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

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`;return h[h.length-1]=" "+h[h.length-1]+"]"+(r?"":m),h}function gp(e){const t=[];for(let s=0;s<e.length;s+=2)t.push([e[s],e[s+1]]);return t}class cn{constructor(e,t,s){if(this.dtype=t,this.shape=e.slice(),this.size=We(e),s!=null){const n=s.length;I(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=s||ty(t,this.size),this.strides=ni(e)}set(e,...t){t.length===0&&(t=[0]),I(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);const s=this.locToIndex(t);this.values[s]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(const n of e){if(n<0||n>=this.shape[t]){const i=`Requested out of range element at ${e}.   Buffer shape=${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 ri().makeTensor(this.values,this.shape,this.dtype)}}let ri=null,Ba=null,e_=null;function jL(e){ri=e}function VL(e){Ba=e}function GL(e){e_=e}class me{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=ni(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 Ba.buffer(this.shape,this.dtype,e)}bufferSync(){return Ba.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Do(this.shape,e)}arraySync(){return Do(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=ri().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(s=>Yu(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=ri().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Yu(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 ri().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;ri().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ba.print(this,e)}clone(){return this.throwIfDisposed(),Ba.clone(this)}toString(e=!1){const t=this.dataSync();return PL(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ba.cast(this,e)}variable(e=!0,t,s){return this.throwIfDisposed(),ri().makeVariable(this,e,t,s)}}Object.defineProperty(me,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class oi extends me{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(!Nt(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ri().disposeTensor(this),this.dataId=e.dataId,ri().incRef(this,null)}dispose(){ri().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(oi,Symbol.hasInstance,{value:e=>e instanceof me&&e.assign!=null&&e.assign instanceof Function});var ay;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(ay||(ay={}));var ly;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(ly||(ly={}));var cy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(cy||(cy={}));var py;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(py||(py={}));var uy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(uy||(uy={}));const t_={float32:py,int32:ly,bool:cy,complex64:uy};function Ft(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return t_[e][t]}function yp(e){return Ft(e,"int32")}const pn={};Ee(pn,{assertTypesMatch:()=>hy,getTensorsInContainer:()=>bp,isTensorInList:()=>s_,makeTypesMatch:()=>Ce});function Ce(e,t){if(e.dtype===t.dtype)return[e,t];const s=Ft(e.dtype,t.dtype);return[e.cast(s),t.cast(s)]}function hy(e,t){I(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function s_(e,t){return t.some(s=>s.id===e.id)}function bp(e){const t=[],s=new Set;return qL(e,t,s),t}function qL(e,t,s){if(e==null)return;if(e instanceof me){t.push(e);return}if(!n_(e))return;const n=e;for(const i in n){const r=n[i];s.has(r)||(s.add(r),qL(r,t,s))}}function n_(e){return Array.isArray(e)||typeof e=="object"}class HL{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class wp{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new HL}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const s=e[t],n=await this.initializeBackend(s).success;if(n){await this.setBackend(s);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 UL(this.backendInstance),!0}setupRegisteredKernels(){const e=up(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=up(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(s&&!(s instanceof go)&&typeof s.then=="function"){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 wp.nextTensorId++}nextVariableId(){return wp.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 v.runKernelFunc(c=>c.cast(r,o),a,null,Ii,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=pp(n,this.backendName);let m;if(d!=null)h=()=>{const g=this.backend.numDataIds();m=d.kernelFunc({inputs:t,attrs:i,backend:this.backend});const y=Array.isArray(m)?m:[m];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,g,y);const w=y.map(({dataId:x,shape:T,dtype:A})=>this.makeTensorFromDataId(x,T,A));if(c){let x=this.getTensorsForGradient(n,t,w);if(x==null){o==null&&(o=[]);const T=w.filter((A,_)=>o[_]);x=(r||[]).slice().concat(T)}l=this.saveTensorsForBackwardMode(x)}return w};else{const g=y=>{if(!c)return;l=y.map(w=>this.keep(this.clone(w)))};h=()=>{const y=this.backend.numDataIds();m=this.tidy(()=>e(this.backend,g));const w=Array.isArray(m)?m:[m];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,y,w),w}}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(g=>t[g]!=null?t[g].shape:null),outputShapes:a.map(g=>g.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=Gu(e);if(n!=null){const i=n.inputsToSave||[],r=n.outputsToSave||[];let o;n.saveAllInputs?(I(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"&&kn(e[0])&&(i=e.map(a=>qu(a)));const r=n.write(i,t,s),o=new me(t,s,r,this.nextTensorId());if(this.incRef(o,n),s==="string"){const a=this.state.tensorInfo.get(r),l=ry(i);this.state.numBytes+=l-a.bytes,a.bytes=l}return o}makeTensorFromDataId(e,t,s,n){s=s||"float32";const i=new me(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 oi(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*iy(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 oi||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=Gu(e);a!=null&&(n=a.gradFunc),n!=null&&(o.gradient=l=>(l=l.map((c,p)=>{if(c==null){const u=s[p],h=ii(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=bp(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(I(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));I(i instanceof me,()=>"The result y returned by f() must be a tensor.");const r=$L(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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e==null?null:e.rank===0?O(e,[e.size]):e.rank===1?e:e.rank===2?O(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?O(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e}const Ys=S({batchNorm_:Gk});function qk(e,t,s,n,i,r){const o=b(e,"x","batchNorm"),a=b(t,"mean","batchNorm"),l=b(s,"variance","batchNorm");let c;i!=null&&(c=b(i,"scale","batchNorm"));let p;return n!=null&&(p=b(n,"offset","batchNorm")),I(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),I(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),I(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&I(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),p!=null&&I(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),Ys(o,a,l,p,c,r)}const uh=S({batchNorm2d_:qk});function Hk(e,t,s,n,i,r){const 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batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),I(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),I(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&I(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),p!=null&&I(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Ys(o,a,l,p,c,r)}const dh=S({batchNorm4d_:Yk});function Kk(e,t){let s=b(e,"broadcastTo","x");const n=s.shape;if(t.some(p=>!(p>0)||p%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<s.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${s.rank}.`);if(t.length>s.rank){const p=s.shape.slice();for(;p.length<t.length;)p.unshift(1);s=O(s,p)}const i=s.shape,r=Array.from(t);for(let p=t.length-1;p>=0;p--)if(i[p]===t[p])r[p]=1;else if(s.shape[p]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);const o=r.map((p,u)=>p>1?u:-1).filter(p=>p>=0);if(o.length===0)return Ds(s);const a=p=>p.tile(s,r),l={x:s},c={shape:t,inputShape:i};return v.runKernelFunc(a,l,null,Ic,c)}const zo=S({broadcastTo_:Kk});function Xk(e){const t=b(e,"x","ceil"),s={x:t};return v.runKernelFunc(n=>n.ceil(t),s,null,cr)}const mh=S({ceil_:Xk});function Jk(e,t,s){const n=b(e,"x","clipByValue");I(t<=s,()=>`Error in clip: min (${t}) must be less than or equal to max (${s}).`);const i={x:n},r={clipValueMin:t,clipValueMax:s};return v.runKernelFunc((o,a)=>{const l=o.clip(n,t,s);return a([n]),l},i,null,pr,r)}const wt=S({clipByValue_:Jk});function Zk(e){return be(e,0)}const fh=S({concat1d_:Zk});function Qk(e,t){return be(e,t)}const gh=S({concat2d_:Qk});function eD(e,t){return be(e,t)}const yh=S({concat3d_:eD});function tD(e,t){return be(e,t)}const bh=S({concat4d_:tD});function sD(e,t,s,n,i="NHWC",r=[1,1],o){const 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o=i.selu(t);return r([t]),o},n={x:t};return v.runKernelFunc(s,n,null,Ar)}const dl=S({selu_:XF});function JF(e,t,s,n,i,r=[1,1],o="NHWC"){const a=b(e,"x","separableConv2d"),l=b(t,"depthwiseFilter","separableConv2d"),c=b(s,"pointwiseFilter","separableConv2d");let p=a,u=!1;if(a.rank===3&&(u=!0,p=O(a,[1,a.shape[0],a.shape[1],a.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");I(p.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${p.rank}.`),I(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),I(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),I(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${c.shape[0]}.`),I(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got 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v.runKernelFunc(n=>n.sign(t),s,null,Cr)}const $h=S({sign_:QF});function eM(e){const t=b(e,"x","sin"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.sin(t);return i([t]),r},s,null,Zn)}const ml=S({sin_:eM});function tM(e){const t=b(e,"x","sinh"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.sinh(t);return i([t]),r},s,null,Nr)}const fl=S({sinh_:tM});function sM(e,t,s){const n=b(e,"x","slice1d");return I(n.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${n.rank} tensor`),he(n,[t],[s])}const gl=S({slice1d_:sM});function nM(e,t,s){const n=b(e,"x","slice2d");return I(n.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${n.rank} tensor`),he(n,t,s)}const Cp=S({slice2d_:nM});function iM(e,t,s){const n=b(e,"x","slice3d");return I(n.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${n.rank} tensor`),he(n,t,s)}const Gr=S({slice3d_:iM});function rM(e,t,s){const n=b(e,"x","slice4d");return I(n.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${n.rank} tensor`),he(n,t,s)}const jo=S({slice4d_:rM});function oM(e,t=-1){const s=b(e,"logits","softmax","float32");if(t===-1&&(t=s.rank-1),t!==s.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${s.rank} and dim was ${t}`);const n={logits:s},i={dim:t};return v.runKernelFunc((r,o)=>{const a=r.softmax(s,t);return o([a]),a},n,null,ip,i)}const es=S({softmax_:oM});function aM(e){I(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);const t={input:e};return v.runKernelFunc(s=>{const n=e.shape[e.shape.length-1],i=e.size/n,r=e.as2D(i,n),o=s.fft(r);return o.reshape(e.shape)},t,null,Oc)}const qr=S({fft_:aM});function lM(e){I(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);const t={input:e};return v.runKernelFunc(s=>{const n=e.shape[e.shape.length-1],i=e.size/n,r=O(e,[i,n]),o=s.ifft(r);return O(o,e.shape)},t,null,Dc)}const Mi=S({ifft_:lM});function cM(e){const t=e.shape[e.shape.length-1],s=e.size/t;let n;if(t<=2){const i=O(e,[s,t]);n=Mi(i)}else{const i=[s,2*(t-1)],r=O(Xs(e),[s,t]),o=O(dn(e),[s,t]),a=Et(he(r,[0,1],[s,t-2]),1),l=R(Et(he(o,[0,1],[s,t-2]),1),j(-1)),c=be([r,a],1),p=be([o,l],1),u=O(Gt(c,p),[i[0],i[1]]);n=Mi(u)}if(n=Xs(n),e.rank===3&&e.shape[0]!==0){const i=n,r=e.shape[0];n=O(n,[r,n.shape[0]/r,n.shape[1]]),i.dispose()}return n}const yl=S({irfft_:cM});function Py(e,t,s=0){let n=[];if(typeof t=="number")I(e.shape[s]%t===0,()=>"Number of splits must evenly divide the axis."),n=new Array(t).fill(e.shape[s]/t);else{const i=t.reduce((o,a)=>(a===-1&&(o+=1),o),0);I(i<=1,()=>"There should be only one negative value in split array.");const r=t.indexOf(-1);if(r!==-1){const o=t.reduce((a,l)=>l>0?a+l:a);t[r]=e.shape[s]-o}I(e.shape[s]===t.reduce((o,a)=>o+a),()=>"The sum of sizes must match the size of the axis dimension."),n=t}return n}function pM(e,t,s=0){const n=b(e,"x","split"),i=(a,l)=>{const c=Ne(s,n.shape)[0],p=Py(n,t,c);return a.split(n,p,c)},r={x:n},o={numOrSizeSplits:t,axis:s};return v.runKernelFunc(i,r,null,np,o)}const 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v.runKernelFunc(i,r,null,Qn,o)}const Yr=S({squaredDifference_:dM});function mM(e,t){const s=b(e,"x","squeeze");return O(s,ey(s.shape,t).newShape)}const Js=S({squeeze_:mM});function fM(e,t=0){const s=Ei(e,"tensors","stack");if(I(s.length>=1,()=>"Pass at least one tensor to tf.stack"),s.length===1)return Mt(s[0],t);const n=s[0].rank,i=s[0].shape,r=s[0].dtype;I(t<=n,()=>"Axis must be <= rank of the tensor"),s.forEach(a=>{Se(i,a.shape,"All tensors passed to stack must have matching shapes"),I(r===a.dtype,()=>"All tensors passed to stack must have matching dtypes")});const o=s.map(a=>Mt(a,t));return be(o,t)}const Ve=S({stack_:fM});function gM(e,t=0){const s=b(e,"x","step"),n={x:s},i={alpha:t};return v.runKernelFunc(r=>r.step(s,t),n,null,Dr,i)}const ui=S({step_:gM});function yM(e,t,s,n,i=0,r=0,o=0,a=0,l=0){let c=b(e,"x","stridedSlice");const p=d=>{n==null&&(n=new Array(t.length));const m=Ip(o);if(m.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&a!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");const f=c.rank-t.length,g=Ip(a),y=c.shape.slice();g.forEach(M=>{t[M]=0,s[M]=1,y.splice(M,0,1)}),c=O(c,y);const{begin:w,end:x,strides:T}=Ny(c.shape,m,f,t,s,n,i,r,o);t=w,s=x,n=T;const A=Ip(l);A.forEach(M=>{s[M]=t[M]+1,n[M]=1});const _=Ay(t,s,n),E=_.filter((M,P)=>A.indexOf(P)===-1),F=n.every(M=>M===1);if(F)return O(he(c,t,_),E);const D=d.stridedSlice(c,t,s,n);return O(D,E)},u={x:c},h={begin:t,end:s,strides:n,beginMask:i,endMask:r,ellipsisMask:o,newAxisMask:a,shrinkAxisMask:l};return v.runKernelFunc(p,u,null,Kg,h)}const Wh=S({stridedSlice_:yM});function bM(e){const t=b(e,"x","tan"),s={x:t};return v.runKernelFunc((n,i)=>{const r=n.tan(t);return i([t]),r},s,null,ei)}const zh=S({tan_:bM});function as(e,t,s){if(qs(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");const n=Zt(e,s);if(n.length!==2&&n.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return us(e,t,n,s)}function ts(e,t,s){if(qs(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");const n=Zt(e,s);if(n.length!==4&&n.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return us(e,t,n,s)}function YS(e,t,s){if(qs(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");const n=Zt(e,s);if(n.length!==5&&n.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a 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a=0;a<o.values.length;a++)o.values[a]=r.nextValue();return o.toTensor()}const Kr=S({truncatedNormal_:xM});function LM(e,t=0){const s=b(e,"x","unique",null);I(s.rank>0,()=>"The input tensor must be at least 1D");const n={x:s},i={axis:t},[r,o]=v.runKernel(Eo,n,i);return{values:r,indices:o}}const Rp=S({unique_:LM});function SM(e,t,s){const n=b(e,"x","unsortedSegmentSum"),i=b(t,"segmentIds","unsortedSegmentSum","int32");I(ke(s),()=>"numSegments must be of dtype int");const r={x:n,segmentIds:i},o={numSegments:s},a=(l,c)=>{const p=l.unsortedSegmentSum(n,i,s);return c([i]),p};return v.runKernelFunc(a,r,null,ap,o)}const Bh=S({unsortedSegmentSum_:SM});function IM(e,t=0){const s=b(e,"x","unstack");I(t>=-s.shape.length&&t<s.shape.length,()=>`Axis = ${t} is not in [-${s.shape.length}, ${s.shape.length})`),t<0&&(t+=s.shape.length);const n={value:s},i={axis:t},r=o=>o.unstack(s,t);return v.runKernelFunc(r,n,null,op,i)}const Ge=S({unstack_:IM});function jh(e,t=!0,s,n){return v.makeVariable(e,t,s,n)}function Vh(e,t){const s=[];for(let r=0;r<t.length;r++)t[r]&&s.push(r);const n=ge(e,"int32"),i=ge([s.length,e.length],"int32");for(let r=0;r<s.length;r++){const o=n.indexToLoc(s[r]),a=r*e.length;i.values.set(o,a)}return i.toTensor()}async function vM(e){const t=b(e,"condition","whereAsync","bool"),s=await t.data(),n=Vh(t.shape,s);return e!==t&&t.dispose(),n}const bl=vM;async function TM(e,t,s){const n=b(e,"tensor","boolMask"),i=b(t,"mask","boolMask","bool"),r=s??0,o=i.rank,a=n.shape;I(o>0,()=>"mask cannot be scalar"),Se(a.slice(r,r+o),i.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let f=r;f<r+o;f++)l*=a[f];const c=a.slice(0,r).concat([l],a.slice(r+o)),p=O(n,c),u=O(i,[-1]),h=await bl(u),d=Js(h,[1]),m=ci(p,d,r);return e!==n&&n.dispose(),t!