if(r==null&&(r=gs()),dt(r),e.rank!==3&&e.rank!==4)throw new _(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new _(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=yu(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Hs.conv2d({x:l,filter:t,strides:n,pad:i==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:s,activation:a}),r==="channelsFirst"&&(l=se(l,[0,3,1,2])),l})}function i$(e,t,s,n=[1,1,1],i="valid",r,o){return N(()=>{if(r==null&&(r=gs()),dt(r),e.rank!==4&&e.rank!==5)throw new _(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new _(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let a=Zb(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Yr(a,t,n,i==="same"?"same":"valid","NDHWC",o),s!=null&&(a=Ys(a,s)),r==="channelsFirst"&&(a=se(a,[0,4,1,2,3])),a})}class Kd extends be{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Kd.verifyArgs(t),this.rank=e,jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ae(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yd(t.kernelSize,e,"kernelSize"),this.strides=Yd(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,_s(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,dt(this.dataFormat),this.activation=Un(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=it(t.biasConstraint),this.biasRegularizer=Ye(t.biasRegularizer),this.activityRegularizer=Ye(t.activityRegularizer),this.dilationRate=Yd(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new _(`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 _(`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 _(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(qs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!sd(e.kernelSize,"number",1,3))throw new _(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:xs(this.activation),useBias:this.useBias,biasInitializer:Me(this.biasInitializer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class bu extends Kd{constructor(e,t){super(e,t);this.kernel=null,bu.verifyArgs(t),this.filters=t.filters,jt(this.filters,"filters"),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=it(t.kernelConstraint),this.kernelRegularizer=Ye(t.kernelRegularizer)}build(e){e=Ue(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new _(`The channel dimension of the input should be defined. Found ${e[t]}`);const s=e[t],n=this.kernelSize.concat([s,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:s}}],this.built=!0}call(e,t){return N(()=>{e=fe(e);let s;const n=this.bias==null?null:this.bias.read(),i=nd(this.activation.getClassName());if(i!=null&&this.rank===2)s=WA(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)s=n$(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)s=WA(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)s=i$(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ae("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(s=this.activation.apply(s))}return s})}computeOutputShape(e){e=Ue(e);const t=[],s=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<s.length;++i){const r=yn(s[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(r)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){const e={filters:this.filters,kernelInitializer:Me(this.kernelInitializer),kernelRegularizer:Ae(this.kernelRegularizer),kernelConstraint:Be(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new _(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class _a extends bu{constructor(e){super(2,e);_a.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!sd(e.kernelSize,"number",1,2))throw new _(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}_a.className="Conv2D";P.registerClass(_a);class wu extends bu{constructor(e){super(3,e);wu.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new _(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}wu.className="Conv3D";P.registerClass(wu);class Xd extends _a{constructor(e){super(e);if(this.inputSpec=[new rt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new _(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ue(e),e.length!==4)throw new _("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new _("The channel dimension of the inputs should be defined. Found `None`.");const s=e[t],n=this.kernelSize.concat([this.filters,s]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new rt({ndim:4,axes:{[t]:s}})],this.built=!0}call(e,t){return N(()=>{let s=fe(e);if(s.shape.length!==4)throw new _(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${s.shape.length}`);const n=s.shape,i=n[0];let r,o;this.dataFormat==="channelsFirst"?(r=2,o=3):(r=1,o=2);const a=n[r],l=n[o],c=this.kernelSize[0],p=this.kernelSize[1],u=this.strides[0],h=this.strides[1],d=gu(a,u,c,this.padding),m=gu(l,h,p,this.padding),f=[i,d,m,this.filters];this.dataFormat!=="channelsLast"&&(s=se(s,[0,2,3,1]));let y=qr(s,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=se(y,[0,3,1,2])),this.bias!=null&&(y=Ys(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=Ue(e);const t=e.slice();let s,n,i;this.dataFormat==="channelsFirst"?(s=1,n=2,i=3):(s=3,n=1,i=2);const r=this.kernelSize[0],o=this.kernelSize[1],a=this.strides[0],l=this.strides[1];return t[s]=this.filters,t[n]=gu(t[n],a,r,this.padding),t[i]=gu(t[i],l,o,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Xd.className="Conv2DTranspose";P.registerClass(Xd);class zA extends bu{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 _("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new _("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 _(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ye(t.depthwiseRegularizer),this.depthwiseConstraint=it(t.depthwiseConstraint),this.pointwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ye(t.pointwiseRegularizer),this.pointwiseConstraint=it(t.pointwiseConstraint)}build(e){if(e=Ue(e),e.length<this.rank+2)throw new _(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new _(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const s=e[t],n=this.kernelSize.concat([s,this.depthMultiplier]),i=[];for(let o=0;o<this.rank;++o)i.push(1);i.push(s*this.depthMultiplier,this.filters);const r=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,r,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,r,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,r,this.biasConstraint):this.bias=null,this.inputSpec=[new rt({ndim:this.rank+2,axes:{[t]:s}})],this.built=!0}call(e,t){return N(()=>{e=fe(e);let s;if(this.rank===1)throw new ae("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=se(e,[0,2,3,1])),s=Ni(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(s=Ys(s,this.bias.read(),this.dataFormat)),this.activation!=null&&(s=this.activation.apply(s)),this.dataFormat==="channelsFirst"&&(s=se(s,[0,3,1,2])),s})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Me(this.depthwiseInitializer),e.pointwiseInitializer=Me(this.pointwiseInitializer),e.depthwiseRegularizer=Ae(this.depthwiseRegularizer),e.pointwiseRegularizer=Ae(this.pointwiseRegularizer),e.depthwiseConstraint=Be(this.depthwiseConstraint),e.pointwiseConstraint=Be(this.pointwiseConstraint),e}}zA.className="SeparableConv";class Jd extends zA{constructor(e){super(2,e)}}Jd.className="SeparableConv2D";P.registerClass(Jd);class xu extends bu{constructor(e){super(1,e);xu.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!sd(e.kernelSize,"number",1,1))throw new _(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}xu.className="Conv1D";P.registerClass(xu);class Zd extends be{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 N(()=>{if(e=fe(e),this.dataFormat==="channelsLast"){const s=tu(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return tu(s,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const s=tu(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return tu(s,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Zd.className="Cropping2D";P.registerClass(Zd);class Qd extends be{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}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],s=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,s]}else{const t=e[1]==null?null:this.size[0]*e[1],s=e[2]==null?null:this.size[1]*e[2];return[e[0],t,s,e[3]]}}call(e,t){return N(()=>{let s=fe(e);const n=s.shape;if(this.dataFormat==="channelsFirst"){s=se(s,[0,2,3,1]);const i=this.size[0]*n[2],r=this.size[1]*n[3],o=s.resizeNearestNeighbor([i,r]);return se(o,[0,3,1,2])}else{const i=this.size[0]*n[1],r=this.size[1]*n[2];return s.resizeNearestNeighbor([i,r])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Qd.className="UpSampling2D";P.registerClass(Qd);function r$(e,t,s=[1,1],n="valid",i,r){return N(()=>{i==null&&(i=gs()),dt(i);let o=yu(e,i);if(e.rank!==4)throw new _(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new _(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=In(o,t,s,n==="same"?"same":"valid","NHWC",r),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}class em extends Kd{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=it(e.depthwiseConstraint),this.depthwiseRegularizer=Ye(e.depthwiseRegularizer)}build(e){if(e=Ue(e),e.length<4)throw new _(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new _(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const s=e[t],n=[this.kernelSize[0],this.kernelSize[1],s,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[s*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return N(()=>{e=fe(e);let s=r$(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(s=Ys(s,this.bias.read(),this.dataFormat)),this.activation!=null&&(s=this.activation.apply(s)),s})}computeOutputShape(e){e=Ue(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=yn(t,this.kernelSize[0],this.padding,this.strides[0]),r=yn(s,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,i,r]:[e[0],i,r,n]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Me(this.depthwiseInitializer),e.depthwiseRegularizer=Ae(this.depthwiseRegularizer),e.depthwiseConstraint=Be(this.depthwiseRegularizer),e}}em.className="DepthwiseConv2D";P.registerClass(em);class Lu extends be{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;const t=e.shape,s=[];for(let n=0;n<this.noiseShape.length;++n)s.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return s}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e);if(0<this.rate&&this.rate<1){const n=t.training==null?!1:t.training,i=this.getNoiseShape(s),r=Ui(()=>dd(s,this.rate,i,this.seed),()=>s,n);return r}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Lu.className="Dropout";P.registerClass(Lu);class tm extends Lu{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}tm.className="SpatialDropout1D";P.registerClass(tm);class sm extends be{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,jt(this.units,"units"),this.activation=Un(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=it(e.kernelConstraint),this.biasConstraint=it(e.biasConstraint),this.kernelRegularizer=Ye(e.kernelRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.activityRegularizer=Ye(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ue(e);const 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=Ue(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e),n=nd(this.activation.getClassName());let i;return n!=null?i=mn(s,this.kernel.read(),n,this.bias?this.bias.read():null):(i=mn(s,this.kernel.read()),this.bias!=null&&(i=Ys(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:xs(this.activation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),biasInitializer:Me(this.biasInitializer),kernelRegularizer:Ae(this.kernelRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}sm.className="Dense";P.registerClass(sm);class nm extends be{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ue(e);for(const t of e.slice(1))if(t==null)throw new _(`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],dn(e,1)]}call(e,t){return N(()=>{this.invokeCallHook(e,t);let s=fe(e);if(this.dataFormat==="channelsFirst"&&s.rank>1){const n=[0];for(let i=2;i<s.rank;++i)n.push(i);n.push(1),s=s.transpose(n)}return PI(s)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}nm.className="Flatten";P.registerClass(nm);class im extends be{constructor(e){super(e);this.supportsMasking=!0,this.activation=Un(e.activation)}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e);return this.activation.apply(s)})}getConfig(){const e={activation:xs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}im.className="Activation";P.registerClass(im);class rm extends be{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 N(()=>(e=fe(e),zI(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}rm.className="RepeatVector";P.registerClass(rm);class om extends be{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){const s="Total size of new array must be unchanged.",n=t.slice();let i=1,r=null;for(let a=0;a<n.length;++a){const l=n[a];if(this.isUnknown(l))if(r===null)r=a;else throw new _("Can only specifiy one unknown dimension.");else i*=l}const o=dn(e);if(r!==null){if(i===0||o%i!==0)throw new _(s);n[r]=o/i}else if(o!==i)throw new _(s);return n}computeOutputShape(e){let t=!1;for(let s=0;s<e.length;++s)if(this.isUnknown(e[s])){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 N(()=>{this.invokeCallHook(e,t);const s=fe(e),n=s.shape,i=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return s.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}om.className="Reshape";P.registerClass(om);class am extends be{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.`);const t=ps(1,e.dims.length+1);if(!E.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 rt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Ue(e);const t=e.slice();return this.dims.forEach((s,n)=>{t[n+1]=e[s]}),t}call(e,t){return se(fe(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}am.className="Permute";P.registerClass(am);class lm extends be{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(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const s=fe(e),n=-1;return sr(Es(s,this.maskValue),n)}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e),n=-1,i=!0,r=sr(Es(s,this.maskValue),n,i),o=s.mul(r.asType(s.dtype));return o})}}lm.className="Masking";P.registerClass(lm);class cm extends be{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(qe(e.inputLength))}this.inputDim=e.inputDim,jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,jt(this.outputDim,"outputDim"),this.embeddingsInitializer=je(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ye(e.embeddingsRegularizer),this.activityRegularizer=Ye(e.activityRegularizer),this.embeddingsConstraint=it(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 N(()=>this.maskZero?(e=fe(e),Es(e,ne(e))):null)}computeOutputShape(e){if(e=Ue(e),this.inputLength==null)return[...e,this.outputDim];const t=qe(this.inputLength);if(t.length!==e.length-1)throw new _(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let s=0;for(let n=0;n<t.length;++n){const i=t[n],r=e[n+1];if(i!=null&&r!=null&&i!==r)throw new _(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[s]=r),s++}}return[e[0],...t,this.outputDim]}call(e,t){return N(()=>{this.invokeCallHook(e,t);let s=fe(e);s.dtype!=="int32"&&(s=Di(s,"int32"));const n=hd(this.embeddings.read(),s.as1D());return n.reshape(Ue(this.computeOutputShape(s.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Me(this.embeddingsInitializer),embeddingsRegularizer:Ae(this.embeddingsRegularizer),activityRegularizer:Ae(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}cm.className="Embedding";P.registerClass(cm);class Eo extends be{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ae()}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;const s=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){const i=e[e.length-t.length+n],r=t[n];if(i==null||r==null||i<0||r<0)s.push(null);else if(i===1)s.push(r);else if(r===1)s.push(i);else{if(i!==r)throw new _("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));s.push(i)}}return s}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Ue(e)]),e=e,e.length<2)throw new _(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=hn(t),t.length>1)throw new _(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let s=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const r=e[i]==null?null:e[i].slice(1);s=this.computeElementwiseOpOutputShape(s,r)}const n=e.map(i=>i.length);e.indexOf(null)===-1&&hn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return N(()=>{if(e=e,this.reshapeRequired){const s=[],n=e.map(i=>i.rank);if(n.indexOf(null)===-1){const i=Fn(n);for(let r of e){const o=r.rank;for(let a=0;a<i-o;++a)r=Fi(r,1);s.push(r)}return this.mergeFunction(s)}else{let i=!1;for(const a of e){const l=a.rank;if(l==null){const c=a.shape,p=c[0],u=c.slice(1).concat([p]);let h=a.reshape([p].concat(dn(c.slice(1))));h=se(h,[1,0]),h=h.reshape(u),s.push(h),i=!0}else if(l>1){const c=ps(1,l).concat([0]);s.push(se(a,c)),i=!0}else s.push(a)}let r=this.mergeFunction(s);const o=r.rank;if(i){if(o==null){const a=r.shape,l=a.length,c=a[l-1],p=[c].concat(a.slice(0,a.length-1));r=se(r.reshape([-1,c]),[1,0]).reshape(p)}else if(o>1){const a=[o-1].concat(ps(0,o-1));r=se(r,a)}}return r}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){const i=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let s=[];for(const n of e)n!=null&&n[0]!==null&&s.push(n[0]);return s=hn(s),s.length===1?t=s.concat(t):t=[null].concat(t),t}computeMask(e,t){return N(()=>{if(t==null)return null;if(!Array.isArray(t))throw new _("`mask` should be an Array");if(!Array.isArray(e))throw new _("`inputs` should be an Array");if(t.length!==e.length)throw new _(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Nt(n,0));let s=t[0];for(let n=1;n<t.length-1;++n)s=zt(s,t[n]);return s})}}class pm extends Eo{constructor(e){super(e)}mergeFunction(e){return N(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=W(t,e[s]);return t})}}pm.className="Add";P.registerClass(pm);class um extends Eo{constructor(e){super(e)}mergeFunction(e){return N(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=C(t,e[s]);return t})}}um.className="Multiply";P.registerClass(um);class hm extends Eo{constructor(e){super(e)}mergeFunction(e){return N(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=W(t,e[s]);return C(1/e.length,t)})}}hm.className="Average";P.registerClass(hm);class dm extends Eo{constructor(e){super(e)}mergeFunction(e){return N(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=Wt(t,e[s]);return t})}}dm.className="Maximum";P.registerClass(dm);class mm extends Eo{constructor(e){super(e)}mergeFunction(e){return N(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=Vs(t,e[s]);return t})}}mm.className="Minimum";P.registerClass(mm);class fm extends Eo{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 _("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const n of e)if(n!=null){t=!1;break}if(t)return;const s=[];for(let n=0;n<e.length;++n){const i=e[n].slice();i.splice(this.axis,1);let r=!1;for(const o of s)if(E.arraysEqual(o,i)){r=!0;break}r||s.push(i)}if(s.length>1)throw new _("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return N(()=>ud(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new _("A `Concatenate` layer should be called on a list of inputs.");const t=e,s=t[0].slice(),n=this.axis<0?s.length+this.axis:this.axis;for(const i of t.slice(1)){if(s[n]==null||i[n]==null){s[n]=null;break}s[n]+=i[n]}return s}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new _("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new _("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new _(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return N(()=>{let s=!0;if(t.forEach(r=>{if(r!=null){s=!1;return}}),s)return null;const n=[];for(let r=0;r<e.length;++r)t[r]==null?n.push(Bt(e[r]).asType("bool")):t[r].rank<e[r].rank?n.push(Nt(t[r],-1)):n.push(t[r]);const i=ge(n,this.axis);return jr(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}fm.className="Concatenate";P.registerClass(fm);function Su(e,t){for(;e<0;)e+=t;return e}function o$(e,t,s){if(e.shape.length>3||t.shape.length>3)throw new ae("batchDot is not implemented for tensors of 4D or higher rank yet");if(E.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),E.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof s=="number"&&(s=[s,s]),e.dtype==="complex64"||t.dtype==="complex64")throw new ae("batchDot is not implemented for complex64-type Tensors yet.");const n=e.shape.length,i=t.shape.length;s==null&&(s=[n-1,i-2]);const r=s;return N(()=>{let o;if(n>i){o=n-i;const l=[];for(let c=0;c<o;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(i>n){o=i-n;const l=[];for(let c=0;c<o;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else o=0;let a;if(e.shape.length===2&&t.shape.length===2)r[0]===r[1]?a=e.mul(t).sum(r[0]):a=e.transpose([1,0]).mul(t).sum(r[1]);else{const l=r[0]!==e.shape.length-1,c=r[1]===t.shape.length-1;a=e.matMul(t,l,c)}if(o>0){let l;n>i?l=n+i-3:l=n-1;const c=[];for(let p=l;p<l+o;++p)c.push(p);a=a.squeeze(c)}return a.shape.length===1&&(a=a.expandDims(1)),a})}class gm extends Eo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){E.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.");const t=e[0],s=e[1];if(t.length>3||s.length>3)throw new ae("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(t,s);if(t[n[0]]!==s[n[1]])throw new _(`Dimension incompatibility: ${t[n[0]]} !== ${s[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new _(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],s=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((i,r)=>Su(i,e[r].shape.length)):n=[Su(this.axes,t.shape.length),Su(this.axes,s.shape.length)],this.normalize&&(t=cu(t,n[0]),s=cu(s,n[1])),o$(t,s,n)}interpretAxes(e,t){let s;return Array.isArray(this.axes)?s=this.axes:s=[Su(this.axes,e.length),Su(this.axes,t.length)],s}computeOutputShape(e){E.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.");const t=e[0].slice(),s=e[1].slice();if(t.length>3||s.length>3)throw new ae("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(t,s);t.splice(n[0],1),s.splice(n[1],1),s.splice(0,1);const i=t.concat(s);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}gm.className="Dot";P.registerClass(gm);class ym extends be{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e),n=()=>va(s.shape,0,this.stddev).add(s),i=Ui(n,()=>s,t.training||!1);return i})}}ym.className="GaussianNoise";P.registerClass(ym);class bm extends be{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return N(()=>{this.invokeCallHook(e,t);const s=fe(e);if(this.rate>0&&this.rate<1){const n=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return s.mul(va(s.shape,1,i))};return Ui(n,()=>s,t.training||!1)}return s})}}bm.className="GaussianDropout";P.registerClass(bm);class wm extends be{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(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return N(()=>{if(this.rate<1&&this.rate>0){const s=this._getNoiseShape(e),n=()=>{const i=fe(e),r=1.6732632423543772,o=1.0507009873554805,a=-r*o;let l=as(Gs(s),this.rate);l=Di(l,"float32");const c=((1-this.rate)*(1+this.rate*a**2))**-.5,p=-c*a*this.rate,u=i.mul(l).add(l.add(-1).mul(a));return u.mul(c).add(p)};return Ui(n,()=>fe(e),t.training||!1)}return e})}}wm.className="AlphaDropout";P.registerClass(wm);function vu(e,t,s,n,i,r=.001){let o;if(e.rank===2)o=gp(e,t,s,n,i,r);else if(e.rank===3)o=yp(e,t,s,n,i,r);else if(e.rank===4)o=bp(e,t,s,n,i,r);else throw new ae(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function a$(e,t,s,n,i=.001){return N(()=>{const r=lr(e,n),o=r.mean,a=r.variance,l=vu(e,o,a,s,t,i);return[l,o,a]})}function l$(e,t,s,n,i=.001){return N(()=>{const r=lr(e,n),o=r.mean,a=r.variance,l=[];for(const m of ps(0,e.rank))n.indexOf(m)!==-1?l.push(1):l.push(e.shape[m]);const c=o.reshape(l),p=a.reshape(l),u=t==null?null:t.reshape(l),h=s==null?null:s.reshape(l),d=vu(e,c,p,h,u,i);return[d,o,a]})}function c$(e,t,s,n,i=.001){return E.arraysEqual(n.slice().sort(),ps(0,e.rank-1))?a$(e,t,s,n,i):l$(e,t,s,n,i)}class xm extends be{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=je(e.betaInitializer||"zeros"),this.gammaInitializer=je(e.gammaInitializer||"ones"),this.movingMeanInitializer=je(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(e.movingVarianceInitializer||"ones"),this.betaConstraint=it(e.betaConstraint),this.gammaConstraint=it(e.gammaConstraint),this.betaRegularizer=Ye(e.betaRegularizer),this.gammaRegularizer=Ye(e.gammaRegularizer)}build(e){e=Ue(e);const t=this.axis>=0?this.axis:this.axis+e.length,s=e[t];if(s==null)throw new _(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new rt({ndim:e.length,axes:{[t]:s}})];const n=[s];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return N(()=>{const s=t.training==null?!1:t.training,n=fe(e),i=n.shape,r=i.length,o=ps(0,r),a=this.axis>=0?this.axis:this.axis+r;o.splice(a,1);const l=cn(1,r);l[a]=i[a];const c=o.slice();c.sort();const p=!E.arraysEqual(c,ps(0,r).slice(0,r-1)),u=()=>{if(p){const b=this.movingMean.read().reshape(l),S=this.movingVariance.read().reshape(l),x=this.center?this.beta.read().reshape(l):null,I=this.scale?this.gamma.read().reshape(l):null;return vu(n,b,S,x,I,this.epsilon)}else return vu(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!s)return u();const[h,d,m]=c$(n,this.gamma.read(),this.beta.read(),o,this.epsilon),f=(b,S,x)=>{N(()=>{const I=1-x,A=b.read(),k=A.sub(S).mul(I);b.write(A.sub(k))})},y=()=>{f(this.movingMean,d,this.momentum),f(this.movingVariance,m,this.momentum)};return y(),h})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Me(this.betaInitializer),gammaInitializer:Me(this.gammaInitializer),movingMeanInitializer:Me(this.movingMeanInitializer),movingVarianceInitializer:Me(this.movingVarianceInitializer),betaRegularizer:Ae(this.betaRegularizer),gammaRegularizer:Ae(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}xm.className="BatchNormalization";P.registerClass(xm);class Lm extends be{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(const 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=je(e.betaInitializer||"zeros"),this.gammaInitializer=je(e.gammaInitializer||"ones"),this.betaRegularizer=Ye(e.betaRegularizer),this.gammaRegularizer=Ye(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Ue(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==hn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const s=this.axis.map(i=>e[i]),n=!0;this.scale?this.gamma=this.addWeight("gamma",s,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",s,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){const s=fe(e),n=s.shape,i=n.length;return N(()=>{const r=!0;let{mean:o,variance:a}=lr(s,this.axis,r);const l=cn(1,i);for(const m of this.axis)l[m]=n[m];const c=m=>m!=null&&m.shape.length!==i&&this.axis!==[i-1]?m.reshape(l):m;let p=c(this.gamma.read()),u=c(this.beta.read());const h=[],d=[];for(let m=0;m<i;++m)this.axis.indexOf(m)!==-1?(h.push(n[m]),d.push(1)):(h.push(1),d.push(n[m]));return o=o.tile(h),a=a.tile(h),p=p.tile(d),u=u.tile(d),vu(s,o,a,u,p,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Me(this.betaInitializer),gammaInitializer:Me(this.gammaInitializer),betaRegularizer:Ae(this.betaRegularizer),gammaRegularizer:Ae(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}Lm.className="LayerNormalization";P.registerClass(Lm);function p$(e,t,s){return N(()=>{if(e.rank!==4)throw new _(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new _("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(s==null&&(s=gs()),s!=="channelsLast"&&s!=="channelsFirst")throw new _(`Unknown data format: ${s}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return s==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Ut(e,n)})}class Sm extends be{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?gs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new _(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,s;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],s=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new _(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new _(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);s=e.padding[1]}this.padding=[t,s]}this.inputSpec=[new rt({ndim:4})]}computeOutputShape(e){e=Ue(e);let t,s;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?s=e[3]+this.padding[1][0]+this.padding[1][1]:s=null,[e[0],e[1],t,s]):(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?s=e[2]+this.padding[1][0]+this.padding[1][1]:s=null,[e[0],t,s,e[3]])}call(e,t){return N(()=>p$(fe(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Sm.className="ZeroPadding2D";P.registerClass(Sm);function vm(e,t,s,n,i,r){return N(()=>{dt(i),Db(r),_s(n),s==null&&(s=[1,1]),n==null&&(n="valid"),i==null&&(i=gs()),r==null&&(r="max"),e=yu(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=lt(e,t,s,a):o=rs(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}function BA(e,t,s,n,i,r){return N(()=>{dt(i),Db(r),_s(n),s==null&&(s=[1,1,1]),n==null&&(n="valid"),i==null&&(i=gs()),r==null&&(r="max"),e=Zb(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=no(e,t,s,a):o=Gr(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,4,1,2,3])),o})}class PA extends be{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 _(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(jt(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 _(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,_s(this.padding),this.inputSpec=[new rt({ndim:3})]}computeOutputShape(e){e=Ue(e);const t=yn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return N(()=>{this.invokeCallHook(e,t),e=Fi(fe(e),2);const s=this.poolingFunction(fe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return En(s,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Tm extends PA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),vm(e,t,s,n,i,"max")}}Tm.className="MaxPooling1D";P.registerClass(Tm);class Im extends PA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),vm(e,t,s,n,i,"avg")}}Im.className="AveragePooling1D";P.registerClass(Im);class jA extends be{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 _(`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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,dt(this.dataFormat),_s(this.padding),this.inputSpec=[new rt({ndim:4})]}computeOutputShape(e){e=Ue(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=yn(t,this.poolSize[0],this.padding,this.strides[0]),s=yn(s,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,s]:[e[0],t,s,e[3]]}call(e,t){return N(()=>(this.invokeCallHook(e,t),this.poolingFunction(fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Am extends jA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),vm(e,t,s,n,i,"max")}}Am.className="MaxPooling2D";P.registerClass(Am);class Nm extends jA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),vm(e,t,s,n,i,"avg")}}Nm.className="AveragePooling2D";P.registerClass(Nm);class VA extends be{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 _(`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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,dt(this.dataFormat),_s(this.padding),this.inputSpec=[new rt({ndim:5})]}computeOutputShape(e){e=Ue(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],s=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=yn(t,this.poolSize[0],this.padding,this.strides[0]),s=yn(s,this.poolSize[1],this.padding,this.strides[1]),n=yn(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,s,n]:[e[0],t,s,n,e[4]]}call(e,t){return N(()=>(this.invokeCallHook(e,t),this.poolingFunction(fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Cm extends VA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),BA(e,t,s,n,i,"max")}}Cm.className="MaxPooling3D";P.registerClass(Cm);class Rm extends VA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return dt(i),_s(n),BA(e,t,s,n,i,"avg")}}Rm.className="AveragePooling3D";P.registerClass(Rm);class GA extends be{constructor(e){super(e);this.inputSpec=[new rt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ae()}}class Om extends GA{constructor(e){super(e||{})}call(e,t){return N(()=>{const s=fe(e);return Ge(s,1)})}}Om.className="GlobalAveragePooling1D";P.registerClass(Om);class Em extends GA{constructor(e){super(e||{})}call(e,t){return N(()=>{const s=fe(e);return st(s,1)})}}Em.className="GlobalMaxPooling1D";P.registerClass(Em);class HA extends be{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,dt(this.dataFormat),this.inputSpec=[new rt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ae()}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class km extends HA{call(e,t){return N(()=>{const s=fe(e);return this.dataFormat==="channelsLast"?Ge(s,[1,2]):Ge(s,[2,3])})}}km.className="GlobalAveragePooling2D";P.registerClass(km);class _m extends HA{call(e,t){return N(()=>{const s=fe(e);return this.dataFormat==="channelsLast"?st(s,[1,2]):st(s,[2,3])})}}_m.className="GlobalMaxPooling2D";P.registerClass(_m);function Qb(e,t,s,n){if(Array.isArray(e)){if(t!=null||s!=null)throw new _("When inputs is an array, neither initialState or constants should be provided");n!=null&&(s=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(r){return r==null||Array.isArray(r)?r:[r]}return t=i(t),s=i(s),{inputs:e,initialState:t,constants:s}}function ew(e,t,s,n=!1,i,r,o=!1,a=!1){return N(()=>{const l=t.shape.length;if(l<3)throw new _(`Input should be at least 3D, but is ${l}D.`);const c=[1,0].concat(ps(2,l));if(t=se(t,c),r!=null)throw new ae("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===l-1&&(i=Nt(i,-1)),i=se(i,c)),n&&(t=Lt(t,0),i!=null&&(i=Lt(i,0)));const p=[];let u,h=s;const d=t.shape[0],m=nt(t);let f;i!=null&&(f=nt(i));for(let b=0;b<d;++b){const S=m[b],x=N(()=>e(S,h));if(i==null)u=x[0],h=x[1];else{const I=N(()=>{const A=f[b],k=Bt(A).sub(A),R=x[0].mul(A).add(h[0].mul(k)),D=h.map((F,M)=>x[1][M].mul(A).add(F.mul(k)));return{output:R,newStates:D}});u=I.output,h=I.newStates}a&&p.push(u)}let y;if(a){const b=1;y=Xe(p,b)}return[u,y,h]})}class ri extends be{constructor(e){super(e);let t;if(e.cell==null)throw new _("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Tu({cells:e.cell}):t=e.cell,t.stateSize==null)throw new _("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 rt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ps(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){vd(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const s=t[0];let n;if(this.returnSequences?n=[e[0],e[1],s]:n=[e[0],s],this.returnState){const i=[];for(const r of t)i.push([e[0],r]);return[n].concat(i)}else return n}computeMask(e,t){return N(()=>{Array.isArray(t)&&(t=t[0]);const s=this.returnSequences?t:null;if(this.returnState){const n=this.states.map(i=>null);return[s].concat(n)}else return s})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let s=0;s<e;++s)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ae("Constants support is not implemented in RNN yet.");vd(e)&&(e=e[0]),e=e;const s=this.stateful?e[0]:null,n=e[e.length-1];this.inputSpec[0]=new rt({shape:[s,null,n]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ae("Constants support is not implemented in RNN yet.");this.cell.build(i);let r;if(Array.isArray(this.cell.stateSize)?r=this.cell.stateSize:r=[this.cell.stateSize],this.stateSpec!=null){if(!E.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),r))throw new _(`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=r.map(o=>new rt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){N(()=>{if(!this.stateful)throw new ti("Cannot call resetStates() on an RNN Layer that is not stateful.");const s=this.inputSpec[0].shape[0];if(s==null)throw new _("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=>Ie([s,n])):this.states_=[Ie([s,this.cell.stateSize])];else if(e==null)ce(this.states_),this.keptStates!=null&&(ce(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Ie([s,n])):this.states_[0]=Ie([s,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new _(`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()):ce(this.states_);for(let n=0;n<this.states_.length;++n){const i=e[n],r=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,o=[s,r];if(!E.arraysEqual(i.shape,o))throw new _(`State ${n} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[n]=i}}this.states_=this.states_.map(n=>gt(n.clone()))})}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=Qb(e,s,n,this.numConstants);e=i.inputs,s=i.initialState,n=i.constants;let r=[],o=[];if(s!=null){t.initialState=s,r=r.concat(s),this.stateSpec=[];for(const l of s)this.stateSpec.push(new rt({shape:l.shape}));o=o.concat(this.stateSpec)}n!=null&&(t.constants=n,r=r.concat(n),this.numConstants=n.length);const a=r[0]instanceof bs;if(a){const l=[e].concat(r),c=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=c;const u=super.apply(l,t);return this.inputSpec=p,u}else return super.apply(e,t)}call(e,t){return N(()=>{const s=t==null?null:t.mask,n=t==null?null:t.training;let i=t==null?null:t.initialState;e=fe(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const r=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==r)throw new _(`RNN Layer has ${r} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const o={training:n},a=(d,m)=>{const f=this.cell.call([d].concat(m),o);return[f[0],f.slice(1)]},l=ew(a,e,i,this.goBackwards,s,null,this.unroll,this.returnSequences),c=l[0],p=l[1],u=l[2];this.stateful&&this.resetStates(u,n);const h=this.returnSequences?p:c;return this.returnState?[h].concat(u):h})}getInitialState(e){return N(()=>{let t=Ie(e.shape);return t=te(t,[1,2]),t=Fi(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(s=>s>1?pd(t,[1,s]):t):this.cell.stateSize>1?[pd(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(){const e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);const t=this.cell.getConfig();e.cell={className:this.cell.getClassName(),config:t};const s=super.getConfig();return Object.assign(e,s),e}static fromConfig(e,t,s={}){const n=t.cell,i=ws(n,s);return new e(Object.assign(t,{cell:i}))}}ri.className="RNN";P.registerClass(ri);class Da extends be{}class Iu extends Da{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,jt(this.units,"units"),this.activation=Un(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=it(e.kernelConstraint),this.recurrentConstraint=it(e.recurrentConstraint),this.biasConstraint=it(e.biasConstraint),this.dropout=To([1,Fn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=To([1,Fn([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ue(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 N(()=>{if(e=e,e.length!==2)throw new _(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let s=e[1];e=e[0];const n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Fa(()=>Bt(e),this.dropout,n)),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Fa(()=>Bt(s),this.recurrentDropout,n));let i;const r=this.dropoutMask,o=this.recurrentDropoutMask;r!=null?i=mn(C(e,r),this.kernel.read()):i=mn(e,this.kernel.read()),this.bias!=null&&(i=Ys(i,this.bias.read())),o!=null&&(s=C(s,o));let a=W(i,mn(s,this.recurrentKernel.read()));return this.activation!