==i&&i.dispose(),d.dispose(),p.dispose(),u.dispose(),h.dispose(),m}const AM=TM;function NM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","notEqualStrict"),n=b(t,"b","notEqualStrict");return Se(s.shape,n.shape,"Error in notEqualStrict: "),Ks(s,n)}function CM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","lessStrict"),n=b(t,"b","lessStrict");return Se(s.shape,n.shape,"Error in lessStrict: "),zr(s,n)}function RM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","equalStrict"),n=b(t,"b","equalStrict");return Se(s.shape,n.shape,"Error in equalStrict: "),os(s,n)}function OM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","lessEqualStrict"),n=b(t,"b","lessEqualStrict");return Se(s.shape,n.shape,"Error in lessEqualStrict: "),$s(s,n)}function EM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","greaterStrict"),n=b(t,"b","greaterStrict");return Se(s.shape,n.shape,"Error in greaterStrict: "),Ut(s,n)}function _M(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","greaterEqualStrict"),n=b(t,"b","greaterEqualStrict");return Se(s.shape,n.shape,"Error in greaterEqualStrict: "),ds(s,n)}const kM=S({equalStrict_:RM}),DM=S({greaterEqualStrict_:_M}),FM=S({greaterStrict_:EM}),MM=S({lessEqualStrict_:OM}),UM=S({lessStrict_:CM}),$M=S({notEqualStrict_:NM});function WM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","addStrict"),n=b(t,"b","addStrict");return Se(s.shape,n.shape,"Error in addStrict: "),$(s,n)}function zM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","subStrict"),n=b(t,"b","subStrict");return Se(s.shape,n.shape,"Error in subStrict: "),X(s,n)}function PM(e,t){return It("strict variants of ops have been deprecated and will be removed in future"),Se(e.shape,t.shape,"Error in powStrict: "),Qt(e,t)}function BM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","mul"),n=b(t,"b","mul");return Se(s.shape,n.shape,"Error in multiplyStrict: "),R(s,n)}function jM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","div"),n=b(t,"b","div");return Se(s.shape,n.shape,"Error in divideStrict: "),Z(s,n)}function VM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","modStrict"),n=b(t,"b","modStrict");return Se(s.shape,n.shape,"Error in modStrict: "),pl(s,n)}function GM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","minimumStrict"),n=b(t,"b","minimumStrict");return Se(s.shape,n.shape,"Error in minimumStrict: "),mn(s,n)}function qM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","maximumStrict"),n=b(t,"b","maximumStrict");return Se(s.shape,n.shape,"Error in maximumStrict: "),Ht(s,n)}function HM(e,t){It("strict variants of ops have been deprecated and will be removed in future");const s=b(e,"a","squaredDifferenceStrict"),n=b(t,"b","squaredDifferenceStrict");return Se(s.shape,n.shape,"Error in squaredDifferenceStrict: "),Yr(s,n)}const YM=S({addStrict_:WM}),KM=S({divStrict_:jM}),XM=S({maximumStrict_:qM}),JM=S({minimumStrict_:GM}),ZM=S({modStrict_:VM}),QM=S({mulStrict_:BM}),eU=S({powStrict_:PM}),tU=S({squaredDifferenceStrict_:HM}),sU=S({subStrict_:zM});function nU(e,t="euclidean",s=null,n=!1){e=b(e,"x","norm");const i=XS(e,t,s);let r=i.shape;if(n){const o=Ne(s,e.shape);r=bt(i.shape,o)}return O(i,r)}function XS(e,t,s=null){if(e.rank===0)return et(e);if(e.rank!==1&&s===null)return XS(O(e,[-1]),t,s);if(e.rank===1||typeof s=="number"||Array.isArray(s)&&s.length===1){if(t===1)return te(et(e),s);if(t===Infinity)return xt(et(e),s);if(t===-Infinity)return Di(et(e),s);if(t==="euclidean"||t===2)return Xe(te(Qt(et(e),j(2,"int32")),s));throw new Error(`Error in norm: 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l=JU(r,o);return ss(l,a,i)}const vI=S({sigmoidCrossEntropy_:ZU});function QU(e,t,s=-1){if(s===-1&&(s=t.rank-1),s!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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t$={fft:qr,ifft:Mi,rfft:Hr,irfft:yl},s$={hammingWindow:QS,hannWindow:Hh,frame:Yh,stft:eI},Zs={flipLeftRight:sI,resizeNearestNeighbor:dI,resizeBilinear:hI,rotateWithOffset:nI,cropAndResize:tI,nonMaxSuppression:iI,nonMaxSuppressionAsync:aI,nonMaxSuppressionWithScore:lI,nonMaxSuppressionWithScoreAsync:cI,nonMaxSuppressionPadded:pI,nonMaxSuppressionPaddedAsync:uI},Hy={bandPart:mI,gramSchmidt:fI,qr:yI},n$={absoluteDifference:bI,computeWeightedLoss:ss,cosineDistance:wI,hingeLoss:xI,huberLoss:LI,logLoss:SI,meanSquaredError:II,sigmoidCrossEntropy:vI,softmaxCrossEntropy:TI};class fs extends Cy{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 Rh(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(fs,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class Yo extends fs{constructor(e,t,s=null){super();this.learningRate=e,this.rho=t,this.epsilon=s,this.accumulatedGrads=[],this.accumulatedUpdates=[],s==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${s}/accum_grad`,variable:C(()=>re(i).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${s}/accum_var`,variable:C(()=>re(i).variable(r))});const o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;const a=this.accumulatedGrads[n].variable,l=this.accumulatedUpdates[n].variable;C(()=>{const c=$(R(a,this.rho),R(xe(o),1-this.rho)),p=R(Z(Xe($(l,this.epsilon)),Xe($(a,this.epsilon))),o),u=$(R(l,this.rho),R(xe(p),1-this.rho));a.assign(c),l.assign(u);const h=$(R(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)}}Yo.className="Adadelta";vs(Yo);class Ko 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=v.registeredVariables[s];if(this.accumulatedGrads[n]==null){const a=!1;this.accumulatedGrads[n]={originalName:`${s}/accumulator`,variable:C(()=>Wt(i.shape,this.initialAccumulatorValue).variable(a))}}const r=Array.isArray(e)?e[n].tensor:e[s];if(r==null)return;const o=this.accumulatedGrads[n].variable;C(()=>{const a=$(o,xe(r));o.assign(a);const l=$(R(Z(r,Xe($(a,v.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)}}Ko.className="Adagrad";vs(Ko);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=[],C(()=>{this.accBeta1=j(t).variable(),this.accBeta2=j(s).variable()}),n==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);C(()=>{const s=X(1,this.accBeta1),n=X(1,this.accBeta2);t.forEach((i,r)=>{const o=v.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:C(()=>re(o).variable(a))}),this.accumulatedSecondMoment[r]==null&&(this.accumulatedSecondMoment[r]={originalName:`${i}/v`,variable:C(()=>re(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=$(R(c,this.beta1),R(l,1-this.beta1)),h=$(R(p,this.beta2),R(xe(l),1-this.beta2)),d=Z(u,s),m=Z(h,n);c.assign(u),p.assign(h);const f=$(R(Z(d,$(Xe(m),this.epsilon)),-this.learningRate),o);o.assign(f)}),this.accBeta1.assign(R(this.accBeta1,this.beta1)),this.accBeta2.assign(R(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),C(()=>{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";vs(Xo);class Jo 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=[],C(()=>{this.iteration=j(0).variable(),this.accBeta1=j(t).variable()}),n==null&&(this.epsilon=v.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);C(()=>{const s=X(1,this.accBeta1),n=Z(-this.learningRate,$(R(this.iteration,this.decay),1));t.forEach((i,r)=>{const o=v.registeredVariables[i],a=!1;this.accumulatedFirstMoment[r]==null&&(this.accumulatedFirstMoment[r]={originalName:`${i}/m`,variable:re(o).variable(a)}),this.accumulatedWeightedInfNorm[r]==null&&(this.accumulatedWeightedInfNorm[r]={originalName:`${i}/v`,variable:re(o).variable(a)});const l=Array.isArray(e)?e[r].tensor:e[i];if(l==null)return;const c=this.accumulatedFirstMoment[r].variable,p=this.accumulatedWeightedInfNorm[r].variable,u=$(R(c,this.beta1),R(l,1-this.beta1)),h=R(p,this.beta2),d=et(l),m=Ht(h,d);c.assign(u),p.assign(m);const f=$(R(Z(n,s),Z(u,$(m,this.epsilon))),o);o.assign(f)}),this.iteration.assign($(this.iteration,1)),this.accBeta1.assign(R(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ce(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ce(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}Jo.className="Adamax";vs(Jo);class Ui extends fs{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=Array.isArray(e)?e[n].tensor:e[s];if(i==null)return;const r=v.registeredVariables[s];C(()=>{const o=$(R(this.c,i),r);r.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=pt(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)}}Ui.className="SGD";vs(Ui);class Zo extends Ui{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=v.registeredVariables[s];if(this.accumulations[n]==null){const a=!1;this.accumulations[n]={originalName:`${s}/momentum`,variable:C(()=>re(i).variable(a))}}const r=this.accumulations[n].variable,o=Array.isArray(e)?e[n].tensor:e[s];if(o==null)return;C(()=>{let a;const l=$(R(this.m,r),o);this.useNesterov?a=$(R(this.c,$(o,R(l,this.m))),i):a=$(R(this.c,l),i),r.assign(l),i.assign(a)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ce(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(s=>({originalName:s.name,variable:s.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Zo.className="Momentum";vs(Zo);class Qo extends fs{constructor(e,t=.9,s=0,n=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=s,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,n==null&&(this.epsilon=v.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(s=>s.name):Object.keys(e);t.forEach((s,n)=>{const i=v.registeredVariables[s],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${s}/rms`,variable:C(()=>re(i).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${s}/momentum`,variable:C(()=>re(i).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${s}/mg`,variable:C(()=>re(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;C(()=>{const c=$(R(a,this.decay),R(xe(o),1-this.decay));if(this.centered){const p=this.accumulatedMeanGrads[n].variable,u=$(R(p,this.decay),R(o,1-this.decay)),h=Z(R(o,this.learningRate),Xe(X(c,$(xe(u),this.epsilon)))),d=$(R(l,this.momentum),h);a.assign(c),p.assign(u),l.assign(d);const m=X(i,d);i.assign(m)}else{const p=$(R(a,this.decay),R(xe(o),1-this.decay)),u=$(R(l,this.momentum),Z(R(o,this.learningRate),Xe($(p,this.epsilon))));a.assign(p),l.assign(u);const h=X(i,u);i.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ce(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ce(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ce(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,s=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(s)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Qo.className="RMSProp";vs(Qo);class Xr{static sgd(e){return new Ui(e)}static momentum(e,t,s=!1){return new Zo(e,t,s)}static rmsprop(e,t=.9,s=0,n=null,i=!1){return new Qo(e,t,s,n,i)}static adam(e=.001,t=.9,s=.999,n=null){return new Xo(e,t,s,n)}static adadelta(e=.001,t=.95,s=null){return new Yo(e,t,s)}static adamax(e=.002,t=.9,s=.999,n=null,i=0){return new Jo(e,t,s,n,i)}static adagrad(e,t=.1){return new Ko(e,t)}}Zo,Ui,Yo,Ko,Qo,Jo,Xo;const Jr={sgd:Xr.sgd,momentum:Xr.momentum,adadelta:Xr.adadelta,adagrad:Xr.adagrad,rmsprop:Xr.rmsprop,adamax:Xr.adamax,adam:Xr.adam};const i$=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function kp(){return new Promise(e=>i$(()=>e()))}function r$(e,t,s){const n=s*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[n,i]}function o$(e,t,s,n=!0){let i=[];if(n)i=i.concat(t.slice(0)),i.push(e[0]/s),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const r=t.length;for(let o=0;o<r;++o)i=i.concat([e[o+1]/t[o],t[o]]);i=i.concat(e.slice(r+1))}return i}function a$(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 l$(e,t,s,n=!0){const i=[];n?i.push(e[0]/s):i.push(e[0]*s);for(let 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U={};Ee(U,{ERF_A1:()=>h$,ERF_A2:()=>d$,ERF_A3:()=>m$,ERF_A4:()=>f$,ERF_A5:()=>g$,ERF_P:()=>u$,PARALLELIZE_THRESHOLD:()=>Th,SELU_SCALE:()=>Ky,SELU_SCALEALPHA:()=>Yy,applyActivation:()=>qo,assertAndGetBroadcastShape:()=>Ie,assertAxesAreInnerMostDims:()=>Sk,assertParamsConsistent:()=>$y,assignToTypedArray:()=>v$,axesAreInnerMostDims:()=>Dy,calculateShapes:()=>cS,castTensor:()=>N$,combineLocations:()=>LS,complexWithEvenIndex:()=>L$,complexWithOddIndex:()=>S$,computeConv2DInfo:()=>is,computeConv3DInfo:()=>Ur,computeDefaultPad:()=>Uy,computeDilation2DInfo:()=>_k,computeOptimalWindowSize:()=>OD,computeOutAndReduceShapes:()=>Fy,computeOutShape:()=>Wy,computePool2DInfo:()=>Fn,computePool3DInfo:()=>_i,convertConv2DDataFormat:()=>Mr,eitherStridesOrDilationsAreOne:()=>tt,expandShapeToKeepDim:()=>bt,exponent:()=>A$,exponents:()=>T$,getAxesPermutation:()=>ht,getBroadcastDims:()=>gD,getComplexWithIndex:()=>I$,getFusedBiasGradient:()=>Go,getFusedDyActivation:()=>Vo,getImageCenter:()=>r$,getInnerMostAxes:()=>qt,getPermuted:()=>a$,getReductionAxes:()=>Ye,getReshaped:()=>o$,getReshapedPermuted:()=>l$,getSliceBeginCoords:()=>c$,getSliceSize:()=>p$,getUndoAxesPermutation:()=>Mo,linspaceImpl:()=>R$,log:()=>b$,mergeRealAndImagArrays:()=>w$,prepareAndValidate:()=>aS,prepareSplitSize:()=>Py,reshapeTensor:()=>C$,segment_util:()=>NS,shouldFuse:()=>Ho,slice_util:()=>Fs,splitRealAndImagArrays:()=>x$,tupleValuesAreOne:()=>Hs,upcastType:()=>Ft,validateInput:()=>Qu,validateUpdateShape:()=>vy,warn:()=>y$});function 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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 k(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Al(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 BT="Variable";class xd{constructor(e,t="float32",s=BT,n=!0,i=null){this.dtype=t??"float32",this.shape=e.shape,this.id=yd(),s=s??BT,this.originalName=rd(s),this.name=od(this.originalName),this.trainable_=n,this.constraint=i,this.val=jh(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),uW(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 uW(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Vp(e){return e.map(t=>t.read())}function Nl(e){e.forEach(t=>{const s=t[0];s.write(t[1])})}class st{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 ws{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=yd(),r!=null&&(this.originalName=rd(r),this.name=od(this.originalName)),this.rank=t.length}}let hW=0;class sa{constructor(e,t){this.callArgs=t,this.id=hW++,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 dW=0;class Le extends V.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=dW++,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=xn(s)+"_"+Qr(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 Ts(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new k(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return jt(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return jt(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new bn(`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 bn(`Layer ${this.name} is not connected, no input to return.`);return jt(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new bn(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new bn(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return jt(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 k(`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 k(`Input ${s} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${r}`);if(i.maxNDim!=null&&r>i.maxNDim)throw new k(`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 k(`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 k(`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 k(`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 k(`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 ws)){n=!1;break}let i=!0;for(const r of s)if(r instanceof ws){i=!1;break}if(n===i)throw new k("Arguments to apply() must be all SymbolicTensors or all Tensors");return Mn(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const r=[];for(const o of qe(e))r.push(o.shape);this.build(jt(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=jt(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=mW(e),o=this.computeOutputShape(r);let a;const l=fW(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 ws(l,c,this,qe(e),t,this.name,p)):a=new ws(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 bn(`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 bn(`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 Ts(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Al(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Vp(e?this.trainableWeights:this.weights)}setWeights(e){C(()=>{const t=this.weights;if(t.length!==e.length)throw new k(`You called setWeights(weights) on layer "${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=Vp(t);for(let i=0;i<n.length;++i){const r=n[i],o=t[i],a=e[i];if(!N.arraysEqual(r.shape,a.shape))throw new k(`Layer weight shape ${r.shape} not compatible with provided weight shape ${a.shape}`);s.push([o,a])}Nl(s)})}addWeight(e,t,s,n,i,r,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new k(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),s==null&&(s="float32"),this.fastWeightInitDuringBuild&&(n=Be("zeros"));const a=n.apply(t,s),l=new xd(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,s,n,i,r,o=null){const a=qe(e);t=qe(t),s=qe(s),n=qe(n),i=Tl(i),r=Tl(r);const l=[],c=[],p=[];for(const u of a)l.push(u.sourceLayer),c.push(u.nodeIndex),p.push(u.tensorIndex);new sa({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:p,inputTensors:a,outputTensors:t,inputMasks:s,outputMasks:n,inputShapes:i,outputShapes:r},o);for(let u=0;u<t.length;u++)t[u].sourceLayer=this,t[u].nodeIndex=this.inboundNodes.length-1,t[u].tensorIndex=u}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 mW(e){e=qe(e);const t=[];for(const s of e)t.push(s.shape);return jt(t)}function fW(e){return"float32"}function ub(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=ub(o,a,l);for(const p of c)i.indexOf(p)===-1&&i.push(p)}return i}}}class ji extends Le{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Qr("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 k("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 k("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new k("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 ws(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new sa({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new k(`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}}}ji.className="InputLayer";V.registerClass(ji);function Ld(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new k("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let s=e.dtype;s==null&&(s="float32");const n=new ji({batchInputShape:t,name:e.name,dtype:s,sparse:e.sparse}),i=n.inboundNodes[0].outputTensors;return i[0]}async function di(e){if(e==null)return;const t=[],s=[],n=[];for(const i in e){const r=e[i];if(typeof r!="number"){const o=r;t.push(o.data()),s.push(i),n.push(o)}}if(t.length>0){const i=await Promise.all(t);for(let r=0;r<i.length;++r)e[s[r]]=i[r][0];ce(n)}}function Sd(e){if(e==null)return;for(const t in e){const s=e[t];typeof s!="number"&&s.dispose()}}var jT;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(jT||(jT={}));const gW=125;class na{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class hb{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(const s of this.callbacks)await s.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(const s of this.callbacks)await s.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(const s of this.callbacks)await s.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(const s of this.callbacks)await s.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class yW extends na{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});const s=t.size==null?0:t.size;this.seen+=s;for(const n in t){const i=t[n];if(typeof i=="number")this.totals.hasOwnProperty(n)||(this.totals[n]=0),this.totals[n]=this.totals[n]+i*s;else{let r;n in this.totals?r=this.totals[n]:this.totals[n]=0;const o=C(()=>$(this.totals[n],R(i,s)));this.totals[n]=o,r!=null&&r.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(const s of this.params.metrics){if(this.totals[s]==null)continue;typeof this.totals[s]=="number"?t[s]=this.totals[s]/this.seen:C(()=>{const n=R(Z(1,this.seen),this.totals[s]);t[s]=n,this.totals[s].dispose(),pt(t[s])})}}}class db extends na{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(const s in t)this.history[s]==null&&(this.history[s]=[]),this.history[s].push(t[s])}async syncData(){const e=[],t=[],s=[];for(const i in this.history){const r=this.history[i];for(let o=0;o<r.length;++o)if(typeof r[o]!="number"){const a=r[o];e.push(a.data()),t.push(i),s.push(o)}}const n=await Promise.all(e);for(let i=0;i<n.length;++i){const r=this.history[t[i]][s[i]];r.dispose(),this.history[t[i]][s[i]]=n[i][0]}}}class mb extends na{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=gW),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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The following previous layers were accessed without issue: ${f}`);for(const T of w.outputTensors)m.push(T);f.push(x.name)}}this.nodesByDepth=u;const g=this.layers.map(y=>y.name);for(const y of g){const w=g.filter(x=>x===y).length;if(w!==1)throw new Ts(`The name "${y}" is used ${w} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sa({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 k("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 k(`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 k(`Provided weight data has no target variable: ${r}`);delete s[o]}if(t){const r=[];for(const o in s)r.push(o);if(r.length>0)throw new k(`${r.length} of ${n} weights are not set: ${r}`)}Nl(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${to}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const s=Od(this.updatedConfig());return t?JSON.stringify(s):s}call(e,t){return C(()=>{e=qe(e);const s=new Vi;for(let n=0;n<this.inputs.length;++n)s.add(this.inputs[n],e[n]);return oa(this.outputs,s,t)})}computeMask(e,t){return C(()=>{e=qe(e);let s;return t==null?s=wn(null,e.length):s=qe(t),this.runInternalGraph(e,s)[1]})}computeOutputShape(e){const t=Tl(e);if(t.length!==this.inputLayers.length)throw new k(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const s={};for(let o=0;o<t.length;o++){const a=this.inputLayers[o],l=t[o],c=a.name+"_0_0";s[c]=l}const n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Dp);if(n.length>1)for(const o of n){const a=this.nodesByDepth[o];for(const l of a){const c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;const p=[];for(let m=0;m<l.inboundLayers.length;m++){const f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],w=`${f.name}_${g}_${y}`,x=s[w];p.push(x)}const u=c.computeOutputShape(jt(p)),h=Tl(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 jt(i)}runInternalGraph(e,t){t==null&&(t=wn(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(Dp);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,g,y,w;if(c.callArgs!=null&&(m=c.callArgs),d.length===1){const[x,T]=d[0];m.mask==null&&(m.mask=T),y=qe(p.call(x,m)),w=qe(p.computeMask(x,T)),f=[x],g=[T]}else f=d.map(x=>x[0]),g=d.map(x=>x[1]),m.mask==null&&(m.mask=g),y=qe(p.call(f,m)),w=qe(p.computeMask(f,g));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 T=h[x],A=y[x],_=w[x];s[T.id]=[A,_]}}}}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 $n?1:0;for(let i=0;i<n.inboundNodes.length;i++){const r=$n.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 k(`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 k("Provide either a layer name or layer index");for(const s of this.layers)if(s.name===e)return s;throw new k(`No such layer: ${e}`)}calculateLosses(){return C(()=>{const e=[];for(const t of this.layers)for(let s=0;s<t.inboundNodes.length;++s){const n=$n.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=$n.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: ${u.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(u.inboundLayers.length>0){const m=[];for(let f=0;f<u.inboundLayers.length;f++){const g=u.inboundLayers[f],y=u.nodeIndices[f],w=u.tensorIndices[f],x=$n.nodeKey(g,y);let T=t[x];T==null&&(T=0),m.push([g.name,T,w,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=$n.nodeKey(o,a);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);const p=this.inputLayersTensorIndices[r];n.push([o.name,c,p])}e.inputLayers=n;const i=[];for(let r=0;r<this.outputLayers.length;r++){const o=this.outputLayers[r],a=this.outputLayersNodeIndices[r],l=$n.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,g){f.name in r?r[f.name].push(g):r[f.name]=[g]}function a(f,g){const y=[];let w;for(const x of g){const T=x[0],A=x[1],_=x[2];if(w=x[3]==null?{}:x[3],!(T in i)){o(f,g);return}const E=i[T];if(E.inboundNodes.length<=A){o(f,g);return}const F=E.inboundNodes[A];y.push(F.outputTensors[_])}y.length>0&&f.apply(jt(y),w)}function l(f){const g=f.name,y=xs(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),i[g]=y;const w=f.inboundNodes;w.forEach(x=>{if(!(x instanceof Array))throw new k(`Corrupted configuration, expected array for nodeData: ${x}`);o(y,x)})}const c=t.name,p=t.layers;for(const f of p)l(f);for(;!wT(r);)for(const f of p){const g=i[f.name];if(g.name in r){const y=r[g.name];delete r[g.name];for(const w of y)a(g,w)}}const u=[],h=[],d=t.inputLayers;for(const f of d){const g=f[0],y=f[1],w=f[2];Qs(g in i);const x=i[g],T=x.inboundNodes[y].outputTensors;u.push(T[w])}const m=t.outputLayers;for(const f of m){const g=f[0],y=f[1],w=f[2];Qs(g in i);const x=i[g],T=x.inboundNodes[y].outputTensors;h.push(T[w])}return new e({inputs:u,outputs:h,name:c})}get stateful(){if(this._stateful)throw new k("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(){C(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function jW(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: ${JSON.stringify(e)}`)}function Ed(e,t){return jW(e,t,"classWeight")}async function _d(e,t,s,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(s!=null){const i=C(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const a=1;return e.argMax(a)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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])}),Oe(o,"float32")}else return null}function QT(e,t){return R(e,t)}const VW=32;function tA(e,t){let s,n;const i=t;s=i.xs,n=i.ys,N.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=eA("input",e.inputNames,s),o=eA("output",e.outputNames,n),a=r[0].shape[0];N.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)})`),N.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++)N.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++)N.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 eA(e,t,s){if(s instanceof me)return[s];if(Array.isArray(s))return N.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 k(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);n.push(s[i])}return n}}function GW(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 nA(e,t,s){const n=s.batchesPerEpoch!=null;if(N.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),N.assert(s!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),N.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}`),N.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}`),N.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(sA(s.validationData))N.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 g=GW(s.validationData);r=g.xs,o=g.ys}const a=e.makeTrainFunction(),l=e.getDedupedMetricsNames();let c;i?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();const p=Id(s.callbacks,s.yieldEvery),u=s.verbose==null?1:s.verbose,{callbackList:h,history:d}=vd(p,u,s.epochs,null,null,qW(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 g={};await h.onEpochBegin(m);let y=0,w=0;for(n||(f=await t.iterator());n?y<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 ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${s.batchesPerEpoch*s.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){const{xs:T,ys:A}=tA(e,x.value),_={};_.batch=w,_.size=T[0].shape[0],await h.onBatchBegin(w,_);const E=[];if(s.classWeight!=null){const M=Ed(s.classWeight,e.outputNames);for(let P=0;P<M.length;++P)E.push(await _d(A[P],null,M[P]))}const F=T.concat(A).concat(E),D=a(F);ce(F);for(let M=0;M<l.length;++M){const P=l[M],B=D[M];_[P]=B,pt(B)}await h.onBatchEnd(w,_),Sd(_),w++,y++}if(n?y>=s.batchesPerEpoch:x.done){if(i){let T;sA(s.validationData)?T=qe(await e.evaluateDataset(s.validationData,{batches:s.validationBatches})):T=qe(e.evaluate(r,o,{batchSize:s.validationBatchSize==null?VW:s.validationBatchSize,verbose:0}));for(let A=0;A<e.metricsNames.length;++A)g[`val_${e.metricsNames[A]}`]=T[A]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(m,g),m++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function qW(e,t){let s=null;return t.batchesPerEpoch!=null?s=t.batchesPerEpoch:Number.isFinite(e.size)&&(s=e.size),s}function sA(e){return typeof e.iterator=="function"}function HW(e){return typeof e.next=="function"}async function iA(e,t,s){s=s||{};const n=s.batches!=null,i=e.testFunction;let r=[];if(s.verbose>0)throw new ae("Verbose mode is not implemented yet.");N.assert(!n||s.batches>0&&Number.isInteger(s.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(s.batches)}`);const o=HW(t)?t:await t.iterator();let a=0,l=0;for(;n?l<s.batches:!0;){const c=await o.next();if(r=C(()=>{if(c.value){const{xs:p,ys:u}=tA(e,c.value),h=p.concat(u),d=C(()=>i(h));if(ce(h),l===0)for(let f=0;f<d.length;++f)r.push(j(0));const m=h[0].shape[0];for(let f=0;f<d.length;++f){const g=d[f],y=r[f];r[f]=C(()=>$(r[f],R(m,g))),l>0&&ce(y)}ce(d),a+=m,++l}return r}),c.done){n&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${s.batches} batches). 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d=Ke(d),this.calculateLosses().forEach(m=>{d=$(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=>C(()=>{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 Vi(r),a=oa(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){const c=this.lossFunctions[l],p=Ke(c(i[l],a[l]));l===0?s=p:s=$(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=Ke(c(i[p],a[p]));t.push(u)}return t})}async fit(e,t,s={}){return rA(this,e,t,s)}async fitDataset(e,t){return nA(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),jt(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=Ya().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ya().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=xn(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=>xn(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]=xn(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[xn(Jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>xn(Jp(e)));{const e={};for(const t in this.metrics)e[t]=xn(Jp(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=ra(e.optimizer_config),s=xs(t);let n;if(typeof e.loss=="string")n=$i(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(r=>$i(r));else if(e.loss!=null){n={};for(const r in e.loss)n[r]=$i(e.loss[r])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(r=>$i(r));else if(e.metrics!=null){i={};for(const r in e.metrics)i[r]=$i(e.metrics[r])}this.compile({loss:n,metrics:i,optimizer:s})}async save(e,t){if(typeof e=="string"){const l=Rt.getSaveHandlers(e);if(l.length===0)throw new k(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new k(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new k("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const s=await Rt.encodeWeights(this.getNamedWeights(t)),n=!1,i=null,r=this.toJSON(i,n),o={modelTopology:r,format:QW,generatedBy:`TensorFlow.js tfjs-layers v${to}`,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 Rt.encodeWeights(await this.optimizer.getWeights(),l);s.specs.push(...p),s.data=Rt.concatenateArrayBuffers([s.data,c])}if(this.userDefinedMetadata!=null){const l=!0;bb(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=s.data,o.weightSpecs=s.specs,e.save(o)}setUserDefinedMetadata(e){bb(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}tn.className="Model";V.registerClass(tn);class cA extends tn{}cA.className="Functional";V.registerClass(cA);async function pA(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let s=e.modelTopology;s.model_config!=null&&(s=s.model_config);const n=ra(s),i=xs(n,t);if(e.weightsManifest!=null){const r=await Rt.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 uA(e,t){if(t==null&&(t={}),typeof e=="string"){const s=Rt.getLoadHandlers(e,t);if(s.length===0)s.push(Rt.browserHTTPRequest(e,t));else if(s.length>1)throw new k(`Found more than one (${s.length}) load handlers for URL '${e}'`);e=s[0]}return ez(e,void 0,t)}async function ez(e,t,s){if(s==null&&(s={}),e.load==null)throw new k("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=xs(ra(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 k("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:c,optimizerWeights:p}=tz(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 tz(e,t){const s=Rt.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 no extends tn{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Qr("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 k(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof no||e instanceof tn;let s;if(t){if(s=e,s.outputs.length!==1)throw new k("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(e,t,s)}async evaluateDataset(e,t){if(!this.built)throw new Ts("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 Ts("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 Ts("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 k("Legacy serialization format not supported yet.");i=t}else N.