=null&&(a=this.activation.apply(a)),[a,a]})}getConfig(){const e={units:this.units,activation:xs(this.activation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},t=super.getConfig();return Object.assign(e,t),e}}Iu.className="SimpleRNNCell";P.registerClass(Iu);class Dm extends ri{constructor(e){e.cell=new Iu(e),super(e)}call(e,t){return N(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}get units(){return this.cell.units}get activation(){return this.cell.activation}get useBias(){return this.cell.useBias}get kernelInitializer(){return this.cell.kernelInitializer}get recurrentInitializer(){return this.cell.recurrentInitializer}get biasInitializer(){return this.cell.biasInitializer}get kernelRegularizer(){return this.cell.kernelRegularizer}get recurrentRegularizer(){return this.cell.recurrentRegularizer}get biasRegularizer(){return this.cell.biasRegularizer}get kernelConstraint(){return this.cell.kernelConstraint}get recurrentConstraint(){return this.cell.recurrentConstraint}get biasConstraint(){return this.cell.biasConstraint}get dropout(){return this.cell.dropout}get recurrentDropout(){return this.cell.recurrentDropout}getConfig(){const e={units:this.units,activation:xs(this.activation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},t=super.getConfig();return delete t.cell,Object.assign(e,t),e}static fromConfig(e,t){return new e(t)}}Dm.className="SimpleRNN";P.registerClass(Dm);class Au extends Da{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 _("GRUCell does not support reset_after parameter set to true.");this.units=e.units,jt(this.units,"units"),this.activation=Un(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Un(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=it(e.kernelConstraint),this.recurrentConstraint=it(e.recurrentConstraint),this.biasConstraint=it(e.biasConstraint),this.dropout=To([1,Fn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=To([1,Fn([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ue(e);const 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 N(()=>{if(e=e,e.length!==2)throw new _(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const s=t.training==null?!1:t.training;let n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Fa(()=>Bt(e),this.dropout,s,3)),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Fa(()=>Bt(n),this.recurrentDropout,s,3));const i=this.dropoutMask,r=this.recurrentDropoutMask;let o,a,l;0<this.dropout&&this.dropout<1&&(e=C(e,i[0]));let c=mn(e,this.kernel.read());this.useBias&&(c=Ys(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=C(n,r[0]));const p=this.recurrentKernel.read(),[u,h]=ls(p,[2*this.units,this.units],p.rank-1),d=mn(n,u),[m,f,y]=ls(c,3,c.rank-1),[b,S]=ls(d,2,d.rank-1);o=this.recurrentActivation.apply(W(m,b)),a=this.recurrentActivation.apply(W(f,S));const x=mn(C(a,n),h);l=this.activation.apply(W(y,x));const I=W(C(o,n),C(W(1,Oe(o)),l));return[I,I]})}getConfig(){const e={units:this.units,activation:xs(this.activation),recurrentActivation:xs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1},t=super.getConfig();return Object.assign(e,t),e}}Au.className="GRUCell";P.registerClass(Au);class Fm extends ri{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 Au(e),super(e)}call(e,t){return N(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}get units(){return this.cell.units}get activation(){return this.cell.activation}get recurrentActivation(){return this.cell.recurrentActivation}get useBias(){return this.cell.useBias}get kernelInitializer(){return this.cell.kernelInitializer}get recurrentInitializer(){return this.cell.recurrentInitializer}get biasInitializer(){return this.cell.biasInitializer}get kernelRegularizer(){return this.cell.kernelRegularizer}get recurrentRegularizer(){return this.cell.recurrentRegularizer}get biasRegularizer(){return this.cell.biasRegularizer}get kernelConstraint(){return this.cell.kernelConstraint}get recurrentConstraint(){return this.cell.recurrentConstraint}get biasConstraint(){return this.cell.biasConstraint}get dropout(){return this.cell.dropout}get recurrentDropout(){return this.cell.recurrentDropout}get implementation(){return this.cell.implementation}getConfig(){const e={units:this.units,activation:xs(this.activation),recurrentActivation:xs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1},t=super.getConfig();return delete t.cell,Object.assign(e,t),e}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Fm.className="GRU";P.registerClass(Fm);class Nu extends Da{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,jt(this.units,"units"),this.activation=Un(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Un(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ye(e.kernelRegularizer),this.recurrentRegularizer=Ye(e.recurrentRegularizer),this.biasRegularizer=Ye(e.biasRegularizer),this.kernelConstraint=it(e.kernelConstraint),this.recurrentConstraint=it(e.recurrentConstraint),this.biasConstraint=it(e.biasConstraint),this.dropout=To([1,Fn([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=To([1,Fn([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=Ue(e);const s=e[e.length-1];this.kernel=this.addWeight("kernel",[s,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){const i=this.biasInitializer,r=this.units;n=new(t=class extends fn{apply(a,l){const c=i.apply([r]),p=new Ta().apply([r]),u=i.apply([r*2]);return Mb(Mb(c,p),u)}},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 N(()=>{const s=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new _(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Fa(()=>Bt(e),this.dropout,s,4)),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Fa(()=>Bt(n),this.recurrentDropout,s,4));const r=this.dropoutMask,o=this.recurrentDropoutMask;let a,l,c,p;0<this.dropout&&this.dropout<1&&(e=C(e,r[0]));let u=mn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=C(n,o[0])),u=W(u,mn(n,this.recurrentKernel.read())),this.useBias&&(u=Ys(u,this.bias.read()));const[h,d,m,f]=ls(u,4,u.rank-1);a=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(d),c=W(C(l,i),C(a,this.activation.apply(m))),p=this.recurrentActivation.apply(f);const y=C(p,this.activation.apply(c));return[y,y,c]})}getConfig(){const e={units:this.units,activation:xs(this.activation),recurrentActivation:xs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},t=super.getConfig();return Object.assign(e,t),e}}Nu.className="LSTMCell";P.registerClass(Nu);class Mm extends ri{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 Nu(e),super(e)}call(e,t){return N(()=>{this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const s=t==null?null:t.mask,n=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:s,training:n,initialState:i})})}get units(){return this.cell.units}get activation(){return this.cell.activation}get recurrentActivation(){return this.cell.recurrentActivation}get useBias(){return this.cell.useBias}get kernelInitializer(){return this.cell.kernelInitializer}get recurrentInitializer(){return this.cell.recurrentInitializer}get biasInitializer(){return this.cell.biasInitializer}get unitForgetBias(){return this.cell.unitForgetBias}get kernelRegularizer(){return this.cell.kernelRegularizer}get recurrentRegularizer(){return this.cell.recurrentRegularizer}get biasRegularizer(){return this.cell.biasRegularizer}get kernelConstraint(){return this.cell.kernelConstraint}get recurrentConstraint(){return this.cell.recurrentConstraint}get biasConstraint(){return this.cell.biasConstraint}get dropout(){return this.cell.dropout}get recurrentDropout(){return this.cell.recurrentDropout}get implementation(){return this.cell.implementation}getConfig(){const e={units:this.units,activation:xs(this.activation),recurrentActivation:xs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Me(this.kernelInitializer),recurrentInitializer:Me(this.recurrentInitializer),biasInitializer:Me(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Ae(this.kernelRegularizer),recurrentRegularizer:Ae(this.recurrentRegularizer),biasRegularizer:Ae(this.biasRegularizer),activityRegularizer:Ae(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},t=super.getConfig();return delete t.cell,Object.assign(e,t),e}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Mm.className="LSTM";P.registerClass(Mm);class Tu extends Da{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return N(()=>{e=e;let s=e.slice(1);const n=[];for(const o of this.cells.slice().reverse())Array.isArray(o.stateSize)?n.push(s.splice(0,o.stateSize.length)):n.push(s.splice(0,1));n.reverse();const i=[];let r;for(let o=0;o<this.cells.length;++o){const a=this.cells[o];s=n[o],o===0?r=[e[0]].concat(s):r=[r[0]].concat(s),r=a.call(r,t),i.push(r.slice(1))}s=[];for(const o of i.slice().reverse())s.push(...o);return[r[0]].concat(s)})}build(e){vd(e)&&(e=e[0]),e=e;let t;this.cells.forEach((s,n)=>{Dn(`RNNCell_${n}`,()=>{s.build(e),Array.isArray(s.stateSize)?t=s.stateSize[0]:t=s.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=[];for(const n of this.cells)e.push({className:n.getClassName(),config:n.getConfig()});const t={cells:e},s=super.getConfig();return Object.assign(t,s),t}static fromConfig(e,t,s={}){const n=[];for(const i of t.cells)n.push(ws(i,s));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const s of this.cells)t.push(...s.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return lu(e)}setWeights(e){const t=[];for(const s of this.cells){const n=s.weights.length,i=e.splice(n);for(let r=0;r<s.weights.length;++r)t.push([s.weights[r],i[r]])}Na(t)}}Tu.className="StackedRNNCells";P.registerClass(Tu);function Fa(e,t,s=null,n=1){function i(){return dd(e(),t)}if(n>1){const r=[];for(let o=0;o<n;o++)r.push(Ui(i,e,s));return r.map(o=>gt(o.clone()))}else return gt(Ui(i,e,s).clone())}class qA extends be{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(){const 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,s={}){const n=t.layer,i=ws(n,s);delete t.layer;const r={layer:i};return Object.assign(r,t),new e(r)}}class Um extends qA{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Ue(e),e.length<3)throw new _(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Ue(e);const t=[e[0]].concat(e.slice(2)),s=this.layer.computeOutputShape(t),n=e[1];return[s[0],n].concat(s.slice(1))}call(e,t){return N(()=>{e=fe(e);const s=(r,o)=>{const a=fe(this.layer.call(r,t));return[a,[]]},n=ew(s,e,[],!1,null,null,!1,!0),i=n[1];return i})}}Um.className="TimeDistributed";P.registerClass(Um);function u$(e){_i(FI,"BidirectionalMergeMode",e)}const h$="concat";class $m extends qA{constructor(e){super(e);const t=e.layer.getConfig(),s={};s.className=e.layer.getClassName(),s.config=t,this.forwardLayer=ws(s),t.goBackwards=!(t.goBackwards===!0);const n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=ws(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?h$:e.mergeMode,u$(this.mergeMode),e.weights)throw new ae("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){const t=e.length,s=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,s)),this.backwardLayer.setWeights(e.slice(s))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let s,n,i;return this.returnState&&(i=t.slice(1)),s=t[0],s=s,this.mergeMode==="concat"?(s[s.length-1]*=2,n=[s]):this.mergeMode==null?n=[s,s.slice()]:n=[s],this.returnState?this.mergeMode==null?n.concat(i).concat(i.slice()):[s].concat(i).concat(i.slice()):Pt(n)}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=Qb(e,s,n,this.numConstants);if(e=i.inputs,s=i.initialState,n=i.constants,Array.isArray(e)&&(s=e.slice(1),e=e[0]),(s==null||s.length===0)&&n==null)return super.apply(e,t);const r=[],o=[];if(s!=null){const l=s.length;if(l%2>0)throw new _("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=s,r.push(...s);const c=s.map(p=>new rt({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(n!=null)throw new ae("Support for constants in Bidirectional layers is not implemented yet.");const a=r[0]instanceof bs;for(const l of r)if(l instanceof bs!==a)throw new _("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(a){const l=[e].concat(r),c=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=c;const u=super.apply(l,t);return this.inputSpec=p,u}else return super.apply(e,t)}call(e,t){return N(()=>{const s=t.initialState;let n,i;if(s==null)n=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const a=s.slice(0,s.length/2),l=s.slice(s.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:a})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let r;this.returnState&&(Array.isArray(n)&&(r=n.slice(1).concat(i.slice(1))),n=n[0],i=i[0]),this.returnSequences&&(i=Lt(i,1));let o;return this.mergeMode==="concat"?o=ud([n,i]):this.mergeMode==="sum"?o=W(n,i):this.mergeMode==="ave"?o=C(.5,W(n,i)):this.mergeMode==="mul"?o=C(n,i):this.mergeMode==null&&(o=[n,i]),this.returnState?this.mergeMode==null?o.concat(r):[o].concat(r):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Dn(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Dn(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let s;if(this.returnSequences?this.mergeMode==null?s=[t,t]:s=t:this.mergeMode==null?s=[null,null]:s=null,this.returnState){const n=this.forwardLayer.states,i=n.map(r=>null);return Array.isArray(s)?s.concat(i).concat(i):[s].concat(i).concat(i)}else return s}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const s=ws(t.layer);if(delete t.layer,t.numConstants!=null)throw new ae("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const n=t;return n.layer=s,new e(n)}}$m.className="Bidirectional";P.registerClass($m);const tw={};Re(tw,{Layer:()=>be,RNN:()=>ri,RNNCell:()=>Da,activation:()=>C$,add:()=>U$,alphaDropout:()=>bW,average:()=>$$,averagePooling1d:()=>sw,averagePooling2d:()=>nw,averagePooling3d:()=>iw,avgPool1d:()=>q$,avgPool2d:()=>K$,avgPool3d:()=>J$,avgPooling1d:()=>Y$,avgPooling2d:()=>X$,avgPooling3d:()=>Z$,batchNormalization:()=>V$,bidirectional:()=>pW,concatenate:()=>W$,conv1d:()=>x$,conv2d:()=>L$,conv2dTranspose:()=>S$,conv3d:()=>v$,cropping2D:()=>I$,dense:()=>R$,depthwiseConv2d:()=>N$,dot:()=>j$,dropout:()=>O$,elu:()=>m$,embedding:()=>M$,flatten:()=>k$,gaussianDropout:()=>yW,gaussianNoise:()=>gW,globalAveragePooling1d:()=>Q$,globalAveragePooling2d:()=>eW,globalMaxPool1d:()=>hW,globalMaxPool2d:()=>dW,globalMaxPooling1d:()=>YA,globalMaxPooling2d:()=>KA,gru:()=>sW,gruCell:()=>nW,input:()=>zd,inputLayer:()=>d$,layerNormalization:()=>G$,leakyReLU:()=>g$,lstm:()=>iW,lstmCell:()=>rW,masking:()=>wW,maxPool1d:()=>mW,maxPool2d:()=>fW,maxPooling1d:()=>XA,maxPooling2d:()=>JA,maxPooling3d:()=>tW,maximum:()=>z$,minimum:()=>B$,multiply:()=>P$,permute:()=>F$,prelu:()=>y$,reLU:()=>f$,repeatVector:()=>_$,reshape:()=>D$,rnn:()=>lW,separableConv2d:()=>T$,simpleRNN:()=>oW,simpleRNNCell:()=>aW,softmax:()=>b$,spatialDropout1d:()=>E$,stackedRNNCells:()=>cW,thresholdedReLU:()=>w$,timeDistributed:()=>uW,upSampling2d:()=>A$,zeroPadding2d:()=>H$});function 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bW(e){return new wm(e)}function wW(e){return new lm(e)}const rw={};Re(rw,{MAPE:()=>OW,MSE:()=>_W,binaryAccuracy:()=>xW,binaryCrossentropy:()=>LW,categoricalAccuracy:()=>vW,categoricalCrossentropy:()=>TW,cosineProximity:()=>NW,mape:()=>EW,meanAbsoluteError:()=>CW,meanAbsolutePercentageError:()=>RW,meanSquaredError:()=>kW,mse:()=>DW,precision:()=>IW,recall:()=>AW,sparseCategoricalAccuracy:()=>SW});function xW(e,t){return hu(e,t)}function LW(e,t){return Od(e,t)}function SW(e,t){return Ed(e,t)}function vW(e,t){return du(e,t)}function TW(e,t){return mu(e,t)}function IW(e,t){return jb(e,t)}function AW(e,t){return QI(e,t)}function NW(e,t){return pu(e,t)}function CW(e,t){return Ca(e,t)}function RW(e,t){return mr(e,t)}function OW(e,t){return mr(e,t)}function EW(e,t){return mr(e,t)}function kW(e,t){return ii(e,t)}function _W(e,t){return ii(e,t)}function DW(e,t){return ii(e,t)}const ow={};Re(ow,{modelFromJSON:()=>yA});const aw={};Re(aw,{l1:()=>MW,l1l2:()=>FW,l2:()=>UW});function FW(e){return new ka(e)}function MW(e){return FA(e)}function UW(e){return MA(e)}class lw extends No{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof gn))throw new Error("model must be a LayersModel, not some other Container");this.model=e}}function Wm(e,t){return e<t}function ZA(e,t){return e>t}class cw extends lw{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new ae("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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hw={};Re(hw,{json:()=>zW});const zW=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"clip_value_min",name:"clipValueMin",type:"number"},{tfName:"clip_value_max",name:"clipValueMax",type:"number"}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"clipValueMin",name:"clipValueMin",type:"number",defaultValue:0},{tfName:"clipValueMax",name:"clipValueMax",type:"number",defaultValue:6}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_mat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dw={};Re(dw,{json:()=>BW});const BW=[{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListSetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListReserve",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListFromTensor",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListStack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"},{tfName:"num_elements",name:"numElements",type:"dtype"}]},{tfOpName:"TensorListSplit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"},{start:2,name:"lengths",type:"number[]"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListConcat",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}],attrs:[{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPopBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPushBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]}];const mw={};Re(mw,{json:()=>PW});const PW=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"FusedDepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]}]},{tfOpName:"Conv3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"Dilation2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"rates",name:"dilations",type:"number[]"},{tfName:"padding",name:"pad",type:"string"}]}];const fw={};Re(fw,{json:()=>jW});const jW=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}];const gw={};Re(gw,{json:()=>VW});const VW=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const yw={};Re(yw,{json:()=>GW});const GW=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]}];const bw={};Re(bw,{json:()=>HW});const HW=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];const ww={};Re(ww,{json:()=>qW});const qW=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];const xw={};Re(xw,{json:()=>YW});const YW=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const Lw={};Re(Lw,{json:()=>KW});const KW=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];const Sw={};Re(Sw,{json:()=>XW});const XW=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];const vw={};Re(vw,{json:()=>JW});const JW=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];const Tw={};Re(Tw,{json:()=>ZW});const ZW=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];const Iw={};Re(Iw,{json:()=>QW});const QW=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];const Aw={};Re(Aw,{json:()=>ez});const ez=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];class rN{static get Instance(){return this._instance||(this._instance=new this())}constructor(){const e=[uw,hw,dw,mw,fw,gw,yw,xw,ww,bw,Lw,Sw,vw,Tw,Iw,Aw],t=[].concat(...e.map(s=>s.json));this.opMappers=t.reduce((s,n)=>(s[n.tfOpName]=n,s),{})}transformGraph(e,t={}){const s=e.node,n=[],i=[],r=s.reduce((h,d)=>(h[d.name]=this.mapNode(d),d.op.startsWith("Placeholder")&&n.push(h[d.name]),d.op==="Const"&&i.push(h[d.name]),h),{});let o=[];const a=[];let l={},c={};t!=null&&(l=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));const p=Object.keys(r);p.forEach(h=>{const d=r[h];d.inputNames.forEach(m=>{const[f]=$n(m);d.inputs.push(r[f]),r[f].children.push(d)})}),Object.keys(c).length===0?p.forEach(h=>{const d=r[h];d.children.length===0&&a.push(d)}):Object.keys(c).forEach(h=>{const[d]=$n(h),m=r[d];m!=null&&(m.signatureKey=c[h],a.push(m))}),Object.keys(l).length>0?Object.keys(l).forEach(h=>{const[d]=$n(h),m=r[d];m&&(m.signatureKey=l[h],o.push(m))}):o=n;let u={};return e.library!=null&&e.library.function!=null&&(u=e.library.function.reduce((h,d)=>(h[d.signature.name]=this.mapFunction(d),h),{})),{nodes:r,inputs:o,outputs:a,weights:i,placeholders:n,signature:t,functions:u}}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,s)=>(t[e[s].name]=s,t),{})}mapNode(e){const t=zm(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});const s={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(n=>n.startsWith("^")?n.substr(1):n),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(s.inputParams=t.inputs.reduce((n,i)=>(n[i.name]={type:i.type,inputIndexStart:i.start,inputIndexEnd:i.end},n),{})),t.attrs!=null&&(s.attrParams=t.attrs.reduce((n,i)=>{const r=i.type;let o;switch(i.type){case"string":o=Pm(e.attr,i.tfName,i.defaultValue),o===void 0&&!!i.tfDeprecatedName&&(o=Pm(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"string[]":o=Km(e.attr,i.tfName,i.defaultValue),o===void 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Pm(this.node.rawAttrs,e,t);if(s.b!=null)return jm(this.node.rawAttrs,e,t);if(s.shape!=null)return qm(this.node.rawAttrs,e,t);if(s.type!=null)return Gm(this.node.rawAttrs,e,t);if(s.list!=null){if(s.list.i!=null||s.list.f!=null)return Ym(this.node.rawAttrs,e,t);if(s.list.s!=null)return Km(this.node.rawAttrs,e,t);if(s.list.shape!=null)return Xm(this.node.rawAttrs,e,t);if(s.list.b!=null)return Jm(this.node.rawAttrs,e,t);if(s.list.type!=null)return Hm(this.node.rawAttrs,e,t)}return t}}const cN=(e,t,s)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[W(w("a",e,t,s),w("b",e,t,s))];case"AddN":return[op(w("tensors",e,t,s))];case"FloorMod":case"Mod":return[io(w("a",e,t,s),w("b",e,t,s))];case"Mul":return[C(w("a",e,t,s),w("b",e,t,s))];case"RealDiv":case"Div":return[J(w("a",e,t,s),w("b",e,t,s))];case"DivNoNan":return[Ap(w("a",e,t,s),w("b",e,t,s))];case"FloorDiv":return[Jr(w("a",e,t,s),w("b",e,t,s))];case"Sub":return[X(w("a",e,t,s),w("b",e,t,s))];case"Minimum":return[Vs(w("a",e,t,s),w("b",e,t,s))];case"Maximum":return[Wt(w("a",e,t,s),w("b",e,t,s))];case"Pow":return[qt(w("a",e,t,s),w("b",e,t,s))];case"SquaredDifference":return[Ri(w("a",e,t,s),w("b",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const pN=(e,t,s)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Je(w("x",e,t,s))];case"Acos":return[ip(w("x",e,t,s))];case"Acosh":return[rp(w("x",e,t,s))];case"Asin":return[lp(w("x",e,t,s))];case"Asinh":return[cp(w("x",e,t,s))];case"Atan":return[pp(w("x",e,t,s))];case"Atan2":return[up(w("x",e,t,s),w("y",e,t,s))];case"Atanh":return[hp(w("x",e,t,s))];case"Ceil":return[wp(w("x",e,t,s))];case"Complex":return[Ze(w("real",e,t,s),w("imag",e,t,s))];case"Cos":return[xi(w("x",e,t,s))];case"Cosh":return[Kr(w("x",e,t,s))];case"Elu":return[An(w("x",e,t,s))];case"Erf":return[Np(w("x",e,t,s))];case"Exp":return[At(w("x",e,t,s))];case"Expm1":return[Cp(w("x",e,t,s))];case"Floor":return[Nn(w("x",e,t,s))];case"Log":return[Mt(w("x",e,t,s))];case"Log1p":return[eo(w("x",e,t,s))];case"Imag":return[Ht(w("x",e,t,s))];case"Neg":return[Oe(w("x",e,t,s))];case"Reciprocal":return[Fp(w("x",e,t,s))];case"Real":return[$t(w("x",e,t,s))];case"Relu":return[ke(w("x",e,t,s))];case"Round":return[Up(w("x",e,t,s))];case"Selu":return[ao(w("x",e,t,s))];case"Sigmoid":return[Ss(w("x",e,t,s))];case"Sin":return[lo(w("x",e,t,s))];case"Sign":return[Wp(w("x",e,t,s))];case"Sinh":return[co(w("x",e,t,s))];case"Softplus":return[Rn(w("x",e,t,s))];case"Sqrt":return[He(w("x",e,t,s))];case"Square":return[ye(w("x",e,t,s))];case"Tanh":return[Zn(w("x",e,t,s))];case"Tan":return[Bp(w("x",e,t,s))];case"Relu6":case"ClipByValue":return[Dt(w("x",e,t,s),w("clipValueMin",e,t,s),w("clipValueMax",e,t,s))];case"Rsqrt":return[oo(Vt(e.inputNames[0],t,s))];case"Prod":return[ro(w("x",e,t,s),w("axes",e,t,s))];case"LeakyRelu":return[Qr(w("x",e,t,s),w("alpha",e,t,s))];case"Prelu":return[Ii(w("x",e,t,s),w("alpha",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ds(e,t,s=""){E.assert(sz(e,t),()=>s+` Shapes ${e} and ${t} must match`)}function sz(e,t){if(e.length!==t.length)return!1;for(let s=0;s<e.length;s++)if(e[s]!==-1&&t[s]!==-1&&e[s]!==t[s])return!1;return!0}class uN{constructor(e,t,s,n,i,r,o){this.name=e,this.dtype=t,this.maxSize=s,this.elementShape=n,this.identicalElementShapes=i,this.dynamicSize=r,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=j(0),gt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(){this.tensors.forEach(e=>e.tensor.dispose()),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: 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          tensor.shape[0], but sum of lengths is
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s.functionMap[n].executeFunctionAsync(c,s.tensorArrayMap,s.tensorListMap);const u=c.map(d=>d.id);p.forEach(d=>{!d.kept&&a.indexOf(d.id)===-1&&u.indexOf(d.id)===-1&&d.dispose()});const h=await s.functionMap[i].executeFunctionAsync(c,s.tensorArrayMap,s.tensorListMap);l=await h[0].data(),h.forEach(d=>{!d.kept&&a.indexOf(d.id)===-1&&u.indexOf(d.id)===-1&&d.dispose()})}return c}case"LoopCond":{const n=w("pred",e,t,s);return[Wn(n)]}case"Switch":{const n=w("pred",e,t,s);let i=w("data",e,t,s);return i.kept||(i=Wn(i)),(await n.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const n=e.inputNames.find(i=>Vt(i,t,s)!==void 0);if(n){const i=Vt(n,t,s);return[Wn(i)]}return}case"Enter":{const n=w("frameName",e,t,s),i=w("tensor",e,t,s);return s.enterFrame(n),[Wn(i)]}case"Exit":{const n=w("tensor",e,t,s);return s.exitFrame(),[Wn(n)]}case"NextIteration":{const n=w("tensor",e,t,s);return s.nextIteration(),[Wn(n)]}case"TensorArrayV3":{const n=w("size",e,t,s),i=w("dtype",e,t,s),r=w("elementShape",e,t,s),o=w("dynamicSize",e,t,s),a=w("clearAfterRead",e,t,s),l=w("identicalElementShapes",e,t,s),c=w("name",e,t,s),p=new uN(c,i,n,r,l,o,a);return s.addTensorArray(p),[p.idTensor,j(1)]}case"TensorArrayWriteV3":{const n=w("tensorArrayId",e,t,s),i=w("index",e,t,s),r=w("tensor",e,t,s),o=s.getTensorArray(n.id);return o.write(i,r),[o.idTensor]}case"TensorArrayReadV3":{const n=w("tensorArrayId",e,t,s),i=w("index",e,t,s),r=s.getTensorArray(n.id);return[r.read(i)]}case"TensorArrayGatherV3":{const n=w("tensorArrayId",e,t,s),i=w("indices",e,t,s),r=w("dtype",e,t,s),o=s.getTensorArray(n.id);return[o.gather(i,r)]}case"TensorArrayScatterV3":{const n=w("tensorArrayId",e,t,s),i=w("indices",e,t,s),r=w("tensor",e,t,s),o=s.getTensorArray(n.id);return o.scatter(i,r),[o.idTensor]}case"TensorArrayConcatV3":{const n=w("tensorArrayId",e,t,s),i=s.getTensorArray(n.id),r=w("dtype",e,t,s);return[i.concat(r)]}case"TensorArraySplitV3":{const n=w("tensorArrayId",e,t,s),i=w("tensor",e,t,s),r=w("lengths",e,t,s),o=s.getTensorArray(n.id);return o.split(r,i),[o.idTensor]}case"TensorArraySizeV3":{const n=w("tensorArrayId",e,t,s),i=s.getTensorArray(n.id);return[j(i.size(),"int32")]}case"TensorArrayCloseV3":{const n=w("tensorArrayId",e,t,s),i=s.getTensorArray(n.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const n=w("tensorListId",e,t,s),i=w("index",e,t,s),r=w("tensor",e,t,s),o=s.getTensorList(n.id);return o.setItem(i,r),[o.idTensor]}case"TensorListGetItem":{const n=w("tensorListId",e,t,s),i=w("index",e,t,s),r=w("elementShape",e,t,s),o=w("elementDType",e,t,s),a=s.getTensorList(n.id);return[a.getItem(i,r,o)]}case"TensorListScatterV2":case"TensorListScatter":{const n=w("indices",e,t,s),i=w("tensor",e,t,s),r=w("elementShape",e,t,s),o=w("numElements",e,t,s),a=mN(i,n,r,o);return s.addTensorList(a),[a.idTensor]}case"TensorListReserve":{const n=w("elementShape",e,t,s),i=w("elementDType",e,t,s),r=w("numElements",e,t,s),o=dN(n,i,r);return s.addTensorList(o),[o.idTensor]}case"TensorListGather":{const n=w("tensorListId",e,t,s),i=w("indices",e,t,s),r=w("elementShape",e,t,s),o=w("elementDType",e,t,s),a=s.getTensorList(n.id);return[a.gather(i,o,r)]}case"TensorListStack":{const n=w("tensorListId",e,t,s),i=w("elementShape",e,t,s),r=w("elementDType",e,t,s),o=w("numElements",e,t,s),a=s.getTensorList(n.id);return[a.stack(i,r,o)]}case"TensorListFromTensor":{const n=w("tensor",e,t,s),i=w("elementShape",e,t,s),r=w("elementDType",e,t,s),o=hN(n,i,r);return s.addTensorList(o),[o.idTensor]}case"TensorListConcat":{const n=w("tensorListId",e,t,s),i=s.getTensorList(n.id),r=w("dtype",e,t,s),o=w("elementShape",e,t,s);return[i.concat(r,o)]}case"TensorListPushBack":{const n=w("tensorListId",e,t,s),i=w("tensor",e,t,s),r=s.getTensorList(n.id);return r.pushBack(i),[r.idTensor]}case"TensorListPopBack":{const n=w("tensorListId",e,t,s),i=w("elementShape",e,t,s),r=w("elementDType",e,t,s),o=s.getTensorList(n.id);return[o.popBack(i,r)]}case"TensorListSplit":{const n=w("tensor",e,t,s),i=w("elementShape",e,t,s),r=w("lengths",e,t,s),o=fN(n,r,i);return s.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function yN(e,t,s){const[n,i]=w("fusedOps",e,t,s),r=n==="biasadd",o=i==="prelu",a=n==="fusedbatchnorm",l=w("numArgs",e,t,s);if(r){if(o&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(a)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const c=w("strides",e,t,s),p=Cu(e,t,s),u=w("dataFormat",e,t,s).toUpperCase(),h=w("dilations",e,t,s),[d,m]=w("args",e,t,s);return{stride:c,pad:p,dataFormat:u,dilations:h,biasArg:d,preluArg:m,activationFunc:i}}const bN=(e,t,s)=>{switch(e.op){case"Conv1D":{const n=w("stride",e,t,s),i=w("pad",e,t,s),r=w("dataFormat",e,t,s).toUpperCase(),o=w("dilation",e,t,s);return[Hr(w("x",e,t,s),w("filter",e,t,s),n,i,r,o)]}case"Conv2D":{const n=w("strides",e,t,s),i=Cu(e,t,s),r=w("dataFormat",e,t,s).toUpperCase(),o=w("dilations",e,t,s);return[yt(w("x",e,t,s),w("filter",e,t,s),[n[1],n[2]],i,r,[o[1],o[2]])]}case"_FusedConv2D":{const{stride:n,pad:i,dataFormat:r,dilations:o,biasArg:a,preluArg:l,activationFunc:c}=yN(e,t,s);return[Hs.conv2d({x:w("x",e,t,s),filter:w("filter",e,t,s),strides:[n[1],n[2]],pad:i,dataFormat:r,dilations:[o[1],o[2]],bias:a,activation:c,preluActivationWeights:l})]}case"FusedDepthwiseConv2dNative":{const{stride:n,pad:i,dataFormat:r,dilations:o,biasArg:a,preluArg:l,activationFunc:c}=yN(e,t,s);return[Hs.depthwiseConv2d({x:w("x",e,t,s),filter:w("filter",e,t,s),strides:[n[1],n[2]],pad:i,dataFormat:r,dilations:[o[1],o[2]],bias:a,activation:c,preluActivationWeights:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const n=w("outputShape",e,t,s),i=w("strides",e,t,s),r=Cu(e,t,s);return[qr(w("x",e,t,s),w("filter",e,t,s),n,[i[1],i[2]],r)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const n=w("strides",e,t,s),i=Cu(e,t,s),r=w("dilations",e,t,s),o=w("dataFormat",e,t,s).toUpperCase();return[In(w("input",e,t,s),w("filter",e,t,s),[n[1],n[2]],i,o,[r[1],r[2]])]}case"Conv3D":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("dataFormat",e,t,s).toUpperCase(),o=w("dilations",e,t,s);return[Yr(w("x",e,t,s),w("filter",e,t,s),[n[1],n[2],n[3]],i,r,[o[1],o[2],o[3]])]}case"AvgPool":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("kernelSize",e,t,s);return[rs(w("x",e,t,s),[r[1],r[2]],[n[1],n[2]],i)]}case"MaxPool":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("kernelSize",e,t,s);return[lt(w("x",e,t,s),[r[1],r[2]],[n[1],n[2]],i)]}case"MaxPoolWithArgmax":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("kernelSize",e,t,s),o=w("includeBatchInIndex",e,t,s),{result:a,indexes:l}=kp(w("x",e,t,s),[r[1],r[2]],[n[1],n[2]],i,o);return[a,l]}case"AvgPool3D":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("kernelSize",e,t,s);return[Gr(w("x",e,t,s),[r[1],r[2],r[3]],[n[1],n[2],n[3]],i)]}case"MaxPool3D":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("kernelSize",e,t,s);return[no(w("x",e,t,s),[r[1],r[2],r[3]],[n[1],n[2],n[3]],i)]}case"Dilation2D":{const n=w("strides",e,t,s),i=w("pad",e,t,s),r=w("dilations",e,t,s),o=n[1],a=n[2],l=r[1],c=r[2];return[Ip(w("x",e,t,s),w("filter",e,t,s),[o,a],i,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const wN=(e,t,s)=>{switch(e.op){case"Fill":{const n=w("shape",e,t,s),i=w("dtype",e,t,s),r=w("value",e,t,s);return[Ft(n,r,i)]}case"LinSpace":{const n=w("start",e,t,s),i=w("stop",e,t,s),r=w("num",e,t,s);return[Rp(n,i,r)]}case"Multinomial":{const n=w("logits",e,t,s),i=w("numSamples",e,t,s),r=w("seed",e,t,s);return[_p(n,i,r)]}case"OneHot":{const n=w("indices",e,t,s),i=w("depth",e,t,s),r=w("onValue",e,t,s),o=w("offValue",e,t,s);return[On(n,i,r,o)]}case"Ones":return[es(w("shape",e,t,s),w("dtype",e,t,s))];case"OnesLike":return[Bt(w("x",e,t,s))];case"RandomUniform":return[Gs(w("shape",e,t,s),w("minval",e,t,s),w("maxval",e,t,s),w("dtype",e,t,s))];case"Range":{const n=w("start",e,t,s),i=w("stop",e,t,s),r=w("step",e,t,s);return[ei(n,i,r,w("dtype",e,t,s))]}case"TruncatedNormal":{const n=w("shape",e,t,s),i=w("mean",e,t,s),r=w("stdDev",e,t,s),o=w("seed",e,t,s);return[Oi(n,i,r,w("dtype",e,t,s),o)]}case"Zeros":return[Ie(w("shape",e,t,s),w("dtype",e,t,s))];case"ZerosLike":return[ne(w("x",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Cw(e,t,s){const n=w("boxes",e,t,s),i=w("scores",e,t,s),r=w("maxOutputSize",e,t,s),o=w("iouThreshold",e,t,s),a=w("scoreThreshold",e,t,s),l=w("softNmsSigma",e,t,s);return{boxes:n,scores:i,maxOutputSize:r,iouThreshold:o,scoreThreshold:a,softNmsSigma:l}}const xN=async(e,t,s)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:n,scores:i,maxOutputSize:r,iouThreshold:o,scoreThreshold:a,softNmsSigma:l}=Cw(e,t,s),c=await ks.nonMaxSuppressionWithScoreAsync(n,i,r,o,a,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:n,scores:i,maxOutputSize:r,iouThreshold:o,scoreThreshold:a}=Cw(e,t,s),l=w("padToMaxOutputSize",e,t,s),c=await ks.nonMaxSuppressionPaddedAsync(n,i,r,o,a,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:n,scores:i,maxOutputSize:r,iouThreshold:o,scoreThreshold:a}=Cw(e,t,s);return[await ks.nonMaxSuppressionAsync(n,i,r,o,a)]}case"Where":{const n=w("condition",e,t,s).asType("bool"),i=[await uo(n)];return n.dispose(),i}case"ListDiff":return $p(w("x",e,t,s),w("y",e,t,s));default:throw TypeError(`Node type ${e.op} is not implemented`)}};const LN=(e,t,s)=>{switch(e.op){case"TopKV2":{const n=w("x",e,t,s),i=w("k",e,t,s),r=w("sorted",e,t,s),o=jp(n,i,r);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const SN=(e,t,s)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const n=w("default",e,t,s);return[Vt(e.name,t,s)||n];case"Placeholder":return[Vt(e.name,t,s)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const c=w("x",e,t,s);return[Wn(c)]}case"IdentityN":return w("x",e,t,s).map(c=>Wn(c));case"Snapshot":const i=w("x",e,t,s);return[Wn(i)];case"Shape":return[Fe(w("x",e,t,s).shape,"int32")];case"ShapeN":return w("x",e,t,s).map(c=>Fe(c.shape));case"Size":return[j(w("x",e,t,s).size,"int32")];case"Rank":return[j(w("x",e,t,s).rank,"int32")];case"NoOp":return[j(1)];case"Print":const r=w("x",e,t,s),o=w("data",e,t,s),a=w("message",e,t,s),l=w("summarize",e,t,s);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(a);for(let c=0;c<o.length;c++)console.log(Array.prototype.slice.call(o[c].dataSync()).slice(0,l));return[r];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const vN=(e,t,s)=>{switch(e.op){case"ResizeBilinear":{const n=w("images",e,t,s),i=w("size",e,t,s),r=w("alignCorners",e,t,s);return[ks.resizeBilinear(n,[i[0],i[1]],r)]}case"ResizeNearestNeighbor":{const n=w("images",e,t,s),i=w("size",e,t,s),r=w("alignCorners",e,t,s);return[ks.resizeNearestNeighbor(n,[i[0],i[1]],r)]}case"CropAndResize":{const n=w("image",e,t,s),i=w("boxes",e,t,s),r=w("boxInd",e,t,s),o=w("cropSize",e,t,s),a=w("method",e,t,s),l=w("extrapolationValue",e,t,s);return[ks.cropAndResize(n,i,r,o,a,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const TN=(e,t,s)=>{switch(e.op){case"Equal":return[Qt(w("a",e,t,s),w("b",e,t,s))];case"NotEqual":return[Es(w("a",e,t,s),w("b",e,t,s))];case"Greater":return[Ct(w("a",e,t,s),w("b",e,t,s))];case"GreaterEqual":return[as(w("a",e,t,s),w("b",e,t,s))];case"Less":return[Si(w("a",e,t,s),w("b",e,t,s))];case"LessEqual":return[vs(w("a",e,t,s),w("b",e,t,s))];case"LogicalAnd":return[zt(w("a",e,t,s),w("b",e,t,s))];case"LogicalNot":return[vi(w("a",e,t,s))];case"LogicalOr":return[so(w("a",e,t,s),w("b",e,t,s))];case"Select":case"SelectV2":return[at(w("condition",e,t,s),w("a",e,t,s),w("b",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const IN=(e,t,s)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Le(w("a",e,t,s),w("b",e,t,s),w("transposeA",e,t,s),w("transposeB",e,t,s))];case"Transpose":return[se(w("x",e,t,s),w("perm",e,t,s))];case"_FusedMatMul":const[n,i]=w("fusedOps",e,t,s),r=n==="biasadd",o=i==="prelu",a=w("numArgs",e,t,s);if(r){if(o&&a!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&a!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[l,c]=w("args",e,t,s);return[Hs.matMul({a:w("a",e,t,s),b:w("b",e,t,s),transposeA:w("transposeA",e,t,s),transposeB:w("transposeB",e,t,s),bias:l,activation:i,preluActivationWeights:c})];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const AN=(e,t,s)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[on(w("x",e,t,s),w("mean",e,t,s),w("variance",e,t,s),w("offset",e,t,s),w("scale",e,t,s),w("epsilon",e,t,s))];case"FusedBatchNormV3":return[on(w("x",e,t,s),w("mean",e,t,s),w("variance",e,t,s),w("offset",e,t,s),w("scale",e,t,s),w("epsilon",e,t,s))];case"LRN":return[Op(w("x",e,t,s),w("radius",e,t,s),w("bias",e,t,s),w("alpha",e,t,s),w("beta",e,t,s))];case"Softmax":return[Yt(w("x",e,t,s))];case"LogSoftmax":return[to(w("x",e,t,s))];case"SparseToDense":return[fa(w("sparseIndices",e,t,s),w("outputShape",e,t,s),w("sparseValues",e,t,s),w("defaultValue",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const NN=(e,t,s)=>{switch(e.op){case"Max":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[st(w("x",e,t,s),n,i)]}case"Mean":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[Ge(w("x",e,t,s),n,i)]}case"Min":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[an(w("x",e,t,s),n,i)]}case"Sum":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[te(w("x",e,t,s),n,i)]}case"All":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[jr(w("x",e,t,s),n,i)]}case"Any":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[sr(w("x",e,t,s),n,i)]}case"ArgMax":{const n=w("axis",e,t,s);return[nr(w("x",e,t,s),n)]}case"ArgMin":{const n=w("axis",e,t,s);return[ap(w("x",e,t,s),n)]}case"Prod":{const n=w("axis",e,t,s),i=w("keepDims",e,t,s);return[ro(w("x",e,t,s),n,i)]}case"Cumsum":{const n=w("axis",e,t,s),i=w("exclusive",e,t,s),r=w("reverse",e,t,s);return[Xr(w("x",e,t,s),n,i,r)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const CN=(e,t,s)=>{switch(e.op){case"ConcatV2":case"Concat":{const n=w("n",e,t,s),i=w("axis",e,t,s);let r=w("tensors",e,t,s);return r=r.slice(0,n),[ge(r,i)]}case"GatherV2":case"Gather":{const n=w("axis",e,t,s),i=w("x",e,t,s),r=w("indices",e,t,s);return[Cn(i,r.asType("int32"),n)]}case"ReverseV2":case"Reverse":{const n=w("axis",e,t,s),i=w("x",e,t,s);return[Lt(i,n)]}case"Slice":{const n=w("begin",e,t,s),i=w("size",e,t,s);return[he(w("x",e,t,s),n,i)]}case"StridedSlice":{const n=w("begin",e,t,s),i=w("end",e,t,s),r=w("strides",e,t,s),o=w("beginMask",e,t,s),a=w("endMask",e,t,s),l=w("ellipsisMask",e,t,s),c=w("newAxisMask",e,t,s),p=w("shrinkAxisMask",e,t,s),u=w("x",e,t,s);return[zp(u,n,i,r,o,a,l,c,p)]}case"Pack":return N(()=>{const n=w("axis",e,t,s),i=w("tensors",e,t,s),r=i[0].shape,o=i[0].squeeze().shape,a=i.map(l=>{const c=E.arraysEqual(l.shape,r);if(!c&&!E.arraysEqual(l.squeeze().shape,o))throw new Error("the input tensors shape does not match");return c?l:l.reshape(r)});return[Xe(a,n)]});case"Unpack":{const n=w("axis",e,t,s),i=w("tensor",e,t,s);return nt(i,n)}case"Tile":{const n=w("reps",e,t,s);return[js(w("x",e,t,s),n)]}case"Split":case"SplitV":{const n=w("axis",e,t,s),i=w("numOrSizeSplits",e,t,s),r=w("x",e,t,s);return ls(r,i,n)}case"ScatterNd":{const n=w("indices",e,t,s),i=w("values",e,t,s),r=w("shape",e,t,s);return[Bh(n,i,r)]}case"GatherNd":{const n=w("x",e,t,s),i=w("indices",e,t,s);return[Ph(n,i)]}case"SparseToDense":{const n=w("sparseIndices",e,t,s),i=w("outputShape",e,t,s),r=w("sparseValues",e,t,s),o=w("defaultValue",e,t,s);return[fa(n,r,i,r.dtype===o.dtype?o:o.asType(r.