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 no))throw new ae(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(const a of i){const l=void 0,c=xs(a,l,n);n&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new k("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 k("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}}}no.className="Sequential";V.registerClass(no);function hA(e){return new tn(e)}function dA(e){return new no(e)}function mA(e,t){return t==null&&(t={}),uA(e,t)}function Md(e){return Ld(e)}function fA(e,t){en.registerCallbackConstructor(e,t)}class zs extends V.Serializable{getConfig(){return{}}}class gA extends zs{apply(e,t=1){return FT(e,t)}}gA.className="elu";V.registerClass(gA);class yA extends zs{apply(e){return dl(e)}}yA.className="selu";V.registerClass(yA);class bA extends zs{apply(e){return De(e)}}bA.className="relu";V.registerClass(bA);class wA extends zs{apply(e){return C(()=>mn(6,De(e)))}}wA.className="relu6";V.registerClass(wA);class xA extends zs{apply(e){return e}}xA.className="linear";V.registerClass(xA);class LA extends zs{apply(e){return rs(e)}}LA.className="sigmoid";V.registerClass(LA);class SA extends zs{apply(e){return UT(e)}}SA.className="hardSigmoid";V.registerClass(SA);class IA extends zs{apply(e){return pi(e)}}IA.className="softplus";V.registerClass(IA);class vA extends zs{apply(e){return MT(e)}}vA.className="softsign";V.registerClass(vA);class TA extends zs{apply(e){return ki(e)}}TA.className="tanh";V.registerClass(TA);class Ud extends zs{apply(e,t=-1){return es(e,t)}}Ud.className="softmax";V.registerClass(Ud);class AA extends zs{apply(e,t=-1){return al(e,t)}}AA.className="logSoftmax";V.registerClass(AA);class NA extends zs{apply(e,t=1){return C(()=>rs(e.mul(t)).mul(e))}}NA.className="swish";V.registerClass(NA);function Wn(e){return e.getClassName()}function Sb(e,t={}){return hi(e,V.SerializationMap.getMap().classNameMap,t,"activation")}function zn(e){if(e==null){const t={};return t.className="linear",t.config={},Sb(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Sb(t)}else return e instanceof zs?e:Sb(e)}function Ib(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class CA extends V.Serializable{}class _l extends CA{constructor(e){super();Ib(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return C(()=>{let t=ye([1]);return this.hasL1&&(t=$(t,te(R(this.l1,et(e))))),this.hasL2&&(t=$(t,te(R(this.l2,ta(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}_l.className="L1L2";V.registerClass(_l);function RA(e){return Ib(e),new _l({l1:e!=null?e.l1:null,l2:0})}function OA(e){return Ib(e),new _l({l2:e!=null?e.l2:null,l1:0})}const EA={l1l2:"L1L2"};function Pe(e){return xl(e)}function _A(e,t={}){return hi(e,V.SerializationMap.getMap().classNameMap,t,"regularizer")}function He(e){if(e==null)return null;if(typeof e=="string"){const t=e in EA?EA[e]:e,s={className:t,config:{}};return _A(s)}else return e instanceof CA?e:_A(e)}class $d extends Le{constructor(e){super(e??{});this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=we(e);let s=De(e);return this.maxValue!=null&&(s=wt(s,0,this.maxValue)),s}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}$d.className="ReLU";V.registerClass($d);class Wd extends Le{constructor(e){super(e??{});this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const s=we(e);return rl(s,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Wd.className="LeakyReLU";V.registerClass(Wd);class zd extends Le{constructor(e){super(e??{});if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Be(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=He(e.alphaRegularizer),this.alphaConstraint=rt(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 k(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Ue(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const s={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)s[n]=e[n];this.inputSpec=[new st({ndim:e.length,axes:s})],this.built=!0}call(e,t){return e=we(e),jr(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Je(this.alphaInitializer),alphaRegularizer:Pe(this.alphaRegularizer),alphaConstraint:it(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}zd.className="PReLU";V.registerClass(zd);class Pd extends Le{constructor(e){super(e??{});if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ae(`Non-default alpha value (${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=we(e);return hn(s)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Pd.className="ELU";V.registerClass(Pd);class Bd extends Le{constructor(e){super(e??{});this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const s=we(e);return s.mul(zi(s.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Bd.className="ThresholdedReLU";V.registerClass(Bd);class jd extends Le{constructor(e){super(e??{});this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ud().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const s=we(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}}jd.className="Softmax";V.registerClass(jd);function io(e,t,s){if(typeof e=="number")return wn(e,t);if(e.length!==t)throw new k(`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(!OT(i))throw new k(`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 Ns(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 Zp(e,t,s,n){if(e==null)return null;if(n==="valid")e=e*t+Un([s-t,0]);else if(n==="same")e=e*t;else throw new k(`Unsupport padding mode: ${n}.`);return e}function Qp(e,t){return C(()=>(ot(t),t==="channelsFirst"?se(e,[0,2,3,1]):e))}function vb(e,t){return C(()=>(ot(t),t==="channelsFirst"?se(e,[0,2,3,4,1]):e))}function sz(e,t,s,n=1,i="valid",r,o=1){return C(()=>{if(r==null&&(r=gs()),ot(r),e.shape.length!==3)throw new k(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new k(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(s!=null&&s.shape.length!==1)throw new k(`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=Qa(e,t,n,i==="same"?"same":"valid","NWC",o);return s!=null&&(a=As(a,s)),a})}function kA(e,t,s,n=[1,1],i="valid",r,o,a=null){return C(()=>{if(r==null&&(r=gs()),ot(r),e.rank!==3&&e.rank!==4)throw new k(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new k(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Qp(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=gn.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 nz(e,t,s,n=[1,1,1],i="valid",r,o){return C(()=>{if(r==null&&(r=gs()),ot(r),e.rank!==4&&e.rank!==5)throw new k(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new k(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let a=vb(e,r);if(i==="causal")throw new ae("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=sl(a,t,n,i==="same"?"same":"valid","NDHWC",o),s!=null&&(a=As(a,s)),r==="channelsFirst"&&(a=se(a,[0,4,1,2,3])),a})}class Vd extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Vd.verifyArgs(t),this.rank=e,gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ae(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=io(t.kernelSize,e,"kernelSize"),this.strides=io(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ys(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,ot(this.dataFormat),this.activation=zn(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Be(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=rt(t.biasConstraint),this.biasRegularizer=He(t.biasRegularizer),this.activityRegularizer=He(t.activityRegularizer),this.dilationRate=io(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new k(`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 k(`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 k(`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"&&!Qh(e.kernelSize,"number",1,3))throw new k(`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:Wn(this.activation),useBias:this.useBias,biasInitializer:Je(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:it(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class eu extends Vd{constructor(e,t){super(e,t);this.kernel=null,eu.verifyArgs(t),this.filters=t.filters,gt(this.filters,"filters"),this.kernelInitializer=Be(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=rt(t.kernelConstraint),this.kernelRegularizer=He(t.kernelRegularizer)}build(e){e=Ue(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new k(`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 C(()=>{e=we(e);let s;const n=this.bias==null?null:this.bias.read(),i=ed(this.activation.getClassName());if(i!=null&&this.rank===2)s=kA(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)s=sz(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)s=kA(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)s=nz(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=Ns(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:Je(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:it(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 k(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class kl extends eu{constructor(e){super(2,e);kl.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Qh(e.kernelSize,"number",1,2))throw new k(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}kl.className="Conv2D";V.registerClass(kl);class tu extends eu{constructor(e){super(3,e);tu.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 k(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}tu.className="Conv3D";V.registerClass(tu);class Gd extends kl{constructor(e){super(e);if(this.inputSpec=[new st({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new k(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ue(e),e.length!==4)throw new k("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new k("The channel dimension of the inputs should be defined. Found `None`.");const s=e[t],n=this.kernelSize.concat([this.filters,s]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new st({ndim:4,axes:{[t]:s}})],this.built=!0}call(e,t){return C(()=>{let s=we(e);if(s.shape.length!==4)throw new k(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Zp(a,u,c,this.padding),m=Zp(l,h,p,this.padding),f=[i,d,m,this.filters];this.dataFormat!=="channelsLast"&&(s=se(s,[0,2,3,1]));let g=tl(s,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=se(g,[0,3,1,2])),this.bias!=null&&(g=As(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}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]=Zp(t[n],a,r,this.padding),t[i]=Zp(t[i],l,o,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Gd.className="Conv2DTranspose";V.registerClass(Gd);class DA extends eu{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 k("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new k("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 k(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Be(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=He(t.depthwiseRegularizer),this.depthwiseConstraint=rt(t.depthwiseConstraint),this.pointwiseInitializer=Be(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=He(t.pointwiseRegularizer),this.pointwiseConstraint=rt(t.pointwiseConstraint)}build(e){if(e=Ue(e),e.length<this.rank+2)throw new k(`Inputs to SeparableConv${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 k(`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 st({ndim:this.rank+2,axes:{[t]:s}})],this.built=!0}call(e,t){return C(()=>{e=we(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=Vr(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(s=As(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=Je(this.depthwiseInitializer),e.pointwiseInitializer=Je(this.pointwiseInitializer),e.depthwiseRegularizer=Pe(this.depthwiseRegularizer),e.pointwiseRegularizer=Pe(this.pointwiseRegularizer),e.depthwiseConstraint=it(this.depthwiseConstraint),e.pointwiseConstraint=it(this.pointwiseConstraint),e}}DA.className="SeparableConv";class qd extends DA{constructor(e){super(2,e)}}qd.className="SeparableConv2D";V.registerClass(qd);class su extends eu{constructor(e){super(1,e);su.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"&&!Qh(e.kernelSize,"number",1,1))throw new k(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}su.className="Conv1D";V.registerClass(su);class Hd extends Le{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 C(()=>{if(e=we(e),this.dataFormat==="channelsLast"){const s=Up(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Up(s,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const s=Up(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Up(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}}Hd.className="Cropping2D";V.registerClass(Hd);class Yd extends Le{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 C(()=>{let s=we(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}}Yd.className="UpSampling2D";V.registerClass(Yd);function iz(e,t,s=[1,1],n="valid",i,r){return C(()=>{i==null&&(i=gs()),ot(i);let o=Qp(e,i);if(e.rank!==4)throw new k(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new k(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=un(o,t,s,n==="same"?"same":"valid","NHWC",r),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}class Kd extends Vd{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Be(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=rt(e.depthwiseConstraint),this.depthwiseRegularizer=He(e.depthwiseRegularizer)}build(e){if(e=Ue(e),e.length<4)throw new k(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new k(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 C(()=>{e=we(e);let s=iz(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(s=As(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=Ns(t,this.kernelSize[0],this.padding,this.strides[0]),r=Ns(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=Je(this.depthwiseInitializer),e.depthwiseRegularizer=Pe(this.depthwiseRegularizer),e.depthwiseConstraint=it(this.depthwiseRegularizer),e}}Kd.className="DepthwiseConv2D";V.registerClass(Kd);function Tb(e,t,s,n){if(Array.isArray(e)){if(t!=null||s!=null)throw new k("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 Ab(e,t,s,n=!1,i,r,o=!1,a=!1){return C(()=>{const l=t.shape.length;if(l<3)throw new k(`Input should be at least 3D, but is ${l}D.`);const c=[1,0].concat(ls(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=Mt(i,-1)),i=se(i,c)),n&&(t=Et(t,0),i!=null&&(i=Et(i,0)));const p=[];let u,h=s;const d=t.shape[0],m=Ge(t);let f;i!=null&&(f=Ge(i));for(let y=0;y<d;++y){const w=m[y],x=C(()=>e(w,h));if(i==null)u=x[0],h=x[1];else{const T=C(()=>{const A=f[y],_=Ot(A).sub(A),E=x[0].mul(A).add(h[0].mul(_)),F=h.map((D,M)=>x[1][M].mul(A).add(D.mul(_)));return{output:E,newStates:F}});u=T.output,h=T.newStates}a&&p.push(u)}let g;if(a){const y=1;g=Ve(p,y)}return[u,g,h]})}class sn extends Le{constructor(e){super(e);let t;if(e.cell==null)throw new k("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new nu({cells:e.cell}):t=e.cell,t.stateSize==null)throw new k("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new st({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ls(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){wd(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 C(()=>{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.");wd(e)&&(e=e[0]),e=e;const s=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new st({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(!N.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),r))throw new k(`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 st({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){C(()=>{if(!this.stateful)throw new bn("Cannot call resetStates() on an RNN Layer that is not stateful.");const s=this.inputSpec[0].shape[0];if(s==null)throw new k("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=>ye([s,n])):this.states_=[ye([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=>ye([s,n])):this.states_[0]=ye([s,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new k(`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(!N.arraysEqual(i.shape,o))throw new k(`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=>pt(n.clone()))})}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=Tb(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 st({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 ws;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 C(()=>{const s=t==null?null:t.mask,n=t==null?null:t.training;let i=t==null?null:t.initialState;e=we(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 k(`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=Ab(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 C(()=>{let t=ye(e.shape);return t=te(t,[1,2]),t=Pi(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(s=>s>1?ad(t,[1,s]):t):this.cell.stateSize>1?[ad(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=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const s=this.cell.getConfig();return this.getClassName()===sn.className&&(t.cell={className:this.cell.getClassName(),config:s}),Object.assign({},s,e,t)}static fromConfig(e,t,s={}){const n=t.cell,i=xs(n,s);return new e(Object.assign(t,{cell:i}))}}sn.className="RNN";V.registerClass(sn);class ro extends Le{}class iu extends ro{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,gt(this.units,"units"),this.activation=zn(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Be(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Be(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Be(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=He(e.kernelRegularizer),this.recurrentRegularizer=He(e.recurrentRegularizer),this.biasRegularizer=He(e.biasRegularizer),this.kernelConstraint=rt(e.kernelConstraint),this.recurrentConstraint=rt(e.recurrentConstraint),this.biasConstraint=rt(e.biasConstraint),this.dropout=ea([1,Un([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ea([1,Un([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 C(()=>{if(e=e,e.length!==2)throw new k(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let s=e[1];e=e[0];const n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gi({ones:()=>Ot(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gi({ones:()=>Ot(s),rate:this.recurrentDropout,training:n}));let i;const r=this.dropoutMask,o=this.recurrentDropoutMask;r!=null?i=vn(R(e,r),this.kernel.read()):i=vn(e,this.kernel.read()),this.bias!=null&&(i=As(i,this.bias.read())),o!=null&&(s=R(s,o));let a=$(i,vn(s,this.recurrentKernel.read()));return this.activation!=null&&(a=this.activation.apply(a)),[a,a]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Wn(this.activation),useBias:this.useBias,kernelInitializer:Je(this.kernelInitializer),recurrentInitializer:Je(this.recurrentInitializer),biasInitializer:Je(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:it(this.kernelConstraint),recurrentConstraint:it(this.recurrentConstraint),biasConstraint:it(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}iu.className="SimpleRNNCell";V.registerClass(iu);class Xd extends sn{constructor(e){e.cell=new iu(e),super(e)}call(e,t){return C(()=>{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})})}static fromConfig(e,t){return new e(t)}}Xd.className="SimpleRNN";V.registerClass(Xd);class ru extends ro{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 k("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gt(this.units,"units"),this.activation=zn(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zn(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Be(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Be(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Be(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=He(e.kernelRegularizer),this.recurrentRegularizer=He(e.recurrentRegularizer),this.biasRegularizer=He(e.biasRegularizer),this.kernelConstraint=rt(e.kernelConstraint),this.recurrentConstraint=rt(e.recurrentConstraint),this.biasConstraint=rt(e.biasConstraint),this.dropout=ea([1,Un([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ea([1,Un([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 C(()=>{if(e=e,e.length!==2)throw new k(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const s=t.training==null?!1:t.training;let n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gi({ones:()=>Ot(e),rate:this.dropout,training:s,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gi({ones:()=>Ot(n),rate:this.recurrentDropout,training:s,count:3}));const i=this.dropoutMask,r=this.recurrentDropoutMask;let o,a,l;0<this.dropout&&this.dropout<1&&(e=R(e,i[0]));let c=vn(e,this.kernel.read());this.useBias&&(c=As(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=R(n,r[0]));const p=this.recurrentKernel.read(),[u,h]=Bt(p,[2*this.units,this.units],p.rank-1),d=vn(n,u),[m,f,g]=Bt(c,3,c.rank-1),[y,w]=Bt(d,2,d.rank-1);o=this.recurrentActivation.apply($(m,y)),a=this.recurrentActivation.apply($(f,w));const x=vn(R(a,n),h);l=this.activation.apply($(g,x));const T=$(R(o,n),R($(1,_e(o)),l));return[T,T]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Wn(this.activation),recurrentActivation:Wn(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Je(this.kernelInitializer),recurrentInitializer:Je(this.recurrentInitializer),biasInitializer:Je(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:it(this.kernelConstraint),recurrentConstraint:it(this.recurrentConstraint),biasConstraint:it(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}ru.className="GRUCell";V.registerClass(ru);class Jd extends sn{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 ru(e),super(e)}call(e,t){return C(()=>{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})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Jd.className="GRU";V.registerClass(Jd);class aa extends ro{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,gt(this.units,"units"),this.activation=zn(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zn(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Be(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Be(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Be(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=He(e.kernelRegularizer),this.recurrentRegularizer=He(e.recurrentRegularizer),this.biasRegularizer=He(e.biasRegularizer),this.kernelConstraint=rt(e.kernelConstraint),this.recurrentConstraint=rt(e.recurrentConstraint),this.biasConstraint=rt(e.biasConstraint),this.dropout=ea([1,Un([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ea([1,Un([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 Ws{apply(a,l){const c=i.apply([r]),p=new vl().apply([r]),u=i.apply([r*2]);return lb(lb(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 C(()=>{const s=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new k(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gi({ones:()=>Ot(e),rate:this.dropout,training:s,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gi({ones:()=>Ot(n),rate:this.recurrentDropout,training:s,count:4}));const r=this.dropoutMask,o=this.recurrentDropoutMask;let a,l,c,p;0<this.dropout&&this.dropout<1&&(e=R(e,r[0]));let u=vn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=R(n,o[0])),u=$(u,vn(n,this.recurrentKernel.read())),this.useBias&&(u=As(u,this.bias.read()));const[h,d,m,f]=Bt(u,4,u.rank-1);a=this.recurrentActivation.apply(h),l=this.recurrentActivation.apply(d),c=$(R(l,i),R(a,this.activation.apply(m))),p=this.recurrentActivation.apply(f);const g=R(p,this.activation.apply(c));return[g,g,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Wn(this.activation),recurrentActivation:Wn(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Je(this.kernelInitializer),recurrentInitializer:Je(this.recurrentInitializer),biasInitializer:Je(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:it(this.kernelConstraint),recurrentConstraint:it(this.recurrentConstraint),biasConstraint:it(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}aa.className="LSTMCell";V.registerClass(aa);class Zd extends sn{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 aa(e),super(e)}call(e,t){return C(()=>{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})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Zd.className="LSTM";V.registerClass(Zd);class nu extends ro{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 C(()=>{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){wd(e)&&(e=e[0]),e=e;let t;this.cells.forEach((s,n)=>{Mn(`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=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),s=this.cells.map(t),n={cells:s};return Object.assign({},e,n)}static fromConfig(e,t,s={}){const n=[];for(const i of t.cells)n.push(xs(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 Vp(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]])}Nl(t)}}nu.className="StackedRNNCells";V.registerClass(nu);function Gi(e){const{ones:t,rate:s,training:n=!1,count:i=1}=e,r=()=>cd(t(),s),o=()=>Zr(r,t,n);if(!i||i<=1)return pt(o().clone());const a=Array(i).fill(void 0).map(o);return a.map(l=>pt(l.clone()))}var rz=function(e,t){var s={};for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&t.indexOf(n)<0&&(s[n]=e[n]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,n=Object.getOwnPropertySymbols(e);i<n.length;i++)t.indexOf(n[i])<0&&Object.prototype.propertyIsEnumerable.call(e,n[i])&&(s[n[i]]=e[n[i]]);return s};class XFe extends ro{}class FA extends sn{constructor(e){if(e.unroll)throw new ae("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ae("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new st({ndim:5})]}call(e,t){return C(()=>{if(this.cell.dropoutMask!=null&&(ce(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ce(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new k("ConvRNN2D cell does not support constants");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})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return C(()=>{const{stateSize:t}=this.cell,s=e.shape,n=this.computeSingleOutputShape(s),i=[n[0],...n.slice(2)],r=ye(i);return Array.isArray(t)?Array(t.length).fill(r):[r]})}resetStates(e,t=!1){C(()=>{if(!this.stateful)throw new bn("Cannot call resetStates() on an RNN Layer that is not stateful.");const s=this.inputSpec[0].shape,n=this.computeSingleOutputShape(s),i=[n[0],...n.slice(2)],r=s[0];if(r==null)throw new k("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ye(i)):this.states_=[ye(i)];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(()=>ye(i)):this.states_[0]=ye(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new k(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ce(this.states_);for(let o=0;o<this.states_.length;++o){const a=e[o],l=i;if(!N.arraysEqual(a.shape,l))throw new k(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${a.shape}`);this.states_[o]=a}}this.states_=this.states_.map(o=>pt(o.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:s,kernelSize:n,padding:i,strides:r,dilationRate:o}=this.cell,a=t==="channelsFirst",l=e[a?3:2],c=e[a?4:3],p=Ns(l,n[0],i,r[0],o[0]),u=Ns(c,n[1],i,r[1],o[1]),h=[...e.slice(0,2),...a?[s,p,u]:[p,u,s]];return h}}FA.className="ConvRNN2D";class ou extends aa{constructor(e){const{filters:t,kernelSize:s,strides:n,padding:i,dataFormat:r,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,gt(this.filters,"filters"),this.kernelSize=io(s,2,"kernelSize"),this.kernelSize.forEach(a=>gt(a,"kernelSize")),this.strides=io(n||1,2,"strides"),this.strides.forEach(a=>gt(a,"strides")),this.padding=i||"valid",ys(this.padding),this.dataFormat=r||"channelsLast",ot(this.dataFormat),this.dilationRate=io(o||1,2,"dilationRate"),this.dilationRate.forEach(a=>gt(a,"dilationRate"))}build(e){var t;e=Ue(e);const s=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[s]==null)throw new k(`The channel dimension of the input should be defined. Found ${e[s]}`);const n=e[s],i=4,r=this.kernelSize.concat([n,this.filters*i]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const o=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let a;if(this.unitForgetBias){const l=this.biasInitializer,c=this.filters;a=new(t=class extends Ws{apply(u,h){const d=l.apply([c]),m=Kt([c]),f=l.apply([c*2]);return Sl([d,m,f])}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,a,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return C(()=>{if(e.length!==3)throw new k(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const s=t.training||!1,n=e[0],i=e[1],r=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gi({ones:()=>Ot(n),rate:this.dropout,training:s,count:o}));const a=this.dropoutMask,l=(ie,ne,le)=>!ne||!ne[le]?ie:R(ne[le],ie);let c=l(n,a,0),p=l(n,a,1),u=l(n,a,2),h=l(n,a,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gi({ones:()=>Ot(i),rate:this.recurrentDropout,training:s,count:o}));const d=this.recurrentDropoutMask;let m=l(i,d,0),f=l(i,d,1),g=l(i,d,2),y=l(i,d,3);const w=3,[x,T,A,_]=Bt(this.kernel.read(),o,w),[E,F,D,M]=this.useBias?Bt(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,T,F,this.padding),u=this.inputConv(u,A,D,this.padding),h=this.inputConv(h,_,M,this.padding);const[P,B,Y,q]=Bt(this.recurrentKernel.read(),o,w);m=this.recurrentConv(m,P),f=this.recurrentConv(f,B),g=this.recurrentConv(g,Y),y=this.recurrentConv(y,q);const K=this.recurrentActivation.apply($(c,m)),H=this.recurrentActivation.apply($(p,f)),Q=$(R(H,r),R(K,this.activation.apply($(u,g)))),J=R(this.recurrentActivation.apply($(h,y)),this.activation.apply(Q));return[J,J,Q]})}getConfig(){const e=super.getConfig(),{units:t}=e,s=rz(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},s,n)}inputConv(e,t,s,n){const i=nt(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return s?As(i,s,this.dataFormat):i}recurrentConv(e,t){const s=1;return nt(e,t,s,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}ou.className="ConvLSTM2DCell";V.registerClass(ou);class Qd extends FA{constructor(e){const t=new ou(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Qd.className="ConvLSTM2D";V.registerClass(Qd);class au extends Le{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 C(()=>{this.invokeCallHook(e,t);const s=we(e);if(0<this.rate&&this.rate<1){const n=t.training==null?!1:t.training,i=this.getNoiseShape(s),r=Zr(()=>cd(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()}}au.className="Dropout";V.registerClass(au);class em extends au{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}em.className="SpatialDropout1D";V.registerClass(em);class tm extends Le{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,gt(this.units,"units"),this.activation=zn(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Be(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Be(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=rt(e.kernelConstraint),this.biasConstraint=rt(e.biasConstraint),this.kernelRegularizer=He(e.kernelRegularizer),this.biasRegularizer=He(e.biasRegularizer),this.activityRegularizer=He(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 C(()=>{this.invokeCallHook(e,t);const s=we(e),n=ed(this.activation.getClassName());let i;return n!=null?i=vn(s,this.kernel.read(),n,this.bias?this.bias.read():null):(i=vn(s,this.kernel.read()),this.bias!=null&&(i=As(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Wn(this.activation),useBias:this.useBias,kernelInitializer:Je(this.kernelInitializer),biasInitializer:Je(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:it(this.kernelConstraint),biasConstraint:it(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}tm.className="Dense";V.registerClass(tm);class sm extends Le{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 k(`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],In(e,1)]}call(e,t){return C(()=>{this.invokeCallHook(e,t);let s=we(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 DT(s)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}sm.className="Flatten";V.registerClass(sm);class nm extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=zn(e.activation)}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e);return this.activation.apply(s)})}getConfig(){const e={activation:Wn(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}nm.className="Activation";V.registerClass(nm);class im extends Le{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 C(()=>(e=we(e),_T(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}im.className="RepeatVector";V.registerClass(im);class rm extends Le{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 k("Can only specifiy one unknown dimension.");else i*=l}const o=In(e);if(r!==null){if(i===0||o%i!==0)throw new k(s);n[r]=o/i}else if(o!==i)throw new k(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 C(()=>{this.invokeCallHook(e,t);const s=we(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}}rm.className="Reshape";V.registerClass(rm);class om extends Le{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=ls(1,e.dims.length+1);if(!N.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new st({ndim:this.dims.length+1})]}computeOutputShape(e){e=Ue(e);const t=e.slice();return this.dims.forEach((s,n)=>{t[n+1]=e[s]}),t}call(e,t){return se(we(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}om.className="Permute";V.registerClass(om);class am extends Le{constructor(e){super(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=we(e),n=-1;return Uo(Ks(s,this.maskValue),n)}call(e,t){return C(()=>{this.invokeCallHook(e,t);const s=we(e),n=-1,i=!0,r=Uo(Ks(s,this.maskValue),n,i),o=s.mul(r.asType(s.dtype));return o})}}am.className="Masking";V.registerClass(am);class lm extends Le{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,gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,gt(this.outputDim,"outputDim"),this.embeddingsInitializer=Be(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=He(e.embeddingsRegularizer),this.activityRegularizer=He(e.activityRegularizer),this.embeddingsConstraint=rt(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 C(()=>this.maskZero?(e=we(e),Ks(e,re(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 k(`"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 k(`"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 C(()=>{this.invokeCallHook(e,t);let s=we(e);s.dtype!=="int32"&&(s=zi(s,"int32"));const n=ld(this.embeddings.read(),s.as1D());return n.reshape(Ue(this.computeOutputShape(s.