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};const RN=(e,t,s)=>{switch(e.op){case"FFT":return[Li(w("x",e,t,s))];case"IFFT":return[Qn(w("x",e,t,s))];case"RFFT":return[Ai(w("x",e,t,s))];case"IRFFT":return[Zr(w("x",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const ON=(e,t,s)=>{switch(e.op){case"Cast":return[K(w("x",e,t,s),w("dtype",e,t,s))];case"ExpandDims":{const n=w("axis",e,t,s);return[Nt(w("x",e,t,s),n)]}case"Squeeze":{const n=w("axis",e,t,s);return[En(w("x",e,t,s),n)]}case"Reshape":return[O(w("x",e,t,s),w("shape",e,t,s))];case"PadV2":case"Pad":return[Ut(w("x",e,t,s),w("padding",e,t,s),w("constantValue",e,t,s))];case"SpaceToBatchND":{const n=w("blockShape",e,t,s),i=w("paddings",e,t,s);return[Ti(w("x",e,t,s),n,i)]}case"BatchToSpaceND":{const n=w("blockShape",e,t,s),i=w("crops",e,t,s);return[wi(w("x",e,t,s),n,i)]}case"DepthToSpace":{const n=w("blockSize",e,t,s),i=w("dataFormat",e,t,s).toUpperCase();return[Tp(w("x",e,t,s),n,i)]}case"BroadcastTo":return[or(w("x",e,t,s),w("shape",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Rw(e,t,s){const n=((i,r,o)=>{switch(i.category){case"arithmetic":return N(()=>cN(i,r,o));case"basic_math":return N(()=>pN(i,r,o));case"control":return gN(i,r,o);case"convolution":return N(()=>bN(i,r,o));case"creation":return N(()=>wN(i,r,o));case"dynamic":return xN(i,r,o);case"evaluation":return N(()=>LN(i,r,o));case"image":return N(()=>vN(i,r,o));case"graph":return N(()=>SN(i,r,o));case"logical":return N(()=>TN(i,r,o));case"matrices":return N(()=>IN(i,r,o));case"normalization":return N(()=>AN(i,r,o));case"reduction":return N(()=>NN(i,r,o));case"slice_join":return N(()=>CN(i,r,o));case"spectral":return N(()=>RN(i,r,o));case"transformation":return N(()=>ON(i,r,o));case"custom":const a=zm(i.op);if(a&&a.customExecutor)return a.customExecutor(new lN(i,r,o));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,s);return n instanceof Promise?n.then(i=>[].concat(i)):[].concat(n)}class Ow{constructor(e={},t={},s={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=s,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const s=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(s))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return 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++;const 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(){for(const e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose();for(const e in this.tensorListMap)this.tensorListMap[e].clearAndClose()}}function kw(e,t,s){const n=new Set(),i=[];let r=null,o=null;const a=new Set(),l=Object.keys(e).map(p=>As(p)[0]),c=[...t];for(;c.length>0;){const p=c.pop();if((Ew(p)||nz(p))&&(r==null&&(r=p,o=r.children.map(u=>u.name).filter(u=>n.has(u)))),n.add(p.name),s[p.name]!=null)continue;if(l.indexOf(p.name)!==-1)continue;if(p.inputs.length===0){i.push(p.name);continue}p.inputs.forEach(u=>{if(a.has(u.name))return;a.add(u.name),c.push(u)})}return{inputs:e,outputs:t,usedNodes:n,missingInputs:i,dynamicNode:r,syncInputs:o}}function EN(e,t,s){const{usedNodes:n,inputs:i}=s,r=[],o=Object.keys(i).map(c=>As(c)[0]).map(c=>e.nodes[c]);o.forEach(c=>{n.has(c.name)&&r.push(c)}),e.weights.forEach(c=>{n.has(c.name)&&r.push(c)});const a=new Set(),l=[];for(;r.length>0;){const c=r.pop();a.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!a.has(p.name)&&n.has(p.name)&&p.inputs.every(u=>a.has(u.name))&&r.push(p)})}return l}const iz=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],rz=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function Ew(e){return iz.indexOf(e.op)>=0}function nz(e){return rz.indexOf(e.op)>=0}class _w{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._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(s=>{this._functionExecutorMap[s]=new _w(e.functions[s],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){const t=Object.keys(e).map(s=>e[s].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=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=>{const 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){const s=e.map(i=>i.name).sort(),n=t.map(i=>i.name).sort();return s.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){const s=kw(e,t,this.weightMap),{missingInputs:n,dynamicNode:i,syncInputs:r}=s;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${r}]`);if(n.length>0){const o=t.map(l=>l.name),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${a}]. Missing the following inputs: [${n}]`)}return EN(this.graph,this.weightMap,s)}execute(e,t){e=this.mapInputs(e);const s=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const n=s.map(c=>this.graph.nodes[As(c)[0]]),i=t.map(c=>this.graph.nodes[As(c)[0]]),r=this.getCompilationKey(n,i);let o=this.compiledMap.get(r);o==null&&(o=this.compile(e,i),this.compiledMap.set(r,o));const a={},l={};return N(()=>{const c=new Ow(this.weightMap,a,l,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(d=>{const[m,f]=As(d),y=[];y[f]=e[d],p[m]=y});const u=this.getFrozenTensorIds(p),h={};for(let d=0;d<o.length;d++){const m=o[d];if(!p[m.name]){const f=Rw(m,p,c);if(f instanceof Promise)throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=f,this.checkTensorForDisposal(m.name,m,p,c,u,t,h)}}return this.parent==null&&c.dispose(),t.map(d=>Vt(d,p,c))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(s=>e[s]).map(s=>s.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,s,n,i,r,o){if(t.category==="control"||r.indexOf(e)!==-1)return;s[e].forEach(a=>{a!=null&&(o[a.id]=(o[a.id]||0)+t.children.length)}),t.inputs.forEach(a=>{if(a.category!=="control"){const l=nN(a.name,s,n);l!=null&&l.forEach(c=>{if(c&&!i.has(c.id)){const p=o[c.id];p===1?(c.dispose(),delete o[c.id]):p!=null&&o[c.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,s=!1,n={},i={}){s||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const r=new Ow(this.weightMap,n,i,this.functionExecutorMap),o=await this.executeWithControlFlow(e,r,t,s),a=t.map(p=>Vt(p,o,r)),l=new Set(a.map(p=>p.id)),c=new Set(Object.keys(e).map(p=>e[p].id));return Object.keys(o).forEach(p=>{const u=o[p];u.forEach(h=>{h&&!h.isDisposed&&!l.has(h.id)&&!c.has(h.id)&&this.weightIds.indexOf(h.id)===-1&&h.dispose()})}),this.parent==null&&r.dispose(),a}async executeFunctionAsync(e,t,s){const n=e.reduce((i,r,o)=>(i[this.inputs[o].name]=r,i),{});return this._executeAsync(n,this.outputNodes,!0,t,s)}async executeWithControlFlow(e,t,s,n){const i=Object.keys(e),r=i.map(b=>this.graph.nodes[As(b)[0]]),o=s.map(b=>this.graph.nodes[As(b)[0]]),{usedNodes:a,missingInputs:l,dynamicNode:c,syncInputs:p}=kw(e,o,this.weightMap),u=[...r,...this.graph.weights].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{const[S,x]=As(b),I=[];I[x]=e[b],h[S]=I});const d={},m=this.getFrozenTensorIds(h),f={};for(;u.length>0;){const b=this.processStack(r,u,t,h,f,m,s,d,a);await Promise.all(b)}c==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=o.filter(b=>!Ew(b)&&!Vt(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw c!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${i}]. Consider providing the following inputs: [${l}]. ${b}`)}return h}processStack(e,t,s,n,i,r,o,a,l){const c=[];for(;t.length>0;){const p=t.pop();s.currentContext=p.contexts;let u="";if(p.node.op==="Enter"&&w("isConstant",p.node,n,s)&&([u]=$n(p.node.name,s)),e.indexOf(p.node)===-1){const h=Rw(p.node,n,s);u||([u]=$n(p.node.name,s));const d=s.currentContext;h instanceof Promise?c.push(h.then(m=>(n[u]=m,s.currentContext=d,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l),m))):(n[u]=h,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l))}else this.processChildNodes(p.node,t,s,n,i,l)}return c}processChildNodes(e,t,s,n,i,r){e.children.forEach(o=>{const[a]=$n(o.name,s);if(i[a]||!r.has(o.name))return;o.op==="Merge"?o.inputNames.some(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o})):o.inputNames.every(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const s=e[t],[n]=As(t),i=this.graph.nodes[n];if(i.attrParams.shape&&i.attrParams.shape.value){const r=i.attrParams.shape.value,o=r.length===s.shape.length&&s.shape.every((a,l)=>r[l]===-1||r[l]===a);E.assert(o,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${r}], but was [${s.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&E.assert(s.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${s.dtype}`)})}mapInputs(e){const t={};for(const s in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[s]!=null){const n=this._signature.inputs[s];t[n.name]=e[s]}else t[s]=e[s];return t}checkInputs(e){const t=Object.keys(e).filter(s=>{const[n]=As(s);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const s=this._signature.outputs[t];return s.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[s]=As(t);if(!this.graph.nodes[s])throw new Error(`The output '${t}' is not found in the graph`)})}}const oz="?tfjs-format=file",az="model.json";class Dw{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ot.browserHTTPRequest(e,this.loadOptions);else{const t=Ot.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ot.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let s={};this.artifacts.userDefinedMetadata!=null&&(s=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const n=Ot.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);return this.executor=new _w(rN.Instance.transformGraph(t,s)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),!0}async save(e,t){if(typeof e=="string"){const s=Ot.getSaveHandlers(e);if(s.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(s.length>1)throw new Error(`Found more than one (${s.length}) save handlers for URL '${e}'`);e=s[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 ue)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,s,n)=>(t[s]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=this.executor.execute(e,t);return s.length>1?s:s[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=await this.executor.executeAsync(e,t);return s.length>1?s:s[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,s)=>(t[s]=[e[s]],t),{})}dispose(){this.executor.dispose()}}async function kN(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${az}${oz}`));const s=new Dw(e,t);return await s.load(),s}const Zm="2.3.0";function _N(e,t){return Qm(e,t)}function Qm(e,t,s=new Map(),n=new Set()){if(e==null)return null;if(n.has(e))throw new Error("Circular references are not supported.");if(s.has(e))return s.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(br(e)){const r=Array.isArray(e)?[]:{};n.add(e);for(const o in e){const a=e[o],l=Qm(a,t,s,n);r[o]=l}return n.delete(e),r}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return s.set(e,i.value),i.value}function FN(e,t=Fw){return DN(e,t)}function DN(e,t,s=new Set()){const n=e[0];if(s.has(n))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(br(n)){const r=Array.isArray(n)?[]:{};s.add(n);for(const o in n){const a=e.map(c=>c[o]),l=DN(a,t,s);r[o]=l}return s.delete(n),r}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return i.value}function Fw(e){return e===null?null:br(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function ef(e,t){const s=new Map();Qm(e,t,s);for(const i of Array.from(s.keys())){const r=s.get(i);if(r instanceof Promise){const o=await r;s.set(i,o)}}const n=Qm(e,t,s);return n}function br(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof ue))}function MN(e){return e==null||lz(e)||Array.isArray(e)||typeof e=="object"&&e instanceof ue||E.isTypedArray(e)}function lz(e){return e===null||typeof e!="object"&&typeof e!="function"}function UN(e){return _N(e,cz)}function cz(e){return e instanceof ue?{value:e.clone(),recurse:!1}:br(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class tf{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),s=this.get(t);return this.set(t,this.pop()),s}}class sf extends tf{constructor(){super(sf.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),s=this.length();for(let n=0;n<s;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=s}}sf.INITIAL_CAPACITY=32;const $N=nl(il());function Mw(e){return new pz(e)}function Ou(e){return new uz(e)}function zN(e,t){return new WN(e,t)}function BN(e,t=zi.FAIL){return new hz(e,t)}class Tt{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let s=await e.next();for(;!s.done;)t.push(s.value),s=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(),s=e(t.value);for(;!t.done&&s;)t=await this.next(),s=e(t.value)}handleErrors(e){return new wz(this,e)}filter(e){return new yz(this,e)}map(e){return new bz(this,e)}mapAsync(e){return new PN(this,e)}serialMapAsync(e){return new PN(this,e).serial()}flatmap(e){return new xz(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 gz(this,e,t)}columnMajorBatch(e,t=!0,s=Fw){const n=this.rowMajorBatch(e,t);return n.map(i=>FN(i,s))}concatenate(e,t){return new WN(Mw([this,e]),t)}take(e){return e<0||e==null?this:new fz(this,e)}skip(e){return e<0||e==null?this:new mz(this,e)}prefetch(e){return new jN(this,e)}shuffle(e,t){return new Lz(this,e,t)}serial(){return new dz(this)}}class pz extends Tt{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};const e=this.items[this.trav];return this.trav++,{value:UN(e),done:!1}}}class uz extends Tt{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: ${e.message}`,e}}}class dz extends Tt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class mz extends Tt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;ce(e.value)}return this.upstream.next()}}class fz extends Tt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class gz extends Tt{constructor(e,t,s=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=s,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class yz extends Tt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ce(e.value)}}}class bz extends Tt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=rn.getTensorsInContainer(e.value),s=this.transform(e.value),n=rn.getTensorsInContainer(s);for(const i of t)rn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class wz extends Tt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}}class PN extends Tt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=rn.getTensorsInContainer(e.value),s=await this.transform(e.value),n=rn.getTensorsInContainer(s);for(const i of t)rn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class Eu extends Tt{constructor(){super();this.outputQueue=new sf(),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class xz extends Eu{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=rn.getTensorsInContainer(e.value),s=this.transform(e.value),n=rn.getTensorsInContainer(s);this.outputQueue.pushAll(s);for(const i of t)rn.isTensorInList(i,n)||i.dispose();return!0}}class WN extends Tt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const s=await this.moreIterators.next();if(s.done)return{value:null,done:!0};this.iterator=s.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var zi;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(zi||(zi={}));class hz extends Tt{constructor(e,t=zi.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,s=0;function n(r){if(r instanceof Tt){const o=r.next();return{value:o.then(a=>(t++,a.done&&s++,a.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await ef(this.iterators,n);if(t===s)return{value:null,done:!0};if(s>0)switch(this.mismatchMode){case zi.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case zi.SHORTEST:return{value:null,done:!0};case zi.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class jN extends Tt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new tf(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class Lz extends jN{constructor(e,t,s){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=$N.alea(s||E.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}const VN=nl(il());class Bi{constructor(){this.size=null}batch(e,t=!0){const s=this;E.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),Ns(async()=>(await s.iterator()).columnMajorBatch(e,t,Sz),n)}concatenate(e){const t=this;let s;return this.size===Infinity||e.size===Infinity?s=Infinity:this.size!=null&&e.size!=null?s=this.size+e.size:s=null,Ns(async()=>(await t.iterator()).concatenate(await e.iterator()),s)}filter(e){const t=this;let s;return this.size===Infinity?s=Infinity:s=null,Ns(async()=>(await t.iterator()).filter(n=>N(()=>e(n))),s)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return Ns(async()=>(await t.iterator()).map(s=>N(()=>e(s))),this.size)}mapAsync(e){const t=this;return Ns(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return Ns(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let s;return this.size!=null&&e>0?s=this.size*e:e===0?s=0:this.size!=null&&(e===void 0||e<0)?s=Infinity:s=null,Ns(async()=>{const n=Ou(async()=>({value:await t.iterator(),done:!1}));return zN(n.take(e))},s)}skip(e){const t=this;let s;return this.size!=null&&e>=0&&this.size>=e?s=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?s=0:s=null,Ns(async()=>(await t.iterator()).skip(e),s)}shuffle(e,t,s=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const n=this,i=VN.alea(t||E.now().toString());return Ns(async()=>{let r=i.int32();return s&&(r+=i.int32()),(await n.iterator()).shuffle(e,r.toString())},this.size)}take(e){const t=this;let s;return this.size!=null&&this.size>e?s=e:this.size!=null&&this.size<=e?s=this.size:s=null,Ns(async()=>(await t.iterator()).take(e),s)}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()}}Bi.MAX_BUFFER_SIZE=1e4;function Ns(e,t=null){return new class extends Bi{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}()}function GN(e){return Ns(async()=>Mw(e),e.length)}function HN(e){if(!br(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let s=0;s<e.length;s++)t=t==null?e[s].size:Math.min(t,e[s].size);else if(e instanceof Object)for(const s in e)t=t==null?e[s].size:Math.min(t,e[s].size);return Ns(async()=>{const s=await ef(e,n=>{if(n instanceof Bi)return{value:n.iterator(),recurse:!1};if(br(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return BN(s,zi.SHORTEST)},t)}function Sz(e){if(e===null)return null;const t=e[0];if(MN(t)){const s=vz(e);return{value:s,recurse:!1}}return{value:null,recurse:!0}}function vz(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ue?Xe(e):ze(e)}class nf extends Bi{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),s=t.split(`
`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n));return s}}const rf='"',ku=Symbol("out"),qN=Symbol("field"),of=Symbol("quote"),Uw=Symbol("quoteafterquote"),YN=Symbol("quoteinquote");class af extends Bi{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 nf(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(E.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(){const 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&&E.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);const t=this.fullColumnNames.reduce((n,i)=>(n[i]=n[i]+1||1,n),{}),s=Object.keys(t).filter(n=>t[n]>1);if(E.assert(s.length===0,()=>"Duplicate column names found: "+s.toString()),this.columnConfigs)for(const n of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(n);if(i===-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){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const s=t.value,n=this.parseRow(s,!1);return n}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){const t=this.parseRow(e),s={},n={};for(let i=0;i<this.fullColumnNames.length;i++){const r=this.fullColumnNames[i],o=this.columnConfigs?this.columnConfigs[r]:null;if(this.configuredColumnsOnly&&!o)continue;{const a=t[i];let l=null;if(a==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${r} is empty in this line: ${e}`);l=void 0}else{const c=Number(a);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(a):l=a;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(a);break;default:l=c}}o&&o.isLabel?n[r]=l:s[r]=l}}return Object.keys(n).length===0?s:{xs:s,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const s=[];let n=0;const i=e.length;let r=ku;for(let o=0;o<i;o++)switch(r){case ku:switch(e.charAt(o)){case rf:n=o+1,r=of;break;case this.delimiter:if(n=o+1,this.delimiter===" "&&this.delimWhitespace)break;s.push(""),r=ku;break;default:r=qN,n=o;break}break;case qN:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o)),r=ku,n=o+1;break;default:}break;case of:switch(e.charAt(o)){case rf:r=Uw;break;default:}break;case Uw:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o-1)),r=ku,n=o+1;break;case rf:r=of;break;default:r=YN;break}break;case YN:switch(e.charAt(o)){case rf:r=of;break;default:}break;default:}if(r===Uw?s.push(e.substring(n,i-1)):s.push(e.substring(n)),t&&s.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${s}`);return s}}class $w extends Tt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const 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 ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if($().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new $w(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(s){throw new Error(`Error thrown while initializing video stream: ${s.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const 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}`);const 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);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const s=await this.getAudioData();if(this.includeSpectrogram){const n=this.flattenQueue(s.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const n=this.flattenQueue(s.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(){const e=[],t=[];let s=0;return new Promise(n=>{const i=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())),++s===this.numFrames&&(clearInterval(i),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){const t=e[0].length,s=new Float32Array(e.length*t);return e.forEach((n,i)=>s.set(n,i*t)),s}getTensorFromAudioDataArray(e,t){const s=new Float32Array(E.sizeFromShape(t));return s.set(e,s.length-e.length),ze(s,t)}}class Ww extends Tt{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=Fe([0],"int32"),this.webcamConfig.centerCrop){const s=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-s)/2,r=(1-n)/2,o=i+s,a=n+r;this.cropBox=ts([r,i,a,o],[1,4])}else this.cropBox=ts([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if($().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}const s=new Ww(e,t);return await s.start(),s}async start(){this.webcamConfig.facingMode&&E.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=pr.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return N(()=>{const t=e.toFloat().expandDims(0);let s;s=ks.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const n=s.shape;return s.reshape(n.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class lf{}class zw extends Tt{split(e){return new Tz(this,e)}}class Tz extends zw{constructor(e,t){super();this.upstream=e,this.impl=new Iz(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Iz extends Eu{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const s of t.slice(0,-1))this.outputQueue.push(s);return this.carryover=t[t.length-1],!0}}class KN extends Tt{decodeUTF8(){return new Az(this)}}class Az extends zw{constructor(e){super();this.upstream=e,this.impl=new Nz(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Nz extends Eu{constructor(e){super();if(this.upstream=e,$().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=dL();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let s;return $().get("IS_BROWSER")?s=this.decoder.decode(t,{stream:!0}):s=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(s),!0}}class cf extends KN{constructor(e,t={}){super();this.file=e,this.options=t,E.assert(e instanceof Uint8Array||($().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 ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,s)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{const i=new FileReader();i.onload=o=>{let a=i.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return s(new TypeError("FileReader returned unknown type."));t(a)},i.onabort=o=>s(new Error("Aborted")),i.onerror=o=>s(new Error(o.type));const r=this.file.slice(this.offset,n);i.readAsArrayBuffer(r)}this.offset=n});return{value:await e,done:!1}}}async function XN(e,t={}){let s,n;typeof e=="string"?s=e:(s=e.url,n=Cz(e));const i=await E.fetch(s,n);if(i.ok){const r=new Uint8Array(await i.arrayBuffer());return new cf(r,t)}else throw new Error(i.statusText)}const Cz=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function pf(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class uf extends lf{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(pf(this.input)&&$().get("IS_NODE")){const e=mL();this.input=e.readFileSync(this.input.substr(7))}return new cf(this.input,this.options)}}class hf extends lf{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return pf(this.url)?new uf(this.url,this.fileOptions).iterator():XN(this.url,this.fileOptions)}}function JN(e,t={}){return new af(new hf(e),t)}function ZN(e){const t=Ou(e);return Ns(async()=>t)}function QN(e){return Ns(async()=>{const t=await e();return Ou(()=>t.next())})}async function eC(e,t){return Ww.create(e,t)}async function tC(e){return $w.create(e)}const df="2.3.0";const Bw={};Re(Bw,{CSVDataset:()=>af,Dataset:()=>Bi,FileDataSource:()=>uf,TextLineDataset:()=>nf,URLDataSource:()=>hf,array:()=>GN,csv:()=>JN,func:()=>ZN,generator:()=>QN,microphone:()=>tC,version_data:()=>df,webcam:()=>eC,zip:()=>HN});function H(e,t){Array.isArray(e)||(e=[e]),e.forEach(s=>{s!=null&&E.assert(s.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}function _u(e,t,s,n,i,r){const o=i.strideHeight,a=i.strideWidth,l=i.dilationHeight,c=i.dilationWidth,p=i.effectiveFilterHeight,u=i.effectiveFilterWidth,h=i.padInfo.top,d=i.padInfo.left,m=r==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=le(i.outShape,s),y=f.values,b=i.outShape[1]*i.outShape[2]*i.outShape[3],S=i.outShape[2]*i.outShape[3],x=i.outShape[3];for(let I=0;I<i.batchSize;++I){const A=I*b,k=I*n[0];for(let R=0;R<i.inChannels;++R)for(let D=0;D<i.outHeight;++D){const F=D*o-h,M=Math.max(0,F),B=Math.min(i.inHeight,p+F),V=A+D*S;for(let Y=0;Y<i.outWidth;++Y){const G=Y*a-d,Z=Math.max(0,G),q=Math.min(i.inWidth,u+G);let ee=m,Q=0,ie=0;for(let de=M;de<B;de+=l){const pe=k+de*n[1];for(let me=Z;me<q;me+=c){const we=pe+me*n[2],$e=e[we+R];r==="max"&&$e>ee?ee=$e:r==="avg"&&(Q+=$e,ie++)}if(isNaN(ee))break}const re=V+Y*x+R;y[re]=r==="avg"?Q/ie:ee}}}return f}function mf(e,t,s,n,i=!1,r=!1){const o=le(n.outShape,"int32"),a=n.strideHeight,l=n.strideWidth,c=n.dilationHeight,p=n.dilationWidth,u=n.effectiveFilterHeight,h=n.effectiveFilterWidth,d=n.padInfo.top,m=n.padInfo.left,f=le(t,s,e);for(let y=0;y<n.batchSize;++y)for(let b=0;b<n.inChannels;++b)for(let S=0;S<n.outHeight;++S){const x=S*a-d;let I=x;for(;I<0;)I+=c;const A=Math.min(n.inHeight,u+x);for(let k=0;k<n.outWidth;++k){const R=k*l-m;let D=R;for(;D<0;)D+=p;const F=Math.min(n.inWidth,h+R);let M=Number.NEGATIVE_INFINITY,B=-1;for(let V=I;V<A;V+=c){const Y=V-x;for(let G=D;G<F;G+=p){const Z=G-R,q=f.get(y,V,G,b);q>M&&(M=q,i?B=r?((y*n.inHeight+V)*n.inWidth+G)*n.inChannels+b:(V*n.inWidth+G)*n.inChannels+b:B=Y*h+Z)}}o.set(B,y,S,k,b)}}return o}const sC=nl(il());const Rz=St.nonMaxSuppressionV3Impl,Oz=St.split,Ez=St.tile,kz=St.topkImpl,_z=St.whereImpl;function Pw(e,t,s,n){if(s==="linear")return e.linear(t);if(s==="relu")return e.relu(t);if(s==="elu")return e.elu(t);if(s==="relu6")return e.relu6(t);if(s==="prelu")return e.prelu(t,n);throw new Error(`Activation ${s} has not been implemented for the CPU backend.`)}class jw extends Jp{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Xp(this,os())}write(e,t,s){this.firstUse&&(this.firstUse=!1,$().get("IS_NODE")&&U.warn(`
============================
Hi there 👋. 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.
============================`));const n={};return this.data.set(n,{values:e,dtype:s}),n}move(e,t,s,n){this.data.set(e,{values:t,dtype:n})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensors:s}=this.data.get(e);if(t==="complex64"){const n=this.readSync(s.real.dataId),i=this.readSync(s.imag.dataId);return U.mergeRealAndImagArrays(n,i)}return this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let s=t;if(e.dtype==="string")try{s=t.map(n=>E.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,s)}makeOutput(e,t,s){const n=this.write(e,t,s);return os().makeTensorFromDataId(n,t,s,this)}disposeData(e){if(this.data.has(e)){const{complexTensors:t}=this.data.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e)}}async time(e){const t=E.now();e();const s=E.now()-t;return{kernelMs:s}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}complex(e,t){const s=this.makeOutput(null,e.shape,"complex64"),n=this.data.get(s.dataId);return n.complexTensors={real:os().keep(e.clone()),imag:os().keep(t.clone())},s}real(e){const t=this.data.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.data.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,s){H(e,"slice");const n=Jn.isSliceContinous(e.shape,t,s);if(n){const o=Jn.computeFlatOffset(t,e.strides),a=E.sizeFromShape(s),l=this.readSync(e.dataId);return ze(l.subarray(o,o+a),s,e.dtype)}const i=le(s,e.dtype),r=this.bufferSync(e);for(let o=0;o<i.size;++o){const a=i.indexToLoc(o),l=a.map((c,p)=>c+t[p]);i.values[o]=r.get(...l)}return i.toTensor()}stridedSlice(e,t,s,n){H(e,"stridedSlice");const i=Jn.computeOutShape(t,s,n);if(i.some(a=>a===0))return ze([],i);const r=le(i,e.dtype),o=this.bufferSync(e);for(let a=0;a<r.size;a++){const l=r.indexToLoc(a),c=new Array(l.length);for(let p=0;p<c.length;p++)c[p]=l[p]*n[p]+t[p];r.set(o.get(...c),...l)}return r.toTensor()}diag(e){const t=this.readSync(e.dataId),s=le([e.size,e.size],e.dtype),n=s.values;for(let i=0;i<t.length;i++)n[i*e.size+i]=t[i];return s.toTensor()}unstack(e,t){const s=e.shape[t],n=new Array(e.rank-1);let i=0;for(let l=0;l<e.rank;l++)l!==t&&(n[i++]=e.shape[l]);const r=new Array(e.rank).fill(0),o=e.shape.slice();o[t]=1;const a=new Array(s);for(let l=0;l<a.length;l++)r[t]=l,a[l]=this.slice(e,r,o).reshape(n);return a}reverse(e,t){H(e,"reverse");const s=le(e.shape,e.dtype),n=this.bufferSync(e);for(let i=0;i<s.size;i++){const r=s.indexToLoc(i),o=r.slice();t.forEach(a=>o[a]=e.shape[a]-1-o[a]),s.set(n.get(...o),...r)}return s.toTensor()}concat(e,t){if(e[0].dtype==="complex64"){const o=e.map(l=>$t(l)),a=e.map(l=>Ht(l));return Ze(this.concat(o,t),this.concat(a,t))}const s=e.map(o=>{const a=E.sizeFromShape(o.shape.slice(t));return o.as2D(-1,a)}),n=U.computeOutShape(s.map(o=>o.shape),1),i=le(n,e[0].dtype).values;if(s[0].shape[0]===1){let o=0;s.forEach(a=>{i.set(this.readSync(a.dataId),o),o+=a.size})}else{let o=0;s.forEach(a=>{const l=this.readSync(a.dataId);let c=0;for(let p=0;p<a.shape[0];++p){const u=p*n[1]+o;for(let h=0;h<a.shape[1];++h)i[u+h]=l[c++]}o+=a.shape[1]})}const r=U.computeOutShape(e.map(o=>o.shape),t);return ze(i,r,e[0].dtype)}neg(e){return H(e,"neg"),this.multiply(j(-1),e)}add(e,t){return e.dtype==="complex64"||t.dtype==="complex64"?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),(s,n,i,r)=>({real:s+i,imag:n+r})):this.broadcastedBinaryOp(e,t,wt(e.dtype,t.dtype),(s,n)=>s+n)}addN(e){H(e,"addN");const t=e.map(i=>this.readSync(i.dataId)),s=le(e[0].shape,e[0].dtype),n=s.values;for(let i=0;i<e.length;i++){const r=t[i];for(let o=0;o<n.length;o++)n[o]+=r[o]}return s.toTensor()}softmax(e,t){const s=E.parseAxisParam([t],e.shape),n=st(e,s),i=U.expandShapeToKeepDim(n.shape,s),r=this.subtract(e,n.reshape(i)),o=this.exp(r),a=this.sum(o,s).reshape(i);return J(o,a)}subtract(e,t){return e.dtype==="complex64"||t.dtype==="complex64"?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),(s,n,i,r)=>({real:s-i,imag:n-r})):this.broadcastedBinaryOp(e,t,wt(e.dtype,t.dtype),(s,n)=>s-n)}pow(e,t){return H([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.pow(s,n))}batchMatMul(e,t,s,n){H([e,t],"matMul");const i=s?e.shape[1]:e.shape[2],r=s?e.shape[2]:e.shape[1],o=n?t.shape[1]:t.shape[2],a=e.shape[0],l=this.readSync(e.dataId),c=this.readSync(t.dataId),[p,u,h]=s?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],[d,m,f]=n?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],y=r*o,b=le([a,r,o],e.dtype),S=b.values,x=this.blockSize;for(let I=0;I<a;I++)for(let A=0;A<r;A+=x)for(let k=0;k<o;k+=x)for(let R=0;R<i;R+=x){const D=Math.min(A+x,r),F=Math.min(k+x,o),M=Math.min(R+x,i);for(let B=A;B<D;B++)for(let V=k;V<F;V++){let Y=0;for(let G=R;G<M;G++)Y+=l[I*p+B*u+G*h]*c[G*d+V*m+I*f];S[I*y+(B*o+V)]+=Y}}return b.toTensor()}fusedBatchMatMul({a:e,b:t,transposeA:s,transposeB:n,bias:i,activation:r,preluActivationWeights:o}){let a=this.batchMatMul(e,t,s,n);return i&&(a=this.add(a,i)),r&&(a=Pw(this,a,r,o)),a}multiply(e,t){return e.dtype==="complex64"||t.dtype==="complex64"?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),(s,n,i,r)=>({real:s*i-n*r,imag:s*r+n*i})):this.broadcastedBinaryOp(e,t,wt(e.dtype,t.dtype),(s,n)=>s*n)}floorDiv(e,t){H([e,t],"floorDiv");const s=(i,r)=>Math.floor(i/r),n="int32";return this.broadcastedBinaryOp(e,t,n,s)}sum(e,t){H(e,"sum"),U.assertAxesAreInnerMostDims("sum",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=wt(e.dtype,"int32"),r=Ie(s,i),o=E.sizeFromShape(n),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=0;for(let h=0;h<o;++h)u+=l[p+h];a[c]=u}return r}prod(e,t){H(e,"sum");const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=wt(e.dtype,"int32"),r=Ie(s,i),o=E.sizeFromShape(n),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=1;for(let h=0;h<o;++h)u*=l[p+h];a[c]=u}return r}unsortedSegmentSum(e,t,s){H(e,"unsortedSegmentSum");const n=[],i=e.rank-t.rank;for(let r=0;r<i;++r)t=t.expandDims(r+1);for(let r=0;r<s;++r){const o=j(r,"int32"),a=Qt(o,t).asType("float32"),l=a.mul(e).sum(0);n.push(l)}return Xe(n)}argMin(e,t){H(e,"argMin");const s=[t];U.assertAxesAreInnerMostDims("argMin",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=Ie(n,"int32"),o=E.sizeFromShape(i),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=l[p],h=0;for(let d=0;d<o;++d){const m=l[p+d];m<u&&(u=m,h=d)}a[c]=h}return r}argMax(e,t){H(e,"argMax");const s=[t];U.assertAxesAreInnerMostDims("argMax",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=Ie(n,"int32"),o=E.sizeFromShape(i),a=this.readSync(r.dataId),l=this.readSync(e.dataId);for(let c=0;c<a.length;++c){const p=c*o;let u=l[p],h=0;for(let d=0;d<o;++d){const m=l[p+d];m>u&&(u=m,h=d)}a[c]=h}return r}cumsum(e,t,s,n){if(H(e,"cumsum"),t!==e.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=wt(e.dtype,"int32"),r=Ie(e.shape,i),o=this.readSync(r.dataId),a=this.readSync(e.dataId),l=e.shape[e.rank-1],c=n?(p,u)=>p+l-u-1:(p,u)=>p+u;for(let p=0;p<a.length;p+=l)for(let u=0;u<l;u++){const h=c(p,u);if(u===0)o[h]=s?0:a[h];else{const d=c(p,u-1);o[h]=s?a[d]+o[d]:a[h]+o[d]}}return r}equal(e,t){return H([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s===n?1:0)}notEqual(e,t){return H([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s!==n?1:0)}less(e,t){return H([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<n?1:0)}lessEqual(e,t){return H([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<=n?1:0)}greater(e,t){return H([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>n?1:0)}greaterEqual(e,t){return H([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>=n?1:0)}logicalNot(e){H(e,"logicalNot");const t=this.readSync(e.dataId),s=new Uint8Array(t.length);for(let n=0;n<t.length;++n)s[n]=t[n]?0:1;return this.makeOutput(s,e.shape,"bool")}logicalAnd(e,t){return H([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s&&n)}logicalOr(e,t){return H([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s||n)}select(e,t,s){H([e,t,s],"select");const n=this.readSync(e.dataId),i=this.readSync(t.dataId),r=this.readSync(s.dataId),o=Ie(t.shape,wt(t.dtype,s.dtype)),a=this.readSync(o.dataId);let l=0;const c=e.rank===0||e.rank>1||t.rank===1?1:E.sizeFromShape(t.shape.slice(1));for(let p=0;p<n.length;p++)for(let u=0;u<c;u++)n[p]===1?a[l++]=i[p]:a[l++]=r[p];return o}where(e){H([e],"where");const t=this.readSync(e.dataId);return _z(e.shape,t)}topk(e,t,s){H(e,"topk");const n=this.readSync(e.dataId);return kz(n,e.shape,e.dtype,t,s)}min(e,t){H(e,"min"),U.assertAxesAreInnerMostDims("min",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ie(s,e.dtype),r=E.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];h<p&&(p=h)}o[l]=p}return i}minimum(e,t){return H([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.min(s,n))}mod(e,t){return H([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>{const i=s%n;return s<0&&n<0||s>=0&&n>=0?i:(i+n)%n})}maximum(e,t){return H([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.max(s,n))}all(e,t){H(e,"all"),U.assertAxesAreInnerMostDims("all",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ie(s,e.dtype),r=E.