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Je(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:it(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}lm.className="Embedding";V.registerClass(lm);class la extends Le{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 k("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 k(`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=Sn(t),t.length>1)throw new k(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let s=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const r=e[i]==null?null:e[i].slice(1);s=this.computeElementwiseOpOutputShape(s,r)}const n=e.map(i=>i.length);e.indexOf(null)===-1&&Sn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return C(()=>{if(e=e,this.reshapeRequired){const s=[],n=e.map(i=>i.rank);if(n.indexOf(null)===-1){const i=Un(n);for(let r of e){const o=r.rank;for(let a=0;a<i-o;++a)r=Pi(r,1);s.push(r)}return this.mergeFunction(s)}else{let i=!1;for(const a of e){const l=a.rank;if(l==null){const c=a.shape,p=c[0],u=c.slice(1).concat([p]);let h=a.reshape([p].concat(In(c.slice(1))));h=se(h,[1,0]),h=h.reshape(u),s.push(h),i=!0}else if(l>1){const c=ls(1,l).concat([0]);s.push(se(a,c)),i=!0}else s.push(a)}let r=this.mergeFunction(s);const o=r.rank;if(i){if(o==null){const a=r.shape,l=a.length,c=a[l-1],p=[c].concat(a.slice(0,a.length-1));r=se(r.reshape([-1,c]),[1,0]).reshape(p)}else if(o>1){const a=[o-1].concat(ls(0,o-1));r=se(r,a)}}return r}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){const i=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let s=[];for(const n of e)n!=null&&n[0]!==null&&s.push(n[0]);return s=Sn(s),s.length===1?t=s.concat(t):t=[null].concat(t),t}computeMask(e,t){return C(()=>{if(t==null)return null;if(!Array.isArray(t))throw new k("`mask` should be an Array");if(!Array.isArray(e))throw new k("`inputs` should be an Array");if(t.length!==e.length)throw new k(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Mt(n,0));let s=t[0];for(let n=1;n<t.length-1;++n)s=Yt(s,t[n]);return s})}}class cm extends la{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=$(t,e[s]);return t})}}cm.className="Add";V.registerClass(cm);class pm extends la{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=R(t,e[s]);return t})}}pm.className="Multiply";V.registerClass(pm);class um extends la{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0].clone();for(let s=1;s<e.length;++s)t=$(t,e[s]);return R(1/e.length,t)})}}um.className="Average";V.registerClass(um);class hm extends la{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=Ht(t,e[s]);return t})}}hm.className="Maximum";V.registerClass(hm);class dm extends la{constructor(e){super(e)}mergeFunction(e){return C(()=>{let t=e[0];for(let s=1;s<e.length;++s)t=mn(t,e[s]);return t})}}dm.className="Minimum";V.registerClass(dm);class mm extends la{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new k("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const n of e)if(n!=null){t=!1;break}if(t)return;const s=[];for(let n=0;n<e.length;++n){const i=e[n].slice();i.splice(this.axis,1);let r=!1;for(const o of s)if(N.arraysEqual(o,i)){r=!0;break}r||s.push(i)}if(s.length>1)throw new k("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return C(()=>Sl(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new k("A `Concatenate` layer should be called on a list of inputs.");const t=e,s=t[0].slice(),n=this.axis<0?s.length+this.axis:this.axis;for(const i of t.slice(1)){if(s[n]==null||i[n]==null){s[n]=null;break}s[n]+=i[n]}return s}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new k("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new k("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new k(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return C(()=>{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(Ot(e[r]).asType("bool")):t[r].rank<e[r].rank?n.push(Mt(t[r],-1)):n.push(t[r]);const i=be(n,this.axis);return Xa(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}mm.className="Concatenate";V.registerClass(mm);function lu(e,t){for(;e<0;)e+=t;return e}function oz(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(N.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),N.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 C(()=>{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 fm extends la{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){N.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 k(`Dimension incompatibility: ${t[n[0]]} !== ${s[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new k(`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)=>lu(i,e[r].shape.length)):n=[lu(this.axes,t.shape.length),lu(this.axes,s.shape.length)],this.normalize&&(t=Gp(t,n[0]),s=Gp(s,n[1])),oz(t,s,n)}interpretAxes(e,t){let s;return Array.isArray(this.axes)?s=this.axes:s=[lu(this.axes,e.length),lu(this.axes,t.length)],s}computeOutputShape(e){N.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}}fm.className="Dot";V.registerClass(fm);class gm extends Le{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 C(()=>{this.invokeCallHook(e,t);const s=we(e),n=()=>Il(s.shape,0,this.stddev).add(s),i=Zr(n,()=>s,t.training||!1);return i})}}gm.className="GaussianNoise";V.registerClass(gm);class ym extends Le{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 C(()=>{this.invokeCallHook(e,t);const s=we(e);if(this.rate>0&&this.rate<1){const n=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return s.mul(Il(s.shape,1,i))};return Zr(n,()=>s,t.training||!1)}return s})}}ym.className="GaussianDropout";V.registerClass(ym);class bm extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||we(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 C(()=>{if(this.rate<1&&this.rate>0){const s=this._getNoiseShape(e),n=()=>{const i=we(e),r=1.6732632423543772,o=1.0507009873554805,a=-r*o;let l=ds(fn(s),this.rate);l=zi(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 Zr(n,()=>we(e),t.training||!1)}return e})}}bm.className="AlphaDropout";V.registerClass(bm);function cu(e,t,s,n,i,r=.001){let o;if(e.rank===2)o=uh(e,t,s,n,i,r);else if(e.rank===3)o=hh(e,t,s,n,i,r);else if(e.rank===4)o=dh(e,t,s,n,i,r);else throw new ae(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function az(e,t,s,n,i=.001){return C(()=>{const r=Bo(e,n),o=r.mean,a=r.variance,l=cu(e,o,a,s,t,i);return[l,o,a]})}function lz(e,t,s,n,i=.001){return C(()=>{const r=Bo(e,n),o=r.mean,a=r.variance,l=[];for(const m of ls(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=cu(e,c,p,h,u,i);return[d,o,a]})}function cz(e,t,s,n,i=.001){return N.arraysEqual(n.slice().sort(),ls(0,e.rank-1))?az(e,t,s,n,i):lz(e,t,s,n,i)}class wm extends Le{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=Be(e.betaInitializer||"zeros"),this.gammaInitializer=Be(e.gammaInitializer||"ones"),this.movingMeanInitializer=Be(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Be(e.movingVarianceInitializer||"ones"),this.betaConstraint=rt(e.betaConstraint),this.gammaConstraint=rt(e.gammaConstraint),this.betaRegularizer=He(e.betaRegularizer),this.gammaRegularizer=He(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 k(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new st({ndim:e.length,axes:{[t]: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 C(()=>{const s=t.training==null?!1:t.training,n=we(e),i=n.shape,r=i.length,o=ls(0,r),a=this.axis>=0?this.axis:this.axis+r;o.splice(a,1);const l=wn(1,r);l[a]=i[a];const c=o.slice();c.sort();const p=!N.arraysEqual(c,ls(0,r).slice(0,r-1)),u=()=>{if(p){const y=this.movingMean.read().reshape(l),w=this.movingVariance.read().reshape(l),x=this.center?this.beta.read().reshape(l):null,T=this.scale?this.gamma.read().reshape(l):null;return cu(n,y,w,x,T,this.epsilon)}else return cu(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]=cz(n,this.gamma.read(),this.beta.read(),o,this.epsilon),f=(y,w,x)=>{C(()=>{const T=1-x,A=y.read(),_=A.sub(w).mul(T);y.write(A.sub(_))})},g=()=>{f(this.movingMean,d,this.momentum),f(this.movingVariance,m,this.momentum)};return g(),h})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Je(this.betaInitializer),gammaInitializer:Je(this.gammaInitializer),movingMeanInitializer:Je(this.movingMeanInitializer),movingVarianceInitializer:Je(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:it(this.betaConstraint),gammaConstraint:it(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}wm.className="BatchNormalization";V.registerClass(wm);class xm extends Le{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=Be(e.betaInitializer||"zeros"),this.gammaInitializer=Be(e.gammaInitializer||"ones"),this.betaRegularizer=He(e.betaRegularizer),this.gammaRegularizer=He(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!==Sn(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=we(e),n=s.shape,i=n.length;return C(()=>{const r=!0;let{mean:o,variance:a}=Bo(s,this.axis,r);const l=wn(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),cu(s,o,a,u,p,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Je(this.betaInitializer),gammaInitializer:Je(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}xm.className="LayerNormalization";V.registerClass(xm);function pz(e,t,s){return C(()=>{if(e.rank!==4)throw new k(`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 k("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 k(`Unknown data format: ${s}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return s==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Pt(e,n)})}class Lm extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?gs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new k(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 k(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new k(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);s=e.padding[1]}this.padding=[t,s]}this.inputSpec=[new st({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 C(()=>pz(we(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Lm.className="ZeroPadding2D";V.registerClass(Lm);function Sm(e,t,s,n,i,r){return C(()=>{ot(i),ob(r),ys(n),s==null&&(s=[1,1]),n==null&&(n="valid"),i==null&&(i=gs()),r==null&&(r="max"),e=Qp(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=mt(e,t,s,a):o=hs(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,3,1,2])),o})}function MA(e,t,s,n,i,r){return C(()=>{ot(i),ob(r),ys(n),s==null&&(s=[1,1,1]),n==null&&(n="valid"),i==null&&(i=gs()),r==null&&(r="max"),e=vb(e,i);let o;const a=n==="same"?"same":"valid";return r==="max"?o=cl(e,t,s,a):o=Za(e,t,s,a),i==="channelsFirst"&&(o=se(o,[0,4,1,2,3])),o})}class UA extends Le{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 k(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gt(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 k(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ys(this.padding),this.inputSpec=[new st({ndim:3})]}computeOutputShape(e){e=Ue(e);const t=Ns(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return C(()=>{this.invokeCallHook(e,t),e=Pi(we(e),2);const s=this.poolingFunction(we(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Js(s,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Im extends UA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),Sm(e,t,s,n,i,"max")}}Im.className="MaxPooling1D";V.registerClass(Im);class vm extends UA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),Sm(e,t,s,n,i,"avg")}}vm.className="AveragePooling1D";V.registerClass(vm);class $A extends Le{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 k(`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];gt(this.poolSize,"poolSize"),gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,ot(this.dataFormat),ys(this.padding),this.inputSpec=[new st({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=Ns(t,this.poolSize[0],this.padding,this.strides[0]),s=Ns(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 C(()=>(this.invokeCallHook(e,t),this.poolingFunction(we(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 Tm extends $A{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),Sm(e,t,s,n,i,"max")}}Tm.className="MaxPooling2D";V.registerClass(Tm);class Am extends $A{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),Sm(e,t,s,n,i,"avg")}}Am.className="AveragePooling2D";V.registerClass(Am);class WA extends Le{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 k(`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];gt(this.poolSize,"poolSize"),gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,ot(this.dataFormat),ys(this.padding),this.inputSpec=[new st({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=Ns(t,this.poolSize[0],this.padding,this.strides[0]),s=Ns(s,this.poolSize[1],this.padding,this.strides[1]),n=Ns(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 C(()=>(this.invokeCallHook(e,t),this.poolingFunction(we(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 Nm extends WA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),MA(e,t,s,n,i,"max")}}Nm.className="MaxPooling3D";V.registerClass(Nm);class Cm extends WA{constructor(e){super(e)}poolingFunction(e,t,s,n,i){return ot(i),ys(n),MA(e,t,s,n,i,"avg")}}Cm.className="AveragePooling3D";V.registerClass(Cm);class zA extends Le{constructor(e){super(e);this.inputSpec=[new st({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ae}}class Rm extends zA{constructor(e){super(e||{})}call(e,t){return C(()=>{const s=we(e);return Ke(s,1)})}}Rm.className="GlobalAveragePooling1D";V.registerClass(Rm);class Om extends zA{constructor(e){super(e||{})}call(e,t){return C(()=>{const s=we(e);return xt(s,1)})}}Om.className="GlobalMaxPooling1D";V.registerClass(Om);class PA extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,ot(this.dataFormat),this.inputSpec=[new st({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ae}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Em extends PA{call(e,t){return C(()=>{const s=we(e);return this.dataFormat==="channelsLast"?Ke(s,[1,2]):Ke(s,[2,3])})}}Em.className="GlobalAveragePooling2D";V.registerClass(Em);class _m extends PA{call(e,t){return C(()=>{const s=we(e);return this.dataFormat==="channelsLast"?xt(s,[1,2]):xt(s,[2,3])})}}_m.className="GlobalMaxPooling2D";V.registerClass(_m);class BA extends Le{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=xs(n,s);delete t.layer;const r={layer:i};return Object.assign(r,t),new e(r)}}class km extends BA{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Ue(e),e.length<3)throw new k(`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 C(()=>{e=we(e);const s=(r,o)=>{const a=we(this.layer.call(r,t));return[a,[]]},n=Ab(s,e,[],!1,null,null,!1,!0),i=n[1];return i})}}km.className="TimeDistributed";V.registerClass(km);function uz(e){Wi(NT,"BidirectionalMergeMode",e)}const hz="concat";class Dm extends BA{constructor(e){super(e);const t=e.layer.getConfig(),s={};s.className=e.layer.getClassName(),s.config=t,this.forwardLayer=xs(s),t.goBackwards=!(t.goBackwards===!0);const n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=xs(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?hz:e.mergeMode,uz(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()):jt(n)}apply(e,t){let s=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});const i=Tb(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 k("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 st({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 ws;for(const l of r)if(l instanceof ws!==a)throw new k("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 C(()=>{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=Et(i,1));let o;return this.mergeMode==="concat"?o=Sl([n,i]):this.mergeMode==="sum"?o=$(n,i):this.mergeMode==="ave"?o=R(.5,$(n,i)):this.mergeMode==="mul"?o=R(n,i):this.mergeMode==null&&(o=[n,i]),this.returnState?this.mergeMode==null?o.concat(r):[o].concat(r):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Mn(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Mn(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let s;if(this.returnSequences?this.mergeMode==null?s=[t,t]:s=t:this.mergeMode==null?s=[null,null]:s=null,this.returnState){const n=this.forwardLayer.states,i=n.map(r=>null);return Array.isArray(s)?s.concat(i).concat(i):[s].concat(i).concat(i)}else return s}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const s=xs(t.layer);if(delete t.layer,t.numConstants!