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];p=p&&h}o[l]=p}return i}any(e,t){H(e,"any"),U.assertAxesAreInnerMostDims("any",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ie(s,e.dtype),r=E.sizeFromShape(n),o=this.readSync(i.dataId),a=this.readSync(e.dataId);for(let l=0;l<o.length;++l){const c=l*r;let p=a[c];for(let u=0;u<r;++u){const h=a[c+u];p=p||h}o[l]=p}return i}squaredDifference(e,t){return H([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>{const i=s-n;return i*i})}ceil(e){H(e,"ceil");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)s[n]=Math.ceil(t[n]);return this.makeOutput(s,e.shape,"float32")}floor(e){H(e,"floor");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)s[n]=Math.floor(t[n]);return this.makeOutput(s,e.shape,"float32")}sign(e){H(e,"x");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)t[n]<0?s[n]=-1:t[n]>0?s[n]=1:s[n]=0;return this.makeOutput(s,e.shape,"float32")}isNaN(e){H(e,"x");const t=this.readSync(e.dataId),s=new Uint8Array(t.length);for(let n=0;n<t.length;++n)Number.isNaN(t[n])&&(s[n]=1);return this.makeOutput(s,e.shape,"bool")}isInf(e){H(e,"x");const t=this.readSync(e.dataId),s=new Uint8Array(t.length);for(let n=0;n<t.length;++n)Math.abs(t[n])===Infinity&&(s[n]=1);return this.makeOutput(s,e.shape,"bool")}isFinite(e){H(e,"x");const t=this.readSync(e.dataId),s=new Uint8Array(t.length);for(let n=0;n<t.length;++n)Number.isFinite(t[n])&&(s[n]=1);return this.makeOutput(s,e.shape,"bool")}round(e){H(e,"round");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n){const i=Math.floor(t[n]);t[n]-i<.5?s[n]=Math.floor(t[n]):t[n]-i>.5?s[n]=Math.ceil(t[n]):i%2===0?s[n]=i:s[n]=i+1}return this.makeOutput(s,e.shape,"float32")}exp(e){H(e,"exp");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)s[n]=Math.exp(t[n]);return this.makeOutput(s,e.shape,"float32")}expm1(e){H(e,"expm1");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)s[n]=Math.expm1(t[n]);return this.makeOutput(s,e.shape,"float32")}log(e){H(e,"log");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n){const i=t[n];s[n]=Math.log(i)}return this.makeOutput(s,e.shape,"float32")}log1p(e){H(e,"log1p");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n){const i=t[n];s[n]=Math.log1p(i)}return this.makeOutput(s,e.shape,"float32")}sqrt(e){H(e,"sqrt");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n){const i=t[n];s[n]=Math.sqrt(i)}return this.makeOutput(s,e.shape,"float32")}rsqrt(e){H(e,"rsqrt");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n){const i=t[n];s[n]=1/Math.sqrt(i)}return this.makeOutput(s,e.shape,"float32")}reciprocal(e){H(e,"reciprocal");const t=this.readSync(e.dataId),s=new Float32Array(t.length);for(let n=0;n<t.length;++n)s[n]=1/t[n];return this.makeOutput(s,e.shape,"float32")}linear(e){return e}relu(e){H(e,"relu");const t=Ie(e.shape,e.dtype),s=this.readSync(t.dataId),n=this.readSync(e.dataId);for(let i=0;i<n.length;++i)s[i]=Math.max(0,n[i]);return t}relu6(e){H(e,"relu");const t=Ie(e.shape,e.dtype),s=this.readSync(t.dataId),n=this.readSync(e.dataId);for(let i=0;i<n.length;++i)s[i]=Math.min(Math.max(0,n[i]),6);return t}prelu(e,t){return H([e,t],"prelu"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>s<0?n*s:s)}elu(e){H(e,"elu");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n){const i=s[n];i>=0?t[n]=i:t[n]=Math.exp(i)-1}return this.makeOutput(t,e.shape,"float32")}eluDer(e,t){H([e,t],"eluDer");const s=new Float32Array(t.size),n=this.readSync(t.dataId),i=this.readSync(e.dataId);for(let r=0;r<n.length;++r){const o=n[r];o>=1?s[r]=i[r]:s[r]=i[r]*(o+1)}return this.makeOutput(s,t.shape,"float32")}selu(e){H(e,"selu");const t=U.SELU_SCALEALPHA,s=U.SELU_SCALE,n=new Float32Array(e.size),i=this.readSync(e.dataId);for(let r=0;r<i.length;++r){const o=i[r];o>=0?n[r]=s*o:n[r]=t*(Math.exp(o)-1)}return this.makeOutput(n,e.shape,"float32")}clip(e,t,s){H(e,"clip");const n=new Float32Array(e.size),i=this.readSync(e.dataId);for(let r=0;r<i.length;++r){const o=i[r];n[r]=o>s?s:o<t?t:o}return this.makeOutput(n,e.shape,"float32")}abs(e){const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.abs(s[n]);return this.makeOutput(t,e.shape,"float32")}complexAbs(e){const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<e.size;++n){const i=s[n*2],r=s[n*2+1];t[n]=Math.hypot(i,r)}return this.makeOutput(t,e.shape,"float32")}int(e){H(e,"int");const t=new Int32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=s[n];return this.makeOutput(t,e.shape,"int32")}sigmoid(e){H(e,"sigmoid");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=1/(1+Math.exp(-s[n]));return this.makeOutput(t,e.shape,"float32")}softplus(e){H(e,"softplus");const t=11920928955078125e-23,s=Math.log(t)+2,n=new Float32Array(e.size),i=this.readSync(e.dataId);for(let r=0;r<i.length;++r){const o=i[r]>-s,a=i[r]<s,l=Math.exp(i[r]);let c;a?c=l:o?c=i[r]:c=Math.log(1+l),n[r]=c}return this.makeOutput(n,e.shape,"float32")}sin(e){H(e,"sin");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.sin(s[n]);return this.makeOutput(t,e.shape,"float32")}cos(e){H(e,"cos");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.cos(s[n]);return this.makeOutput(t,e.shape,"float32")}tan(e){H(e,"tan");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.tan(s[n]);return this.makeOutput(t,e.shape,"float32")}asin(e){H(e,"asin");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.asin(s[n]);return this.makeOutput(t,e.shape,"float32")}acos(e){H(e,"acos");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.acos(s[n]);return this.makeOutput(t,e.shape,"float32")}atan(e){H(e,"atan");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.atan(s[n]);return this.makeOutput(t,e.shape,"float32")}atan2(e,t){return H([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.atan2(s,n))}sinh(e){H(e,"sinh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.sinh(s[n]);return this.makeOutput(t,e.shape,"float32")}cosh(e){H(e,"cosh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.cosh(s[n]);return this.makeOutput(t,e.shape,"float32")}tanh(e){H(e,"tanh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=E.tanh(s[n]);return this.makeOutput(t,e.shape,"float32")}asinh(e){H(e,"asinh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.asinh(s[n]);return this.makeOutput(t,e.shape,"float32")}acosh(e){H(e,"acosh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.acosh(s[n]);return this.makeOutput(t,e.shape,"float32")}atanh(e){H(e,"atanh");const t=new Float32Array(e.size),s=this.readSync(e.dataId);for(let n=0;n<s.length;++n)t[n]=Math.atanh(s[n]);return this.makeOutput(t,e.shape,"float32")}erf(e){H(e,"erf");const t=new Float32Array(e.size),s=this.readSync(e.dataId),n=U.ERF_P,i=U.ERF_A1,r=U.ERF_A2,o=U.ERF_A3,a=U.ERF_A4,l=U.ERF_A5;for(let c=0;c<s.length;++c){const p=Math.sign(s[c]),u=Math.abs(s[c]),h=1/(1+n*u);t[c]=p*(1-((((l*h+a)*h+o)*h+r)*h+i)*h*Math.exp(-u*u))}return this.makeOutput(t,e.shape,"float32")}step(e,t=0){H(e,"step");const s=new Float32Array(e.size),n=this.readSync(e.dataId);for(let i=0;i<n.length;++i){const r=n[i];isNaN(r)?s[i]=NaN:s[i]=r>0?1:t}return this.makeOutput(s,e.shape,"float32")}fusedConv2d({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){let o=this.conv2d(e,t,s);return n&&(o=this.add(o,n)),i&&(o=Pw(this,o,i,r)),o}conv2d(e,t,s){H([e,t],"conv2d");const n=s.filterHeight,i=s.filterWidth,r=s.dilationHeight,o=s.dilationWidth,a=s.padInfo.left,l=s.padInfo.top,c=s.dataFormat==="channelsLast",p=le(s.outShape,e.dtype),u=e.strides[0],h=c?e.strides[1]:e.strides[2],d=c?e.strides[2]:1,m=c?1:e.strides[1],f=p.strides[0],y=c?p.strides[1]:p.strides[2],b=c?p.strides[2]:1,S=c?1:p.strides[1],x=this.readSync(e.dataId),I=this.readSync(t.dataId),A=p.values;for(let k=0;k<s.batchSize;++k){const R=k*u,D=k*f;for(let F=0;F<s.outHeight;++F){const M=D+F*y,B=F*s.strideHeight-l;for(let V=0;V<n;V++){const Y=B+V*r;if(Y<0||Y>=s.inHeight)continue;const G=V*t.strides[0],Z=R+Y*h;for(let q=0;q<s.outWidth;++q){const ee=M+q*b,Q=q*s.strideWidth-a;for(let ie=0;ie<i;ie++){const re=Q+ie*o;if(re<0||re>=s.inWidth)continue;const de=G+ie*t.strides[1],pe=Z+re*d;let me=de;for(let we=0;we<s.inChannels;++we){const $e=x[pe+we*m];for(let Pe=0;Pe<s.outChannels;++Pe)A[ee+Pe*S]+=$e*I[me+Pe];me+=s.outChannels}}}}}}return p.toTensor()}conv3d(e,t,s){const n=s.filterDepth,i=s.filterHeight,r=s.filterWidth,o=s.dilationDepth,a=s.dilationHeight,l=s.dilationWidth,c=s.padInfo.front,p=s.padInfo.left,u=s.padInfo.top,h=le(s.outShape,e.dtype),d=this.readSync(e.dataId),m=this.readSync(t.dataId),f=h.values;for(let y=0;y<s.batchSize;++y){const b=y*e.strides[0],S=y*h.strides[0];for(let x=0;x<s.outDepth;++x){const I=S+x*h.strides[1],A=x*s.strideDepth-c;for(let k=0;k<n;k++){const R=A+k*o;if(R<0||R>=s.inDepth)continue;const D=k*t.strides[0],F=b+R*e.strides[1];for(let M=0;M<s.outHeight;++M){const B=I+M*h.strides[2],V=M*s.strideHeight-u;for(let Y=0;Y<i;Y++){const G=V+Y*a;if(G<0||G>=s.inHeight)continue;const Z=D+Y*t.strides[1],q=F+G*e.strides[2];for(let ee=0;ee<s.outWidth;++ee){const Q=B+ee*s.outChannels,ie=ee*s.strideWidth-p;for(let re=0;re<r;re++){const de=ie+re*l;if(de<0||de>=s.inWidth)continue;const pe=Z+re*t.strides[2],me=q+de*s.inChannels;let we=pe;for(let $e=0;$e<s.inChannels;++$e){const Pe=d[me+$e];for(let ct=0;ct<s.outChannels;++ct)f[Q+ct]+=Pe*m[we+ct];we+=s.outChannels}}}}}}}}return h.toTensor()}conv2dDerInput(e,t,s){H([e,t],"conv2dDerInput");const n=le(s.inShape,"float32"),i=n.values,r=this.readSync(e.dataId),o=this.readSync(t.dataId),[a,l,c]=t.strides,{batchSize:p,filterHeight:u,filterWidth:h,inChannels:d,inHeight:m,inWidth:f,outChannels:y,outHeight:b,outWidth:S,strideHeight:x,strideWidth:I,dataFormat:A}=s,k=u-1-s.padInfo.top,R=h-1-s.padInfo.left,D=A==="channelsLast",F=n.strides[0],M=D?n.strides[1]:n.strides[2],B=D?n.strides[2]:1,V=D?1:n.strides[1],Y=e.strides[0],G=D?e.strides[1]:e.strides[2],Z=D?e.strides[2]:1,q=D?1:e.strides[1];for(let ee=0;ee<p;++ee)for(let Q=0;Q<d;++Q)for(let ie=0;ie<m;++ie){const re=ie-k,de=Math.max(0,Math.ceil(re/x)),pe=Math.min(b,(u+re)/x);for(let me=0;me<f;++me){const we=me-R,$e=Math.max(0,Math.ceil(we/I)),Pe=Math.min(S,(h+we)/I);let ct=0;for(let Rs=de;Rs<pe;++Rs){const xn=Rs*x-re;for(let kt=$e;kt<Pe;++kt){const Us=kt*I-we,ci=Y*ee+G*Rs+Z*kt,Ln=a*(u-1-xn)+l*(h-1-Us)+c*Q;for(let Os=0;Os<y;++Os){const tn=r[ci+q*Os],sl=o[Ln+Os];ct+=tn*sl}}}const Jt=F*ee+M*ie+B*me+V*Q;i[Jt]=ct}}return n.toTensor()}conv3dDerInput(e,t,s){const n=le(s.inShape,"float32"),i=n.values,[r,o,a,l]=n.strides,c=this.readSync(e.dataId),[p,u,h,d]=e.strides,m=this.readSync(t.dataId),[f,y,b,S]=t.strides,{batchSize:x,filterDepth:I,filterHeight:A,filterWidth:k,inChannels:R,inDepth:D,inHeight:F,inWidth:M,outChannels:B,outDepth:V,outHeight:Y,outWidth:G,strideDepth:Z,strideHeight:q,strideWidth:ee}=s,Q=I-1-s.padInfo.front,ie=A-1-s.padInfo.top,re=k-1-s.padInfo.left;for(let de=0;de<x;++de)for(let pe=0;pe<R;++pe)for(let me=0;me<D;++me){const we=me-Q,$e=Math.max(0,Math.ceil(we/Z)),Pe=Math.min(V,(I+we)/Z);for(let ct=0;ct<F;++ct){const Jt=ct-ie,Rs=Math.max(0,Math.ceil(Jt/q)),xn=Math.min(Y,(A+Jt)/q);for(let kt=0;kt<M;++kt){const Us=kt-re,ci=Math.max(0,Math.ceil(Us/ee)),Ln=Math.min(G,(k+Us)/ee);let Os=0;for(let tn=$e;tn<Pe;++tn){const sl=tn*Z-we;for(let Bo=Rs;Bo<xn;++Bo){const Po=Bo*q-Jt;for(let jo=ci;jo<Ln;++jo){const Jf=jo*ee-Us,Zf=p*de+u*tn+h*Bo+d*jo,MO=f*(I-1-sl)+y*(A-1-Po)+b*(k-1-Jf)+S*pe;for(let qu=0;qu<B;++qu){const UO=c[Zf+qu],$O=m[MO+qu];Os+=UO*$O}}}}i[r*de+o*me+a*ct+l*kt+pe]=Os}}}return n.toTensor()}conv2dDerFilter(e,t,s){H([e,t],"conv2dDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=s.dataFormat==="channelsLast",l=le(s.filterShape,"float32"),c=s.padInfo.left,p=s.padInfo.top,u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<r;++d){const m=Math.max(0,Math.ceil((p-d)/n)),f=Math.min(s.outHeight,(s.inHeight+p-d)/n);for(let y=0;y<o;++y){const b=Math.max(0,Math.ceil((c-y)/i)),S=Math.min(s.outWidth,(s.inWidth+c-y)/i);for(let x=0;x<s.inChannels;++x)for(let I=0;I<s.outChannels;++I){let A=0;for(let k=0;k<s.batchSize;++k)for(let R=m;R<f;++R){const D=d+R*n-p;for(let F=b;F<S;++F){const M=y+F*i-c;a?A+=u.get(k,D,M,x)*h.get(k,R,F,I):A+=u.get(k,x,D,M)*h.get(k,I,R,F)}}l.set(A,d,y,x,I)}}}return l.toTensor()}conv3dDerFilter(e,t,s){const n=s.strideDepth,i=s.strideHeight,r=s.strideWidth,o=s.filterDepth,a=s.filterHeight,l=s.filterWidth,c=le(s.filterShape,"float32"),p=c.values,[u,h,d,m]=c.strides,f=this.readSync(t.dataId),[y,b,S,x]=t.strides,I=this.readSync(e.dataId),[A,k,R,D]=e.strides,F=s.padInfo.front,M=s.padInfo.left,B=s.padInfo.top;for(let V=0;V<o;++V){const Y=Math.max(0,Math.ceil((F-V)/n)),G=Math.min(s.outDepth,(s.inDepth+F-V)/n),Z=V*u;for(let q=0;q<a;++q){const ee=Math.max(0,Math.ceil((B-q)/i)),Q=Math.min(s.outHeight,(s.inHeight+B-q)/i),ie=q*h+Z;for(let re=0;re<l;++re){const de=Math.max(0,Math.ceil((M-re)/r)),pe=Math.min(s.outWidth,(s.inWidth+M-re)/r),me=re*d+ie;for(let we=0;we<s.inChannels;++we){const $e=we*m+me;for(let Pe=0;Pe<s.outChannels;++Pe){let ct=0;for(let Jt=0;Jt<s.batchSize;++Jt){const Rs=Jt*A,xn=Jt*y;for(let kt=Y;kt<G;++kt){const Us=V+kt*n-F,ci=Us*k+Rs,Ln=kt*b+xn;for(let Os=ee;Os<Q;++Os){const tn=q+Os*i-B,sl=tn*R+ci,Bo=Os*S+Ln;for(let Po=de;Po<pe;++Po){const jo=re+Po*r-M,Jf=jo*D+sl,Zf=Po*x+Bo;ct+=I[Jf+we]*f[Zf+Pe]}}}}p[$e+Pe]=ct}}}}}return c.toTensor()}fusedDepthwiseConv2D({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){let o=this.depthwiseConv2D(e,t,s);return n&&(o=this.add(o,n)),i&&(o=Pw(this,o,i,r)),o}depthwiseConv2D(e,t,s){H([e,t],"depthwiseConv2D");const n=s.filterHeight,i=s.filterWidth,r=s.dilationHeight,o=s.dilationWidth,a=s.padInfo.left,l=s.padInfo.top,c=s.outChannels/s.inChannels,p=le(s.outShape,e.dtype),u=this.readSync(e.dataId),h=this.readSync(t.dataId),d=p.values;for(let m=0;m<s.batchSize;++m){const f=m*e.strides[0],y=m*p.strides[0];for(let b=0;b<s.outHeight;++b){const S=y+b*p.strides[1],x=b*s.strideHeight-a;for(let I=0;I<n;++I){const A=x+I*r;if(A<0||A>=s.inHeight)continue;const k=I*t.strides[0],R=f+A*e.strides[1];for(let D=0;D<s.outWidth;++D){const F=S+D*p.strides[2],M=D*s.strideWidth-l;for(let B=0;B<i;++B){const V=M+B*o;if(V<0||V>=s.inWidth)continue;const Y=k+B*t.strides[1],G=R+V*s.inChannels;let Z=F,q=Y;for(let ee=0;ee<s.inChannels;++ee){const Q=u[G+ee];for(let ie=0;ie<c;++ie)d[Z+ie]+=Q*h[q+ie];Z+=c,q+=c}}}}}}return p.toTensor()}depthwiseConv2DDerInput(e,t,s){H([e,t],"depthwiseConv2DDerInput");const n=le(s.inShape,"float32"),i=n.values,[r,o,a]=n.strides,l=this.readSync(e.dataId),[c,p,u]=e.strides,h=this.readSync(t.dataId),[d,m,f]=t.strides,{batchSize:y,filterHeight:b,filterWidth:S,inChannels:x,inHeight:I,inWidth:A,outChannels:k,outHeight:R,outWidth:D,strideHeight:F,strideWidth:M}=s,B=b-1-s.padInfo.top,V=S-1-s.padInfo.left,Y=k/x;for(let G=0;G<y;++G)for(let Z=0;Z<x;++Z)for(let q=0;q<I;++q){const ee=q-B,Q=Math.max(0,Math.ceil(ee/F)),ie=Math.min(R,(b+ee)/F);for(let re=0;re<A;++re){const de=re-V,pe=Math.max(0,Math.ceil(de/M)),me=Math.min(D,(S+de)/M);let we=0;for(let $e=Q;$e<ie;++$e){const Pe=$e*F-ee;for(let ct=pe;ct<me;++ct){const Jt=ct*M-de,Rs=c*G+p*$e+u*ct,xn=d*(b-1-Pe)+m*(S-1-Jt)+f*Z;for(let kt=0;kt<Y;++kt){const Us=Z*Y+kt,ci=l[Rs+Us],Ln=h[xn+kt];we+=ci*Ln}}}i[r*G+o*q+a*re+Z]=we}}return n.toTensor()}depthwiseConv2DDerFilter(e,t,s){H([e,t],"depthwiseConv2DDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=le(s.filterShape,"float32"),l=s.padInfo.left,c=s.padInfo.top,p=s.outChannels/s.inChannels,u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<r;++d){const m=Math.max(0,Math.ceil((c-d)/n)),f=Math.min(s.outHeight,(s.inHeight+c-d)/n);for(let y=0;y<o;++y){const b=Math.max(0,Math.ceil((l-y)/i)),S=Math.min(s.outWidth,(s.inWidth+l-y)/i);for(let x=0;x<s.outChannels;++x){const I=Math.trunc(x/p),A=x%p;let k=0;for(let R=0;R<s.batchSize;++R)for(let D=m;D<f;++D){const F=d+D*n-c;for(let M=b;M<S;++M){const B=y+M*i-l;k+=u.get(R,F,B,I)*h.get(R,D,M,x)}}a.set(k,d,y,I,A)}}}return a.toTensor()}tile(e,t){return H(e,"tile"),Ez(this.bufferSync(e),t)}pad(e,t,s){H(e,"pad");const n=t.map((a,l)=>a[0]+e.shape[l]+a[1]),i=t.map(a=>a[0]),r=this.bufferSync(e),o=le(n,e.dtype);s!==0&&o.values.fill(s);for(let a=0;a<e.size;a++){const l=r.indexToLoc(a),c=l.map((p,u)=>p+i[u]);o.set(r.get(...l),...c)}return o.toTensor()}gather(e,t,s){H([e,t],"gather");const n=e.shape.slice(),i=this.readSync(t.dataId);n[s]=i.length;const r=le(n,e.dtype),o=this.bufferSync(e);for(let a=0;a<r.size;++a){const l=r.indexToLoc(a),c=l.slice();c[s]=i[l[s]];const p=o.locToIndex(c);r.values[a]=o.values[p]}return r.toTensor()}batchToSpaceND(e,t,s){H([e],"batchToSpaceND");const n=t.reduce((c,p)=>c*p),i=U.getReshaped(e.shape,t,n),r=U.getPermuted(i.length,t.length),o=U.getReshapedPermuted(e.shape,t,n),a=U.getSliceBeginCoords(s,t.length),l=U.getSliceSize(o,s,t.length);return se(e.reshape(i),r).reshape(o).slice(a,l)}spaceToBatchND(e,t,s){H([e],"spaceToBatchND");const n=t.reduce((p,u)=>p*u),i=[[0,0]];i.push(...s);for(let p=1+t.length;p<e.shape.length;++p)i.push([0,0]);const r=e.pad(i),o=U.getReshaped(r.shape,t,n,!1),a=U.getPermuted(o.length,t.length,!1),l=U.getReshapedPermuted(r.shape,t,n,!1),c=se(r.reshape(o),a);return O(c,l)}maxPool(e,t){H(e,"maxPool");const s=this.readSync(e.dataId);return _u(s,e.shape,e.dtype,e.strides,t,"max").toTensor()}maxPoolBackprop(e,t,s,n){H([t,s],"maxPoolBackprop");const i=this.readSync(t.dataId),r=le(n.outShape,t.dtype,mf(i,t.shape,t.dtype,n).values),o=n.strideHeight,a=n.strideWidth,l=n.dilationHeight,c=n.dilationWidth,p=n.effectiveFilterHeight,u=n.effectiveFilterWidth,h=u-1-n.padInfo.left,d=p-1-n.padInfo.top,m=le(t.shape,"float32"),f=this.bufferSync(e);for(let y=0;y<n.batchSize;++y)for(let b=0;b<n.inChannels;++b)for(let S=0;S<n.inHeight;++S)for(let x=0;x<n.inWidth;++x){const I=S-d,A=x-h;let k=0;for(let R=0;R<p;R+=l){const D=(I+R)/o;if(D<0||D>=n.outHeight||Math.floor(D)!==D)continue;for(let F=0;F<u;F+=c){const M=(A+F)/a;if(M<0||M>=n.outWidth||Math.floor(M)!==M)continue;const B=p*u-1-r.get(y,D,M,b),V=R*u+F,Y=B===V?1:0;if(Y===0)continue;const G=f.get(y,D,M,b);k+=G*Y}}m.set(k,y,S,x,b)}return m.toTensor()}avgPoolBackprop(e,t,s){H([e,t],"avgPoolBackprop");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=s.dilationHeight,l=s.dilationWidth,c=s.effectiveFilterHeight,p=s.effectiveFilterWidth,u=p-1-s.padInfo.left,h=c-1-s.padInfo.top,d=le(t.shape,"float32"),m=1/(r*o),f=this.bufferSync(e);for(let y=0;y<s.batchSize;++y)for(let b=0;b<s.inChannels;++b)for(let S=0;S<s.inHeight;++S)for(let x=0;x<s.inWidth;++x){const I=S-h,A=x-u;let k=0;for(let R=0;R<c;R+=a){const D=(I+R)/n;if(D<0||D>=s.outHeight||Math.floor(D)!==D)continue;for(let F=0;F<p;F+=l){const M=(A+F)/i;if(M<0||M>=s.outWidth||Math.floor(M)!==M)continue;const B=f.get(y,D,M,b);k+=B}}d.set(k*m,y,S,x,b)}return d.toTensor()}pool3d(e,t,s){H(e,"pool3d");const n=t.strideDepth,i=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,h=t.padInfo.front,d=t.padInfo.top,m=t.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,y=this.readSync(e.dataId),b=le(t.outShape,e.dtype),S=b.values,x=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],I=t.outShape[2]*t.outShape[3]*t.outShape[4],A=t.outShape[3]*t.outShape[4],k=t.outShape[4];for(let R=0;R<t.batchSize;++R){const D=R*x,F=R*e.strides[0];for(let M=0;M<t.inChannels;++M)for(let B=0;B<t.outDepth;++B){const V=B*n-h;let Y=V;for(;Y<0;)Y+=o;const G=Math.min(t.inDepth,c+V),Z=D+B*I;for(let q=0;q<t.outHeight;++q){const ee=q*i-d;let Q=ee;for(;Q<0;)Q+=a;const ie=Math.min(t.inHeight,p+ee),re=Z+q*A;for(let de=0;de<t.outWidth;++de){const pe=de*r-m;let me=pe;for(;me<0;)me+=l;const we=Math.min(t.inWidth,u+pe),$e=re+de*k;let Pe=f,ct=0,Jt=0;for(let xn=Y;xn<G;xn+=o){const kt=F+xn*e.strides[1];for(let Us=Q;Us<ie;Us+=a){const ci=kt+Us*e.strides[2];for(let Ln=me;Ln<we;Ln+=l){const Os=ci+Ln*e.strides[3],tn=y[Os+M];if(s==="max"&&tn>Pe?Pe=tn:s==="avg"&&(ct+=tn,Jt++),isNaN(Pe))break}if(isNaN(Pe))break}if(isNaN(Pe))break}const Rs=$e+M;S[Rs]=s==="avg"?ct/Jt:Pe}}}}return b.toTensor()}avgPool3d(e,t){return H(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,s){H([e,t],"avgPool3dBackprop");const n=s.strideDepth,i=s.strideHeight,r=s.strideWidth,o=s.filterDepth,a=s.filterHeight,l=s.filterWidth,c=s.dilationDepth,p=s.dilationHeight,u=s.dilationWidth,h=s.effectiveFilterDepth,d=s.effectiveFilterHeight,m=s.effectiveFilterWidth,f=h-1-s.padInfo.front,y=m-1-s.padInfo.left,b=d-1-s.padInfo.top,S=le(t.shape,"float32"),x=1/(o*a*l),I=this.bufferSync(e);for(let A=0;A<s.batchSize;++A)for(let k=0;k<s.inChannels;++k)for(let R=0;R<s.inDepth;++R)for(let D=0;D<s.inHeight;++D)for(let F=0;F<s.inWidth;++F){const M=R-f,B=D-b,V=F-y;let Y=0;for(let G=0;G<h;G+=c){const Z=(M+G)/n;if(Z<0||Z>=s.outDepth||Math.floor(Z)!==Z)continue;for(let q=0;q<d;q+=p){const ee=(B+q)/i;if(ee<0||ee>=s.outHeight||Math.floor(ee)!==ee)continue;for(let Q=0;Q<m;Q+=u){const ie=(V+Q)/r;if(ie<0||ie>=s.outWidth||Math.floor(ie)!==ie)continue;const re=I.get(A,Z,ee,ie,k);Y+=re}}}S.set(Y*x,A,R,D,F,k)}return S.toTensor()}maxPool3d(e,t){return H(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const s=le(t.outShape,"int32"),n=t.strideDepth,i=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,h=t.padInfo.front,d=t.padInfo.top,m=t.padInfo.left,f=this.bufferSync(e);for(let y=0;y<t.batchSize;++y)for(let b=0;b<t.inChannels;++b)for(let S=0;S<t.outDepth;++S){const x=S*n-h;let I=x;for(;I<0;)I+=o;const A=Math.min(t.inDepth,c+x);for(let k=0;k<t.outHeight;++k){const R=k*i-d;let D=R;for(;D<0;)D+=a;const F=Math.min(t.inHeight,p+R);for(let M=0;M<t.outWidth;++M){const B=M*r-m;let V=B;for(;V<0;)V+=l;const Y=Math.min(t.inWidth,u+B);let G=Number.NEGATIVE_INFINITY,Z=-1;for(let q=I;q<A;q+=o){const ee=q-x;for(let Q=D;Q<F;Q+=a){const ie=Q-R;for(let re=V;re<Y;re+=l){const de=re-B,pe=f.get(y,q,Q,re,b);pe>=G&&(G=pe,Z=ee*p*u+ie*p+de)}}}s.set(Z,y,S,k,M,b)}}}return s.toTensor()}maxPool3dBackprop(e,t,s,n){H([t,s],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,n),r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,l=n.dilationDepth,c=n.dilationHeight,p=n.dilationWidth,u=n.effectiveFilterDepth,h=n.effectiveFilterHeight,d=n.effectiveFilterWidth,m=u-1-n.padInfo.front,f=d-1-n.padInfo.left,y=h-1-n.padInfo.top,b=le(t.shape,"float32"),S=this.bufferSync(i),x=this.bufferSync(e);for(let I=0;I<n.batchSize;++I)for(let A=0;A<n.inChannels;++A)for(let k=0;k<n.inDepth;++k)for(let R=0;R<n.inHeight;++R)for(let D=0;D<n.inWidth;++D){const F=k-m,M=R-y,B=D-f;let V=0;for(let Y=0;Y<u;Y+=l){const G=(F+Y)/r;if(G<0||G>=n.outDepth||Math.floor(G)!==G)continue;for(let Z=0;Z<h;Z+=c){const q=(M+Z)/o;if(q<0||q>=n.outHeight||Math.floor(q)!==q)continue;for(let ee=0;ee<d;ee+=p){const Q=(B+ee)/a;if(Q<0||Q>=n.outWidth||Math.floor(Q)!==Q)continue;const ie=u*h*d-1-S.get(I,G,q,Q,A),re=Y*h*d+Z*d+ee,de=ie===re?1:0;if(de===0)continue;const pe=x.get(I,G,q,Q,A);V+=pe*de}}}b.set(V,I,k,R,D,A)}return b.toTensor()}cast(e,t){return U.castTensor(e,t,this)}reshape(e,t){return U.reshapeTensor(e,t)}avgPool(e,t){H(e,"avgPool"),H(e,"maxPool");const s=this.readSync(e.dataId);return _u(s,e.shape,e.dtype,e.strides,t,"avg").toTensor().toFloat()}resizeBilinear(e,t,s,n){H(e,"resizeBilinear");const[i,r,o,a]=e.shape,l=this.readSync(e.dataId),c=new Float32Array(E.sizeFromShape([i,t,s,a])),p=[n&&t>1?r-1:r,n&&s>1?o-1:o],u=[n&&t>1?t-1:t,n&&s>1?s-1:s];let h=0;const d=p[0]/u[0],m=p[1]/u[1];for(let f=0;f<i;f++)for(let y=0;y<t;y++){const b=d*y,S=Math.floor(b),x=b-S,I=Math.min(r-1,Math.ceil(b)),A=f*e.strides[0]+S*e.strides[1],k=f*e.strides[0]+I*e.strides[1];for(let R=0;R<s;R++){const D=m*R,F=Math.floor(D),M=D-F,B=Math.min(o-1,Math.ceil(D)),V=A+F*e.strides[2],Y=k+F*e.strides[2],G=A+B*e.strides[2],Z=k+B*e.strides[2];for(let q=0;q<a;q++){const ee=l[V+q],Q=l[Y+q],ie=l[G+q],re=l[Z+q],de=ee+(ie-ee)*M,pe=Q+(re-Q)*M,me=de+(pe-de)*x;c[h++]=me}}}return ze(c,[i,t,s,a])}resizeBilinearBackprop(e,t,s){H([e,t],"resizeBilinearBackprop");const[n,i,r,o]=t.shape,[,a,l]=e.shape,c=new Float32Array(n*i*r*o),p=[s&&a>1?i-1:i,s&&l>1?r-1:r],u=[s&&a>1?a-1:a,s&&l>1?l-1:l],h=p[0]/u[0],d=p[1]/u[1],m=this.readSync(e.dataId);let f=0;for(let y=0;y<n;y++){const b=y*t.strides[0];for(let S=0;S<a;S++){const x=S*h,I=Math.floor(x),A=Math.min(Math.ceil(x),i-1),k=b+I*t.strides[1],R=b+A*t.strides[1],D=x-I,F=1-D;for(let M=0;M<l;M++){const B=M*d,V=Math.floor(B),Y=Math.min(Math.ceil(B),r-1),G=B-V,Z=1-G,q=k+V*t.strides[2],ee=k+Y*t.strides[2],Q=R+V*t.strides[2],ie=R+Y*t.strides[2],re=F*Z,de=F*G,pe=D*Z,me=D*G;for(let we=0;we<o;we++){const $e=m[f++];c[q+we]+=$e*re,c[ee+we]+=$e*de,c[Q+we]+=$e*pe,c[ie+we]+=$e*me}}}}return cs(c,[n,r,i,o],t.dtype)}resizeNearestNeighbor(e,t,s,n){H(e,"resizeNearestNeighbor");const[i,r,o,a]=e.shape,l=this.readSync(e.dataId),c=new Float32Array(i*t*s*a),p=[n&&t>1?r-1:r,n&&s>1?o-1:o],u=[n&&t>1?t-1:t,n&&s>1?s-1:s],h=p[0]/u[0],d=p[1]/u[1];let m=0;for(let f=0;f<i;f++){const y=f*e.strides[0];for(let b=0;b<t;b++){const S=h*b,x=Math.min(r-1,n?Math.round(S):Math.floor(S)),I=y+x*e.strides[1];for(let A=0;A<s;A++){const k=d*A,R=Math.min(o-1,n?Math.round(k):Math.floor(k)),D=I+R*e.strides[2];for(let F=0;F<a;F++){const M=l[D+F];c[m++]=M}}}}return ze(c,[i,t,s,a],e.dtype)}resizeNearestNeighborBackprop(e,t,s){H([e,t],"resizeNearestNeighborBackprop");const[n,i,r,o]=t.shape,[,a,l]=e.shape,c=new Float32Array(n*i*r*o),p=this.readSync(e.dataId),u=[s&&a>1?i-1:i,s&&l>1?r-1:r],h=[s&&a>1?a-1:a,s&&l>1?l-1:l],d=u[0]/h[0],m=u[1]/h[1],f=1/d,y=1/m,b=Math.ceil(f)*2+2,S=Math.ceil(y)*2+2;for(let x=0;x<n;x++){const I=x*t.strides[0];for(let A=0;A<i;A++){const k=I+A*t.strides[1],R=Math.floor(A*f),D=Math.floor(R-b/2);for(let F=0;F<r;F++){const M=k+F*t.strides[2],B=Math.floor(F*y),V=Math.floor(B-S/2);for(let Y=0;Y<o;Y++){let G=0;for(let Z=0;Z<b;Z++){const q=Z+D;if(q<0||q>=a)continue;const ee=I+q*e.strides[1],Q=q*d,ie=Math.min(i-1,s?Math.round(Q):Math.floor(Q));if(A!==ie)continue;for(let re=0;re<S;re++){const de=re+V;if(de<0||de>=l)continue;const pe=ee+de*e.strides[2],me=de*m,we=Math.min(r-1,s?Math.round(me):Math.floor(me));F===we&&(G+=p[pe+Y])}}c[M+Y]=G}}}}return cs(c,t.shape,t.dtype)}batchNorm(e,t,s,n,i,r){H([e,t,s,i,n],"batchNorm");const o=this.readSync(e.dataId),a=this.readSync(t.dataId),l=this.readSync(s.dataId),c=i?this.readSync(i.dataId):new Float32Array([1]),p=n?this.readSync(n.dataId):new Float32Array([0]),u=new Float32Array(o.length),h=p.length,d=c.length,m=l.length,f=a.length;let y=0,b=0,S=0,x=0;for(let I=0;I<o.length;++I)u[I]=p[y++]+(o[I]-a[b++])*c[S++]/Math.sqrt(l[x++]+r),y>=h&&(y=0),b>=f&&(b=0),S>=d&&(S=0),x>=m&&(x=0);return cs(u,e.shape)}localResponseNormalization4D(e,t,s,n,i){H(e,"localResponseNormalization4D");const r=e.shape[3],o=r-1,a=this.readSync(e.dataId),l=e.size,c=new Float32Array(l);function p(u){const h=u%r;let d=u-h+Math.max(0,h-t);const m=u-h+Math.min(h+t,o);let f=0;for(;d<=m;d++){const y=a[d];f+=y*y}return f}for(let u=0;u<l;u++){const h=p(u),d=a[u]*Math.pow(s+n*h,-i);c[u]=d}return cs(c,e.shape)}LRNGrad(e,t,s,n,i,r,o){H(e,"LRNGrad");const a=e.shape[3],l=this.readSync(e.dataId),c=this.readSync(t.dataId),p=this.readSync(s.dataId),u=new Float32Array(e.size),h=e.size;for(let d=0;d<h;d++){const m=d%a,f=d-m+Math.max(0,m-n),y=d-m+Math.min(a,m+n+1);let b=0;for(let S=f;S<y;S++)b+=Math.pow(c[S],2);b=r*b+i;for(let S=f;S<y;S++){let x=-2*r*o*c[S]*p[d]/b;d===S&&(x+=Math.pow(b,-o)),x*=l[d],u[S]+=x}}return cs(u,e.shape)}multinomial(e,t,s,n){H(e,"multinomial");const i=t?e:Yt(e),r=i.shape[0],o=i.shape[1],a=Ie([r,s],"int32"),l=this.readSync(a.dataId),c=this.readSync(i.dataId);for(let p=0;p<r;++p){const u=p*o,h=new Float32Array(o-1);h[0]=c[u];for(let f=1;f<h.length;++f)h[f]=h[f-1]+c[u+f];const d=sC.alea(n.toString()),m=p*s;for(let f=0;f<s;++f){const y=d();l[m+f]=h.length;for(let b=0;b<h.length;b++)if(y<h[b]){l[m+f]=b;break}}}return a}oneHot(e,t,s,n){H(e,"oneHot");const i=new Float32Array(e.size*t);i.fill(n);const r=this.readSync(e.dataId);for(let o=0;o<e.size;++o)r[o]>=0&&r[o]<t&&(i[o*t+r[o]]=s);return ts(i,[e.size,t],"int32")}nonMaxSuppression(e,t,s,n,i){H(e,"nonMaxSuppression");const r=this.readSync(e.dataId),o=this.readSync(t.dataId);return Rz(r,o,s,n,i)}fft(e){return this.fftBatch(e,!1)}ifft(e){return this.fftBatch(e,!0)}fftBatch(e,t){const s=e.shape[0],n=e.shape[1],i=le(e.shape,"float32"),r=le(e.shape,"float32"),o=$t(e).as2D(s,n),a=Ht(e).as2D(s,n);for(let c=0;c<s;c++){const p=o.slice([c,0],[1,n]),u=a.slice([c,0],[1,n]),h=Ze(p,u),d=this.readSync(this.fftImpl(h,t).dataId);for(let m=0;m<n;m++){const f=U.getComplexWithIndex(d,m);i.values[c*n+m]=f.real,r.values[c*n+m]=f.imag}}const l=Ze(i.toTensor(),r.toTensor());return l.as2D(s,n)}fftImpl(e,t){const s=e.as1D(),n=s.size;if(this.isExponentOf2(n)){let i=this.fftRadix2(s,n,t).as2D(e.shape[0],e.shape[1]);return t&&(i=Ze($t(i).div(j(n)),Ht(i).div(j(n)))),i}else{const i=this.readSync(e.dataId),r=this.fourierTransformByMatmul(i,n,t),o=U.splitRealAndImagArrays(r);return Ze(o.real,o.imag).as2D(e.shape[0],e.shape[1])}}isExponentOf2(e){return(e&e-1)===0}fftRadix2(e,t,s){if(t===1)return e;const n=this.readSync(e.dataId),i=t/2,r=U.complexWithEvenIndex(n);let o=Ze(r.real,r.imag).as1D();const a=U.complexWithOddIndex(n);let l=Ze(a.real,a.imag).as1D();o=this.fftRadix2(o,i,s),l=this.fftRadix2(l,i,s);const c=U.exponents(t,s),p=Ze(c.real,c.imag).mul(l),u=o.add(p),h=o.sub(p),d=$t(u).concat($t(h)),m=Ht(u).concat(Ht(h));return Ze(d,m).as1D()}fourierTransformByMatmul(e,t,s){const n=new Float32Array(t*2);for(let i=0;i<t;i++){let r=0,o=0;for(let a=0;a<t;a++){const l=U.exponent(i*a,t,s),c=U.getComplexWithIndex(e,a);r+=c.real*l.real-c.imag*l.imag,o+=c.real*l.imag+c.imag*l.real}s&&(r/=t,o/=t),U.assignToTypedArray(n,r,o,i)}return n}depthToSpace(e,t,s){E.assert(s==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${s}`),E.assert(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const n=e.shape[0],i=e.shape[1],r=e.shape[2],o=e.shape[3],a=i*t,l=r*t,c=o/(t*t),p=this.readSync(e.dataId),u=new Float32Array(n*a*l*c);let h=0;for(let d=0;d<n;++d)for(let m=0;m<a;++m){const f=Math.floor(m/t),y=m%t;for(let b=0;b<l;++b){const S=Math.floor(b/t),x=b%t,I=(y*t+x)*c;for(let A=0;A<c;++A){const k=A+I,R=k+o*(S+r*(f+i*d));u[h++]=p[R]}}}return cs(u,[n,a,l,c])}broadcastedBinaryOp(e,t,s,n){const i=U.assertAndGetBroadcastShape(e.shape,t.shape),r=le(i,s),o=this.readSync(e.dataId),a=this.readSync(t.dataId),l=U.getBroadcastDims(e.shape,i),c=U.getBroadcastDims(t.shape,i),p=r.values;if(l.length+c.length===0)for(let u=0;u<p.length;++u)p[u]=n(o[u%o.length],a[u%a.length]);else{const u=this.bufferSync(e),h=this.bufferSync(t);for(let d=0;d<p.length;++d){const m=r.indexToLoc(d),f=m.slice(-e.rank);l.forEach(x=>f[x]=0);const y=u.locToIndex(f),b=m.slice(-t.rank);c.forEach(x=>b[x]=0);const S=h.locToIndex(b);p[d]=n(o[y],a[S])}}return r.toTensor()}broadcastedBinaryComplexOp(e,t,s){const n=U.assertAndGetBroadcastShape(e.shape,t.shape),i=le(n,"float32"),r=le(n,"float32"),o=this.readSync(e.dataId),a=this.readSync(t.dataId),l=U.getBroadcastDims(e.shape,n),c=U.getBroadcastDims(t.shape,n),p=i.values,u=r.values;if(l.length+c.length===0)for(let h=0;h<p.length;h++){const d=h%o.length,m=h%a.length,f=s(o[d*2],o[d*2+1],a[m*2],a[m*2+1]);p[h]=f.real,u[h]=f.imag}else{const h=this.bufferSync(this.data.get(e.dataId).complexTensors.real),d=this.bufferSync(this.data.get(t.dataId).complexTensors.real);for(let m=0;m<p.length;m++){const f=i.indexToLoc(m),y=f.slice(-e.rank);l.forEach(A=>y[A]=0);const b=h.locToIndex(y),S=f.slice(-t.rank);c.forEach(A=>S[A]=0);const x=d.locToIndex(S),I=s(o[b*2],o[b*2+1],a[x*2],a[x*2+1]);p[m]=I.real,u[m]=I.imag}}return this.complex(i.toTensor(),r.toTensor())}split(e,t,s){return Oz(e,t,s)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(e,t,s,n,i,r){const[o,a,l,c]=e.shape,p=t.shape[0],[u,h]=n,d=le([p,u,h,c],"float32"),m=this.readSync(t.dataId),f=this.readSync(s.dataId),y=this.readSync(e.dataId),b=e.strides,S=d.strides;for(let x=0;x<p;x++){const I=x*4,A=m[I],k=m[I+1],R=m[I+2],D=m[I+3],F=f[x];if(F>=o)continue;const M=u>1?(R-A)*(a-1)/(u-1):0,B=h>1?(D-k)*(l-1)/(h-1):0;for(let V=0;V<u;V++){const Y=u>1?A*(a-1)+V*M:.5*(A+R)*(a-1);if(Y<0||Y>a-1){for(let G=0;G<h;G++)for(let Z=0;Z<c;Z++){const q=Z+G*S[2]+V*S[1]+x*S[0];d.values[q]=r}continue}if(i==="bilinear"){const G=Math.floor(Y),Z=Math.ceil(Y),q=Y-G;for(let ee=0;ee<h;ee++){const Q=h>1?k*(l-1)+ee*B:.5*(k+D)*(l-1);if(Q<0||Q>l-1){for(let pe=0;pe<c;pe++){const me=pe+ee*S[2]+V*S[1]+x*S[0];d.values[me]=r}continue}const ie=Math.floor(Q),re=Math.ceil(Q),de=Q-ie;for(let pe=0;pe<c;pe++){let me=pe+ie*b[2]+G*b[1]+F*b[0];const we=y[me];me=pe+re*b[2]+G*b[1]+F*b[0];const $e=y[me];me=pe+ie*b[2]+Z*b[1]+F*b[0];const Pe=y[me];me=pe+re*b[2]+Z*b[1]+F*b[0];const ct=y[me],Jt=we+($e-we)*de,Rs=Pe+(ct-Pe)*de;me=pe+ee*S[2]+V*S[1]+x*S[0],d.values[me]=Jt+(Rs-Jt)*q}}}else for(let G=0;G<h;++G){const Z=h>1?k*(l-1)+G*B:.5*(k+D)*(l-1);if(Z<0||Z>l-1){for(let Q=0;Q<c;Q++){const ie=Q+G*S[2]+V*S[1]+x*S[0];d.values[ie]=r}continue}const q=Math.round(Z),ee=Math.round(Y);for(let Q=0;Q<c;Q++){const ie=Q+q*b[2]+ee*b[1]+F*b[0],re=Q+G*S[2]+V*S[1]+x*S[0];d.values[re]=y[ie]}}}}return d.toTensor()}sparseToDense(e,t,s,n){const{sliceRank:i,numUpdates:r,sliceSize:o,strides:a,outputSize:l}=U.calculateShapes(t,e,s),c=!1;return this.scatter(e,t,s,l,o,r,i,a,n,c)}gatherND(e,t){const s=t.shape,n=s[s.length-1],[i,r,o,a]=U.prepareAndValidate(e,t);if(r===0)return ze([],i,e.dtype);const l=new fi([r,o],e.dtype),c=this.readSync(t.dataId),p=this.readSync(e.dataId);for(let u=0;u<r;u++){const h=[];let d=0;for(let m=0;m<n;m++){const f=c[u*n+m];d+=f*a[m],h.push(f)}if(d<0||d>=e.size/o)throw new Error(`Invalid indices: ${h} does not index into ${e.shape}`);for(let m=0;m<o;m++)l.values[u*o+m]=p[d*o+m]}return l.toTensor().reshape(i)}scatterND(e,t,s){const{sliceRank:n,numUpdates:i,sliceSize:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=j(0),c=!0;return this.scatter(e,t,s,a,r,i,n,o,l,c)}fill(e,t,s){s=s||E.inferDtype(t);const n=E.getArrayFromDType(s,E.sizeFromShape(e));return n.fill(t),os().makeTensor(n,e,s,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=E.getArrayFromDType(e.dtype,E.sizeFromShape(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,s){return U.linspaceImpl(e,t,s)}scatter(e,t,s,n,i,r,o,a,l,c){const p=[n/i,i],u=this.readSync(e.dataId),h=this.readSync(t.dataId);if(n===0)return ze([],s,t.dtype);const d=new fi(p,t.dtype);d.values.fill(this.readSync(l.dataId)[0]);for(let m=0;m<r;m++){const f=[];let y=0;for(let b=0;b<o;b++){const S=u[m*o+b];f.push(S),y+=S*a[b]}if(y<0||y>=n/i)throw new Error(`Invalid indices: ${f} does not index into ${s}`);for(let b=0;b<i;b++)c?d.values[y*i+b]+=h[m*i+b]:d.values[y*i+b]=t.rank===0?h[0]:h[m*i+b]}return d.toTensor().reshape(s)}}function ff(e,t,s,n){const i=E.getTypedArrayFromDType(n,E.sizeFromShape(s));for(let r=0;r<i.length;++r){const o=r*t;let a=e[o];for(let l=0;l<t;++l){const c=e[o+l];c>a&&(a=c)}i[r]=a}return i}function Ma(e,t,s,n,i){const r=t.length,o=E.sizeFromShape(t),a=E.computeStrides(t),l=E.computeStrides(i),c=E.getTypedArrayFromDType(s,E.sizeFromShape(i));for(let p=0;p<o;++p){const u=E.indexToLoc(p,r,a),h=new Array(u.length);for(let m=0;m<h.length;m++)h[m]=u[n[m]];const d=E.locToIndex(h,r,l);c[d]=e[p]}return c}const Vw={};Re(Vw,{maxImpl:()=>ff,transposeImpl:()=>Ma});const Gw="2.3.0";dp("cpu",()=>new jw(),1);const nC={kernelName:_r,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i}=e,{strides:r,pad:o,dilations:a}=s,l=t,c=E.toNestedArray(n.shape,l.data.get(n.dataId).values),p=E.toNestedArray(i.shape,l.data.get(i.dataId).values),{batchSize:u,inHeight:h,inWidth:d,inChannels:m,outHeight:f,outWidth:y,padInfo:b,strideHeight:S,strideWidth:x,filterHeight:I,filterWidth:A,dilationHeight:k,dilationWidth:R,outShape:D}=U.computeDilation2DInfo(n.shape,i.shape,r,o,"NHWC",a),F=E.makeZerosNestedTypedArray(D,n.dtype);for(let B=0;B<u;++B)for(let V=0;V<f;++V){const Y=V*S-b.top;for(let G=0;G<y;++G){const Z=G*x-b.left;for(let q=0;q<m;++q){let ee=Number.MIN_SAFE_INTEGER;for(let Q=0;Q<I;++Q){const ie=Y+Q*k;if(ie>=0&&ie<h)for(let re=0;re<A;++re){const de=Z+re*R;if(de>=0&&de<d){const pe=c[B][ie][de][q]+p[Q][re][q];pe>ee&&(ee=pe)}}}F[B][V][G][q]=ee}}}const M=l.write(E.toTypedArray(F,n.dtype),D,n.dtype);return{dataId:M,shape:D,dtype:n.dtype}}};const iC={kernelName:ea,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i,dy:r}=e,{strides:o,pad:a,dilations:l}=s,c=t,p=E.toNestedArray(n.shape,c.data.get(n.dataId).values),u=E.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:y,outWidth:b,padInfo:S,strideHeight:x,strideWidth:I,filterHeight:A,filterWidth:k,dilationHeight:R,dilationWidth:D,outShape:F}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);E.assert(r.rank===F.length,()=>`Error in ${ea}, dy must have the same rank as output ${F.length}, but got ${r.rank}`);const M=E.toNestedArray(F,c.data.get(r.dataId).values),B=E.makeZerosNestedTypedArray(i.shape,i.dtype);for(let Y=0;Y<h;++Y)for(let G=0;G<y;++G){const Z=G*x-S.top;for(let q=0;q<b;++q){const ee=q*I-S.left;for(let Q=0;Q<f;++Q){let ie=Number.MIN_SAFE_INTEGER,re=0,de=0;for(let pe=0;pe<A;++pe){const me=Z+pe*R;if(me>=0&&me<d)for(let we=0;we<k;++we){const $e=ee+we*D;if($e>=0&&$e<m){const Pe=p[Y][me][$e][Q]+u[pe][we][Q];Pe>ie&&(ie=Pe,re=pe,de=we)}}}B[re][de][Q]+=M[Y][G][q][Q]}}}const V=c.write(E.toTypedArray(B,n.dtype),i.shape,i.dtype);return{dataId:V,shape:i.shape,dtype:i.dtype}}};const rC={kernelName:Qo,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:s})=>{const{x:n,filter:i,dy:r}=e,{strides:o,pad:a,dilations:l}=s,c=t,p=E.toNestedArray(n.shape,c.data.get(n.dataId).values),u=E.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:y,outWidth:b,padInfo:S,strideHeight:x,strideWidth:I,filterHeight:A,filterWidth:k,dilationHeight:R,dilationWidth:D,outShape:F}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);E.assert(r.rank===F.length,()=>`Error in ${Qo}, dy must have the same rank as output ${F.length}, but got ${r.rank}`);const M=E.toNestedArray(F,c.data.get(r.dataId).values),B=E.makeZerosNestedTypedArray(n.shape,n.dtype);for(let Y=0;Y<h;++Y)for(let G=0;G<y;++G){const Z=G*x-S.top;for(let q=0;q<b;++q){const ee=q*I-S.left;for(let Q=0;Q<f;++Q){let ie=Number.MIN_SAFE_INTEGER,re=Z<0?0:Z,de=ee<0?0:ee;for(let pe=0;pe<A;++pe){const me=Z+pe*R;if(me>=0&&me<d)for(let we=0;we<k;++we){const $e=ee+we*D;if($e>=0&&$e<m){const Pe=p[Y][me][$e][Q]+u[pe][we][Q];Pe>ie&&(ie=Pe,re=me,de=$e)}}}B[Y][re][de][Q]+=M[Y][G][q][Q]}}}const V=c.write(E.toTypedArray(B,n.dtype),n.shape,n.dtype);return{dataId:V,shape:n.shape,dtype:n.dtype}}};function gf(e,t){return{kernelName:e,backendName:"cpu",kernelFunc:({inputs:s,backend:n})=>{const{a:i,b:r}=s,o=n;H([i,r],e);const a=o.data.get(i.dataId).values,l=o.data.get(r.dataId).values,[c,p]=t(i.shape,r.shape,a,l,i.dtype),u=o.write(c,p,i.dtype);return{dataId:u,shape:p,dtype:i.dtype}}}}function yf(e){return(t,s,n,i,r)=>{const o=U.assertAndGetBroadcastShape(t,s),a=o.length,l=E.computeStrides(o),c=E.sizeFromShape(o),p=E.getTypedArrayFromDType(r,c),u=t.length,h=s.length,d=E.computeStrides(t),m=E.computeStrides(s),f=U.getBroadcastDims(t,o),y=U.getBroadcastDims(s,o);if(f.length+y.length===0)for(let b=0;b<p.length;++b)p[b]=e(n[b%n.length],i[b%i.length]);else for(let b=0;b<p.length;++b){const S=E.indexToLoc(b,a,l),x=S.slice(-u);f.forEach(R=>x[R]=0);const I=E.locToIndex(x,u,d),A=S.slice(-h);y.forEach(R=>A[R]=0);const k=E.locToIndex(A,h,m);p[b]=e(n[I],i[k])}return[p,o]}}const oC=yf((e,t)=>e/t);const aC=gf(ui,oC);const lC={kernelName:Dr,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,i=s,r=E.getTypedArrayFromDType(n.dtype,E.sizeFromShape(n.shape)),[o,a,l,c]=n.shape,p=i.data.get(n.dataId).values;for(let h=0;h<o;h++){const d=h*l*a*c;for(let m=0;m<a;m++){const f=m*(l*c);for(let y=0;y<l;y++){const b=y*c;for(let S=0;S<c;S++){const x=[o,m,y,S],I=x[2],A=Math.round(l-I),k=d+f+b+S;let 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        for (int i = 0; i < ${n}; i++) {
          int inIdx = ${l};
          float candidate = getA(batch, inIdx);
          if (candidate ${a} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}}function ex(e,t){return["x","y","z","w","u","v"].slice(0,t).map(s=>`${e}.${s}`)}function Et(e,t){return t===1?[e]:ex(e,t)}function jC(e,t){if(e===1)return"rc";let s="";for(let n=0;n<e;n++)s+=t[n],n<e-1&&(s+=",");return s}function ot(){let e,t,s,n,i,r,o,a,l,c;return $().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",s="out",n="in",i="texture",r="outputColor",o="out vec4 outputColor;",a=`
      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="",c=`
      #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)));
      }
    `):(e="",t="attribute",s="varying",n="varying",i="texture2D",r="gl_FragColor",o="",a=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,c=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:s,varyingFs:n,texture2D:i,output:r,defineOutput:o,defineSpecialNaN:a,defineSpecialInf:l,defineRound:c}}function zn(e,t,s="index"){const n=E.computeStrides(t);return n.map((i,r)=>{const o=`int ${e[r]} = ${s} / ${i}`,a=r===n.length-1?`int ${e[r+1]} = ${s} - ${e[r]} * ${i}`:`index -= ${e[r]} * ${i}`;return`${o}; ${a};`}).join("")}function Ua(e){const t=E.computeStrides(e).map(s=>s.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}const Sf=`
  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;
  }
`;const{getBroadcastDims:VC}=U;function GC(e,t,s,n){const i=[];e.forEach(m=>{const f=E.sizeFromShape(m.shapeInfo.logicalShape);m.shapeInfo.isUniform?i.push(`uniform float ${m.name}${f>1?`[${f}]`:""};`):(i.push(`uniform sampler2D ${m.name};`),i.push(`uniform int offset${m.name};`))});const r=i.join(`
`),o=e.map(m=>Xz(m,t,n)).join(`
`),a=t.texShape,l=ot(),c=Qz(l);let p,u,h=sB(l);t.isPacked?(p=Jz(t.logicalShape,a),u=tB(l)):(p=Zz(t.logicalShape,a),u=eB(l)),n&&(h+=nB);const d=[h,c,u,r,p,o,s].join(`
`);return d}function $a(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return iB(e);case 1:return rB(e);case 2:return oB(e);case 3:return aB(e);case 4:return lB(e);case 5:return cB(e);case 6:return pB(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function HC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return uB(e);case 1:return hB(e);case 2:return dB(e);case 3:return mB(e);default:return fB(e)}}function Xz(e,t,s=!1){let n="";s?n+=HC(e):n+=$a(e);const i=e.shapeInfo.logicalShape,r=t.logicalShape;return i.length<=r.length&&(s?n+=gB(e,t):n+=yB(e,t)),n}function Jz(e,t){switch(e.length){case 0:return qC();case 1:return bB(e,t);case 2:return LB(e,t);case 3:return wB(e,t);default:return xB(e,t)}}function Zz(e,t){switch(e.length){case 0:return qC();case 1:return SB(e,t);case 2:return NB(e,t);case 3:return vB(e,t);case 4:return TB(e,t);case 5:return IB(e,t);case 6:return AB(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Qz(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function eB(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function tB(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function sB(e){const t=`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.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;
    ${e.defineSpecialNaN}
    ${e.defineSpecialInf}
    ${e.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);
    }

    ${CB}
    ${RB}
    ${OB}
  `;return t}const CB=`
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);
}
`,RB=`
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);
}
`,OB=`
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);
}
`,nB=`
  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 qC(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function bB(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${s[1]}.0);
      }
    `:s[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${s[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
    }
  `}function SB(e,t){return t[0]===1?`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function wB(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[2]/2),i=n*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

      int b = index / ${i};
      index -= b * ${i};

      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec3(b, r, c);
    }
  `}function vB(e,t){const s=zn(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec3(r, c, d);
    }
  `}function xB(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[e.length-1]/2),i=n*Math.ceil(e[e.length-2]/2);let r=i,o="",a="b, r, c";for(let l=2;l<e.length-1;l++)r*=e[e.length-l-1],o=`
      int b${l} = index / ${r};
      index -= b${l} * ${r};
    `+o,a=`b${l}, `+a;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

      ${o}

      int b = index / ${i};
      index -= b * ${i};

      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec${e.length}(${a});
    }
  `}function TB(e,t){const s=zn(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec4(r, c, d, d2);
    }
  `}function IB(e,t){const s=zn(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${s}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function AB(e,t){const s=zn(["r","c","d","d2","d3","d4"],e);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${s}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function LB(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(E.arraysEqual(e,t))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
      }
    `;const n=Math.ceil(e[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));

      int index = resTexRC.x * ${s[1]} + resTexRC.y;
      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec2(r, c);
    }
  `}function NB(e,t){return E.arraysEqual(e,t)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function Do(e){return`offset${e}`}function uB(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1),n=ot();return`
    vec4 ${s}() {
      return ${n.texture2D}(${t}, halfCR);
    }
  `}function iB(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${t};}`;const[n,i]=e.shapeInfo.texShape;if(n===1&&i===1)return`
      float ${s}() {
        return sampleTexture(${t}, halfCR);
      }
    `;const[r,o]=e.shapeInfo.texShape,a=Do(t);return`
    float ${s}() {
      vec2 uv = uvFromFlat(${r}, ${o}, ${a});
      return sampleTexture(${t}, uv);
    }
  `}function hB(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e.shapeInfo.texShape,i=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],r=ot();return`
    vec4 ${s}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${r.texture2D}(${t}, uv);
    }
  `}function rB(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${s}(int index) {
        ${Wa(e)}
      }
    `;const n=e.shapeInfo.texShape,i=n[0],r=n[1];if(r===1&&i===1)return`
      float ${s}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;const o=Do(t);return r===1?`
      float ${s}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${i}.0);
        return sampleTexture(${t}, uv);
      }
    `:i===1?`
      float ${s}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${r}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${s}(int index) {
      vec2 uv = uvFromFlat(${i}, ${r}, index + ${o});
      return sampleTexture(${t}, uv);
    }
  `}function dB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape,r=i[0],o=i[1],a=ot();if(i!=null&&E.arraysEqual(t,i))return`
      vec4 ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${r}.0);

        return ${a.texture2D}(${s}, uv);
      }
    `;const l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=Math.ceil(t[1]/2);return`
    vec4 ${n}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${a.texture2D}(${s}, uv);
    }
  `}function oB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape;if(i!=null&&E.arraysEqual(t,i)){const u=i[0],h=i[1];return`
    float ${n}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${u}.0);
      return sampleTexture(${s}, uv);
    }
  `}const{newShape:r,keptDims:o}=E.squeezeShape(t),a=r;if(a.length<t.length){const u=za(e,a),h=["row","col"];return`
      ${$a(u)}
      float ${n}(int row, int col) {
        return ${n}(${Ba(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${Wa(e)}
      }
    `;const l=i[0],c=i[1],p=Do(s);return c===1?`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${s}, uv);
    }
  `:l===1?`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${s}, uv);
    }
  `:`
  float ${n}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${t[1]} + col + ${p};
    vec2 uv = uvFromFlat(${l}, ${c}, index);
    return sampleTexture(${s}, uv);
  }
`}function mB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=e.shapeInfo.texShape,r=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const u=t.slice(1),h=[1,2],d=za(e,u),m=["b","row","col"];return`
        ${HC(d)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${Ba(m,h)});
        }
      `}const o=r[0],a=r[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),p=ot();return`
    vec4 ${n}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${o}, ${a}, ${c}, ${l}, b, row, col);
      return ${p.texture2D}(${s}, uv);
    }
  `}function aB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[1]*t[2],r=t[2],{newShape:o,keptDims:a}=E.squeezeShape(t),l=o;if(l.length<t.length){const m=za(e,l),f=["row","col","depth"];return`
        ${$a(m)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${Ba(f,a)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${i}, ${r}, 1)));
        ${Wa(e)}
      }
    `;const c=e.shapeInfo.texShape,p=c[0],u=c[1],h=e.shapeInfo.flatOffset;if(u===i&&h==null)return`
        float ${n}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${r}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${u}.0, ${p}.0);
          return sampleTexture(${s}, uv);
        }
      `;if(u===r&&h==null)return`
    float ${n}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}.0, ${p}.0);
      return sampleTexture(${s}, uv);
    }
  `;const d=Do(s);return`
      float ${n}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${i} + col * ${r} + depth + ${d};
        vec2 uv = uvFromFlat(${p}, ${u}, index);
        return sampleTexture(${s}, uv);
      }
  `}function fB(e){const t=e.shapeInfo.logicalShape,s=t.length,n=e.name,i="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=o[0],l=o[1],c=Math.ceil(t[s-1]/2);let p=c*Math.ceil(t[s-2]/2),u="int b, int row, int col",h=`b * ${p} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<s-1;m++)u=`int b${m}, `+u,p*=t[s-m-1],h=`b${m} * ${p} + `+h;const d=ot();return`
    vec4 ${i}(${u}) {
      int index = ${h};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${a});
      return ${d.texture2D}(${n}, uv);
    }
  `}function lB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[3],r=t[2]*i,o=t[1]*r,{newShape:a,keptDims:l}=E.squeezeShape(t);if(a.length<t.length){const m=za(e,a),f=["row","col","depth","depth2"];return`
      ${$a(m)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${Ba(f,l)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${r}, ${i}, 1)));
        ${Wa(e)}
      }
    `;const c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,u=p[0],h=p[1];if(h===o&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${r}, ${i}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${u}.0);
        return sampleTexture(${s}, uv);
      }
    `;if(h===i&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${u}.0);
        return sampleTexture(${s}, uv);
      }
    `;const d=Do(s);return`
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${r} +
          depth * ${i} + depth2;
      vec2 uv = uvFromFlat(${u}, ${h}, index + ${d});
      return sampleTexture(${s}, uv);
    }
  `}function cB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),i=t[4],r=t[3]*i,o=t[2]*r,a=t[1]*o,{newShape:l,keptDims:c}=E.squeezeShape(t);if(l.length<t.length){const f=za(e,l),y=["row","col","depth","depth2","depth3"];return`
      ${$a(f)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${Ba(y,c)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${a}, ${o}, ${r}, ${i})) +
          depth3;
        ${Wa(e)}
      }
    `;const p=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===a&&p==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${o}, ${r}, ${i}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${h}.0);
        return sampleTexture(${s}, uv);
      }
    `;if(d===i&&p==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${h}.0);
        return sampleTexture(${s}, uv);
      }
    `;const m=Do(s);return`
    float ${n}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${a} + col * ${o} + depth * ${r} +
          depth2 * ${i} + depth3 + ${m};
      vec2 uv = uvFromFlat(${h}, ${d}, index);
      return sampleTexture(${s}, uv);
    }
  `}function pB(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),{newShape:i,keptDims:r}=E.squeezeShape(t);if(i.length<t.length){const y=za(e,i),b=["row","col","depth","depth2","depth3","depth4"];return`
      ${$a(y)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${Ba(b,r)});
      }
    `}const o=t[5],a=t[4]*o,l=t[3]*a,c=t[2]*l,p=t[1]*c;if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${p}, ${c}, ${l}, ${a})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${Wa(e)}
      }
    `;const u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],m=h[1];if(m===p&&u==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${c}, ${l}, ${a}, ${o})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${m}.0, ${d}.0);
        return sampleTexture(${s}, uv);
      }
    `;if(m===o&&u==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${m}.0, ${d}.0);
        return sampleTexture(${s}, uv);
      }
    `;const f=Do(s);return`
    float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${p} + col * ${c} + depth * ${l} +
          depth2 * ${a} + depth3 * ${o} + depth4 + ${f};
      vec2 uv = uvFromFlat(${d}, ${m}, index);
      return sampleTexture(${s}, uv);
    }
  `}function Wa(e){const t=e.name,s=E.sizeFromShape(e.shapeInfo.logicalShape);return s<2?`return ${t};`:`
    for (int i = 0; i < ${s}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function gB(e,t){const s=e.name,n=s.charAt(0).toUpperCase()+s.slice(1),i="get"+n+"AtOutCoords",r=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,a=VC(e.shapeInfo.logicalShape,t.logicalShape),l=Ce(o),c=o-r;let p;const u=["x","y","z","w","u","v"];r===0?p="":o<2&&a.length>=1?p="coords = 0;":p=a.map(S=>`coords.${u[S+c]} = 0;`).join(`
`);let h="";o<2&&r>0?h="coords":h=e.shapeInfo.logicalShape.map((S,x)=>`coords.${u[x+c]}`).join(", ");let d="return outputValue;";const m=E.sizeFromShape(e.shapeInfo.logicalShape),f=m===1,y=E.sizeFromShape(t.logicalShape),b=y===1;if(r===1&&!f&&!b)d=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(f&&!b)o===1?d=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:d=`
        return vec4(outputValue.x);
      `;else if(a.length){const S=r-2,x=r-1;a.indexOf(S)>-1&&a.indexOf(x)>-1?d="return vec4(outputValue.x);":a.indexOf(S)>-1?d="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(x)>-1&&(d="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${i}() {
      ${l} coords = getOutputCoords();
      ${p}
      vec4 outputValue = get${n}(${h});
      ${d}
    }
  `}function yB(e,t){const s=e.name,n=s.charAt(0).toUpperCase()+s.slice(1),i="get"+n+"AtOutCoords",r=t.texShape,o=e.shapeInfo.texShape,a=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&a===l&&e.shapeInfo.flatOffset==null&&E.arraysEqual(o,r))return`
      float ${i}() {
        return sampleTexture(${s}, resultUV);
      }
    `;const c=Ce(l),p=VC(e.shapeInfo.logicalShape,t.logicalShape),u=l-a;let h;const d=["x","y","z","w","u","v"];a===0?h="":l<2&&p.length>=1?h="coords = 0;":h=p.map(f=>`coords.${d[f+u]} = 0;`).join(`
`);let m="";return l<2&&a>0?m="coords":m=e.shapeInfo.logicalShape.map((f,y)=>`coords.${d[y+u]}`).join(", "),`
    float ${i}() {
      ${c} coords = getOutputCoords();
      ${h}
      return get${n}(${m});
    }
  `}function Ce(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function za(e,t){const s=JSON.parse(JSON.stringify(e));return s.shapeInfo.logicalShape=t,s}function Ba(e,t){return t.map(s=>e[s]).join(", ")}class YC{constructor(e,t,s,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,E.assert(e.length>2,()=>`Packed arg${s.charAt(0).toUpperCase()+s.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],r=Math.ceil(i/t);this.outputShape=e.slice(0,-1),r>1&&this.outputShape.push(r),n||this.variableNames.push("bestIndicesA");const o=this.outputShape,a=o.length,l=Ce(a),c=Et("coords",a);let p,u;if(r===1){u=a+1;const R=Ce(u);p=`
        ${R} sourceLocR = ${R}(${c.join()}, 0);
        ++${c[a-1]};
        ${R} sourceLocG = ${R}(${c.join()}, 0);
        ++${c[a-2]};
        ${R} sourceLocA = ${R}(${c.join()}, 0);
        --${c[a-1]};
        ${R} sourceLocB = ${R}(${c.join()}, 0);
        --${c[a-2]};`}else u=a,p=`
        ${l} sourceLocR = coords;
        ++${c[a-1]};
        ${l} sourceLocG = coords;
        ++${c[a-2]};
        ${l} sourceLocA = coords;
        --${c[a-1]};
        ${l} sourceLocB = coords;
        --${c[a-2]};`;const h=["x","y","z","w","u","v"].slice(0,u),d="."+h[u-1],m=h.map(R=>"int "+R),f=Et("sourceLocR",u-1).concat("inIdx.r"),y=Et("sourceLocG",u-1).concat("inIdx.g"),b=Et("sourceLocB",u-1).concat("inIdx.b"),S=Et("sourceLocA",u-1).concat("inIdx.a"),x=s==="max"?"greaterThan":"lessThan",I=n?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${S.join()})));`,A=`vec4(
            getAChannel(${f.join()}),
            hasNextCol ? getAChannel(${y.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${S.join()}) : 0.)`,k=n?"":`
      float getBestIndicesAChannel(${m.join()}) {
        return getChannel(getBestIndicesA(${h.join()}),
                                          vec2(${h.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${m.join()}) {
        return getChannel(getA(${h.join()}),
                               vec2(${h.slice(-2).join()}));
      }
      ${k}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${c[a-1]} < ${o[a-1]-1};
        bool hasNextRow = ${c[a-2]} < ${o[a-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;
          ${I}
          vec4 candidate = ${A};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}}class KC{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=e.dilationHeight,o=e.dilationWidth,a=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=a-1-e.padInfo.top,p=l-1-e.padInfo.left,u=1/(t*s);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${p});
      const float avgMultiplier = float(${u});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${a};
            wR += ${r}) {
          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 < ${l};
            wC+= ${o}) {
            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(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}}class XC{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,s=e.filterHeight,n=e.filterWidth,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=p-1-e.padInfo.front,m=u-1-e.padInfo.top,f=h-1-e.padInfo.left,y=1/(t*s*n);this.userCode=`
      const ivec3 pads = ivec3(${d}, ${m}, ${f});
      const float avgMultiplier = float(${y});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${p};
            wD += ${a}) {
          float dyD = float(dyDCorner + wD) / ${i}.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 += ${l}) {
            float dyR = float(dyRCorner + wR) / ${r}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${h};
                wC += ${c}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class JC{constructor(e,t,s,n,i,r){this.outputShape=[],this.variableNames=["x","mean","variance"],U.assertAndGetBroadcastShape(e,t),U.assertAndGetBroadcastShape(e,s);let o="0.0";n!=null&&(U.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let a="1.0";i!=null&&(U.assertAndGetBroadcastShape(e,i),this.variableNames.push("scale"),a="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${a};
        float inv = scale * inversesqrt(variance + float(${r}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}}class ZC{constructor(e,t,s,n,i,r){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],U.assertAndGetBroadcastShape(e,t),U.assertAndGetBroadcastShape(e,s);let o="vec4(0.0)";n!=null&&(U.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let a="vec4(1.0)";i!=null&&(U.assertAndGetBroadcastShape(e,i),this.variableNames.push("scale"),a="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${a};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${r}));

        setOutput((x - mean) * inv + offset);
      }
    `}}const tx={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class sx{constructor(e,t,s){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=U.assertAndGetBroadcastShape(t,s),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));
      }
    `}}const nx=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,vf="return a + b;",Tf="return a - b;",ix="return a * b;",QC=`
if (a == b) {
  return 1.0;
};
return a / b;`,e0=`
  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;
  }
`,t0=`
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);
`,s0="return float(a == b);",n0="return float(a != b);",i0="return float(a < b);",r0="return float(a <= b);",o0="return float(a > b);",a0="return float(a >= b);",l0="return float(a >= 1.0 && b >= 1.0);",c0="return float(a >= 1.0 || b >= 1.0);",p0=nx+`
  return max(a, b);
`,u0=nx+`
  return min(a, b);
`,h0=`if (b == 0.0) return NAN;
  return mod(a, b);`,d0=nx+`
  return atan(a, b);
`,m0="return (b >= 1.0) ? a : a * (b + 1.0);",rx="return (a < 0.) ? b * a : a;";class mt{constructor(e,t,s){this.variableNames=["A","B"],this.outputShape=U.assertAndGetBroadcastShape(t,s),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}}const Uu=`
  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;
`,f0=`
  // 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;
`,g0=`
  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);
`,y0=`
  // 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));
  `+Uu+`
  return result;
`,ox=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,b0=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,w0=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Uu+`
  return result;
`,x0=`
  return vec4(equal(a, b));
`,L0=`
  return vec4(notEqual(a, b));
`,S0=`
  return vec4(lessThan(a, b));
`,v0=`
  return vec4(lessThanEqual(a, b));
`,T0=`
  return vec4(greaterThan(a, b));
`,I0=`
  return vec4(greaterThanEqual(a, b));
`,A0=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,N0=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,C0=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Uu+`
  return result;
`,R0=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Uu+`
  return result;
`,O0=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Uu+`
  return result;
`;class Qs{constructor(e,t,s,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=U.assertAndGetBroadcastShape(t,s);const i=this.outputShape.length;let r="";if(n)if(i===0||E.sizeFromShape(this.outputShape)===1)r=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else{const o=Ce(i);if(r=`
          ${o} coords = getOutputCoords();
        `,i===1)r+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{const a=Et("coords",i);r+=`
            bool nextRowOutOfBounds =
              (${a[i-2]} + 1) >= ${this.outputShape[i-2]};
            bool nextColOutOfBounds =
              (${a[i-1]} + 1) >= ${this.outputShape[i-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${r}

        setOutput(result);
      }
    `}}class E0{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(s,n)=>{this.minLoc==null&&(this.minLoc=s.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=s.getUniformLocationNoThrow(n,"maxVal")),s.gl.uniform1f(this.minLoc,e),s.gl.uniform1f(this.maxLoc,t)}}}class k0{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(s,n)=>{this.minLoc==null&&(this.minLoc=s.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=s.getUniformLocationNoThrow(n,"maxVal")),s.gl.uniform1f(this.minLoc,e),s.gl.uniform1f(this.maxLoc,t)}}}class _0{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))
        );
      }
    `}}class D0{constructor(e){this.outputShape=[],this.outputShape=U.computeOutShape(e,1),this.variableNames=e.map((r,o)=>`T${o}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let r=1;r<t.length;r++)t[r]=t[r-1]+e[r][1];const s=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let r=1;r<t.length;r++){const o=t[r-1];s.push(`else if (yC < ${t[r]}) setOutput(getT${r}(yR, yC-${o}));`)}const n=t.length,i=t[t.length-1];s.push(`else setOutput(getT${n}(yR, yC-${i}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${s.join(`
        `)}
      }
    `}}class F0{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=U.computeOutShape(e,t);const s=this.outputShape,n=s.length,i=Ce(n),r=Et("coords",n),o=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${f}`);const a=new Array(e.length-1);a[0]=e[0][t];for(let m=1;m<a.length;m++)a[m]=a[m-1]+e[m][t];const l=o[t],c=o.slice(-2),p=o.join();let u=`if (${l} < ${a[0]}) {
        return getChannel(
            getT0(${p}), vec2(${c.join()}));
        }`;for(let m=1;m<a.length;m++){const f=a[m-1];u+=`
        if (${l} < ${a[m]}  && ${l} >= ${a[m-1]}) {
          return getChannel(
            getT${m}(${If(o,l,f)}),
            vec2(${If(c,l,f)}));
        }`}const h=a.length,d=a[a.length-1];u+=`
        return getChannel(
          getT${h}(${If(o,l,d)}),
          vec2(${If(c,l,d)}));`,this.userCode=`
      float getValue(${o.map(m=>"int "+m)}) {
        ${u}
      }

      void main() {
        ${i} coords = getOutputCoords();
        vec4 result = vec4(getValue(${r}), 0., 0., 0.);

        ${r[n-1]} = ${r[n-1]} + 1;
        if (${r[n-1]} < ${s[n-1]}) {
          result.g = getValue(${r});
        }

        ${r[n-2]} = ${r[n-2]} + 1;
        if (${r[n-2]} < ${s[n-2]}) {
          result.a = getValue(${r});
        }

        ${r[n-1]} = ${r[n-1]} - 1;
        if (${r[n-2]} < ${s[n-2]} &&
            ${r[n-1]} < ${s[n-1]}) {
          result.b = getValue(${r});
        }
        setOutput(result);
      }
    `}}function If(e,t,s){const n=e.indexOf(t),i=e.map((r,o)=>o===n?`${r} - ${s}`:r);return i.join()}class M0{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,s=e.strideWidth,n=e.padInfo.top,i=e.padInfo.left,r=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 * ${s} - ${i};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              if (${r}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class U0{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,a=s-1-e.padInfo.left,l=r?1:2,c=r?2:3,p=r?3:1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${a});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${p}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${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 < ${s}; 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 = ${s} - 1 - wC;

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${r}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class $0{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,s=e.strideHeight,n=e.strideWidth,i=e.padInfo.front,r=e.padInfo.top,o=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} - ${i};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${s} - ${r};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${n} - ${o};

                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);
      }
    `}}class W0{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,s=e.filterHeight,n=e.filterWidth,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.front,l=s-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${a}, ${l}, ${c});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${i}.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 < ${s}; wR++) {
            float dyR = float(dyRCorner + wR) / ${r}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${s} - 1 - wR;

            for (int wC = 0; wC < ${n}; 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);

              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);
      }
    `}}class z0{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,s=e.strideWidth,n=e.padInfo.top,i=e.padInfo.left,r=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 * ${r} + 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 * ${s} - ${i};

              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);
      }
    `}}class B0{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,s=e.filterWidth,n=e.strideHeight,i=e.strideWidth,r=t-1-e.padInfo.top,o=s-1-e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${r}, ${o});

      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 < ${s}; 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 = ${s} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${a}; dm++) {
              int d2 = d1 * ${a} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class ax{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,r=e.padInfo.left,o=e.strideHeight,a=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,u=e.filterWidth,h=Math.floor(e.inChannels/4)*4,d=e.inChannels%4,m=e.dataFormat==="channelsLast",f=m?1:2,y=m?2:3,b=m?3:1;let S="",x="";s&&(n?S=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:S=`
          float activation(float x) {
            ${s}
          }
        `,x="result = activation(result);");const I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${S}

      const ivec2 strides = ivec2(${o}, ${a});
      const ivec2 pads = ivec2(${i}, ${r});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${b}];

        ivec2 xRCCorner =
            ivec2(coords[${f}], coords[${y}]) * 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 < ${p}; wR++) {
          int xR = xRCorner + wR * ${l};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${u}; wC++) {
            int xC = xCCorner + wC * ${c};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${h}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${m}) {
                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 (${d===1}) {

              if (${m}) {
                dotProd +=
                    getX(batch, xR, xC, ${h}) *
                    getW(wR, wC, ${h}, d2);
              } else {
                dotProd +=
                    getX(batch, ${h}, xR, xC) *
                    getW(wR, wC, ${h}, d2);
              }

            } else if (${d===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2)
              );

              if (${m}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${d===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2),
                getW(wR, wC, ${h} + 2, d2)
              );

              if (${m}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1),
                  getX(batch, xR, xC, ${h} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC),
                  getX(batch, ${h} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${I}
        ${x}
        setOutput(result);
      }
    `}}class P0{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,s=e.padInfo.top,n=e.padInfo.left,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,u=e.filterHeight,h=e.filterWidth,d=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${i}, ${r}, ${o});
      const ivec3 pads = ivec3(${t}, ${s}, ${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 * ${a};

          if (xF < 0 || xF >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${u}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${h}; 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 (${m===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${d}) *
                  getW(wF, wR, wC, ${d}, d2);
              } else if (${m===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, ${d} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${m===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);
      }
    `}}class lx{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.inHeight,r=e.inWidth,o=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,m=e.outChannels/e.inChannels;let f="",y="";s&&(n?f=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:f=`
          float activation(float x) {
            ${s}
          }
        `,y="result = activation(result);");const b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${f}

      const ivec2 strides = ivec2(${l}, ${c});
      const ivec2 pads = ivec2(${o}, ${a});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${m};
        int q = d2 - d1 * ${m};

        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 < ${h}; wR++) {
          int xR = xRCorner + wR * ${p};

          if (xR < 0 || xR >= ${i}) {
            continue;
          }

          for (int wC = 0; wC < ${d}; wC++) {
            int xC = xCCorner + wC * ${u};

            if (xC < 0 || xC >= ${r}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${b}
        ${y}
        setOutput(result);
      }
    `}}class cx{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,r=e.inWidth,o=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,m=d;let f="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let I=0;I<d;I++)f+=`
          vec4 xTexelR${x}C${I*2} = vec4(0.);
          vec4 wR${x}C${I} = vec4(0.);
          vec4 xR${x}C${I} = vec4(0.);`;for(let x=0;x<h;x++)for(let I=0;I<m;I++){const A=I*2;if(f+=`
          xR = xRCorner + ${x*p};
          xC = xCCorner + ${A*u};
        `,c===1){if(A<d&&(a%2===1?f+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${r}) {
                  xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${r}) {
                    xTexelR${x}C${A}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${x}C${A} = vec4(0.);
                }

                xCOffset = xC + 1 - 2;
                if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${r}) {
                  vec4 previous = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${r}) {
                    previous.zw = vec2(0.);
                  }

                  xR${x}C${A} = vec4(previous.zw, xTexelR${x}C${A}.xy);
                } else {
                  xR${x}C${A} = vec4(0, 0, xTexelR${x}C${A}.xy);
                }
              `:f+=`
                if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${r}) {
                  xTexelR${x}C${A} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${x}C${A} = vec4(0.);
                }

                xR${x}C${A} = xTexelR${x}C${A};
              `,A+1<d)){const k=a%2===0?E.nearestLargerEven(u):u;u%2===0&&a%2===1||u%2!==0&&a%2!==1?(f+=`
                  xCOffset = xC + ${a%2} + ${k};

                  if(xR >= 0 && xR < ${i} &&
                    xCOffset >= 0 && xCOffset < ${r}) {
                    xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,u>1&&(f+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${i} &&
                      xCOffset >= 0 && xCOffset < ${r}) {
                      xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${x}C${A} = vec4(0.);
                    }
                  `),f+=`
                  xR${x}C${A+1} = vec4(
                    xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.xy);
                `):f+=`
                  xCOffset = xC + ${k};

                  if(xR >= 0 && xR < ${i} &&
                    xCOffset >= 0 && xCOffset < ${r}) {
                    xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${x}C${A+1} = xTexelR${x}C${A+2};
                `}}else A<d&&(f+=`
              if(xR >= 0 && xR < ${i}) {
            `,a%2===1?(f+=`
                xCOffset = xC + 1 - ${c};
                if(xCOffset >= 0 && xCOffset < ${r}) {
                  xTexelR${x}C${A} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${x}C${A} = vec4(0.);
                }

                if(xC + 1 >= 0 && xC + 1 < ${r}) {
                  xTexelR${x}C${A+2} = getX(batch, xR, xC + 1, d1);
                } else {
                  xTexelR${x}C${A+2} = vec4(0.);
                }

                xR${x}C${A} = vec4(
                  xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.zw);
              `,A+1<d&&(f+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${c};
                  if(xCOffset >= 0 && xCOffset < ${r}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${x}C${A+1} = vec4(xTexelR${x}C${A+2}.xy, final.xy);
                `)):(f+=`
                if(xC >= 0 && xC < ${r}) {
                  xTexelR${x}C${A} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${x}C${A} = vec4(0.);
                }

                xCOffset = xC + ${c};
                if(xCOffset >= 0 && xCOffset < ${r}) {
                  xTexelR${x}C${A+2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${x}C${A+2} = vec4(0.);
                }

                xR${x}C${A} = vec4(
                  xTexelR${x}C${A}.xy, xTexelR${x}C${A+2}.xy);
              `,A+1<d&&(f+=`
                  xR${x}C${A+1} = vec4(
                    xTexelR${x}C${A}.zw, xTexelR${x}C${A+2}.zw);
                `)),f+="}");A<d&&(f+=`
            vec4 wTexelR${x}C${A} = getW(${x}, ${A}, d1, q);
            wR${x}C${A} = vec4(wTexelR${x}C${A}.xz, wTexelR${x}C${A}.xz);
          `,A+1<d&&(f+=`
              vec4 wTexelR${x}C${A+1} = getW(${x}, ${A+1}, d1, q);
              wR${x}C${A+1} =
                vec4(wTexelR${x}C${A+1}.xz, wTexelR${x}C${A+1}.xz);`))}for(let x=0;x<h;x++)for(let I=0;I<d;I++)f+=`dotProd += xR${x}C${I} * wR${x}C${I};`;let y="",b="";s&&(n?y=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:y=`vec4 activation(vec4 x) {
          ${s}
        }`,b="result = activation(result);");const S=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${y}

      const ivec2 strides = ivec2(${l}, ${c});
      const ivec2 pads = ivec2(${o}, ${a});

      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.);

        ${f}

        vec4 result = dotProd;
        ${S}
        ${b}
        setOutput(result);
      }
    `}}class j0{constructor(e,t,s,n,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[r,o,a,l]=e,[c]=t,[p,u]=s;this.outputShape=[c,p,u,l];const h=n==="bilinear"?1:0,[d,m]=[`${o-1}.0`,`${a-1}.0`],[f,y,b]=p>1?[`${(o-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[S,x,I]=u>1?[`${(a-1)/(u-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
      const float height_ratio = float(${f});
      const float width_ratio = float(${S});
      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 >= ${r}) {
          return;
        }

        float height_scale = ${y};
        float width_scale = ${x};

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${i}));
          return;
        }
        float in_x = ${I};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${i}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${h} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}}class px{constructor(e,t,s){this.variableNames=["x"],this.outputShape=e;const n=e.length,i=t?"0.0":`getX(${V0(n,"coords")})`,r=e[e.length-1];let o="",a="";t?(o=s?`end != ${r-1}`:"end != 0",a=s?"end + 1":"end - 1"):(o=s?`end + pow2 < ${r}`:"end >= pow2",a=s?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${Ce(n)} coords = getOutputCoords();
        int end = ${G0(n,"coords")};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${o}) {
          int idx = ${a};
          ${G0(n,"coords")} = idx;
          val += getX(${V0(n,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,s)=>{this.index==null&&(this.index=t.getUniformLocation(s,"index")),t.gl.uniform1f(this.index,e)}}}function V0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function G0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class H0{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=wr.DENSE;const t=xr(e),s=ot();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${zn(["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);
        }

        ${s.output} = result;
      }
    `}}class q0{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=wr.DENSE;const t=xr(e),s=ot();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${zn(["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));
        }

        ${s.output} = result;
      }
    `}}class Y0{constructor(e,t,s){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=s,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)"}}class K0{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);
      }
    `}}class X0{constructor(e){this.variableNames=["A"],this.outTexUsage=ns.DOWNLOAD;const t=ot();this.outputShape=e,this.userCode=`
      ${Sf}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}}class J0{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ns.DOWNLOAD;const t=ot();this.outputShape=e,this.userCode=`
      ${Sf}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}}class Z0{constructor(e,t,s=!1){this.variableNames=["A"];const n=ot(),[i,r]=t;this.outputShape=e;let o="result";s&&(o="floor(result * 255. + 0.5)"),this.userCode=`
      ${Ua(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / ${r};
        int c = imod(flatIndex, ${r});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${r}.0, ${i}.0);
        vec4 values = ${n.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${n.output} = vec4(${o}, 0., 0., 0.);
      }
    `}}class Q0{constructor(e,t,s=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const n=ot(),[i,r]=t;this.outputShape=e;let o="",a="result";s&&(a="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){const p=l*2+c;o+=`
          localCoords = coords;
          if(localCoords[2] + ${c} < ${e[2]}) {
            localCoords[2] += ${c};
            if(localCoords[1] + ${l} < ${e[1]}) {
              localCoords[1] += ${l};

              flatIndex = getFlatIndex(localCoords);
              offset = imod(flatIndex, 4);

              flatIndex = idiv(flatIndex, 4, 1.);

              r = flatIndex / ${r};
              c = imod(flatIndex, ${r});
              uv = (vec2(c, r) + halfCR) / vec2(${r}.0, ${i}.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=`
      ${Ua(e)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${o}