=null)throw new ae("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const n=t;return n.layer=s,new e(n)}}Dm.className="Bidirectional";V.registerClass(Dm);const Nb={};Ee(Nb,{Layer:()=>Le,RNN:()=>sn,RNNCell:()=>ro,activation:()=>Cz,add:()=>Uz,alphaDropout:()=>xP,average:()=>$z,averagePooling1d:()=>Cb,averagePooling2d:()=>Rb,averagePooling3d:()=>Ob,avgPool1d:()=>Hz,avgPool2d:()=>Kz,avgPool3d:()=>Jz,avgPooling1d:()=>Yz,avgPooling2d:()=>Xz,avgPooling3d:()=>Zz,batchNormalization:()=>Vz,bidirectional:()=>hP,concatenate:()=>Wz,conv1d:()=>xz,conv2d:()=>Lz,conv2dTranspose:()=>Sz,conv3d:()=>Iz,convLstm2d:()=>lP,convLstm2dCell:()=>cP,cropping2D:()=>Tz,dense:()=>Rz,depthwiseConv2d:()=>Nz,dot:()=>jz,dropout:()=>Oz,elu:()=>mz,embedding:()=>Mz,flatten:()=>_z,gaussianDropout:()=>wP,gaussianNoise:()=>bP,globalAveragePooling1d:()=>Qz,globalAveragePooling2d:()=>eP,globalMaxPool1d:()=>mP,globalMaxPool2d:()=>fP,globalMaxPooling1d:()=>jA,globalMaxPooling2d:()=>VA,gru:()=>sP,gruCell:()=>nP,input:()=>Md,inputLayer:()=>dz,layerNormalization:()=>Gz,leakyReLU:()=>gz,lstm:()=>iP,lstmCell:()=>rP,masking:()=>LP,maxPool1d:()=>gP,maxPool2d:()=>yP,maxPooling1d:()=>GA,maxPooling2d:()=>qA,maxPooling3d:()=>tP,maximum:()=>zz,minimum:()=>Pz,multiply:()=>Bz,permute:()=>Fz,prelu:()=>yz,reLU:()=>fz,repeatVector:()=>kz,reshape:()=>Dz,rnn:()=>pP,separableConv2d:()=>vz,simpleRNN:()=>oP,simpleRNNCell:()=>aP,softmax:()=>bz,spatialDropout1d:()=>Ez,stackedRNNCells:()=>uP,thresholdedReLU:()=>wz,timeDistributed:()=>dP,upSampling2d:()=>Az,zeroPadding2d:()=>qz});function 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Wb={};Ee(Wb,{json:()=>jP});const jP=[{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 zb={};Ee(zb,{json:()=>VP});const VP=[{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 Pb={};Ee(Pb,{json:()=>GP});const GP=[{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 Bb={};Ee(Bb,{json:()=>qP});const qP=[{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 jb={};Ee(jb,{json:()=>HP});const HP=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}];const Vb={};Ee(Vb,{json:()=>YP});const YP=[{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 Gb={};Ee(Gb,{json:()=>KP});const KP=[{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 qb={};Ee(qb,{json:()=>XP});const XP=[{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 Hb={};Ee(Hb,{json:()=>JP});const JP=[{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 Yb={};Ee(Yb,{json:()=>ZP});const ZP=[{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 Kb={};Ee(Kb,{json:()=>QP});const QP=[{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 Xb={};Ee(Xb,{json:()=>eB});const eB=[{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 Jb={};Ee(Jb,{json:()=>tB});const tB=[{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 Zb={};Ee(Zb,{json:()=>sB});const sB=[{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 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nN=(e,t,s)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[$(L("a",e,t,s),L("b",e,t,s))];case"AddN":return[sh(L("tensors",e,t,s))];case"FloorMod":case"Mod":return[pl(L("a",e,t,s),L("b",e,t,s))];case"Mul":return[R(L("a",e,t,s),L("b",e,t,s))];case"RealDiv":case"Div":return[Z(L("a",e,t,s),L("b",e,t,s))];case"DivNoNan":return[Sh(L("a",e,t,s),L("b",e,t,s))];case"FloorDiv":return[Ka(L("a",e,t,s),L("b",e,t,s))];case"Sub":return[X(L("a",e,t,s),L("b",e,t,s))];case"Minimum":return[mn(L("a",e,t,s),L("b",e,t,s))];case"Maximum":return[Ht(L("a",e,t,s),L("b",e,t,s))];case"Pow":return[Qt(L("a",e,t,s),L("b",e,t,s))];case"SquaredDifference":return[Yr(L("a",e,t,s),L("b",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const iN=(e,t,s)=>{switch(e.op){case"Abs":case"ComplexAbs":return[et(L("x",e,t,s))];case"Acos":return[eh(L("x",e,t,s))];case"Acosh":return[th(L("x",e,t,s))];case"Asin":return[ih(L("x",e,t,s))];case"Asinh":return[rh(L("x",e,t,s))];case"Atan":return[oh(L("x",e,t,s))];case"Atan2":return[ah(L("x",e,t,s),L("y",e,t,s))];case"Atanh":return[lh(L("x",e,t,s))];case"Ceil":return[mh(L("x",e,t,s))];case"Complex":return[Gt(L("real",e,t,s),L("imag",e,t,s))];case"Cos":return[Wr(L("x",e,t,s))];case"Cosh":return[nl(L("x",e,t,s))];case"Elu":return[hn(L("x",e,t,s))];case"Erf":return[Ih(L("x",e,t,s))];case"Exp":return[ut(L("x",e,t,s))];case"Expm1":return[vh(L("x",e,t,s))];case"Floor":return[li(L("x",e,t,s))];case"Log":return[zt(L("x",e,t,s))];case"Log1p":return[ol(L("x",e,t,s))];case"Imag":return[dn(L("x",e,t,s))];case"Neg":return[_e(L("x",e,t,s))];case"Reciprocal":return[Dh(L("x",e,t,s))];case"Real":return[Xs(L("x",e,t,s))];case"Relu":return[De(L("x",e,t,s))];case"Round":return[Mh(L("x",e,t,s))];case"Selu":return[dl(L("x",e,t,s))];case"Sigmoid":return[rs(L("x",e,t,s))];case"Sin":return[ml(L("x",e,t,s))];case"Sign":return[$h(L("x",e,t,s))];case"Sinh":return[fl(L("x",e,t,s))];case"Softplus":return[pi(L("x",e,t,s))];case"Sqrt":return[Xe(L("x",e,t,s))];case"Square":return[xe(L("x",e,t,s))];case"Tanh":return[ki(L("x",e,t,s))];case"Tan":return[zh(L("x",e,t,s))];case"Relu6":case"ClipByValue":return[wt(L("x",e,t,s),L("clipValueMin",e,t,s),L("clipValueMax",e,t,s))];case"Rsqrt":return[hl(Vt(e.inputNames[0],t,s))];case"Prod":return[ul(L("x",e,t,s),L("axes",e,t,s))];case"LeakyRelu":return[rl(L("x",e,t,s),L("alpha",e,t,s))];case"Prelu":return[jr(L("x",e,t,s),L("alpha",e,t,s))];default:throw 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n=L("size",e,t,s),i=L("dtype",e,t,s),r=L("elementShape",e,t,s),o=L("dynamicSize",e,t,s),a=L("clearAfterRead",e,t,s),l=L("identicalElementShapes",e,t,s),c=L("name",e,t,s),p=new rN(c,i,n,r,l,o,a);return s.addTensorArray(p),[p.idTensor,j(1)]}case"TensorArrayWriteV3":{const n=L("tensorArrayId",e,t,s),i=L("index",e,t,s),r=L("tensor",e,t,s),o=s.getTensorArray(n.id);return o.write(i,r),[o.idTensor]}case"TensorArrayReadV3":{const n=L("tensorArrayId",e,t,s),i=L("index",e,t,s),r=s.getTensorArray(n.id);return[r.read(i)]}case"TensorArrayGatherV3":{const n=L("tensorArrayId",e,t,s),i=L("indices",e,t,s),r=L("dtype",e,t,s),o=s.getTensorArray(n.id);return[o.gather(i,r)]}case"TensorArrayScatterV3":{const n=L("tensorArrayId",e,t,s),i=L("indices",e,t,s),r=L("tensor",e,t,s),o=s.getTensorArray(n.id);return o.scatter(i,r),[o.idTensor]}case"TensorArrayConcatV3":{const n=L("tensorArrayId",e,t,s),i=s.getTensorArray(n.id),r=L("dtype",e,t,s);return[i.concat(r)]}case"TensorArraySplitV3":{const 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bN=(e,t,s)=>{switch(e.op){case"Equal":return[os(L("a",e,t,s),L("b",e,t,s))];case"NotEqual":return[Ks(L("a",e,t,s),L("b",e,t,s))];case"Greater":return[Ut(L("a",e,t,s),L("b",e,t,s))];case"GreaterEqual":return[ds(L("a",e,t,s),L("b",e,t,s))];case"Less":return[zr(L("a",e,t,s),L("b",e,t,s))];case"LessEqual":return[$s(L("a",e,t,s),L("b",e,t,s))];case"LogicalAnd":return[Yt(L("a",e,t,s),L("b",e,t,s))];case"LogicalNot":return[Pr(L("a",e,t,s))];case"LogicalOr":return[ll(L("a",e,t,s),L("b",e,t,s))];case"Select":case"SelectV2":return[dt(L("condition",e,t,s),L("a",e,t,s),L("b",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const wN=(e,t,s)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Te(L("a",e,t,s),L("b",e,t,s),L("transposeA",e,t,s),L("transposeB",e,t,s))];case"Transpose":return[se(L("x",e,t,s),L("perm",e,t,s))];case"_FusedMatMul":const[n,i]=L("fusedOps",e,t,s),r=n==="biasadd",o=i==="prelu",a=L("numArgs",e,t,s);if(r){if(o&&a!==2)throw new 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xN=(e,t,s)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ys(L("x",e,t,s),L("mean",e,t,s),L("variance",e,t,s),L("offset",e,t,s),L("scale",e,t,s),L("epsilon",e,t,s))];case"FusedBatchNormV3":return[Ys(L("x",e,t,s),L("mean",e,t,s),L("variance",e,t,s),L("offset",e,t,s),L("scale",e,t,s),L("epsilon",e,t,s))];case"LRN":return[Nh(L("x",e,t,s),L("radius",e,t,s),L("bias",e,t,s),L("alpha",e,t,s),L("beta",e,t,s))];case"Softmax":return[es(L("x",e,t,s))];case"LogSoftmax":return[al(L("x",e,t,s))];case"SparseToDense":return[Ep(L("sparseIndices",e,t,s),L("outputShape",e,t,s),L("sparseValues",e,t,s),L("defaultValue",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};const LN=(e,t,s)=>{switch(e.op){case"Max":{const n=L("axis",e,t,s),i=L("keepDims",e,t,s);return[xt(L("x",e,t,s),n,i)]}case"Mean":{const n=L("axis",e,t,s),i=L("keepDims",e,t,s);return[Ke(L("x",e,t,s),n,i)]}case"Min":{const 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n=L("axis",e,t,s);return[Js(L("x",e,t,s),n)]}case"Reshape":return[O(L("x",e,t,s),L("shape",e,t,s))];case"PadV2":case"Pad":return[Pt(L("x",e,t,s),L("padding",e,t,s),L("constantValue",e,t,s))];case"SpaceToBatchND":{const n=L("blockShape",e,t,s),i=L("paddings",e,t,s);return[Br(L("x",e,t,s),n,i)]}case"BatchToSpaceND":{const n=L("blockShape",e,t,s),i=L("crops",e,t,s);return[$r(L("x",e,t,s),n,i)]}case"DepthToSpace":{const n=L("blockSize",e,t,s),i=L("dataFormat",e,t,s).toUpperCase();return[xh(L("x",e,t,s),n,i)]}case"BroadcastTo":return[zo(L("x",e,t,s),L("shape",e,t,s))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function sw(e,t,s){const n=((i,r,o)=>{switch(i.category){case"arithmetic":return C(()=>nN(i,r,o));case"basic_math":return C(()=>iN(i,r,o));case"control":return pN(i,r,o);case"convolution":return C(()=>hN(i,r,o));case"creation":return C(()=>dN(i,r,o));case"dynamic":return mN(i,r,o);case"evaluation":return C(()=>fN(i,r,o));case"image":return C(()=>yN(i,r,o));case"graph":return C(()=>gN(i,r,o));case"logical":return C(()=>bN(i,r,o));case"matrices":return C(()=>wN(i,r,o));case"normalization":return C(()=>xN(i,r,o));case"reduction":return C(()=>LN(i,r,o));case"slice_join":return C(()=>SN(i,r,o));case"spectral":return C(()=>IN(i,r,o));case"transformation":return C(()=>vN(i,r,o));case"custom":const a=Mm(i.op);if(a&&a.customExecutor)return a.customExecutor(new sN(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 nw{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(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function rw(e,t,s,n){const i=new Set,r=[];let o=null,a=null;const l=new Set,c=Object.keys(e).map(h=>Ls(h)[0]);let p=[];n!=null&&(p=n.map(h=>Ls(h.name)[0]));const u=[...t];for(;u.length>0;){const h=u.pop();if((iw(h)||rB(h))&&(o==null&&(o=h,a=o.children.map(d=>d.name).filter(d=>i.has(d)))),i.add(h.name),s[h.name]!=null)continue;if(c.indexOf(h.name)!==-1)continue;if(p.indexOf(h.name)!==-1)continue;if(h.inputs.length===0){r.push(h.name);continue}h.inputs.forEach(d=>{if(l.has(d.name))return;l.add(d.name),u.push(d)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:r,dynamicNode:o,syncInputs:a}}function TN(e,t,s){const{usedNodes:n,inputs:i}=s,r=[],o=Object.keys(i).map(p=>Ls(p)[0]).map(p=>e.nodes[p]),a=e.initNodes;o.forEach(p=>{n.has(p.name)&&r.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&r.push(p)}),a!=null&&a.forEach(p=>{n.has(p.name)&&r.push(p)});const l=new Set,c=[];for(;r.length>0;){const p=r.pop();l.add(p.name),t[p.name]||c.push(p),p.children.forEach(u=>{!l.has(u.name)&&n.has(u.name)&&u.inputs.every(h=>l.has(h.name))&&r.push(u)})}return c}const oB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],aB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function iw(e){return oB.indexOf(e.op)>=0}function rB(e){return aB.indexOf(e.op)>=0}class Ym{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(s=>{this._functionExecutorMap[s]=new Ym(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=rw(e,t,this.weightMap,this._initNodes),{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 TN(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(p=>this.graph.nodes[Ls(p)[0]]),i=t.map(p=>Ls(p)[0]);let r=i.map(p=>this.graph.nodes[p]);r.length===0&&(r=this._outputs);const o=this.getCompilationKey(n,r);let a=this.compiledMap.get(o);a==null&&(a=this.compile(e,r),this.compiledMap.set(o,a));const l={},c={};return C(()=>{const p=new nw(this.weightMap,l,c,this.functionExecutorMap),u=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{const[f,g]=Ls(m),y=[];y[g]=e[m],u[f]=y});const h=this.getFrozenTensorIds(u),d={};for(let m=0;m<a.length;m++){const f=a[m];if(!u[f.name]){const g=sw(f,u,p);if(g instanceof Promise)throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);u[f.name]=g,this.checkTensorForDisposal(f.name,f,u,p,h,i,d)}}return this.parent==null&&p.dispose(h),t.map(m=>Vt(m,u,p))})}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=ZA(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 nw(this.weightMap,n,i,this.functionExecutorMap),o=await this.executeWithControlFlow(e,r,t,s),a=t.map(u=>Vt(u,o,r)),l=a.map(u=>u.id),c=Object.keys(e).map(u=>e[u].id),p=new Set([...l,...c,...this.weightIds]);return Object.keys(o).forEach(u=>{const h=o[u];h.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&r.dispose(p),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(w=>this.graph.nodes[Ls(w)[0]]),o=s.map(w=>Ls(w)[0]),a=o.map(w=>this.graph.nodes[w]),{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:u}=rw(e,a,this.weightMap),h=[...r,...this.graph.weights].map(w=>({node:w,contexts:t.currentContext})),d=Object.assign({},this.weightMap);Object.keys(e).forEach(w=>{const[x,T]=Ls(w),A=[];A[T]=e[w],d[x]=A});const m={},f=this.getFrozenTensorIds(d),g={};for(;h.length>0;){const w=this.processStack(r,h,t,d,g,f,o,m,l);await Promise.all(w)}p==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=a.filter(w=>!iw(w)&&!Vt(w.name,d,t)).map(w=>w.name);if(y.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${u}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${i}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(e,t,s,n,i,r,o,a,l){const c=[];for(;t.length>0;){const p=t.pop();s.currentContext=p.contexts;let u="";if(p.node.op==="Enter"&&L("isConstant",p.node,n,s)&&([u]=Pn(p.node.name,s)),e.indexOf(p.node)===-1){const h=sw(p.node,n,s);u||([u]=Pn(p.node.name,s));const d=s.currentContext;h instanceof Promise?c.push(h.then(m=>(n[u]=m,s.currentContext=d,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l),m))):(n[u]=h,this.checkTensorForDisposal(u,p.node,n,s,r,o,a),this.processChildNodes(p.node,t,s,n,i,l))}else this.processChildNodes(p.node,t,s,n,i,l)}return c}processChildNodes(e,t,s,n,i,r){e.children.forEach(o=>{const[a]=Pn(o.name,s);if(i[a]||!r.has(o.name))return;o.op==="Merge"?o.inputNames.some(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o})):o.inputNames.every(l=>!!Vt(l,n,s))&&(i[a]=!0,t.push({contexts:s.currentContext,node:o}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const s=e[t],[n]=Ls(t),i=this.graph.nodes[n];if(i.attrParams.shape&&i.attrParams.shape.value){const r=i.attrParams.shape.value,o=r.length===s.shape.length&&s.shape.every((a,l)=>r[l]===-1||r[l]===a);N.assert(o,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${r}], but was [${s.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&N.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 ${s.dtype}`)})}mapInputs(e){const t={};for(const s in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[s]!=null){const n=this._signature.inputs[s];t[n.name]=e[s]}else t[s]=e[s];return t}checkInputs(e){const t=Object.keys(e).filter(s=>{const[n]=Ls(s);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Ls(t);if(!this.graph.nodes[s])throw new Error(`The output '${t}' is not found in the graph`)})}}const lB="?tfjs-format=file",cB="model.json";class ow{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Rt.browserHTTPRequest(e,this.loadOptions);else{const t=Rt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Rt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;const n=Rt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ym(ew.Instance.transformGraph(t,s)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),e.modelInitializer!=null){const i=ew.Instance.transformGraph(e.modelInitializer);this.initializer=new Ym(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const s=Rt.getSaveHandlers(e);if(s.length===0)throw new Error(`Cannot find any save handlers for URL '${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 me)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,s,n)=>(t[s]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=this.executor.execute(e,t);return s.length>1?s:s[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const s=await this.executor.executeAsync(e,t);return s.length>1?s:s[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,s)=>(t[s]=[e[s]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function AN(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${cB}${lB}`));const s=new ow(e,t);return await s.load(),s}const Km="2.6.0";function NN(e,t){return Xm(e,t)}function Xm(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(oo(e)){const r=Array.isArray(e)?[]:{};n.add(e);for(const o in e){const a=e[o],l=Xm(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 RN(e,t=aw){return CN(e,t)}function CN(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(oo(n)){const r=Array.isArray(n)?[]:{};s.add(n);for(const o in n){const a=e.map(c=>c[o]),l=CN(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 aw(e){return e===null?null:oo(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Jm(e,t){const s=new Map;Xm(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=Xm(e,t,s);return n}function oo(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof me))}function ON(e){return e==null||pB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof me||N.isTypedArray(e)}function pB(e){return e===null||typeof e!="object"&&typeof e!="function"}function EN(e){return NN(e,uB)}function uB(e){return e instanceof me?{value:e.clone(),recurse:!1}:oo(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class Zm{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 Qm extends Zm{constructor(){super(Qm.