        ${n.output} = ${a};
      }
    `}}const ux={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class hx{constructor(e,t,s){this.variableNames=["real","imag"];const n=t[1];this.outputShape=t;const i=s?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,r=s?`${n}.0`:"1.0";this.userCode=`
      const float exponentMultiplier = ${i};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${e}
      }

      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) / ${r};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}}class eR{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,s)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(s,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class tR{constructor(e,t,s){this.variableNames=["A","indices"];const n=e.slice();n[s]=t,this.outputShape=n,this.rank=n.length;const i=Ce(this.rank),r=EB(e,s);this.userCode=`
      void main() {
        ${i} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}}function EB(e,t){const s=e.length;if(s>4)throw Error(`Gather for rank ${s} is not yet supported`);if(s===1)return"int(getIndices(resRC))";const n=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[];for(let r=0;r<e.length;r++)r===t?i.push(`int(getIndices(${n[r]}))`):i.push(`${n[r]}`);return i.join()}class sR{constructor(e,t,s){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=s;const n=Ce(t.length),i=Ce(s.length),r=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${n} strides = ${n}(${this.strides});
         void main() {
          ${i} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${r};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}}function nR(e){const t=ot(),s=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return LC(e,s)}function iR(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return IC(e,t)}function rR(e){const t=new Uint16Array([0,1,2,2,1,3]);return AC(e,t)}function $u(e,t,s,n,i,r){CC(t,s);const o=NC(e),a=e.TEXTURE_2D;return oe(e,()=>e.bindTexture(a,o)),oe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),oe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),oe(e,()=>e.texParameteri(a,e.TEXTURE_MIN_FILTER,e.NEAREST)),oe(e,()=>e.texParameteri(a,e.TEXTURE_MAG_FILTER,e.NEAREST)),oe(e,()=>e.texImage2D(a,0,n,t,s,0,i,r,null)),oe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function dx(e){return e.internalFormatFloat}function oR(e,t,s,n){const[i,r]=_o(t,s);return $u(e,i,r,dx(n),n.textureFormatFloat,e.FLOAT)}function mx(e){return e.internalFormatHalfFloat}function aR(e,t,s,n){const[i,r]=_o(t,s);return $u(e,i,r,mx(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function fx(e){return e.downloadTextureFormat}function lR(e,t,s,n){const[i,r]=_o(t,s);return $u(e,i,r,fx(n),e.RGBA,e.UNSIGNED_BYTE)}function gx(e){return e.internalFormatPackedFloat}function cR(e,t,s,n){const[i,r]=oi(t,s);return $u(e,i,r,gx(n),e.RGBA,e.FLOAT)}function yx(e){return e.internalFormatPackedHalfFloat}function pR(e,t,s,n){const[i,r]=oi(t,s);return $u(e,i,r,yx(n),e.RGBA,n.textureTypeHalfFloat)}function uR(e,t,s){const n=0,i=3*4,r=3*4+2*4;oe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s));const o=Yw(e,t,"clipSpacePos",s,3,r,n);return o&&Yw(e,t,"uv",s,2,r,i)}function hR(e,t,s,n,i,r){oe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,a,l;i instanceof Uint8Array?(o=new Uint8Array(s*n*4),a=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(s*n*4),a=e.FLOAT,l=r.internalFormatPackedFloat),o.set(i),oe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,s,n,0,e.RGBA,a,o)),oe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function dR(e,t,s){oe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),s.data instanceof Uint8Array?oe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,s.width,s.height,0,e.RGBA,e.UNSIGNED_BYTE,s.data)):oe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,s)),oe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function mR(e,t,s,n){const i=e.createBuffer();oe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const r=4,o=4,a=r*o*t*s;return oe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ)),oe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,0)),oe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function fR(e,t,s){const n=e,i=new Float32Array(s);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,i),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),i}function gR(e,t,s,n){const[i,r]=_o(t,s),o=4,a=new Uint8Array(bC(t*s,o));return oe(e,()=>e.readPixels(0,0,i,r,n.downloadTextureFormat,e.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function yR(e,t,s,n,i,r,o,a){const l=e,c=new Float32Array(wC(r,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function bR(e,t,s){const n=new Float32Array(t*s*4);return oe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,n)),n}class bx{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=$().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,qw(t,e)):this.gl=Js(t);let s="WEBGL_color_buffer_float";const n="EXT_color_buffer_half_float";if($().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",r="OES_texture_half_float";if(this.textureFloatExtension=Fu(this.gl,i),Zs(this.gl,r))this.textureHalfFloatExtension=Fu(this.gl,r);else if($().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(s),Zs(this.gl,n))this.colorBufferHalfFloatExtension=Fu(this.gl,n);else if($().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(s="EXT_color_buffer_float",Zs(this.gl,s))this.colorBufferFloatExtension=this.gl.getExtension(s);else if(Zs(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=iR(this.gl),this.indexBuffer=rR(this.gl),this.framebuffer=RC(this.gl),this.textureConfig=Du(this.gl,this.textureHalfFloatExtension)}get debug(){return $().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;oe(e,()=>e.finish()),oe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),oe(e,()=>e.deleteFramebuffer(this.framebuffer)),oe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),oe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),oe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),oR(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),aR(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),lR(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),dR(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,s,n){this.throwIfDisposed(),hR(this.gl,e,t,s,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),pR(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),cR(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Kw(this.gl,this.framebuffer),this.outputTexture=null),oe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,s){return this.downloadMatrixDriver(e,()=>gR(this.gl,t,s,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,s,n,i,r){return yR(this.gl,e,t,s,n,i,r,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return fR(this.gl,e,t)}createBufferFromTexture(e,t,s){this.bindTextureToFrameBuffer(e);const n=mR(this.gl,t,s,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,s;if($().getBool("WEBGL_FENCE_API_ENABLED")){const n=e,i=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),s=()=>{const r=n.clientWaitSync(i,0,0);return r===n.ALREADY_SIGNALED||r===n.CONDITION_SATISFIED},t=i}else $().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),s=()=>this.isQueryAvailable(t,$().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):s=()=>!0;return{query:t,isFencePassed:s}}downloadMatrixFromPackedTexture(e,t,s){return this.downloadMatrixDriver(e,()=>bR(this.gl,t,s))}createProgram(e){this.throwIfDisposed();const t=this.gl,s=SC(t,e),n=nR(t),i=vC(t);return oe(t,()=>t.attachShader(i,n)),oe(t,()=>t.attachShader(i,s)),TC(t,i),this.debug&&bf(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=uR(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&oe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&bf(this.gl,this.program),oe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,s=!0){return this.throwIfDisposed(),s?OC(this.gl,e,t):EC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),oe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,s){this.throwIfDisposed(),this.throwIfNoProgram(),kC(this.gl,e,t,s)}setOutputMatrixTexture(e,t,s){this.setOutputMatrixTextureDriver(e,s,t)}setOutputPackedMatrixTexture(e,t,s){this.throwIfDisposed();const[n,i]=oi(t,s);this.setOutputMatrixTextureDriver(e,n,i)}setOutputMatrixWriteRegion(e,t,s,n){this.setOutputMatrixWriteRegionDriver(s,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,s,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&bf(this.gl,this.program),Mu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),oe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),oe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Fu(this.gl,$().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if($().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const s=this.gl,n=this.getQueryTimerExtensionWebGL2(),i=s.createQuery();return s.beginQuery(n.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if($().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,s=this.getQueryTimerExtensionWebGL2();t.endQuery(s.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await E.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,$().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,$().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const s=this.gl,n=s.getQueryParameter(e,s.QUERY_RESULT);return n/1e6}else{const s=this.getQueryTimerExtensionWebGL1(),n=s.getQueryObjectEXT(e,s.QUERY_RESULT_EXT);return n/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const s=this.gl,n=this.getQueryTimerExtensionWebGL2(),i=s.getQueryParameter(e,s.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const s=this.getQueryTimerExtensionWebGL1(),n=s.getQueryObjectEXT(e,s.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=kB(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:s}=this.itemsToPoll[t];s()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;E.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),wf(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(wf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Kw(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const s=t();return this.unbindTextureToFrameBuffer(),s}setOutputMatrixTextureDriver(e,t,s){this.throwIfDisposed();const n=this.gl;wf(n,e,this.framebuffer),this.debug&&Mu(n),this.outputTexture=e,oe(n,()=>n.viewport(0,0,t,s)),oe(n,()=>n.scissor(0,0,t,s))}setOutputMatrixWriteRegionDriver(e,t,s,n){this.throwIfDisposed(),oe(this.gl,()=>this.gl.scissor(e,t,s,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 kB(e){let t=0;for(;t<e.length;++t){const s=e[t]();if(!s)break}return t-1}function wR(e,t,s,n){const i=t.userCode,r=s.map((d,m)=>{const f={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(f.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),o=r.map(d=>d.shapeInfo),a={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},l=GC(r,a,i,t.packedInputs),c=e.createProgram(l);let p=null;const u=e.getUniformLocation(c,"NAN",!1);$().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(c,"INFINITY",!1));const h={};for(let d=0;d<t.variableNames.length;d++){const m=t.variableNames[d],f=!1;h[m]=e.getUniformLocation(c,m,f),h[`offset${m}`]=e.getUniformLocation(c,`offset${m}`,f)}return{program:t,source:l,webGLProgram:c,uniformLocations:h,inShapeInfos:o,outShapeInfo:a,infLoc:p,nanLoc:u}}function xR(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((s,n)=>{const i=s.logicalShape,r=t[n],o=r.shape;if(!E.arraysEqual(i,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${o} must match`);if(s.isUniform&&r.isUniform)return;const a=s.texShape,l=r.isUniform?null:r.texData.texShape;if(!E.arraysEqual(a,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${l} must match`)})}function LR(e,t,s,n,i){xR(t.inShapeInfos,s),xR([t.outShapeInfo],[n]);const r=n.texData.texture,o=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(r,o[0],o[1]):e.setOutputMatrixTexture(r,o[0],o[1]),e.setProgram(t.webGLProgram),$().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),s.forEach((a,l)=>{const c=t.program.variableNames[l],p=t.uniformLocations[c],u=t.uniformLocations[`offset${c}`];if(p==null)return;if(a.isUniform){if(E.sizeFromShape(a.shape)<2)e.gl.uniform1f(p,a.uniformValues[0]);else{let h=a.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),e.gl.uniform1fv(p,h)}return}a.texData.slice!=null&&u!=null&&e.gl.uniform1i(u,a.texData.slice.flatOffset),e.setInputMatrixTexture(a.texData.texture,p,l)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function SR(e,t,s){let n="";t.concat(s).forEach(o=>{const a=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0,l=o.isUniform?"uniform":o.texData.texShape;n+=`${o.shape}_${l}_${a}`});const i=e.userCode;let r=e.constructor.name;return r+="_"+n+"_"+i,r}class vR{constructor(e,t,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:n,inChannels:i,strideWidth:r,strideHeight:o,padInfo:a,outWidth:l,dilationWidth:c,dilationHeight:p,dataFormat:u}=s,{left:h,top:d}=a,m=i*n,f=ot(),y=u==="channelsLast",b=y?0:1,S=y?1:2;let x="";for(let I=0;I<=1;I++)for(let A=0;A<=1;A++)x+=`
          blockIndex = rc.y + ${A};
          pos = rc.x + ${I};

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${l})) * ${o} - ${d};
            d0 = offsetY + ${p} * (pos / ${m});

            if(d0 < ${t[b]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${l}.) * ${r}. - ${h}.);
              d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${i}.));

              if(d1 < ${t[S]} && d1 >= 0) {

                ch = int(mod(float(pos), ${i}.));

                if (${y}) {
                  innerDims = vec2(d1, ch);
                  result[${I*2+A}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${I*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;

        ${x}

        ${f.output} = result;
      }
    `}}class TR{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[];const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${s}) + float(${n}) * sum`;i===.5?a=`inversesqrt(${l})`:i===1?a=`1.0/(${l})`:a=`exp(log(${l}) * float(-${i}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${r}; j <= ${r}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${a};
        setOutput(val);
      }
    `}}class IR{constructor(e,t,s,n,i){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=s,this.alpha=n,this.beta=i,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(${n}) * norm + float(${s});

          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(${n})
                * float(${i})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${i});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}}class AR{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${s}) + float(${n}) * sum`;i===.5?a=`inversesqrt(${l})`:i===1?a=`1.0/(${l})`:a=`exp(log(${l}) * float(-${i}));`,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 - ${r};
        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 = - ${r}; j <= ${r}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));

          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 * ${a};
        setOutput(result);
      }
    `}}class NR{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,s=e.strideWidth,n=e.dilationHeight,i=e.effectiveFilterHeight,r=e.effectiveFilterWidth,o=i-1-e.padInfo.top,a=r-1-e.padInfo.left,l=i*r-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${a});

      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 < ${i};
          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 < ${r}; 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);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${r} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}}class CR{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,s=e.strideHeight,n=e.strideWidth,i=e.dilationDepth,r=e.dilationHeight,o=e.dilationWidth,a=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=a-1-e.padInfo.front,u=l-1-e.padInfo.top,h=c-1-e.padInfo.left,d=a*l*c-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${u}, ${h});

      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 < ${a};
           wD += ${i}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${r}) {
            float dyR = float(dyRCorner + wR) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${c};
                wC += ${o}) {
              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 * ${l} * ${c} +
                  wR * ${c} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class Af{constructor(e,t,s=!1,n=!1,i=!1,r=null,o=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;const a=s?e[1]:e[2],l=Math.ceil(a/2),c=s?"i * 2, rc.y":"rc.y, i * 2",p=n?"rc.z, i * 2":"i * 2, rc.z",u=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=n?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let d="",m="";r&&(o?d=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:d=`vec4 activation(vec4 x) {
          ${r}
        }`,m="result = activation(result);");const f=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${d}

      const float sharedDimension = ${l}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${l}; i++) {
          vec4 a = getMatrixA(rc.x, ${c});
          vec4 b = getMatrixB(rc.x, ${p});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${u[0]} * ${h[0]});
          result += (${u[1]} * ${h[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${f}

        ${m}

        setOutput(result);
      }
    `}}class RR{constructor(e,t,s){this.variableNames=["probs"],this.outputShape=[e,s],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(${t-1}));
      }
    `}getCustomSetupFunc(e){return(t,s)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(s,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}class OR{constructor(e,t,s,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${s}),
                      float(index == coords.y)));
      }
    `}}class ER{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(t===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{const s=Et("rc",t),n=Ce(t),i=_B(t,e,s),r=DB(t,e[e.length-1],e[e.length-2],s),o=FB(e,s);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

          if(${i}) {
            setOutput(vec4(0));
          } else {
            ${r}

            setOutput(vec4(${o}));
          }
        }
      `}}}function MB(e,t){const s=[];for(let n=0;n<=1;n++)for(let i=0;i<=1;i++){let r=`${n===0?"r":"rp1"}, ${i===0?"c":"cp1"}`;for(let o=2;o<e;o++)r=`${t[t.length-1-o]},`+r;s.push(r)}return s}function _B(e,t,s){if(e===1)return`rc > ${t[0]}`;let n="";for(let i=e-2;i<e;i++)n+=`${s[i]} >= ${t[i]}`,i<e-1&&(n+="||");return n}function DB(e,t,s,n){if(e===1)return"";const i=n.slice(-2);return`
    int r = ${i[0]};
    int c = ${i[1]};
    int rp1 = r + 1;
    int cp1 = c + 1;

    bool cEdge = cp1 >= ${t};
    bool rEdge = rp1 >= ${s};
  `}function FB(e,t){const s=e.length,n=MB(s,t);return s===1?`getA(rc),
            rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${n[0]}),
          cEdge ? 0. : getA(${n[1]}),
          rEdge ? 0. : getA(${n[2]}),
          rEdge || cEdge ? 0. : getA(${n[3]})`}class kR{constructor(e,t,s){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);const n=e.length,i=Ce(n),r=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),a=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
        int start = ${r};
        int end = ${o};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(float(${s}));
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${i} start = ${i}(${r});
      ${i} end = ${i}(${o});

      void main() {
        ${i} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(float(${s}));
        } else {
          ${i} coords = outC - start;
          setOutput(getX(${a}));
        }
      }
    `}}class _R{constructor(e,t,s){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);const n=e.length,i=Ce(n),r=t.map(m=>m[0]).join(","),o=t.map((m,f)=>m[0]+e[f]).join(","),a=Et("rc",n),l=Et("source",n),c=`${a[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,u=[`${i} rc = outputLoc;`,`${a[n-1]} += 1;
       if(${c}) {
      `,n===1?"":`}
       rc = outputLoc;
       ${a[n-2]} += 1;
       if(${a[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":`  ${a[n-1]} += 1;
         if(${c}) {`],h=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let d="";for(let m=0,f=n===1?2:4;m<f;m++)d+=`
        ${u[m]}
        if (${h}) {
          result[${m}] = float(${s});
        } else {
          ${i} source = rc - start;
          result[${m}] = getChannel(getX(${l.join()}), ${p});
        }
      `;d+=n===1?"} ":"}}",this.userCode=`
      const ${i} start = ${i}(${r});
      const ${i} end = ${i}(${o});

      void main() {
        ${i} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}}class Fo{constructor(e,t,s,n=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&s)throw new Error("Cannot compute positions for average pool.");const r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,h=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;const m=t==="avg",f=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let b="0.0";if(m||(b="-1.0 / 1e-20"),s){const R=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${a});
        const ivec2 pads = ivec2(${h}, ${d});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${p};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${u};
                wC += ${c}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${R} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${n?i?f:y:`wR * ${u} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const S="max";let x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");const I=Math.floor(r/4)*4,A=r%4,k=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${S}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${o}, ${a});
      const ivec2 pads = ivec2(${h}, ${d});
      const float initializationValue = ${b};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${b});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${p};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${I}; 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)
            );

            ${k}
          }

          int xC = xCCorner + ${I};
          if (${A===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${k}
          } else if (${A===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              initializationValue,
              initializationValue
            );

            ${k}
          } else if (${A===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              initializationValue
            );

            ${k}
          }
        }
        setOutput(${x});
      }
    `}}class Nf{constructor(e,t,s,n=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&s)throw new Error("Cannot compute positions for average pool.");const r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,u=e.dilationWidth,h=e.effectiveFilterDepth,d=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,y=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;const S=t==="avg";let x="0.0";if(S||(x="-1.0 / 1e-20"),s){const F=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${o}, ${a}, ${l});
        const ivec3 pads = ivec3(${f}, ${y}, ${b});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${h};
              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 < ${m};
                  wC += ${u}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${F} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${n?i?`(((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} * ${m} +
                      wR * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const I="max";let A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");const k=Math.floor(r/4)*4,R=r%4,D=`
      if (${S}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${I}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${a}, ${l});
      const ivec3 pads = ivec3(${f}, ${y}, ${b});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${h};
            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 < ${k}; wC += 4) {
              int xC = xCCorner + wC * ${u};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${u}, ch),
                getValue(batch, xD, xR, xC + 2 * ${u}, ch),
                getValue(batch, xD, xR, xC + 3 * ${u}, ch)
              );

              ${D}
            }

            int xC = xCCorner + ${k};
            if (${R===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${D}
            } else if (${R===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${u}, ch),
                initializationValue,
                initializationValue
              );

              ${D}
            } else if (${R===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${u}, ch),
                getValue(batch, xD, xR, xC + 2 * ${u}, ch),
                initializationValue
              );

              ${D}
            }
          }
          setOutput(${A});
        }
      }
    `}}class Cf{constructor(e,t){this.variableNames=["x"];const s=e.windowSize,n=e.batchSize,i=e.inSize,r=Math.ceil(i/s);this.outputShape=[n,r];let o="0.0",a="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",a="min"):t==="max"&&(o="-1.0 / 1e-20",a="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");const c=Math.floor(s/4)*4,p=s%4;let u=`
      if (${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 = ${a}(values, minMaxValue);
      }
    `,h="vec4";t==="all"?(o="1.0",u=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,h="bvec4"):t==="any"&&(o="0.0",u=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,h="bvec4");let d="";i%s>0&&(d=`
        if (inIdx < 0 || inIdx >= ${i}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${o};
      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 * ${s};

        vec4 minMaxValue = vec4(${o});
        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;
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${u}
        }

        int inIdx = inOffset + ${c};
        if (${p===1}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${u}
        } else if (${p===2}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${u}
        } else if (${p===3}) {
          ${h} values = ${h}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${u}
        }
        setOutput(${l});
      }
    `}}class Rf{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let s="";for(let n=0;n<4;n++){let i="thisRC = rc;";n%2===1&&(i+="thisRC.z += 1;"),n>1&&(i+="thisRC.y += 1;"),s+=`
        ${i}
        ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${n}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${n>0?"}":""}
      `}this.userCode=`
      ${UB(t)}
      ${Ua(e)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${e[1]};
        int cols = ${e[2]};

        ${s}

        setOutput(result);
      }
    `}}function UB(e){const t=zn(["r","c","d"],e);return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t}
      return ivec3(r, c, d);
    }
  `}class DR{constructor(e,t,s){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,n,i]=t.shape,[,r,o]=e.shape,a=[s&&r>1?n-1:n,s&&o>1?i-1:i],l=[s&&r>1?r-1:r,s&&o>1?o-1:o],c=a[0]/l[0],p=a[1]/l[1],u=1/c,h=1/p,d=Math.ceil(u)*2+2,m=Math.ceil(h)*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(${u});
        const float invWidthScale = float(${h});

        const int winHeight = int(${d});
        const int winWidth = int(${m});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${r}) {
            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 >= ${o}) {
              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), ${i-1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}}class FR{constructor(e,t,s,n){this.variableNames=["A"],this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s];this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${l[0]/c[0]},
          ${l[1]/c[1]});
      const vec2 inputShapeRC = vec2(${r}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `}}class MR{constructor(e,t,s,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s];this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${l[0]/c[0]},
          ${l[1]/c[1]},
          ${l[1]/c[1]});
      const vec3 inputShapeRC = vec3(${r}.0, ${o}.0,
                                     ${o}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${a-1};
        bool hasNextRow = coords.z < ${s-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}}class UR{constructor(e,t,s){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,n,i]=t.shape,[,r,o]=e.shape,a=[s&&r>1?n-1:n,s&&o>1?i-1:i],l=[s&&r>1?r-1:r,s&&o>1?o-1:o],c=a[0]/l[0],p=a[1]/l[1],u=1/c,h=1/p,d=Math.ceil(u)*2+2,m=Math.ceil(h)*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(${u});
        const float invWidthScale = float(${h});

        const int winHeight = int(${d});
        const int winWidth = int(${m});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${r}) {
            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 >= ${o}) {
              continue;
            }

            float sourceFracRow =
              float(${a[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${a[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${n}) - 1),
                ${s} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${i}) - 1),
                ${s} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}}class $R{constructor(e,t,s,n){this.variableNames=["A"],this.outputShape=[];const[i,r,o,a]=e;this.outputShape=[i,t,s,a];const l=[n&&t>1?r-1:r,n&&s>1?o-1:o],c=[n&&t>1?t-1:t,n&&s>1?s-1:s],p=n?"0.5":"0.0";this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${l[0]/c[0]},
          ${l[1]/c[1]});
      const vec2 inputShapeRC = vec2(${r}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));

        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}}class WR{constructor(e,t){this.variableNames=["x"];const s=e.length;if(s>4)throw new Error(`WebGL backend: Reverse of rank-${s} tensor is not yet supported`);if(this.outputShape=e,s===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}const n=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,i=e.map((o,a)=>n(a)).join(","),r=Ce(s);this.userCode=`
      void main() {
        ${r} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}}class zR{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const s=e.length;if(s>4)throw new Error(`WebGL backend: Reverse of rank-${s} tensor is not yet supported`);this.outputShape=e;const n=Et("rc",s),i=`${n[s-1]} + 1 < ${this.outputShape[s-1]}`,r=`${n[s-2]} + 1 < ${this.outputShape[s-2]}`,o=Ce(s);s===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${i}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${a(n.slice())};
          if(${i}){
            result.g = ${l(n.slice())};
          }
          if(${r}) {
            result.b = ${c(n.slice())};
            if(${i}) {
              result.a = ${p(n.slice())};
            }
          }
          setOutput(result);
        }
    `;function a(d){return u(d)}function l(d){return d[s-1]="("+d[s-1]+" + 1)",u(d)}function c(d){return d[s-2]="("+d[s-2]+" + 1)",u(d)}function p(d){return d[s-1]="("+d[s-1]+" + 1)",d[s-2]="("+d[s-2]+" + 1)",u(d)}function u(d){const m=e.map((b,S)=>h(S,d)),f=m.join(","),y=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${y}))`}function h(d,m){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${m[d]} - 1`:`${m[d]}`}}}class wx{constructor(e,t,s,n,i,r,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=r;const a=Ce(i.length),l=Ce(r.length);let c="";s===1?c="i":s===2&&(c="i, j");const p=`getIndices(${c})`;let u="";n===1?u="i":n===2&&(u="i, coords[1]");const h=`getUpdates(${u})`,d=t>1?"strides[j]":"strides";this.userCode=`
        ${a} strides = ${a}(${i});

        void main() {
          ${l} 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 += ${h};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}}class BR{constructor(e,t){this.variableNames=["x","segmentIds"];const s=e.windowSize,n=e.batchSize,i=e.inSize,r=e.numSegments,o=r*Math.ceil(i/s);this.outputShape=[n,o];const a="0.0",l="sumValue",c=Math.floor(s/4)*4,p=s%4,u=`
        sumValue += dot(values, segFilter);
    `;let h="";i%s>0&&(h=`
        if (inIdx < 0 || inIdx >= ${i}) {
          return initializationValue;
        }
      `);let d="";i%s>0&&(d=`
        if (inIdx < 0 || inIdx >= ${i}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${a};

      float getValue(int batch, int inIdx) {
        ${h}
        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(
          ${r})) * float(${s}));
        int currentSeg = int(mod(float(outIdx), float(${r})));

        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
          );

          ${u}
        }

        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
          );

          ${u}
        } else if (${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
          );

          ${u}
        } else if (${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
          );

          ${u}
        }
        setOutput(${l});
      }
    `}}class PR{constructor(e,t,s){this.variableNames=["c","a","b"],this.outputShape=t;let n,i;if(s>4)throw Error(`Where for rank ${s} is not yet supported`);if(s===1)i="resRC",n="resRC";else{const o=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&a.push(`${o[c]}`);n=a.join(),i=l.join()}const r=Ce(s);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        float cVal = getC(${n});
        if (cVal >= 1.0) {
          setOutput(getA(${i}));
        } else {
          setOutput(getB(${i}));
        }
      }
    `}}class jR{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Ce(this.rank),s=`uniform int start[${this.rank}];`,n=$B(this.rank);let i;const r=e.map((o,a)=>`sourceLoc.${xx[a]} = start[${a}] + coords.${xx[a]};`);i=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `,this.userCode=`
      ${s}
      void main() {
        ${i}
        setOutput(getSource(${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,s)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(s,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}const xx=["x","y","z","w","u","v"];function $B(e){if(e===1)return"sourceLoc";if(e<=6)return xx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class VR{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Ce(this.rank),s=Et("coords",this.rank),n=Et("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,r=`getChannel(getSource(${n.join()}), ${i})`,o=`
      result.x = ${r};
      if (++${s[this.rank-1]} < ${e[this.rank-1]}) {
        ++${n[this.rank-1]};
        result.y = ${r};
        --${n[this.rank-1]};
      }
    `,a=this.rank===1?"":`
      --${s[this.rank-1]};
      if (++${s[this.rank-2]} < ${e[this.rank-2]}) {
        ++${n[this.rank-2]};
        result.z = ${r};
        if (++${s[this.rank-1]} < ${e[this.rank-1]}) {
          ++${n[this.rank-1]};
          result.w = ${r};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${s[p]} + start[${p}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${o}
        ${a}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,s)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(s,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class GR{constructor(e,t,s){this.variableNames=["x"],this.outputShape=s;const n=s.length,i=Ce(s.length),r=Ce(s.length);let o="";if(n===1)o="coords * strides + begin";else{let a=0;o=s.map((l,c)=>(a++,s.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${a-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
      ${i} begin = ${i}(${e});
      ${i} strides = ${i}(${t});

      void main() {
        ${r} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}}class KR{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,s){const n=qR(t,s),i=YR(e,n,s);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const r=HR(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,s);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=r,this.log();const a=this.freeTextures[i].shift();return this.usedTextures[i].push(a),a}let o;return n===Kt.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Kt.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Kt.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Kt.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Kt.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(o),this.numUsedTextures++,this._numBytesAllocated+=r,this.log(),o}releaseTexture(e,t,s,n){if(this.freeTextures==null)return;const i=qR(s,n),r=YR(t,i,n);r in this.freeTextures||(this.freeTextures[r]=[]);const o=HR(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,n),a=$().get("WEBGL_DELETE_TEXTURE_THRESHOLD");a!==-1&&this._numBytesAllocated>a?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[r].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;const l=this.usedTextures[r],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function WB(e,t){const s=e;if(t===s.R32F)return 4;if(t===s.R16F)return 2;if(t===s.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===s.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function HR(e,t,s,n,i){const r=zB(t,n);let o;if(i){const[l,c]=oi(e[0],e[1]);o=l*c}else{const[l,c]=_o(e[0],e[1]);o=l*c}const a=WB(s,r);return o*a}function zB(e,t){switch(e){case Kt.PACKED_2X2_FLOAT32:return gx(t);case Kt.PACKED_2X2_FLOAT16:return yx(t);case Kt.UNPACKED_FLOAT32:return dx(t);case Kt.UNPACKED_FLOAT16:return mx(t);case Kt.PACKED_4X1_UNSIGNED_BYTE:return fx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function BB(e){return $().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Kt.PACKED_2X2_FLOAT32:Kt.UNPACKED_FLOAT32:e?Kt.PACKED_2X2_FLOAT16:Kt.UNPACKED_FLOAT16}function qR(e,t){if(e===ns.UPLOAD)return Kt.PACKED_2X2_FLOAT32;if(e===ns.RENDER||e==null)return BB(t);if(e===ns.DOWNLOAD||e===ns.PIXELS)return Kt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function YR(e,t,s){return`${e[0]}_${e[1]}_${t}_${s}`}class XR{constructor(e,t){this.variableNames=["A"];const s=new Array(e.length);for(let r=0;r<s.length;r++)s[r]=e[r]*t[r];this.outputShape=s,this.rank=s.length;const n=Ce(this.rank),i=PB(e);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${i}));
      }
    `}}function PB(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;const s=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let i=0;i<e.length;i++)n.push(`imod(${s[i]}, ${e[i]})`);return n.join()}class _e{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}}const Bn="if (isnan(x)) return x;",JR="return x;",Lx="return abs(x);",Sx=Bn+`
  return (x < 0.0) ? 0.0 : x;
`,vx=Bn+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Tx="return (x >= 0.0) ? x : (exp(x) - 1.0);",ZR=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${U.SELU_SCALEALPHA};
  float scale = ${U.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function QR(e=0){return Bn+`
    return x > 0.0 ? 1.0 : float(${e});
  `}const Ix="return -x;",Ax="return ceil(x);",Nx="return floor(x);",e2=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,t2="return float(isnan(x));",s2="return float(isinf(x));",n2="return float(!isnan(x) && !isinf(x));",i2=`
  // 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;
    }
  }
`,Cx="return exp(x);",Rx="return exp(x) - 1.0;",r2=`if (x < 0.0) return NAN;
  return log(x);`,o2="return log(1.0 + x);",a2="return sqrt(x);",l2="return inversesqrt(x);",c2="return 1.0 / (1.0 + exp(-1.0 * x));",p2=`
  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;
`,u2=Bn+`
  return sin(x);
`,h2=Bn+`
  return cos(x);
`,d2="return tan(x);",m2=Bn+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,f2=Bn+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,g2=Bn+`
  return atan(x);
`,y2=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,b2=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,w2=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,x2=Bn+"return log(x + sqrt(x * x + 1.0));",L2=Bn+`
  if (x < 1.0) return NAN;
  return log(x + sqrt(x * x - 1.0));`,S2=Bn+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,v2=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${U.ERF_P};
  float a1 = ${U.ERF_A1};
  float a2 = ${U.ERF_A2};
  float a3 = ${U.ERF_A3};
  float a4 = ${U.ERF_A4};
  float a5 = ${U.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));
`,T2="return x * x;",I2="return 1.0 / x;",A2="return float(!(x >= 1.0));",N2="return float(int(x));",Wu="return x;";const C2="return x;",R2=`
  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;
`,Ox=`
  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;
`,Ex=`
  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;
`,kx=`
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;class Pa{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}}class O2{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,s=Et("rc",t),n=Ce(t),i=jC(t,s),r=s.slice(-2),o=t<=1?"rc":`vec2(${r.join(",")})`;this.userCode=`
      void main() {
        ${n} rc = getOutputCoords();
        vec4 packedInput = getA(${i});

        setOutput(getChannel(packedInput, ${o}));
      }
    `}}const{segment_util:E2}=U,jB=St.split,VB=St.tile,GB=St.topkImpl,HB=St.whereImpl,qB=1e-7,YB=1e-4,Of={};function KB(e){return e in Of||(Of[e]={}),Of[e]}function Ef(e,t=!1){if(e==="linear")return t?C2:JR;if(e==="relu")return t?Ox:Sx;if(e==="elu")return t?kx:Tx;if(e==="relu6")return t?Ex:vx;if(e==="prelu")return t?ox:rx;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const XB=128,JB=600;function ZB(){return $().global.screen==null?1024:$().global.screen.height*$().global.screen.width*window.devicePixelRatio*JB/1024/1024}const k2=1e3;class _x extends Jp{constructor(e){super();if(this.pendingRead=new WeakMap(),this.pendingDisposal=new WeakSet(),this.dataRefCount=new WeakMap(),this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!$().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Js($().getNumber("WEBGL_VERSION"));this.binaryCache=KB($().getNumber("WEBGL_VERSION")),this.gpgpu=new bx(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new KR(this.gpgpu),this.numMBBeforeWarning=ZB(),this.texData=new Xp(this,os())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,s){if(($().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||$().getBool("DEBUG"))&&this.checkNumericalProblems(e),s==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const n={};return this.texData.set(n,{shape:t,dtype:s,values:e,usage:ns.UPLOAD}),n}move(e,t,s,n){if($().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:s,dtype:n,values:t,usage:ns.UPLOAD})}readSync(e){const t=this.texData.get(e),{values:s,dtype:n,complexTensors:i,slice:r,shape:o,isPacked:a}=t;if(r!=null){let u;a?u=new Pa(o,Wu):u=new _e(o,Wu);const h=this.runWebGLProgram(u,[{dataId:e,shape:o,dtype:n}],n),d=this.readSync(h.dataId);return this.disposeData(h.dataId),d}if(s!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return s;const l=this.activeTimers!=null;let c;l&&(c=E.now());let p;if(n==="complex64"){const u=i.real.dataSync(),h=i.imag.dataSync();p=U.mergeRealAndImagArrays(u,h)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=E.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){const d=this.pendingRead.get(e);return new Promise(m=>d.push(m))}const t=this.texData.get(e),{values:s,shape:n,slice:i,dtype:r,complexTensors:o,isPacked:a}=t;if(i!=null){let d;a?d=new Pa(n,Wu):d=new _e(n,Wu);const m=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:r}],r),f=this.read(m.dataId);return this.disposeData(m.dataId),f}if(s!=null)return this.convertAndCacheOnCPU(e);if(!$().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&$().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(r!=="complex64"&&$().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);const d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture,...xr(n))}this.pendingRead.set(e,[]),r!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(r==="complex64"){const d=await Promise.all([o.real.data(),o.imag.data()]),m=d[0],f=d[1];p=U.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{const d=E.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}c!=null&&this.disposeData(c.dataId);const u=this.convertAndCacheOnCPU(e,p),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach(d=>d(u)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),u}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const s=e[t];if(!xC(s))throw $().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${s} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${s} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:s,isPacked:n}=this.texData.get(e),i=E.sizeFromShape(t);if($().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const u=this.decode(e),h=this.texData.get(u.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(h.texture,...xr(t)).subarray(0,i);return this.disposeData(u.dataId),d}const r=$().getBool("WEBGL_PACK")&&n===!0,o=r?xf(t):t,a=r?new J0(o):new X0(o),l=this.runWebGLProgram(a,[{shape:o,dtype:s,dataId:e}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,i);return this.disposeData(l.dataId),p}async time(e){const t=this.activeTimers,s=[];let n=!1;this.programTimersStack==null?(this.programTimersStack=s,n=!0):this.activeTimers.push(s),this.activeTimers=s,e();const i=E.flatten(this.activeTimers.map(a=>a.query)).filter(a=>a!=null),r=E.flatten(this.activeTimers.map(a=>a.name)).filter(a=>a!=null);this.activeTimers=t,n&&(this.programTimersStack=null);const o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if($().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const a=await Promise.all(i);o.kernelMs=E.sum(a),o.getExtraProfileInfo=()=>a.map((l,c)=>({name:r[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return $().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:E.now(),endMs:null}}endTimer(e){return $().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=E.now(),e)}async getQueryTime(e){if($().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:s,texShape:n,usage:i,isPacked:r,slice:o}=this.texData.get(e),a=o&&o.origDataId||e,l=this.dataRefCount.get(a);l>1?this.dataRefCount.set(a,l-1):(this.dataRefCount.delete(a),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,s),this.textureManager.releaseTexture(t,n,i,r)));const c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return $().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=os().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=XB){const s=this.getCPUBackend();return!this.warnedAboutCPUBackend&&s==null&&!$().getBool("IS_TEST")&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),s!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&E.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const s=this.makeOutput(e.shape,"complex64"),n=this.texData.get(s.dataId);return n.complexTensors={real:os().keep(e.clone()),imag:os().keep(t.clone())},s}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,s){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,s);if(E.sizeFromShape(s)===0)return ze([],s,e.dtype);const{isPacked:n}=this.texData.get(e.dataId),i=Jn.isSliceContinous(e.shape,t,s);if(n||!i){const r=$().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VR(s):new jR(s),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,s)}shallowSlice(e,t,s){const n=this.texData.get(e.dataId),i=this.makeOutput(s,e.dtype),r=this.texData.get(i.dataId);Object.assign(r,n),r.shape=s,r.dtype=e.dtype;let o=Jn.computeFlatOffset(t,e.strides);n.slice&&(o+=n.slice.flatOffset),r.slice={flatOffset:o,origDataId:n.slice&&n.slice.origDataId||e.dataId};const a=this.dataRefCount.get(r.slice.origDataId)||1;return this.dataRefCount.set(r.slice.origDataId,a+1),i}stridedSlice(e,t,s,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,s,n);const i=Jn.computeOutShape(t,s,n);if(i.some(o=>o===0))return ze([],i);const r=new GR(t,n,i);return this.compileAndRun(r,[e])}reverse(e,t){const s=$().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zR(e.shape,t):new WR(e.shape,t);return this.compileAndRun(s,[e])}concat(e,t){if(e[0].dtype==="complex64"){const o=e.map(l=>$t(l)),a=e.map(l=>Ht(l));return Ze(this.concat(o,t),this.concat(a,t))}if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(e.length===1)return e[0];if(e.length>$().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.concat(e.slice(0,o),t),l=this.concat(e.slice(o),t);return this.concat([a,l],t)}if($().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const o=new F0(e.map(a=>a.shape),t);return this.compileAndRun(o,e)}const s=U.computeOutShape(e.map(o=>o.shape),t),n=e.map(o=>o.as2D(-1,E.sizeFromShape(o.shape.slice(t)))),i=new D0(n.map(o=>o.shape)),r=this.compileAndRun(i,n);return r.reshape(s)}neg(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.neg(e);if($().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Ix,e.dtype);const t=new _e(e.shape,Ix);return this.compileAndRun(t,[e])}batchMatMul(e,t,s,n){const i=s?e.shape[2]:e.shape[1],r=n?t.shape[1]:t.shape[2],o=s?e.shape[1]:e.shape[2],[a,,]=e.shape;if((i===1||r===1)&&o>k2){s&&(e=se(e,[0,2,1])),n&&(t=se(t,[0,2,1]));const p=r===1?e:e.as3D(a,o,1),u=r===1?2:1,h=r===1?t.as3D(a,1,o):t;return this.multiply(p,h).sum(u,!0)}const l=wt(e.dtype,t.dtype),c=new Af(e.shape,[a,i,r],s,n);return this.compileAndRun(c,[e,t],l)}fusedBatchMatMul({a:e,b:t,transposeA:s,transposeB:n,bias:i,activation:r,preluActivationWeights:o}){const a=s?e.shape[2]:e.shape[1],l=n?t.shape[1]:t.shape[2],[c,,]=e.shape,p=wt(e.dtype,t.dtype),u=i!=null,h=o!=null,d=r?Ef(r,!0):null,m=new Af(e.shape,[c,a,l],s,n,u,d,h),f=[e,t];return i&&f.push(i),o&&f.push(o),this.compileAndRun(m,f,p)}multiply(e,t){if(e.dtype==="complex64"){const n=this.texData.get(e.dataId),i=this.texData.get(t.dataId),r=new sx(tx.REAL,e.shape,t.shape),o=new sx(tx.IMAG,e.shape,t.shape),a=[this.makeComplexComponentTensorInfo(e,n.complexTensors.real),this.makeComplexComponentTensorInfo(e,n.complexTensors.imag),this.makeComplexComponentTensorInfo(t,i.complexTensors.real),this.makeComplexComponentTensorInfo(t,i.complexTensors.imag)],l=this.compileAndRun(r,a),c=this.compileAndRun(o,a),p=this.complex(l,c);return l.dispose(),c.dispose(),p}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,ix,e.dtype);const s=new mt(ix,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}batchNorm(e,t,s,n,i,r){const o=[e,t,s];let a=null;n!=null&&(a=n.shape,o.push(n));let l=null;if(i!=null&&(l=i.shape,o.push(i)),$().getBool("WEBGL_PACK_NORMALIZATION")){const p=new ZC(e.shape,t.shape,s.shape,a,l,r);return this.compileAndRun(p,o)}const c=new JC(e.shape,t.shape,s.shape,a,l,r);return this.compileAndRun(c,o)}localResponseNormalization4D(e,t,s,n,i){const r=$().getBool("WEBGL_PACK_NORMALIZATION")?new AR(e.shape,t,s,n,i):new TR(e.shape,t,s,n,i);return this.compileAndRun(r,[e])}LRNGrad(e,t,s,n,i,r,o){const a=new IR(t.shape,n,i,r,o);return this.compileAndRun(a,[t,s,e])}tile(e,t){if(e.dtype==="string"){const n=this.readSync(e.dataId),i=n.map(o=>E.decodeString(o)),r=le(e.shape,e.dtype,i);return VB(r,t)}const s=new XR(e.shape,t);return this.compileAndRun(s,[e])}pad(e,t,s){const n=$().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _R(e.shape,t,s):new kR(e.shape,t,s);return this.compileAndRun(n,[e])}gather(e,t,s){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.gather(e,t,s);const n=new tR(e.shape,t.size,s);return this.compileAndRun(n,[e,t])}batchToSpaceND(e,t,s){E.assert(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const n=t.reduce((c,p)=>c*p),i=U.getReshaped(e.shape,t,n),r=U.getPermuted(i.length,t.length),o=U.getReshapedPermuted(e.shape,t,n),a=U.getSliceBeginCoords(s,t.length),l=U.getSliceSize(o,s,t.length);return se(e.reshape(i),r).reshape(o).slice(a,l)}spaceToBatchND(e,t,s){E.assert(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const n=t.reduce((p,u)=>p*u),i=[[0,0]];i.push(...s);for(let p=1+t.length;p<e.shape.length;++p)i.push([0,0]);const r=e.pad(i),o=U.getReshaped(r.shape,t,n,!1),a=U.getPermuted(o.length,t.length,!1),l=U.getReshapedPermuted(r.shape,t,n,!1),c=se(r.reshape(o),a);return O(c,l)}reduce(e,t,s){const n=e.shape[0],i=e.shape[1],r=U.computeOptimalWindowSize(i),o={windowSize:r,inSize:i,batchSize:n},a=new Cf(o,t),l=this.compileAndRun(a,[e],s);return l.shape[1]===1?l:this.reduce(l,t,s)}argReduce(e,t,s=null){let n=e.shape[0],i=e.shape[1];s!=null&&(n=s.shape[0],i=s.shape[1]);const r=U.computeOptimalWindowSize(i),o={windowSize:r,inSize:i,batchSize:n},a=new PC(o,t,s==null),l=[e];s!=null&&l.push(s);const c=this.compileAndRun(a,l,"int32");return c.shape[1]===1?c:this.argReduce(e,t,c)}argReducePacked(e,t,s=null){const n=s!=null?s.shape:e.shape,i=n[n.length-1],r=U.computeOptimalWindowSize(i),o=new YC(n,r,t,s==null),a=s==null?[e]:[e,s],l=this.compileAndRun(o,a,"int32");return l.rank===e.rank?this.argReducePacked(e,t,l):l}sum(e,t){U.assertAxesAreInnerMostDims("sum",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=E.sizeFromShape(n),r=e.as2D(-1,i),o=tp(e.dtype);return this.reduce(r,"sum",o).reshape(s)}prod(e,t){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.prod(e,t);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=E.sizeFromShape(n),r=e.as2D(-1,i),o=tp(e.dtype);return this.reduce(r,"prod",o).reshape(s)}unsortedSegmentSum(e,t,s){let n=0;const i=U.getAxesPermutation([n],e.rank);let r=e;i!=null&&(r=se(e,i),n=U.getInnerMostAxes(1,e.rank)[0]);const o=E2.computeOutShape(r.shape,n,s),a=E.sizeFromShape([r.shape[n]]),l=r.as2D(-1,a),c=tp(e.dtype);let p=this.segOpCompute(l,"unsortedSegmentSum",t,c,s).reshape(o);return i!=null&&(p=se(p,U.getUndoAxesPermutation(i))),p}segOpCompute(e,t,s,n,i){const r=e.shape[0],o=e.shape[1],a=E2.segOpComputeOptimalWindowSize(o,i),l={windowSize:a,inSize:o,batchSize:r,numSegments:i},c=new BR(l,t),p=this.compileAndRun(c,[e,s],n);return p.shape[1]===i?p:(s=ei(0,i).tile([o/a]),this.segOpCompute(p,t,s,n,i))}argMinMaxReduce(e,t,s){const n=[t];if(U.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),n,e.rank),!$().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,r]=U.computeOutAndReduceShapes(e.shape,n),o=E.sizeFromShape(r),a=e.as2D(-1,o);return this.argReduce(a,s).reshape(i)}return this.argReducePacked(e,s)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,s,n){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let r=e;for(let o=0;o<=Math.ceil(Math.log2(i))-1;o++){const a=new px(e.shape,!1,n),l=a.getCustomSetupFunc(o),c=r;r=this.compileAndRun(a,[r],r.dtype,l),c.dispose()}if(s){const o=new px(e.shape,s,n),a=r;r=this.compileAndRun(o,[r]),a.dispose()}return r}equal(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,x0,"bool");const s=new mt(s0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}notEqual(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,L0,"bool");const s=new mt(n0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}less(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,S0,"bool");const s=new mt(i0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,v0,"bool");const s=new mt(r0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greater(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,T0,"bool");const s=new mt(o0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,I0,"bool");const s=new mt(a0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}logicalNot(e){const t=new _e(e.shape,A2);return this.compileAndRun(t,[e])}logicalAnd(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,A0,"bool");const s=new mt(l0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}logicalOr(e,t){if($().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N0,"bool");const s=new mt(c0,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}select(e,t,s){const n=new PR(e.rank,t.shape,t.rank);return this.compileAndRun(n,[e,t,s],wt(t.dtype,s.dtype))}where(e){U.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return HB(e.shape,t)}topk(e,t,s){const n=e.dataSync();return GB(n,e.shape,e.dtype,t,s)}min(e,t){U.assertAxesAreInnerMostDims("min",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=E.sizeFromShape(n),r=e.as2D(-1,i);return this.reduce(r,"min",r.dtype).reshape(s)}minimum(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);const s=$().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qs(R0,e.shape,t.shape):new mt(u0,e.shape,t.shape);return this.compileAndRun(s,[e,t])}mod(e,t){const s=$().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qs(O0,e.shape,t.shape):new mt(h0,e.shape,t.shape);return this.compileAndRun(s,[e,t])}maximum(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);const s=$().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qs(C0,e.shape,t.shape):new mt(p0,e.shape,t.shape);return 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t=this.texData.get(e.dataId),s=new _0(e.shape),n=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(s,n)}sigmoid(e){const t=new _e(e.shape,c2);return this.compileAndRun(t,[e])}softplus(e){const t=new _e(e.shape,p2);return this.compileAndRun(t,[e])}sin(e){const t=new _e(e.shape,u2);return this.compileAndRun(t,[e])}cos(e){const t=new _e(e.shape,h2);return this.compileAndRun(t,[e])}tan(e){const t=new _e(e.shape,d2);return this.compileAndRun(t,[e])}asin(e){const t=new _e(e.shape,m2);return this.compileAndRun(t,[e])}acos(e){const t=new _e(e.shape,f2);return this.compileAndRun(t,[e])}atan(e){const t=new _e(e.shape,g2);return this.compileAndRun(t,[e])}atan2(e,t){const s=$().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qs(w0,e.shape,t.shape):new mt(d0,e.shape,t.shape);return this.compileAndRun(s,[e,t])}sinh(e){const t=new _e(e.shape,y2);return this.compileAndRun(t,[e])}cosh(e){const t=new 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this.reshape(this.fusedBatchMatMul({a:R,b:D,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r}),s.outShape)}const y=u?o[0]*o[1]*(o[2]+1):o[0]*o[2]*(o[3]+1),b={dataId:e.dataId,shape:[1,y,s.inChannels],dtype:e.dtype},S=a.shape;a.shape=a.shape.slice(),a.shape[a.shape.length-2]++,E.assert(vr(a.shape,b.shape),()=>`packed reshape ${a.shape} to ${b.shape} isn't free`);const x=this.reshape(t,[1,s.inChannels,s.outChannels]),I=this.fusedBatchMatMul({a:b,b:x,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r}),A=this.texData.get(I.dataId);return E.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),a.shape=S,A.shape=s.outShape,os().makeTensorFromDataId(I.dataId,s.outShape,I.dtype)}conv2dWithIm2Row(e,t,s,n,i,r){const{filterWidth:o,filterHeight:a,inChannels:l,outWidth:c,outHeight:p,dataFormat:u}=s,h=u==="channelsLast",d=o*a*l,m=p*c,f=[d,m],y=!0,b=!1,S=e.squeeze([0]),x=t.reshape([1,d,-1]),I=new 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n&&p.push(n),r&&p.push(r),this.compileAndRun(c,p)}conv2d(e,t,s){if(s.filterHeight===1&&s.filterWidth===1&&s.dilationHeight===1&&s.dilationWidth===1&&s.strideHeight===1&&s.strideWidth===1&&(s.padInfo.type==="SAME"||s.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,s);if($().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,s);const n=new ax(s);return this.compileAndRun(n,[e,t])}conv2dDerInput(e,t,s){const n=new U0(s);return this.compileAndRun(n,[e,t])}conv2dDerFilter(e,t,s){const n=new M0(s);return this.compileAndRun(n,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){const o=$().getBool("WEBGL_PACK_DEPTHWISECONV")&&s.strideWidth<=2&&s.outChannels/s.inChannels===1,a=i?Ef(i,o):null,l=[e,t],c=n!=null,p=r!=null;c&&l.push(n),p&&l.push(r);let u;return o?(u=new cx(s,c,a,p),this.compileAndRun(u,l)):(u=new lx(s,c,a,p),this.compileAndRun(u,l))}depthwiseConv2D(e,t,s){let n;return 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n=e.shape[0],i=s==="NHWC"?e.shape[1]:e.shape[2],r=s==="NHWC"?e.shape[2]:e.shape[3],o=s==="NHWC"?e.shape[3]:e.shape[1],a=i*t,l=r*t,c=o/(t*t),p=s==="NHWC"?[n,a,l,c]:[n,c,a,l],u=new Y0(p,t,s);return this.compileAndRun(u,[e])}split(e,t,s){return jB(e,t,s)}scatterND(e,t,s){const{sliceRank:n,numUpdates:i,sliceSize:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=[a/r,r],c=e.reshape([i,n]),p=t.reshape([i,r]);if(a===0)return U.reshapeTensor(ze([]),s);const u=j(0),h=new wx(i,n,c.rank,p.rank,o,l),d=this.compileAndRun(h,[p,c,u]);return d.reshape(s)}sparseToDense(e,t,s,n){const{sliceRank:i,numUpdates:r,strides:o,outputSize:a}=U.calculateShapes(t,e,s),l=!1,c=new wx(r,i,e.rank,t.rank,o,[a,1],l),p=this.compileAndRun(c,[t,e,n]);return p.reshape(s)}fft(e){const t=!1;return this.fftImpl(e,t)}ifft(e){const t=!0;return this.fftImpl(e,t)}fftImpl(e,t){const s=this.texData.get(e.dataId),n=new hx(ux.REAL,e.shape,t),i=new hx(ux.IMAG,e.shape,t),r=[this.makeComplexComponentTensorInfo(e,s.complexTensors.real),this.makeComplexComponentTensorInfo(e,s.complexTensors.imag)],o=this.compileAndRun(n,r),a=this.compileAndRun(i,r),l=this.complex(o,a).as2D(e.shape[0],e.shape[1]);return o.dispose(),a.dispose(),l}gatherND(e,t){const s=t.shape,n=s[s.length-1],[i,r,o,a]=U.prepareAndValidate(e,t),l=t.reshape([r,n]),c=e.reshape([e.size/o,o]),p=new sR(n,a,[r,o]),u=this.compileAndRun(p,[c,l]);return u.reshape(i)}fill(e,t,s){if(s=s||E.inferDtype(t),s==="string"){const n=E.getArrayFromDType(s,E.sizeFromShape(e));return n.fill(t),os().makeTensor(n,e,s,this)}else{const n=new eR(e,t),i=n.getCustomSetupFunc(t);return this.compileAndRun(n,[],s,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,s){return U.linspaceImpl(e,t,s)}makeTensorInfo(e,t){const s=this.write(null,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t){const{dataId:s}=this.makeTensorInfo(e,t);return os().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new O2(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new ER(e.shape),s=!0;return this.runWebGLProgram(t,[e],e.dtype,null,s)}packedReshape(e,t){const s=[Lr(e.shape),...Sr(e.shape)],n={dtype:e.dtype,shape:s,dataId:e.dataId},i=[Lr(t),...Sr(t)],r=new Rf(i,s),o=!0,a=this.runWebGLProgram(r,[n],e.dtype,null,o);return{dataId:a.dataId,shape:t,dtype:a.dtype}}decode(e){const t=this.texData.get(e),{isPacked:s,shape:n,dtype:i}=t,r=xf(n);let o;s?o=new q0(r):o=new H0(r);const a=!0,l=this.runWebGLProgram(o,[{shape:r,dtype:i,dataId:e}],i,null,a);return{dtype:i,shape:n,dataId:l.dataId}}runWebGLProgram(e,t,s,n,i=!1){const r=this.makeTensorInfo(e.outputShape,s),o=this.texData.get(r.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===wr.DENSE){const m=xr(e.outputShape);o.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),E.sizeFromShape(r.shape)===0)return o.values=E.getTypedArrayFromDType(r.dtype,0),r;const a=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let f=this.texData.get(m.dataId);if(f.texture==null){if(!e.packedInputs&&E.sizeFromShape(m.shape)<=$().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:f.values};e.packedInputs&&(f.isPacked=!0,f.shape=m.shape)}else if(!!f.isPacked!==!!e.packedInputs)m=f.isPacked?this.unpackTensor(m):this.packTensor(m),a.push(m),f=this.texData.get(m.dataId);else if(f.isPacked&&!vr(f.shape,m.shape)){const y=m,b=m.shape;m.shape=f.shape,m=this.packedReshape(m,b),a.push(m),f=this.texData.get(m.dataId),y.shape=b}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:f,isUniform:!1}});this.uploadToGPU(r.dataId);const c={shape:r.shape,texData:o,isUniform:!1},p=SR(e,l,c),u=this.getAndSaveBinary(p,()=>wR(this.gpgpu,e,l,c)),h=this.activeTimers!=null;let d;if(h&&(d=this.startTimer()),LR(this.gpgpu,u,l,c,n),a.forEach(m=>this.disposeData(m.dataId)),h&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!$().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&i===!1){const m=this.unpackTensor(r);return this.disposeData(r.dataId),m}return r}compileAndRun(e,t,s,n,i=!1){s=s||t[0].dtype;const r=this.runWebGLProgram(e,t,s,n,i);return os().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!$().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=N(()=>{if(!$().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=$().getBool("DEBUG");$().set("DEBUG",!1);const t=this.abs(j(1e-8)).dataSync()[0];if($().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?qB:YB}uploadToGPU(e){const t=this.texData.get(e),{shape:s,dtype:n,values:i,texture:r,usage:o,isPacked:a}=t;if(r!=null)return;const l=this.activeTimers!=null;let c;l&&(c=E.now());let p=t.texShape;if(p==null&&(p=_C(s,a),t.texShape=p),i!=null){const u=xf(s);let h,d=p[1],m=p[0];const f=i instanceof Uint8Array;a?([d,m]=oi(p[0],p[1]),h=new Q0(u,[m,d],f)):h=new Z0(u,[m,d],f);const y=this.makeTensorInfo([m,d],n);f?this.texData.get(y.dataId).usage=ns.PIXELS:this.texData.get(y.dataId).usage=ns.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),d,m,i);const b=!0,S=this.runWebGLProgram(h,[y],n,null,b),x=this.texData.get(S.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeData(y.dataId),this.texData.delete(S.dataId),t.values=null,l&&(this.uploadWaitMs+=E.now()-c)}else{const u=this.acquireTexture(p,o,n,a);t.texture=u}}convertAndCacheOnCPU(e,t){const s=this.texData.get(e),{dtype:n}=s;return this.releaseGPUData(e),t!=null&&(s.values=QB(t,n)),s.values}acquireTexture(e,t,s,n){if(this.numBytesInGPU+=this.computeBytes(e,s),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*E.bytesPerElement(t)}}function QB(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const s=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<s.length;++n)s[n]=Math.round(e[n]);return s}else throw new Error(`Unknown dtype ${t}`)}const Dx="2.3.0";function _2(){$().set("WEBGL_FORCE_F16_TEXTURES",!0)}la.isBrowser()&&dp("webgl",()=>new _x(),2);const a3e={forceHalfFloat:_2};function D2(e,t,s){let n=new mt(QC,e.shape,t.shape);$().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&(n=new Qs(f0,e.shape,t.shape,!0));const i=s.runWebGLProgram(n,[e,t],"float32");return i}const F2={kernelName:ui,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{a:s,b:n}=e,i=t;return D2(s,n,i)}};class M2{constructor(e){this.variableNames=["Image"],this.outputShape=[];const 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);
        }
    `}}const U2={kernelName:Dr,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:s}=e,n=t,i=new M2(s.shape),r=n.runWebGLProgram(i,[s],s.dtype);return r}};class $2{constructor(e){this.variableNames=["A"];const t=ot(),[s,n]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${s}.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));
      }
    `}}class W2{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=ot(),[s,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, ${s}.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);
          }
        }

        ${t.output} = result;
      }
    `}}const z2={kernelName:ta,backendName:"webgl",kernelFunc:eP};let ja;function eP(e){const{inputs:t,backend:s,attrs:n}=e;let{pixels:i}=t;const{numChannels:r}=n,o=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[l,c]=o?[i.videoWidth,i.videoHeight]:[i.width,i.height],p=[c,l],u=[c,l,r];(a||o)&&(ja==null&&(ja=document.createElement("canvas").getContext("2d")),ja.canvas.width=l,ja.canvas.height=c,ja.drawImage(i,0,0,l,c),i=ja.canvas);const h=s.makeTensorInfo(p,"int32");s.texData.get(h.dataId).usage=ns.PIXELS,s.gpgpu.uploadPixelDataToTexture(s.getTexture(h.dataId),i);const d=$().getBool("WEBGL_PACK")?new W2(u):new $2(u),m=s.runWebGLProgram(d,[h],"int32");return s.disposeData(h.dataId),m}const{maxImpl:B2,transposeImpl:kf}=Vw;function Fx(e,t,s,n){const[i,r]=e.shape,o=U.computeOptimalWindowSize(r),a={windowSize:o,inSize:r,batchSize:i},l=new Cf(a,s),c=n.runWebGLProgram(l,[e],t);return c.shape[1]===1?c:Fx(c,t,s,n)}function tP(e,t,s){const n=[Lr(e.shape),...Sr(e.shape)],i={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Lr(t),...Sr(t)],o=new Rf(r,n),a=!0,l=s.runWebGLProgram(o,[i],e.dtype,null,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Mx(e,t,s){const n=s.texData.get(e.dataId);return n.isPacked&&!vr(e.shape,t)&&!(n.texture!==null&&vr(n.shape,t))?tP(e,t,s):{dataId:e.dataId,shape:t,dtype:e.dtype}}function P2(e,t,s,n){const i=E.sizeFromShape(t),r=E.sizeFromShape(e.shape),o=r/i,a=Mx(e,[o,i],n),l=Fx(a,e.dtype,"max",n);return a.dataId!==e.dataId&&n.disposeData(a.dataId),Mx(l,s,n)}class j2{constructor(e,t){this.variableNames=["A"];const s=new Array(e.length);for(let r=0;r<s.length;r++)s[r]=e[t[r]];this.outputShape=s,this.rank=s.length;const n=Ce(this.rank),i=sP(t);this.userCode=`
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${i}));
    }
    `}}function sP(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const s=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let i=0;i<e.length;i++)n[e[i]]=s[i];return n.join()}class V2{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const s=new Array(e.length);for(let c=0;c<s.length;c++)s[c]=e[t[c]];if(this.outputShape=s,this.rank=s.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const n=Ce(this.rank),i=ex("rc",this.rank),r=new Array(this.rank);for(let c=0;c<t.length;c++)r[t[c]]=i[c];const o=`vec2(${r.slice(-2).join()})`,a=`++${i[this.rank-1]} < ${s[this.rank-1]}`,l=`getChannel(getA(${r.join()}), ${o})`;this.userCode=`
    void main() {
      ${n} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${a}) {
        result[1] = ${l};
      }
      --${i[this.rank-1]};
      if(++${i[this.rank-2]} < ${s[this.rank-2]}) {
        result[2] = ${l};
        if(${a}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}}function _f(e,t,s){const n=$().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V2(e.shape,t):new j2(e.shape,t);return s.runWebGLProgram(n,[e],e.dtype)}const G2={kernelName:hi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{reductionIndices:i,keepDims:r}=t,o=s,a=n.shape.length,l=E.parseAxisParam(i,n.shape);let c=l;const p=U.getAxesPermutation(c,a),u=p!=null,h=o.shouldExecuteOnCPU([n]);let d=n;if(u){if(h){const S=o.texData.get(d.dataId),x=S.values,I=new Array(a);for(let R=0;R<I.length;R++)I[R]=n.shape[p[R]];const A=kf(x,n.shape,n.dtype,p,I);d=o.makeTensorInfo(I,n.dtype);const k=o.texData.get(d.dataId);k.values=A}else d=_f(n,p,o);c=U.getInnerMostAxes(c.length,a)}U.assertAxesAreInnerMostDims("max",c,a);const[m,f]=U.computeOutAndReduceShapes(d.shape,c);let y=m;r&&(y=U.expandShapeToKeepDim(m,l));let b;if(h){const S=o.texData.get(d.dataId),x=S.values,I=B2(x,E.sizeFromShape(f),y,n.dtype);b=o.makeTensorInfo(y,n.dtype);const A=o.texData.get(b.dataId);A.values=I}else b=P2(d,f,y,o);return u&&o.disposeData(d.dataId),b}};function H2(e,t,s,n){let i=new Fo(s,"max",!1);const r=n.runWebGLProgram(i,[e],"float32");i=new Fo(s,"max",!0,!0,t);const o=n.runWebGLProgram(i,[e],"float32");return[r,o]}const q2={kernelName:Fr,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{filterSize:i,strides:r,pad:o,includeBatchInIndex:a}=t,l=s;E.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);const c=[1,1];E.assert(U.eitherStridesOrDilationsAreOne(r,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${r} and dilations '${c}'`);const p=U.computePool2DInfo(n.shape,i,r,c,o),[u,h]=H2(n,a,p,l);return[u,h]}};const Y2={kernelName:pc,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a}=s,l=t,c=l.readSync(n.dataId),p=l.readSync(i.dataId),u=r,h=o,d=a;return St.nonMaxSuppressionV3Impl(c,p,u,h,d)}};const nP=St.nonMaxSuppressionV4Impl,K2={kernelName:Mr,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a,padToMaxOutputSize:l}=s,c=t,p=c.readSync(n.dataId),u=c.readSync(i.dataId),{selectedIndices:h,validOutputs:d}=nP(p,u,r,o,a,l);return[h,d]}};const iP=St.nonMaxSuppressionV5Impl,X2={kernelName:Ur,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:s})=>{U.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:n,scores:i}=e,{maxOutputSize:r,iouThreshold:o,scoreThreshold:a,softNmsSigma:l}=s,c=t,p=c.readSync(n.dataId),u=c.readSync(i.dataId),h=r,d=o,m=a,f=l,{selectedIndices:y,selectedScores:b}=iP(p,u,h,d,m,f);return[y,b]}};class J2{constructor(e,t,s,n){this.variableNames=["Image"],this.outputShape=[];const i=e[1],r=e[2],o=Math.sin(t).toFixed(3),a=Math.cos(t).toFixed(3);this.outputShape=e;const[l,c]=U.getImageCenter(n,i,r),p=l.toFixed(3),u=c.toFixed(3);let h="";typeof s=="number"?h=`float outputValue = ${s.toFixed(2)};`:h=`
        vec3 fill = vec3(${s.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - ${p}) * ${a} - (float(y) - ${u}) * ${o};
          float coordYFloat = (float(x) - ${p}) * ${o} + (float(y) - ${u}) * ${a};
          int coordX = int(round(coordXFloat + ${p}));
          int coordY = int(round(coordYFloat + ${u}));
          ${h}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${i}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const Z2={kernelName:Wr,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,{radians:i,fillValue:r,center:o}=t,a=s,l=new J2(n.shape,i,r,o),c=a.runWebGLProgram(l,[n],n.dtype);return c}};const Q2={kernelName:$r,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{x:s}=e,n=t,i=new _e(s.shape,T2);return n.runWebGLProgram(i,[s],s.dtype)}};const eO={kernelName:di,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{a:s,b:n}=e,i="return (a - b) * (a - b);",r=t,o=$().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qs(i,s.shape,n.shape):new mt(i,s.shape,n.shape);return r.compileAndRun(o,[s,n])}};const tO={kernelName:mi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{perm:i}=t,r=s,o=n.shape.length,a=new Array(o);for(let c=0;c<a.length;c++)a[c]=n.shape[i[c]];let l;if(r.shouldExecuteOnCPU([n])){const c=r.texData.get(n.dataId),p=c.values,u=kf(p,n.shape,n.dtype,i,a);l=r.makeTensorInfo(a,n.dtype);const h=r.texData.get(l.dataId);h.values=u}else l=_f(n,i,r);return l}};const rP=[G2,U2,z2,F2,q2,Y2,K2,X2,Z2,Q2,eO,tO];for(const e of rP)Yc(e);const sO="2.3.0";const 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i=Math.abs(s-n),r=Math.round(i*(t?.5:1)),o=s>n?2:1,a=h=>{const d=e.shape.slice();return d[o]=h,Ft(d,0)},l=a(r),c=i-l.shape[o],p=t&&c?a(c):null,u=[p,e,l].filter(h=>!!h).map(h=>h.toFloat());return ge(u,o)})}function wL(e){const t=e.slice();for(let s=t.length-1;s>0;s--){const n=Math.floor(Math.random()*(s+1)),i=t[s];t[s]=t[n],t[n]=i}return t}function Ho(e){return 1/(1+Math.exp(-e))}function xL(e){return Math.log(e/(1-e))}class Sh extends tt{constructor(e,t,s,n,i=!1){super({x:e,y:t,width:s,height:n},i)}}const lP=.5,cP=.43,pP=.45;class Ws{constructor(e,t,s=new Ee(0,0)){const{width:n,height:i}=t;this._imgDims=new Ls(n,i),this._shift=s,this._positions=e.map(r=>r.mul(new Ee(n,i)).add(s))}get shift(){return new Ee(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(e=>e.sub(this._shift).div(new Ee(this.imageWidth,this.imageHeight)))}forSize(e,t){return new this.constructor(this.relativePositions,{width:e,height:t})}shiftBy(e,t){return new this.constructor(this.relativePositions,this._imgDims,new Ee(e,t))}shiftByPoint(e){return this.shiftBy(e.x,e.y)}align(e,t={}){if(e){const i=e instanceof bt?e.box.floor():new tt(e);return this.shiftBy(i.x,i.y).align(null,t)}const{useDlibAlignment:s,minBoxPadding:n}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},t);return s?this.alignDlib():this.alignMinBbox(n)}alignDlib(){const e=this.getRefPointsForAlignment(),[t,s,n]=e,i=p=>n.sub(p).magnitude(),r=(i(t)+i(s))/2,o=Math.floor(r/pP),a=Wo(e),l=Math.floor(Math.max(0,a.x-lP*o)),c=Math.floor(Math.max(0,a.y-cP*o));return new Sh(l,c,Math.min(o,this.imageWidth+l),Math.min(o,this.imageHeight+c))}alignMinBbox(e){const t=nh(this.positions);return t.pad(t.width*e,t.height*e)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class GL extends Ws{getRefPointsForAlignment(){const e=this.positions;return[e[0],e[1],Wo([e[3],e[4]])]}}class Jo extends Ws{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Wo)}}class hl{constructor(e,t){this._label=e,this._distance=t}get label(){return this._label}get distance(){return this._distance}toString(e=!0){return`${this.label}${e?` (${$o(this.distance)})`:""}`}}class ul extends tt{constructor(e,t){super(e);this._label=t}static assertIsValidLabeledBox(e,t){if(tt.assertIsValidBox(e,t),!Pn(e.label))throw new Error(`${t} - expected property label (${e.label}) to be a number`)}get label(){return this._label}}class tr{constructor(e,t){if(!(typeof e=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(t)||t.some(s=>!(s instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=e,this._descriptors=t}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(e=>Array.from(e))}}static fromJSON(e){const t=e.descriptors.map(s=>new Float32Array(s));return new tr(e.label,t)}}class VL extends ul{constructor(e,t,s,n){super(e,t);this._score=s,this._classScore=n}static assertIsValidPredictedBox(e,t){if(ul.assertIsValidLabeledBox(e,t),!Va(e.score)||!Va(e.classScore))throw new Error(`${t} - expected properties score (${e.score}) and (${e.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}}function Sn(e){return e.detection instanceof bt}function Xi(e,t){const s={detection:t};return Object.assign({},e,s)}function zx(){const e=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},t=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:e,readFile:t}}function Mf(e){let t="";if(!e)try{e=require("fs")}catch(n){t=n.toString()}const s=e?function(n){return new Promise((i,r)=>{e.readFile(n,function(o,a){return o?r(o):i(a)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)};return{readFile:s}}function Bx(){const e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,s=function(){if(e)return new e();throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},n=function(){if(t)return new t();throw new Error("createImageElement - missing Image implementation for nodejs environment")},i=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},r=Mf();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:s,createImageElement:n,fetch:i,...r}}function Px(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const jx=nl(gL());let Xt;function uP(){if(!Xt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Xt}function Vx(e){Xt=e}function Gx(){if(Px())return Vx(zx());if(jx.isNodejs())return Vx(Bx())}function hP(e){if(Xt||Gx(),!Xt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:t=Xt.Canvas,Image:s=Xt.Image}=e;Xt.Canvas=t,Xt.Image=s,Xt.createCanvasElement=e.createCanvasElement||(()=>new t()),Xt.createImageElement=e.createImageElement||(()=>new s()),Xt.ImageData=e.ImageData||Xt.ImageData,Xt.Video=e.Video||Xt.Video,Xt.fetch=e.fetch||Xt.fetch,Xt.readFile=e.readFile||Xt.readFile}const Ve={getEnv:uP,setEnv:Vx,initialize:Gx,createBrowserEnv:zx,createFileSystem:Mf,createNodejsEnv:Bx,monkeyPatch:hP,isBrowser:Px,isNodejs:jx.isNodejs};Gx();function Zi(e){return!Ve.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function us(e){const{Canvas:t,CanvasRenderingContext2D:s}=Ve.getEnv();if(e instanceof s)return e;const n=Zi(e);if(!(n instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const i=n.getContext("2d");if(!i)throw new Error("resolveContext2d - canvas 2d context is null");return i}var li;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(li||(li={}));class Uf{constructor(e={}){const{anchorPosition:t,backgroundColor:s,fontColor:n,fontSize:i,fontStyle:r,padding:o}=e;this.anchorPosition=t||li.TOP_LEFT,this.backgroundColor=s||"rgba(0, 0, 0, 0.5)",this.fontColor=n||"rgba(255, 255, 255, 1)",this.fontSize=i||14,this.fontStyle=r||"Georgia",this.padding=o||4}}class Ga{constructor(e,t,s={}){this.text=typeof e=="string"?[e]:e instanceof Ga?e.text:e,this.anchor=t,this.options=new Uf(s)}measureWidth(e){const{padding:t}=this.options;return this.text.map(s=>e.measureText(s).width).reduce((s,n)=>s<n?n:s,0)+2*t}measureHeight(){const{fontSize:e,padding:t}=this.options;return this.text.length*e+2*t}getUpperLeft(e,t){const{anchorPosition:s}=this.options,n=s===li.BOTTOM_RIGHT||s===li.TOP_RIGHT,i=s===li.BOTTOM_LEFT||s===li.BOTTOM_RIGHT,r=this.measureWidth(e),o=this.measureHeight(),a=n?this.anchor.x-r:this.anchor.x,l=i?this.anchor.y-o:this.anchor.y;if(t){const{width:c,height:p}=t,u=Math.max(Math.min(a,c-r),0),h=Math.max(Math.min(l,p-o),0);return{x:u,y:h}}return{x:a,y:l}}draw(e){const t=Zi(e),s=us(t),{backgroundColor:n,fontColor:i,fontSize:r,fontStyle:o,padding:a}=this.options;s.font=`${r}px ${o}`;const l=this.measureWidth(s),c=this.measureHeight();s.fillStyle=n;const p=this.getUpperLeft(s,t);s.fillRect(p.x,p.y,l,c),s.fillStyle=i,this.text.forEach((u,h)=>{const d=a+p.x,m=a+p.y+(h+1)*r;s.fillText(u,d,m)})}}class nO{constructor(e={}){const{boxColor:t,lineWidth:s,label:n,drawLabelOptions:i}=e;this.boxColor=t||"rgba(0, 0, 255, 1)",this.lineWidth=s||2,this.label=n;const r={anchorPosition:li.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Uf(Object.assign({},r,i))}}class Hx{constructor(e,t={}){this.box=new tt(e),this.options=new nO(t)}draw(e){const t=us(e),{boxColor:s,lineWidth:n}=this.options,{x:i,y:r,width:o,height:a}=this.box;t.strokeStyle=s,t.lineWidth=n,t.strokeRect(i,r,o,a);const{label:l}=this.options;l&&new Ga([l],{x:i-n/2,y:r},this.options.drawLabelOptions).draw(e)}}function dP(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof bt?n.score:Sn(n)?n.detection.score:void 0,r=n instanceof bt?n.box:Sn(n)?n.detection.box:new tt(n),o=i?`${$o(i)}`:void 0;new Hx(r,{label:o}).draw(e)})}function Ko(e){const{Image:t,Video:s}=Ve.getEnv();return e instanceof t&&e.complete||e instanceof s&&e.readyState>=3}function Lh(e){return new Promise((t,s)=>{if(e instanceof Ve.getEnv().Canvas||Ko(e))return t();function n(r){if(!r.currentTarget)return;r.currentTarget.removeEventListener("load",n),r.currentTarget.removeEventListener("error",i),t(r)}function i(r){if(!r.currentTarget)return;r.currentTarget.removeEventListener("load",n),r.currentTarget.removeEventListener("error",i),s(r)}e.addEventListener("load",n),e.addEventListener("error",i)})}function xh(e){return new Promise((t,s)=>{if(!(e instanceof Blob))return s("bufferToImage - expected buf to be of type: Blob");const n=new FileReader();n.onload=()=>{if(typeof n.result!="string")return s("bufferToImage - expected reader.result to be a string, in onload");const i=Ve.getEnv().createImageElement();i.onload=()=>t(i),i.onerror=s,i.src=n.result},n.onerror=s,n.readAsDataURL(e)})}function Qi(e){const{Image:t,Video:s}=Ve.getEnv();return e instanceof t?new Ls(e.naturalWidth,e.naturalHeight):e instanceof s?new Ls(e.videoWidth,e.videoHeight):new Ls(e.width,e.height)}function Rr({width:e,height:t}){const{createCanvasElement:s}=Ve.getEnv(),n=s();return n.width=e,n.height=t,n}function Xo(e,t){const{ImageData:s}=Ve.getEnv();if(!(e instanceof s)&&!Ko(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:n,height:i}=t||Qi(e),r=Rr({width:n,height:i});return e instanceof s?us(r).putImageData(e,0,0):us(r).drawImage(e,0,0,n,i),r}async function bh(e,t){const s=t||Ve.getEnv().createCanvasElement(),[n,i,r]=e.shape.slice(en(e)?1:0),o=N(()=>e.as3D(n,i,r).toInt());return await pr.toPixels(o,s),o.dispose(),s}function pl(e){const{Image:t,Canvas:s,Video:n}=Ve.getEnv();return e instanceof t||e instanceof s||e instanceof n}function yh(e,t,s=!1){const{Image:n,Canvas:i}=Ve.getEnv();if(!(e instanceof n||e instanceof i))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const r=Qi(e),o=t/Math.max(r.height,r.width),a=o*r.width,l=o*r.height,c=Rr({width:t,height:t}),p=e instanceof i?e:Xo(e),u=Math.abs(a-l)/2,h=s&&a<l?u:0,d=s&&l<a?u:0;return us(c).drawImage(p,h,d,a,l),c}class pi{constructor(e,t=!1){if(this._imageTensors=[],this._canvases=[],this._treatAsBatchInput=!1,this._inputDimensions=[],!Array.isArray(e))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${e}`);this._treatAsBatchInput=t,this._batchSize=e.length,e.forEach((s,n)=>{if(Uo(s)){this._imageTensors[n]=s,this._inputDimensions[n]=s.shape;return}if(en(s)){const r=s.shape[0];if(r!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${r} passed, but not supported in input array`);this._imageTensors[n]=s,this._inputDimensions[n]=s.shape.slice(1);return}const i=s instanceof Ve.getEnv().Canvas?s:Xo(s);this._canvases[n]=i,this._inputDimensions[n]=[i.height,i.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return ai(this.batchSize,0,1).map((e,t)=>this.getReshapedInputDimensions(t))}getInput(e){return this.canvases[e]||this.imageTensors[e]}getInputDimensions(e){return this._inputDimensions[e]}getInputHeight(e){return this._inputDimensions[e][0]}getInputWidth(e){return this._inputDimensions[e][1]}getReshapedInputDimensions(e){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");const t=this.getInputWidth(e),s=this.getInputHeight(e);return Wx({width:t,height:s},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,N(()=>{const s=ai(this.batchSize,0,1).map(i=>{const r=this.getInput(i);if(r instanceof ue){let o=en(r)?r:r.expandDims();return o=th(o,t),(o.shape[1]!==e||o.shape[2]!==e)&&(o=ks.resizeBilinear(o,[e,e])),o.as3D(e,e,3)}if(r instanceof Ve.getEnv().Canvas)return pr.fromPixels(yh(r,e,t));throw new Error(`toBatchTensor - at batchIdx ${i}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${r}`)}),n=Xe(s.map(i=>i.toFloat())).as4D(this.batchSize,e,e,3);return n})}}async function pt(e){if(e instanceof pi)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");const s=i=>Array.isArray(e)?` at input index ${i}:`:"",n=t.map(Zi);return n.forEach((i,r)=>{if(!pl(i)&&!Uo(i)&&!en(i))throw typeof t[r]=="string"?new Error(`toNetInput -${s(r)} string passed, but could not resolve HTMLElement for element id ${t[r]}`):new Error(`toNetInput -${s(r)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(en(i)){const o=i.shape[0];if(o!==1)throw new Error(`toNetInput -${s(r)} tf.Tensor4D with batchSize ${o} passed, but not supported in input array`)}}),await Promise.all(n.map(i=>pl(i)&&Lh(i))),new pi(n,Array.isArray(e))}async function Nr(e,t){const{Canvas:s}=Ve.getEnv();let n=e;if(!(e instanceof s)){const o=await pt(e);if(o.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const a=o.getInput(0);n=a instanceof s?a:await bh(a)}const i=us(n),r=t.map(o=>o instanceof bt?o.forSize(n.width,n.height).box.floor():o).map(o=>o.clipAtImageBorders(n.width,n.height));return r.map(({x:o,y:a,width:l,height:c})=>{const p=Rr({width:l,height:c});return us(p).putImageData(i.getImageData(o,a,l,c),0,0),p})}async function Cr(e,t){if(!Uo(e)&&!en(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(en(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return N(()=>{const[s,n,i]=e.shape.slice(en(e)?1:0),r=t.map(a=>a instanceof bt?a.forSize(n,s).box:a).map(a=>a.clipAtImageBorders(n,s)),o=r.map(({x:a,y:l,width:c,height:p})=>Ci(e.as3D(s,n,i),[l,a,0],[p,c,i]));return o})}async function er(e,t){const s=Ve.getEnv().fetch,n=await s(e,t);if(!(n.status<400))throw new Error(`failed to fetch: (${n.status}) ${n.statusText}, from url: ${n.url}`);return n}async function jL(e){const t=await er(e),s=await t.blob();if(!s.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${s.type}, for url: ${t.url}`);return xh(s)}async function wh(e){return(await er(e)).json()}async function PL(e){return new Float32Array(await(await er(e)).arrayBuffer())}function $f(e,t){const s=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:s};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${s}`};const n=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(n,"");const i=e.split("/").filter(a=>a),r=e.endsWith(".json")?i[i.length-1]:s;let o=n+(e.endsWith(".json")?i.slice(0,i.length-1):i).join("/");return o=e.startsWith("/")?`/${o}`:o,{modelBaseUri:o,manifestUri:o==="/"?`/${r}`:`${o}/${r}`}}async function gh(e,t){const{manifestUri:s,modelBaseUri:n}=$f(e,t),i=await wh(s);return Ot.loadWeights(i,n)}function BL(e,t,s=!1){const{width:n,height:i}=s?Qi(t):t;return e.width=n,e.height=i,{width:n,height:i}}class hs{constructor(e){this._name=e,this._params=void 0,this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(e){const{obj:t,objProp:s}=this.traversePropertyPath(e);return t[s]}reassignParamFromPath(e,t){const{obj:s,objProp:n}=this.traversePropertyPath(e);s[n].dispose(),s[n]=t}getParamList(){return this._paramMappings.map(({paramPath:e})=>({path:e,tensor:this.getParamFromPath(e)}))}getTrainableParams(){return this.getParamList().filter(e=>e.tensor instanceof Xn)}getFrozenParams(){return this.getParamList().filter(e=>!(e.tensor instanceof Xn))}variable(){this.getFrozenParams().forEach(({path:e,tensor:t})=>{this.reassignParamFromPath(e,t.variable())})}freeze(){this.getTrainableParams().forEach(({path:e,tensor:t})=>{const s=ze(t.dataSync());t.dispose(),this.reassignParamFromPath(e,s)})}dispose(e=!0){this.getParamList().forEach(t=>{if(e&&t.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${t.path}`);t.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:e})=>Array.from(e.dataSync())).reduce((e,t)=>e.concat(t)))}async load(e){if(e instanceof Float32Array){this.extractWeights(e);return}await this.loadFromUri(e)}async loadFromUri(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const t=await gh(e,this.getDefaultModelName());this.loadFromWeightMap(t)}async loadFromDisk(e){if(e&&typeof e!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:t}=Ve.getEnv(),{manifestUri:s,modelBaseUri:n}=$f(e,this.getDefaultModelName()),i=l=>Promise.all(l.map(c=>t(c).then(p=>p.buffer))),r=Ot.weightsLoaderFactory(i),o=JSON.parse((await t(s)).toString()),a=await r(o,n);this.loadFromWeightMap(a)}loadFromWeightMap(e){const{paramMappings:t,params:s}=this.extractParamsFromWeigthMap(e);this._paramMappings=t,this._params=s}extractWeights(e){const{paramMappings:t,params:s}=this.extractParams(e);this._paramMappings=t,this._params=s}traversePropertyPath(e){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const t=e.split("/").reduce((i,r)=>{if(!i.nextObj.hasOwnProperty(r))throw new Error(`traversePropertyPath - object does not have property ${r}, for path ${e}`);return{obj:i.nextObj,objProp:r,nextObj:i.nextObj[r]}},{nextObj:this.params}),{obj:s,objProp:n}=t;if(!s||!n||!(s[n]instanceof ue))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:s,objProp:n}}}function Cs(e,t,s){return N(()=>{let n=Ni(e,t.depthwise_filter,t.pointwise_filter,s,"same");return n=W(n,t.bias),n})}function Wf(e,t,s=!1){return N(()=>{const n=ke(s?W(yt(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Cs(e,t.conv0,[2,2])),i=Cs(n,t.conv1,[1,1]),r=ke(W(n,i)),o=Cs(r,t.conv2,[1,1]);return ke(W(n,W(i,o)))})}function zu(e,t,s=!1,n=!0){return N(()=>{const i=ke(s?W(yt(e,t.conv0.filters,n?[2,2]:[1,1],"same"),t.conv0.bias):Cs(e,t.conv0,n?[2,2]:[1,1])),r=Cs(i,t.conv1,[1,1]),o=ke(W(i,r)),a=Cs(o,t.conv2,[1,1]),l=ke(W(i,W(r,a))),c=Cs(l,t.conv3,[1,1]);return ke(W(i,W(r,W(a,c))))})}function zf(e,t,s="same",n=!1){return N(()=>{const i=W(yt(e,t.filters,[1,1],s),t.bias);return n?ke(i):i})}function Fs(e,t){Object.keys(e).forEach(s=>{t.some(n=>n.originalPath===s)||e[s].dispose()})}function Ha(e,t){return function(s,n,i,r){const o=cs(e(s*n*i*i),[i,i,s,n]),a=Fe(e(n));return t.push({paramPath:`${r}/filters`},{paramPath:`${r}/bias`}),{filters:o,bias:a}}}function Bf(e,t){return function(s,n,i){const r=ts(e(s*n),[s,n]),o=Fe(e(n));return t.push({paramPath:`${i}/weights`},{paramPath:`${i}/bias`}),{weights:r,bias:o}}}class qx{constructor(e,t,s){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=s}}function qa(e,t){return function(s,n,i){const r=cs(e(3*3*s),[3,3,s,1]),o=cs(e(s*n),[1,1,s,n]),a=Fe(e(n));return t.push({paramPath:`${i}/depthwise_filter`},{paramPath:`${i}/pointwise_filter`},{paramPath:`${i}/bias`}),new qx(r,o,a)}}function Ya(e){return function(t){const s=e(`${t}/depthwise_filter`,4),n=e(`${t}/pointwise_filter`,4),i=e(`${t}/bias`,1);return new qx(s,n,i)}}function wn(e,t){return function(s,n,i){const r=e[s];if(!Mo(r,n))throw new Error(`expected weightMap[${s}] to be a Tensor${n}D, instead have ${r}`);return t.push({originalPath:s,paramPath:i||s}),r}}function Ms(e){let t=e;function s(i){const r=t.slice(0,i);return t=t.slice(i),r}function n(){return t}return{extractWeights:s,getRemainingWeights:n}}function Pf(e,t){const s=Ha(e,t),n=qa(e,t);function i(o,a,l,c=!1){const p=c?s(o,a,3,`${l}/conv0`):n(o,a,`${l}/conv0`),u=n(a,a,`${l}/conv1`),h=n(a,a,`${l}/conv2`);return{conv0:p,conv1:u,conv2:h}}function r(o,a,l,c=!1){const{conv0:p,conv1:u,conv2:h}=i(o,a,l,c),d=n(a,a,`${l}/conv3`);return{conv0:p,conv1:u,conv2:h,conv3:d}}return{extractDenseBlock3Params:i,extractDenseBlock4Params:r}}function iO(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Ms(e),{extractDenseBlock4Params:i}=Pf(s,t),r=i(3,32,"dense0",!0),o=i(32,64,"dense1"),a=i(64,128,"dense2"),l=i(128,256,"dense3");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{dense0:r,dense1:o,dense2:a,dense3:l}}}function jf(e){return function(t){const s=e(`${t}/filters`,4),n=e(`${t}/bias`,1);return{filters:s,bias:n}}}function Vf(e,t){const s=wn(e,t),n=jf(s),i=Ya(s);function r(a,l=!1){const c=l?n(`${a}/conv0`):i(`${a}/conv0`),p=i(`${a}/conv1`),u=i(`${a}/conv2`);return{conv0:c,conv1:p,conv2:u}}function o(a,l=!1){const c=l?n(`${a}/conv0`):i(`${a}/conv0`),p=i(`${a}/conv1`),u=i(`${a}/conv2`),h=i(`${a}/conv3`);return{conv0:c,conv1:p,conv2:u,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:o}}function rO(e){const t=[],{extractDenseBlock4Params:s}=Vf(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2"),dense3:s("dense3")};return Fs(e,t),{params:n,paramMappings:t}}class Gf extends hs{constructor(){super("FaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return N(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=sn(s,n).div(j(255));let r=zu(i,t.dense0,!0);return r=zu(r,t.dense1),r=zu(r,t.dense2),r=zu(r,t.dense3),r=rs(r,[7,7],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await pt(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(e){return rO(e)}extractParams(e){return iO(e)}}function Bu(e,t){return N(()=>W(Le(e,t.weights),t.bias))}function oO(e,t,s){const n=[],{extractWeights:i,getRemainingWeights:r}=Ms(e),o=Bf(i,n),a=o(t,s,"fc");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{fc:a}}}function aO(e){const t=[],s=wn(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:n("fc")};return Fs(e,t),{params:i,paramMappings:t}}function Hf(e){const t={},s={};return Object.keys(e).forEach(n=>{const i=n.startsWith("fc")?s:t;i[n]=e[n]}),{featureExtractorMap:t,classifierMap:s}}class qf extends hs{constructor(e,t){super(e);this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return N(()=>{const s=e instanceof pi?this.faceFeatureExtractor.forwardInput(e):e;return Bu(s.as2D(s.shape[0],-1),t.fc)})}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:s}=this.extractClassifierParams(e);this._params=t,this._paramMappings=s}extractClassifierParams(e){return oO(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Hf(e);return this.faceFeatureExtractor.loadFromWeightMap(t),aO(s)}extractParams(e){const t=this.getClassifierChannelsIn(),s=this.getClassifierChannelsOut(),n=s*t+s,i=e.slice(0,e.length-n),r=e.slice(e.length-n);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(r)}}const dh=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class Ji{constructor(e){if(e.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);dh.forEach((t,s)=>{this[t]=e[s]})}asSortedArray(){return dh.map(e=>({expression:e,probability:this[e]})).sort((e,t)=>t.probability-e.probability)}}class mh extends qf{constructor(e=new Gf()){super("FaceExpressionNet",e)}forwardInput(e){return N(()=>Yt(this.runNet(e)))}async forward(e){return this.forwardInput(await pt(e))}async predictExpressions(e){const t=await pt(e),s=await this.forwardInput(t),n=await Promise.all(nt(s).map(async r=>{const o=await r.data();return r.dispose(),o}));s.dispose();const i=n.map(r=>new Ji(r));return t.isBatchInput?i:i[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function uh(e){return e.expressions instanceof Ji}function al(e,t){const s={expressions:t};return Object.assign({},e,s)}function mP(e,t,s=.1,n){const i=Array.isArray(t)?t:[t];i.forEach(r=>{const o=r instanceof Ji?r:uh(r)?r.expressions:void 0;if(!o)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const a=o.asSortedArray(),l=a.filter(u=>u.probability>s),c=Sn(r)?r.detection.box.bottomLeft:n||new Ee(0,0),p=new Ga(l.map(u=>`${u.expression} (${$o(u.probability)})`),c);p.draw(e)})}function Ki(e){return Sn(e)&&e.landmarks instanceof Ws&&e.unshiftedLandmarks instanceof Ws&&e.alignedRect instanceof bt}function Ar(e,t){const{box:s}=e.detection,n=t.shiftBy(s.x,s.y),i=n.align(),{imageDims:r}=e.detection,o=new bt(e.detection.score,i.rescale(r.reverse()),r),a={landmarks:n,unshiftedLandmarks:t,alignedRect:o};return Object.assign({},e,a)}class lO{constructor(e={}){const{drawLines:t=!0,drawPoints:s=!0,lineWidth:n,lineColor:i,pointSize:r,pointColor:o}=e;this.drawLines=t,this.drawPoints=s,this.lineWidth=n||1,this.pointSize=r||2,this.lineColor=i||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}}class cO{constructor(e,t={}){this.faceLandmarks=e,this.options=new lO(t)}draw(e){const t=us(e),{drawLines:s,drawPoints:n,lineWidth:i,lineColor:r,pointSize:o,pointColor:a}=this.options;if(s&&this.faceLandmarks instanceof Jo&&(t.strokeStyle=r,t.lineWidth=i,ji(t,this.faceLandmarks.getJawOutline()),ji(t,this.faceLandmarks.getLeftEyeBrow()),ji(t,this.faceLandmarks.getRightEyeBrow()),ji(t,this.faceLandmarks.getNose()),ji(t,this.faceLandmarks.getLeftEye(),!0),ji(t,this.faceLandmarks.getRightEye(),!0),ji(t,this.faceLandmarks.getMouth(),!0)),n){t.strokeStyle=a,t.fillStyle=a;const l=c=>{t.beginPath(),t.arc(c.x,c.y,o,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(l)}}}function fP(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof Ws?n:Ki(n)?n.landmarks:void 0;if(!i)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new cO(i).draw(e)})}const Ju={};Re(Ju,{AnchorPosition:()=>li,DrawBox:()=>Hx,DrawBoxOptions:()=>nO,DrawFaceLandmarks:()=>cO,DrawFaceLandmarksOptions:()=>lO,DrawTextField:()=>Ga,DrawTextFieldOptions:()=>Uf,drawContour:()=>ji,drawDetections:()=>dP,drawFaceExpressions:()=>mP,drawFaceLandmarks:()=>fP});function gP(e,t){const s=Ha(e,t),n=qa(e,t);function i(o,a,l){const c=n(o,a,`${l}/separable_conv0`),p=n(a,a,`${l}/separable_conv1`),u=s(o,a,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:p,expansion_conv:u}}function r(o,a){const l=n(o,o,`${a}/separable_conv0`),c=n(o,o,`${a}/separable_conv1`),p=n(o,o,`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:s,extractSeparableConvParams:n,extractReductionBlockParams:i,extractMainBlockParams:r}}function pO(e,t){const s=[],{extractWeights:n,getRemainingWeights:i}=Ms(e),{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:a,extractMainBlockParams:l}=gP(n,s),c=r(3,32,3,"entry_flow/conv_in"),p=a(32,64,"entry_flow/reduction_block_0"),u=a(64,128,"entry_flow/reduction_block_1"),h={conv_in:c,reduction_block_0:p,reduction_block_1:u},d={};ai(t,0,1).forEach(b=>{d[`main_block_${b}`]=l(128,`middle_flow/main_block_${b}`)});const m=a(128,256,"exit_flow/reduction_block"),f=o(256,512,"exit_flow/separable_conv"),y={reduction_block:m,separable_conv:f};if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:s,params:{entry_flow:h,middle_flow:d,exit_flow:y}}}function yP(e,t){const s=wn(e,t),n=jf(s),i=Ya(s);function r(a){const l=i(`${a}/separable_conv0`),c=i(`${a}/separable_conv1`),p=n(`${a}/expansion_conv`);return{separable_conv0:l,separable_conv1:c,expansion_conv:p}}function o(a){const l=i(`${a}/separable_conv0`),c=i(`${a}/separable_conv1`),p=i(`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}}function uO(e,t){const s=[],{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}=yP(e,s),a=n("entry_flow/conv_in"),l=r("entry_flow/reduction_block_0"),c=r("entry_flow/reduction_block_1"),p={conv_in:a,reduction_block_0:l,reduction_block_1:c},u={};ai(t,0,1).forEach(f=>{u[`main_block_${f}`]=o(`middle_flow/main_block_${f}`)});const h=r("exit_flow/reduction_block"),d=i("exit_flow/separable_conv"),m={reduction_block:h,separable_conv:d};return Fs(e,s),{params:{entry_flow:p,middle_flow:u,exit_flow:m},paramMappings:s}}function hO(e,t,s){return W(yt(e,t.filters,s,"same"),t.bias)}function Yx(e,t,s=!0){let n=s?ke(e):e;return n=Cs(n,t.separable_conv0,[1,1]),n=Cs(ke(n),t.separable_conv1,[1,1]),n=lt(n,[3,3],[2,2],"same"),n=W(n,hO(e,t.expansion_conv,[2,2])),n}function bP(e,t){let s=Cs(ke(e),t.separable_conv0,[1,1]);return s=Cs(ke(s),t.separable_conv1,[1,1]),s=Cs(ke(s),t.separable_conv2,[1,1]),s=W(s,e),s}class dO extends hs{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return N(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=sn(s,n).div(j(256));let r=ke(hO(i,t.entry_flow.conv_in,[2,2]));return r=Yx(r,t.entry_flow.reduction_block_0,!1),r=Yx(r,t.entry_flow.reduction_block_1),ai(this._numMainBlocks,0,1).forEach(o=>{r=bP(r,t.middle_flow[`main_block_${o}`])}),r=Yx(r,t.exit_flow.reduction_block),r=ke(Cs(r,t.exit_flow.separable_conv,[1,1])),r})}async forward(e){return this.forwardInput(await pt(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(e){return uO(e,this._numMainBlocks)}extractParams(e){return pO(e,this._numMainBlocks)}}function mO(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Ms(e),i=Bf(s,t),r=i(512,1,"fc/age"),o=i(512,2,"fc/gender");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{fc:{age:r,gender:o}}}}function fO(e){const t=[],s=wn(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:{age:n("fc/age"),gender:n("fc/gender")}};return Fs(e,t),{params:i,paramMappings:t}}var Vn;(function(e){e.FEMALE="female",e.MALE="male"})(Vn||(Vn={}));class vh extends hs{constructor(e=new dO(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return N(()=>{const s=e instanceof pi?this.faceFeatureExtractor.forwardInput(e):e,n=rs(s,[7,7],[2,2],"valid").as2D(s.shape[0],-1),i=Bu(n,t.fc.age).as1D(),r=Bu(n,t.fc.gender);return{age:i,gender:r}})}forwardInput(e){return N(()=>{const{age:t,gender:s}=this.runNet(e);return{age:t,gender:Yt(s)}})}async forward(e){return this.forwardInput(await pt(e))}async predictAgeAndGender(e){const t=await pt(e),s=await this.forwardInput(t),n=nt(s.age),i=nt(s.gender),r=n.map((a,l)=>({ageTensor:a,genderTensor:i[l]})),o=await Promise.all(r.map(async({ageTensor:a,genderTensor:l})=>{const c=(await a.data())[0],p=(await l.data())[0],u=p>.5,h=u?Vn.MALE:Vn.FEMALE,d=u?p:1-p;return a.dispose(),l.dispose(),{age:c,gender:h,genderProbability:d}}));return s.age.dispose(),s.gender.dispose(),t.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(e=!0){this.faceFeatureExtractor.dispose(e),super.dispose(e)}loadClassifierParams(e){const{params:t,paramMappings:s}=this.extractClassifierParams(e);this._params=t,this._paramMappings=s}extractClassifierParams(e){return mO(e)}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Hf(e);return this.faceFeatureExtractor.loadFromWeightMap(t),fO(s)}extractParams(e){const t=512*1+1+(512*2+2),s=e.slice(0,e.length-t),n=e.slice(e.length-t);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(n)}}class Yf extends qf{postProcess(e,t,s){const n=s.map(({width:r,height:o})=>{const a=t/Math.max(o,r);return{width:r*a,height:o*a}}),i=n.length;return N(()=>{const r=(p,u)=>Xe([Ft([68],p),Ft([68],u)],1).as2D(1,136).as1D(),o=(p,u)=>{const{width:h,height:d}=n[p];return u(h,d)?Math.abs(h-d)/2:0},a=p=>o(p,(u,h)=>u<h),l=p=>o(p,(u,h)=>h<u),c=e.mul(Ft([i,136],t)).sub(Xe(Array.from(Array(i),(p,u)=>r(a(u),l(u))))).div(Xe(Array.from(Array(i),(p,u)=>r(n[u].width,n[u].height))));return c})}forwardInput(e){return N(()=>{const t=this.runNet(e);return this.postProcess(t,e.inputSize,e.inputDimensions.map(([s,n])=>({height:s,width:n})))})}async forward(e){return this.forwardInput(await pt(e))}async detectLandmarks(e){const t=await pt(e),s=N(()=>nt(this.forwardInput(t))),n=await Promise.all(s.map(async(i,r)=>{const o=Array.from(await i.data()),a=o.filter((c,p)=>Df(p)),l=o.filter((c,p)=>!Df(p));return new Jo(Array(68).fill(0).map((c,p)=>new Ee(a[p],l[p])),{height:t.getInputHeight(r),width:t.getInputWidth(r)})}));return s.forEach(i=>i.dispose()),t.isBatchInput?n:n[0]}getClassifierChannelsOut(){return 136}}class Yo extends Yf{constructor(e=new Gf()){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function gO(e){const t=[],{extractDenseBlock3Params:s}=Vf(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2")};return Fs(e,t),{params:n,paramMappings:t}}function yO(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Ms(e),{extractDenseBlock3Params:i}=Pf(s,t),r=i(3,32,"dense0",!0),o=i(32,64,"dense1"),a=i(64,128,"dense2");if(n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{paramMappings:t,params:{dense0:r,dense1:o,dense2:a}}}class bO extends hs{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return N(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=sn(s,n).div(j(255));let r=Wf(i,t.dense0,!0);return r=Wf(r,t.dense1),r=Wf(r,t.dense2),r=rs(r,[14,14],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await pt(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(e){return gO(e)}extractParams(e){return yO(e)}}class hh extends Yf{constructor(e=new bO()){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class zL extends Yo{}function wO(e,t){return W(C(e,t.weights),t.biases)}function Kx(e,t,s,n,i="same"){const{filters:r,bias:o}=t.conv;let a=yt(e,r,s,i);return a=W(a,o),a=wO(a,t.scale),n?ke(a):a}function xO(e,t){return Kx(e,t,[1,1],!0)}function Xx(e,t){return Kx(e,t,[1,1],!1)}function Kf(e,t){return Kx(e,t,[2,2],!0,"valid")}function wP(e,t){function s(a,l,c){const p=e(a),u=p.length/(l*c*c);if($x(u))throw new Error(`depth has to be an integer: ${u}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${c}`);return N(()=>se(cs(p,[l,u,c,c]),[2,3,1,0]))}function n(a,l,c,p){const u=s(a,l,c),h=Fe(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:u,bias:h}}function i(a,l){const c=Fe(e(a)),p=Fe(e(a));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:p}}function r(a,l,c,p){const u=n(a,l,c,`${p}/conv`),h=i(l,`${p}/scale`);return{conv:u,scale:h}}function o(a,l,c,p,u=!1){const h=r((u?.5:1)*a,l,c,`${p}/conv1`),d=r(a,l,c,`${p}/conv2`);return{conv1:h,conv2:d}}return{extractConvLayerParams:r,extractResidualLayerParams:o}}function LO(e){const{extractWeights:t,getRemainingWeights:s}=Ms(e),n=[],{extractConvLayerParams:i,extractResidualLayerParams:r}=wP(t,n),o=i(4704,32,7,"conv32_down"),a=r(9216,32,3,"conv32_1"),l=r(9216,32,3,"conv32_2"),c=r(9216,32,3,"conv32_3"),p=r(36864,64,3,"conv64_down",!0),u=r(36864,64,3,"conv64_1"),h=r(36864,64,3,"conv64_2"),d=r(36864,64,3,"conv64_3"),m=r(147456,128,3,"conv128_down",!0),f=r(147456,128,3,"conv128_1"),y=r(147456,128,3,"conv128_2"),b=r(589824,256,3,"conv256_down",!0),S=r(589824,256,3,"conv256_1"),x=r(589824,256,3,"conv256_2"),I=r(589824,256,3,"conv256_down_out"),A=N(()=>se(ts(t(256*128),[128,256]),[1,0]));if(n.push({paramPath:"fc"}),s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);const k={conv32_down:o,conv32_1:a,conv32_2:l,conv32_3:c,conv64_down:p,conv64_1:u,conv64_2:h,conv64_3:d,conv128_down:m,conv128_1:f,conv128_2:y,conv256_down:b,conv256_1:S,conv256_2:x,conv256_down_out:I,fc:A};return{params:k,paramMappings:n}}function xP(e,t){const s=wn(e,t);function n(o){const a=s(`${o}/scale/weights`,1),l=s(`${o}/scale/biases`,1);return{weights:a,biases:l}}function i(o){const a=s(`${o}/conv/filters`,4),l=s(`${o}/conv/bias`,1),c=n(o);return{conv:{filters:a,bias:l},scale:c}}function r(o){return{conv1:i(`${o}/conv1`),conv2:i(`${o}/conv2`)}}return{extractConvLayerParams:i,extractResidualLayerParams:r}}function SO(e){const t=[],{extractConvLayerParams:s,extractResidualLayerParams:n}=xP(e,t),i=s("conv32_down"),r=n("conv32_1"),o=n("conv32_2"),a=n("conv32_3"),l=n("conv64_down"),c=n("conv64_1"),p=n("conv64_2"),u=n("conv64_3"),h=n("conv128_down"),d=n("conv128_1"),m=n("conv128_2"),f=n("conv256_down"),y=n("conv256_1"),b=n("conv256_2"),S=n("conv256_down_out"),x=e.fc;if(t.push({originalPath:"fc",paramPath:"fc"}),!Ux(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);const I={conv32_down:i,conv32_1:r,conv32_2:o,conv32_3:a,conv64_down:l,conv64_1:c,conv64_2:p,conv64_3:u,conv128_down:h,conv128_1:d,conv128_2:m,conv256_down:f,conv256_1:y,conv256_2:b,conv256_down_out:S,fc:x};return Fs(e,t),{params:I,paramMappings:t}}function jn(e,t){let s=xO(e,t.conv1);return s=Xx(s,t.conv2),s=W(s,e),s=ke(s),s}function Pu(e,t){let s=Kf(e,t.conv1);s=Xx(s,t.conv2);let n=rs(e,2,2,"valid");const i=Ie(n.shape),r=n.shape[3]!==s.shape[3],o=n.shape[1]!==s.shape[1]||n.shape[2]!==s.shape[2];if(o){const a=[...s.shape];a[1]=1;const l=Ie(a);s=ge([s,l],1);const c=[...s.shape];c[2]=1;const p=Ie(c);s=ge([s,p],2)}return n=r?ge([n,i],3):n,s=W(n,s),s=ke(s),s}class qo extends hs{constructor(){super("FaceRecognitionNet")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceRecognitionNet - load model before inference");return N(()=>{const s=e.toBatchTensor(150,!0).toFloat(),n=[122.782,117.001,104.298],i=sn(s,n).div(j(256));let 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s=Ni(s,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),s=W(s,t.bias),Xa(s)})}function LP(e,t){const s=Ha(e,t);function n(o,a){const l=Fe(e(o)),c=Fe(e(o));return t.push({paramPath:`${a}/sub`},{paramPath:`${a}/truediv`}),{sub:l,truediv:c}}function i(o,a,l){const c=s(o,a,3,`${l}/conv`),p=n(a,`${l}/bn`);return{conv:c,bn:p}}const r=qa(e,t);return{extractConvParams:s,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}}function vO(e,t,s,n){const{extractWeights:i,getRemainingWeights:r}=Ms(e),o=[],{extractConvParams:a,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=LP(i,o);let p;if(t.withSeparableConvs){const[u,h,d,m,f,y,b,S,x]=n,I=t.isFirstLayerConv2d?a(u,h,3,"conv0"):c(u,h,"conv0"),A=c(h,d,"conv1"),k=c(d,m,"conv2"),R=c(m,f,"conv3"),D=c(f,y,"conv4"),F=c(y,b,"conv5"),M=S?c(b,S,"conv6"):void 0,B=x?c(S,x,"conv7"):void 0,V=a(x||S||b,5*s,1,"conv8");p={conv0:I,conv1:A,conv2:k,conv3:R,conv4:D,conv5:F,conv6:M,conv7:B,conv8:V}}else{const[u,h,d,m,f,y,b,S,x]=n,I=l(u,h,"conv0"),A=l(h,d,"conv1"),k=l(d,m,"conv2"),R=l(m,f,"conv3"),D=l(f,y,"conv4"),F=l(y,b,"conv5"),M=l(b,S,"conv6"),B=l(S,x,"conv7"),V=a(x,5*s,1,"conv8");p={conv0:I,conv1:A,conv2:k,conv3:R,conv4:D,conv5:F,conv6:M,conv7:B,conv8:V}}if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:p,paramMappings:o}}function SP(e,t){const s=wn(e,t);function n(a){const l=s(`${a}/sub`,1),c=s(`${a}/truediv`,1);return{sub:l,truediv:c}}function i(a){const l=s(`${a}/filters`,4),c=s(`${a}/bias`,1);return{filters:l,bias:c}}function r(a){const l=i(`${a}/conv`),c=n(`${a}/bn`);return{conv:l,bn:c}}const o=Ya(s);return{extractConvParams:i,extractConvWithBatchNormParams:r,extractSeparableConvParams:o}}function TO(e,t){const s=[],{extractConvParams:n,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}=SP(e,s);let o;if(t.withSeparableConvs){const a=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?n("conv0"):r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:a>7?r("conv6"):void 0,conv7:a>8?r("conv7"):void 0,conv8:n("conv8")}}else o={conv0:i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:i("conv6"),conv7:i("conv7"),conv8:n("conv8")};return Fs(e,s),{params:o,paramMappings:s}}var eh;(function(e){e[e.XS=224]="XS",e[e.SM=320]="SM",e[e.MD=416]="MD",e[e.LG=608]="LG"})(eh||(eh={}));class Vo{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}class Ja extends hs{constructor(e){super("TinyYolov2");Qu(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,t){let s=Vi(e,t.conv0);return s=lt(s,[2,2],[2,2],"same"),s=Vi(s,t.conv1),s=lt(s,[2,2],[2,2],"same"),s=Vi(s,t.conv2),s=lt(s,[2,2],[2,2],"same"),s=Vi(s,t.conv3),s=lt(s,[2,2],[2,2],"same"),s=Vi(s,t.conv4),s=lt(s,[2,2],[2,2],"same"),s=Vi(s,t.conv5),s=lt(s,[2,2],[1,1],"same"),s=Vi(s,t.conv6),s=Vi(s,t.conv7),zf(s,t.conv8,"valid",!1)}runMobilenet(e,t){let s=this.config.isFirstLayerConv2d?Xa(zf(e,t.conv0,"valid",!1)):Gi(e,t.conv0);return s=lt(s,[2,2],[2,2],"same"),s=Gi(s,t.conv1),s=lt(s,[2,2],[2,2],"same"),s=Gi(s,t.conv2),s=lt(s,[2,2],[2,2],"same"),s=Gi(s,t.conv3),s=lt(s,[2,2],[2,2],"same"),s=Gi(s,t.conv4),s=lt(s,[2,2],[2,2],"same"),s=Gi(s,t.conv5),s=lt(s,[2,2],[1,1],"same"),s=t.conv6?Gi(s,t.conv6):s,s=t.conv7?Gi(s,t.conv7):s,zf(s,t.conv8,"valid",!1)}forwardInput(e,t){const{params:s}=this;if(!s)throw new Error("TinyYolov2 - load model before inference");return N(()=>{let n=e.toBatchTensor(t,!1).toFloat();return n=this.config.meanRgb?sn(n,this.config.meanRgb):n,n=n.div(j(256)),this.config.withSeparableConvs?this.runMobilenet(n,s):this.runTinyYolov2(n,s)})}async forward(e,t){return await this.forwardInput(await pt(e),t)}async detect(e,t={}){const{inputSize:s,scoreThreshold:n}=new Vo(t),i=await pt(e),r=await this.forwardInput(i,s),o=N(()=>nt(r)[0].expandDims()),a={width:i.getInputWidth(0),height:i.getInputHeight(0)},l=await this.extractBoxes(o,i.getReshapedInputDimensions(0),n);r.dispose(),o.dispose();const c=l.map(f=>f.box),p=l.map(f=>f.score),u=l.map(f=>f.classScore),h=l.map(f=>this.config.classes[f.label]),d=sh(c.map(f=>f.rescale(s)),p,this.config.iouThreshold,!0),m=d.map(f=>new Or(p[f],u[f],h[f],c[f],a));return m}getDefaultModelName(){return""}extractParamsFromWeigthMap(e){return TO(e,this.config)}extractParams(e){const t=this.config.filterSizes||Ja.DEFAULT_FILTER_SIZES,s=t?t.length:void 0;if(s!==7&&s!==8&&s!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${s} filterSizes in config`);return vO(e,this.config,this.boxEncodingSize,t)}async extractBoxes(e,t,s){const{width:n,height:i}=t,r=Math.max(n,i),o=r/n,a=r/i,l=e.shape[1],c=this.config.anchors.length,[p,u,h]=N(()=>{const y=e.reshape([l,l,c,this.boxEncodingSize]),b=y.slice([0,0,0,0],[l,l,c,4]),S=y.slice([0,0,0,4],[l,l,c,1]),x=this.withClassScores?Yt(y.slice([0,0,0,5],[l,l,c,this.config.classes.length]),3):j(0);return[b,S,x]}),d=[],m=await u.array(),f=await p.array();for(let y=0;y<l;y++)for(let b=0;b<l;b++)for(let S=0;S<c;S++){const x=Ho(m[y][b][S][0]);if(!s||x>s){const I=(b+Ho(f[y][b][S][0]))/l*o,A=(y+Ho(f[y][b][S][1]))/l*a,k=Math.exp(f[y][b][S][2])*this.config.anchors[S].x/l*o,R=Math.exp(f[y][b][S][3])*this.config.anchors[S].y/l*a,D=I-k/2,F=A-R/2,M={row:y,col:b,anchor:S},{classScore:B,label:V}=this.withClassScores?await this.extractPredictedClass(h,M):{classScore:1,label:0};d.push({box:new Zo(D,F,D+k,F+R),score:x,classScore:x*B,label:V,...M})}}return p.dispose(),u.dispose(),h.dispose(),d}async extractPredictedClass(e,t){const{row:s,col:n,anchor:i}=t,r=await e.array();return Array(this.config.classes.length).fill(0).map((o,a)=>r[s][n][i][a]).map((o,a)=>({classScore:o,label:a})).reduce((o,a)=>o.classScore>a.classScore?o:a)}}Ja.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];const IO=.4,AO=[new Ee(1.603231,2.094468),new Ee(6.041143,7.080126),new Ee(2.882459,3.518061),new Ee(4.266906,5.178857),new Ee(9.041765,10.66308)],NO=[117.001,114.697,97.404];class Ir extends Ja{constructor(){const e={withSeparableConvs:!0,iouThreshold:IO,classes:["face"],anchors:AO,meanRgb:NO,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(e)}get anchors(){return this.config.anchors}async locateFaces(e,t){const s=await this.detect(e,t);return s.map(n=>new bt(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}const CO=.4,RO=[new Ee(.738768,.874946),new Ee(2.42204,2.65704),new Ee(4.30971,7.04493),new Ee(10.246,4.59428),new Ee(12.6868,11.8741)],OO=[new Ee(1.603231,2.094468),new Ee(6.041143,7.080126),new Ee(2.882459,3.518061),new Ee(4.266906,5.178857),new Ee(9.041765,10.66308)],EO=[117.001,114.697,97.404],kO="tiny_yolov2_model",_O="tiny_yolov2_separable_conv_model";class Go extends Ja{constructor(e=!0){const t=Object.assign({},{withSeparableConvs:e,iouThreshold:CO,classes:["face"]},e?{anchors:OO,meanRgb:EO}:{anchors:RO,withClassScores:!0});super(t)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(e,t){const s=await this.detect(e,t);return s.map(n=>new bt(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?_O:kO}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}function yL(e,t=!0){const s=new Go(t);return s.extractWeights(e),s}const Qe={tinyFaceDetector:new Ir(),tinyYolov2:new Go(),faceLandmark68Net:new Yo(),faceLandmark68TinyNet:new hh(),faceRecognitionNet:new qo(),faceExpressionNet:new mh(),ageGenderNet:new vh()},LL=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),og=e=>Qe.faceLandmark68Net.detectLandmarks(e),SL=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),vL=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),TL=e=>Qe.faceExpressionNet.predictExpressions(e),IL=e=>Qe.ageGenderNet.predictAgeAndGender(e),ag=e=>Qe.tinyFaceDetector.load(e),AL=e=>Qe.tinyYolov2.load(e),NL=e=>Qe.faceLandmark68Net.load(e),CL=e=>Qe.faceLandmark68TinyNet.load(e),RL=e=>Qe.faceRecognitionNet.load(e),OL=e=>Qe.faceExpressionNet.load(e),EL=e=>Qe.ageGenderNet.load(e),kL=ag,_L=Ir,DL=og;class DO extends nn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class Gu extends DO{async run(){const e=await this.parentTask,t=await zo(e,this.input,async s=>await Promise.all(s.map(n=>Qe.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return e.map((s,n)=>al(s,t[n]))}withAgeAndGender(){return new ju(this,this.input)}}class Hu extends DO{async run(){const e=await this.parentTask;if(!e)return;const t=await Ka(e,this.input,s=>Qe.faceExpressionNet.predictExpressions(s),this.extractedFaces);return al(e,t)}withAgeAndGender(){return new Vu(this,this.input)}}class el extends Gu{withAgeAndGender(){return new Za(this,this.input)}withFaceDescriptors(){return new qi(this,this.input)}}class tl extends Hu{withAgeAndGender(){return new Qa(this,this.input)}withFaceDescriptor(){return new Yi(this,this.input)}}class FO extends nn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class ju extends FO{async run(){const e=await this.parentTask,t=await zo(e,this.input,async s=>await Promise.all(s.map(n=>Qe.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return e.map((s,n)=>{const{age:i,gender:r,genderProbability:o}=t[n];return cl(ol(s,r,o),i)})}withFaceExpressions(){return new Gu(this,this.input)}}class Vu extends FO{async run(){const e=await this.parentTask;if(!e)return;const{age:t,gender:s,genderProbability:n}=await Ka(e,this.input,i=>Qe.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return cl(ol(e,s,n),t)}withFaceExpressions(){return new Hu(this,this.input)}}class Za extends ju{withFaceExpressions(){return new el(this,this.input)}withFaceDescriptors(){return new qi(this,this.input)}}class Qa extends Vu{withFaceExpressions(){return new tl(this,this.input)}withFaceDescriptor(){return new Yi(this,this.input)}}class ph extends nn{constructor(e,t){super();this.parentTask=e,this.input=t}}class qi extends ph{async run(){const e=await this.parentTask,t=await zo(e,this.input,s=>Promise.all(s.map(n=>Qe.faceRecognitionNet.computeFaceDescriptor(n))),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return t.map((s,n)=>ll(e[n],s))}withFaceExpressions(){return new el(this,this.input)}withAgeAndGender(){return new Za(this,this.input)}}class Yi extends ph{async run(){const e=await this.parentTask;if(!e)return;const t=await Ka(e,this.input,s=>Qe.faceRecognitionNet.computeFaceDescriptor(s),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return ll(e,t)}withFaceExpressions(){return new tl(this,this.input)}withAgeAndGender(){return new Qa(this,this.input)}}class Hi extends Vo{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class ah extends nn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}}class lh extends ah{async run(){const e=await this.parentTask,t=e.map(i=>i.detection),s=this.input instanceof ue?await Cr(this.input,t):await Nr(this.input,t),n=await Promise.all(s.map(i=>this.landmarkNet.detectLandmarks(i)));return s.forEach(i=>i instanceof ue&&i.dispose()),e.map((i,r)=>Ar(i,n[r]))}withFaceExpressions(){return new el(this,this.input)}withAgeAndGender(){return new Za(this,this.input)}withFaceDescriptors(){return new qi(this,this.input)}}class ch extends ah{async run(){const e=await this.parentTask;if(!e)return;const{detection:t}=e,s=this.input instanceof ue?await Cr(this.input,[t]):await Nr(this.input,[t]),n=await this.landmarkNet.detectLandmarks(s[0]);return s.forEach(i=>i instanceof ue&&i.dispose()),Ar(e,n)}withFaceExpressions(){return new tl(this,this.input)}withAgeAndGender(){return new Qa(this,this.input)}withFaceDescriptor(){return new Yi(this,this.input)}}class rh extends nn{constructor(e,t=new Hi()){super();this.input=e,this.options=t}}class rl extends rh{async run(){const{input:e,options:t}=this,s=t instanceof Hi?n=>Qe.tinyFaceDetector.locateFaces(n,t):null;if(!s)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return s(e)}runAndExtendWithFaceDetections(){return new Promise(async e=>{const t=await this.run();return e(t.map(s=>Xi({},s)))})}withFaceLandmarks(e=!1){return new lh(this.runAndExtendWithFaceDetections(),this.input,e)}withFaceExpressions(){return new Gu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ju(this.runAndExtendWithFaceDetections(),this.input)}}class oh extends rh{async run(){const e=await new rl(this.input,this.options);let t=e[0];return e.forEach(s=>{s.score>t.score&&(t=s)}),t}runAndExtendWithFaceDetection(){return new Promise(async e=>{const t=await this.run();return e(t?Xi({},t):void 0)})}withFaceLandmarks(e=!1){return new ch(this.runAndExtendWithFaceDetection(),this.input,e)}withFaceExpressions(){return new Hu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Vu(this.runAndExtendWithFaceDetection(),this.input)}}function FL(e,t=new Hi()){return new oh(e,t)}function ML(e,t=new Hi()){return new rl(e,t)}function fh(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const s=Array.from(e),n=Array.from(t);return Math.sqrt(s.map((i,r)=>i-n[r]).reduce((i,r)=>i+Math.pow(r,2),0))}class lg{constructor(e,t=.6){this._distanceThreshold=t;const s=Array.isArray(e)?e:[e];if(!s.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1;const i=()=>`person ${n++}`;this._labeledDescriptors=s.map(r=>{if(r instanceof tr)return r;if(r instanceof Float32Array)return new tr(i(),[r]);if(r.descriptor&&r.descriptor instanceof Float32Array)return new tr(i(),[r.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(e,t){return t.map(s=>fh(s,e)).reduce((s,n)=>s+n,0)/(t.length||1)}matchDescriptor(e){return this.labeledDescriptors.map(({descriptors:t,label:s})=>new hl(s,this.computeMeanDistance(e,t))).reduce((t,s)=>t.distance<s.distance?t:s)}findBestMatch(e){const t=this.matchDescriptor(e);return t.distance<this.distanceThreshold?t:new hl("unknown",t.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(e=>e.toJSON())}}static fromJSON(e){const t=e.labeledDescriptors.map(s=>tr.fromJSON(s));return new lg(t,e.distanceThreshold)}}function bL(e){const t=new Ir();return t.extractWeights(e),t}function rg(e,t){const{width:s,height:n}=new Ls(t.width,t.height);if(s<=0||n<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:s,height:n})}`);if(Array.isArray(e))return e.map(i=>rg(i,{width:s,height:n}));if(Ki(e)){const i=e.detection.forSize(s,n),r=e.unshiftedLandmarks.forSize(i.box.width,i.box.height);return Ar(Xi(e,i),r)}return Sn(e)?Xi(e,e.detection.forSize(s,n)):e instanceof Ws||e instanceof bt?e.forSize(s,n):e}return HL();})();
/**
 * @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. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/** @license See the LICENSE file. */
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