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}}Qm.INITIAL_CAPACITY=32;const _N=mc(fc());function lw(e){return new hB(e)}function hu(e){return new dB(e)}function DN(e,t){return new kN(e,t)}function FN(e,t=qi.FAIL){return new mB(e,t)}class At{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 LB(this,e)}filter(e){return new wB(this,e)}map(e){return new xB(this,e)}mapAsync(e){return new MN(this,e)}serialMapAsync(e){return new MN(this,e).serial()}flatmap(e){return new SB(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 bB(this,e,t)}columnMajorBatch(e,t=!0,s=aw){const n=this.rowMajorBatch(e,t);return n.map(i=>RN(i,s))}concatenate(e,t){return new kN(lw([this,e]),t)}take(e){return e<0||e==null?this:new yB(this,e)}skip(e){return e<0||e==null?this:new gB(this,e)}prefetch(e){return new UN(this,e)}shuffle(e,t){return new IB(this,e,t)}serial(){return new fB(this)}}class hB extends At{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:EN(e),done:!1}}}class dB extends At{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: ${e.message}`,e}}}class fB extends At{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class gB extends At{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;ce(e.value)}return this.upstream.next()}}class yB extends At{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class bB extends At{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class wB extends At{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 xB extends At{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=pn.getTensorsInContainer(e.value),s=this.transform(e.value),n=pn.getTensorsInContainer(s);for(const i of t)pn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class LB extends At{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 MN extends At{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=pn.getTensorsInContainer(e.value),s=await this.transform(e.value),n=pn.getTensorsInContainer(s);for(const i of t)pn.isTensorInList(i,n)||i.dispose();return{value:s,done:!1}}}class du extends At{constructor(){super();this.outputQueue=new Qm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class SB extends du{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=pn.getTensorsInContainer(e.value),s=this.transform(e.value),n=pn.getTensorsInContainer(s);this.outputQueue.pushAll(s);for(const i of t)pn.isTensorInList(i,n)||i.dispose();return!0}}class kN extends At{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`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const s=await this.moreIterators.next();if(s.done)return{value:null,done:!0};this.iterator=s.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var qi;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(qi||(qi={}));class mB extends At{constructor(e,t=qi.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,s=0;function n(r){if(r instanceof At){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 Jm(this.iterators,n);if(t===s)return{value:null,done:!0};if(s>0)switch(this.mismatchMode){case qi.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case qi.SHORTEST:return{value:null,done:!0};case qi.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class UN extends At{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Zm(t)}summary(){return`${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 IB extends UN{constructor(e,t,s){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=_N.alea(s||N.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 $N=mc(fc());class Hi{constructor(){this.size=null}batch(e,t=!0){const s=this;N.assert(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),Cs(async()=>(await s.iterator()).columnMajorBatch(e,t,vB),n)}concatenate(e){const t=this;let s;return this.size===Infinity||e.size===Infinity?s=Infinity:this.size!=null&&e.size!=null?s=this.size+e.size:s=null,Cs(async()=>(await t.iterator()).concatenate(await e.iterator()),s)}filter(e){const t=this;let s;return this.size===Infinity?s=Infinity:s=null,Cs(async()=>(await t.iterator()).filter(n=>C(()=>e(n))),s)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return Cs(async()=>(await t.iterator()).map(s=>C(()=>e(s))),this.size)}mapAsync(e){const t=this;return Cs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return Cs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let s;return this.size!=null&&e>0?s=this.size*e:e===0?s=0:this.size!=null&&(e===void 0||e<0)?s=Infinity:s=null,Cs(async()=>{const n=hu(async()=>({value:await t.iterator(),done:!1}));return DN(n.take(e))},s)}skip(e){const t=this;let s;return this.size!=null&&e>=0&&this.size>=e?s=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?s=0:s=null,Cs(async()=>(await t.iterator()).skip(e),s)}shuffle(e,t,s=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const n=this,i=$N.alea(t||N.now().toString());return Cs(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,Cs(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()}}Hi.MAX_BUFFER_SIZE=1e4;function Cs(e,t=null){return new class extends Hi{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function WN(e){return Cs(async()=>lw(e),e.length)}function zN(e){if(!oo(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 Cs(async()=>{const s=await Jm(e,n=>{if(n instanceof Hi)return{value:n.iterator(),recurse:!1};if(oo(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return FN(s,qi.SHORTEST)},t)}function vB(e){if(e===null)return null;const t=e[0];if(ON(t)){const s=TB(e);return{value:s,recurse:!1}}return{value:null,recurse:!0}}function TB(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof me?Ve(e):ze(e)}class ef extends Hi{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 tf='"',mu=Symbol("out"),PN=Symbol("field"),sf=Symbol("quote"),cw=Symbol("quoteafterquote"),BN=Symbol("quoteinquote");class nf extends Hi{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 ef(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(N.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&&N.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(N.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=mu;for(let o=0;o<i;o++)switch(r){case mu:switch(e.charAt(o)){case tf:n=o+1,r=sf;break;case this.delimiter:if(n=o+1,this.delimiter===" "&&this.delimWhitespace)break;s.push(""),r=mu;break;default:r=PN,n=o;break}break;case PN:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o)),r=mu,n=o+1;break;default:}break;case sf:switch(e.charAt(o)){case tf:r=cw;break;default:}break;case cw:switch(e.charAt(o)){case this.delimiter:s.push(e.substring(n,o-1)),r=mu,n=o+1;break;case tf:r=sf;break;default:r=BN;break}break;case BN:switch(e.charAt(o)){case tf:r=sf;break;default:}break;default:}if(r===cw?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 pw extends At{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(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new pw(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(N.sizeFromShape(t));return s.set(e,s.length-e.length),ze(s,t)}}class uw extends At{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=Oe([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=as([r,i,a,o],[1,4])}else this.cropBox=as([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().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 uw(e,t);return await s.start(),s}async start(){this.webcamConfig.facingMode&&N.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=Fr.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return C(()=>{const t=e.toFloat().expandDims(0);let s;s=Zs.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const n=s.shape;return s.reshape(n.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class rf{}class hw extends At{split(e){return new AB(this,e)}}class AB extends hw{constructor(e,t){super();this.upstream=e,this.impl=new NB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class NB extends du{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const s of t.slice(0,-1))this.outputQueue.push(s);return this.carryover=t[t.length-1],!0}}class jN extends At{decodeUTF8(){return new CB(this)}}class CB extends hw{constructor(e){super();this.upstream=e,this.impl=new RB(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class RB extends du{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=AL();this.decoder=new t("utf8")}}summary(){return`${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 W().get("IS_BROWSER")?s=this.decoder.decode(t,{stream:!0}):s=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(s),!0}}class of extends jN{constructor(e,t={}){super();this.file=e,this.options=t,N.assert(e instanceof Uint8Array||(W().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 VN(e,t={}){let s,n;typeof e=="string"?s=e:(s=e.url,n=OB(e));const i=await N.fetch(s,n);if(i.ok){const r=new Uint8Array(await i.arrayBuffer());return new of(r,t)}else throw new Error(i.statusText)}const OB=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 af(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class lf extends rf{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(af(this.input)&&W().get("IS_NODE")){const e=NL();this.input=e.readFileSync(this.input.substr(7))}return new of(this.input,this.options)}}class cf extends rf{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return af(this.url)?new lf(this.url,this.fileOptions).iterator():VN(this.url,this.fileOptions)}}function GN(e,t={}){return new nf(new cf(e),t)}function qN(e){const t=hu(e);return Cs(async()=>t)}function HN(e){return Cs(async()=>{const t=await e();return hu(()=>t.next())})}async function YN(e,t){return uw.create(e,t)}async function KN(e){return pw.create(e)}const pf="2.6.0";const dw={};Ee(dw,{CSVDataset:()=>nf,Dataset:()=>Hi,FileDataSource:()=>lf,TextLineDataset:()=>ef,URLDataSource:()=>cf,array:()=>WN,csv:()=>GN,func:()=>qN,generator:()=>HN,microphone:()=>KN,version_data:()=>pf,webcam:()=>YN,zip:()=>zN});function ee(e,t){Array.isArray(e)||(e=[e]),e.forEach(s=>{s!=null&&N.assert(s.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const XN=mc(fc());const EB=vt.nonMaxSuppressionV3Impl,_B=vt.split,kB=vt.tile,DB=vt.topkImpl,FB=vt.whereImpl;function mw(e,t,s,n){if(s==="linear")return e.linear(t);if(s==="relu")return e.relu(t);if(s==="elu")return hn(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 fw extends go{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new gc(this,Ms())}write(e,t,s){this.firstUse&&(this.firstUse=!1,W().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,refCount:1}),n}makeTensorInfo(e,t,s){const n=this.write(s,e,t);return{dataId:n,shape:e,dtype:t}}incRef(e){const t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){const t=this.data.get(e);t.refCount--}}move(e,t,s,n){this.data.set(e,{values:t,dtype:n,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos: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=>N.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ge(e.shape,e.dtype,s)}makeOutput(e,t,s){const n=this.write(e,t,s);return Ms().makeTensorFromDataId(n,t,s,this)}disposeData(e){if(this.data.has(e)){const{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.data.has(t)){const s=this.data.get(t);s.refCount--,s.refCount<1&&this.disposeData(t)}}async time(e){const t=N.now();e();const s=N.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."]}}stridedSlice(e,t,s,n){ee(e,"stridedSlice");const i=Fs.computeOutShape(t,s,n);if(i.some(a=>a===0))return ze([],i);const r=ge(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=ge([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]=he(e,r,o).reshape(n);return a}reverse(e,t){ee(e,"reverse");const s=ge(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()}neg(e){return ee(e,"neg"),R(j(-1),e)}addN(e){ee(e,"addN");const t=e.map(i=>this.readSync(i.dataId)),s=ge(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=N.parseAxisParam([t],e.shape),n=xt(e,s),i=U.expandShapeToKeepDim(n.shape,s),r=X(e,n.reshape(i)),o=ut(r),a=this.sum(o,s).reshape(i);return Z(o,a)}pow(e,t){return ee([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.pow(s,n))}batchMatMul(e,t,s,n){ee([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]],g=r*o,y=ge([a,r,o],e.dtype),w=y.values,x=this.blockSize;for(let T=0;T<a;T++)for(let A=0;A<r;A+=x)for(let _=0;_<o;_+=x)for(let E=0;E<i;E+=x){const F=Math.min(A+x,r),D=Math.min(_+x,o),M=Math.min(E+x,i);for(let P=A;P<F;P++)for(let B=_;B<D;B++){let Y=0;for(let q=E;q<M;q++)Y+=l[T*p+P*u+q*h]*c[q*d+B*m+T*f];w[T*g+(P*o+B)]+=Y}}return y.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=$(a,i)),r&&(a=mw(this,a,r,o)),a}floorDiv(e,t){ee([e,t],"floorDiv");const s=(i,r)=>Math.floor(i/r),n="int32";return this.broadcastedBinaryOp(e,t,n,s)}sum(e,t){ee(e,"sum"),U.assertAxesAreInnerMostDims("sum",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ft(e.dtype,"int32"),r=ye(s,i),o=N.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){ee(e,"sum");const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=Ft(e.dtype,"int32"),r=ye(s,i),o=N.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){ee(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=os(o,t).asType("float32"),l=a.mul(e).sum(0);n.push(l)}return Ve(n)}argMin(e,t){ee(e,"argMin");const s=[t];U.assertAxesAreInnerMostDims("argMin",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=ye(n,"int32"),o=N.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){ee(e,"argMax");const s=[t];U.assertAxesAreInnerMostDims("argMax",s,e.rank);const[n,i]=U.computeOutAndReduceShapes(e.shape,s),r=ye(n,"int32"),o=N.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(ee(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=Ft(e.dtype,"int32"),r=ye(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 ee([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s===n?1:0)}notEqual(e,t){return ee([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s!==n?1:0)}less(e,t){return ee([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<n?1:0)}lessEqual(e,t){return ee([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s<=n?1:0)}greater(e,t){return ee([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>n?1:0)}greaterEqual(e,t){return ee([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s>=n?1:0)}logicalAnd(e,t){return ee([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s&&n)}logicalOr(e,t){return ee([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",(s,n)=>s||n)}select(e,t,s){ee([e,t,s],"select");const n=this.readSync(e.dataId),i=this.readSync(t.dataId),r=this.readSync(s.dataId),o=ye(t.shape,Ft(t.dtype,s.dtype)),a=this.readSync(o.dataId);let l=0;const c=e.rank===0||e.rank>1||t.rank===1?1:N.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){ee([e],"where");const t=this.readSync(e.dataId);return FB(e.shape,t)}topk(e,t,s){ee(e,"topk");const n=this.readSync(e.dataId);return DB(n,e.shape,e.dtype,t,s)}min(e,t){ee(e,"min"),U.assertAxesAreInnerMostDims("min",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.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 ee([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.min(s,n))}mod(e,t){return ee([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 ee([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.max(s,n))}all(e,t){ee(e,"all"),U.assertAxesAreInnerMostDims("all",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.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){ee(e,"any"),U.assertAxesAreInnerMostDims("any",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=ye(s,e.dtype),r=N.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 ee([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>{const i=s-n;return i*i})}linear(e){return e}relu(e){ee(e,"relu");const t=ye(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){ee(e,"relu");const t=ye(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 ee([e,t],"prelu"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>s<0?n*s:s)}eluDer(e,t){ee([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")}atan2(e,t){return ee([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,(s,n)=>Math.atan2(s,n))}fusedConv2d({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){let o=this.conv2d(e,t,s);return n&&(o=$(o,n)),i&&(o=mw(this,o,i,r)),o}conv2d(e,t,s){ee([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=ge(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],g=c?p.strides[1]:p.strides[2],y=c?p.strides[2]:1,w=c?1:p.strides[1],x=this.readSync(e.dataId),T=this.readSync(t.dataId),A=p.values;for(let _=0;_<s.batchSize;++_){const E=_*u,F=_*f;for(let D=0;D<s.outHeight;++D){const M=F+D*g,P=D*s.strideHeight-l;for(let B=0;B<n;B++){const Y=P+B*r;if(Y<0||Y>=s.inHeight)continue;const q=B*t.strides[0],K=E+Y*h;for(let H=0;H<s.outWidth;++H){const Q=M+H*y,J=H*s.strideWidth-a;for(let ie=0;ie<i;ie++){const ne=J+ie*o;if(ne<0||ne>=s.inWidth)continue;const le=q+ie*t.strides[1],ue=K+ne*d;let oe=le;for(let de=0;de<s.inChannels;++de){const Ae=x[ue+de*m];for(let Me=0;Me<s.outChannels;++Me)A[Q+Me*w]+=Ae*T[oe+Me];oe+=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=ge(s.outShape,e.dtype),d=this.readSync(e.dataId),m=this.readSync(t.dataId),f=h.values;for(let g=0;g<s.batchSize;++g){const y=g*e.strides[0],w=g*h.strides[0];for(let x=0;x<s.outDepth;++x){const T=w+x*h.strides[1],A=x*s.strideDepth-c;for(let _=0;_<n;_++){const E=A+_*o;if(E<0||E>=s.inDepth)continue;const F=_*t.strides[0],D=y+E*e.strides[1];for(let M=0;M<s.outHeight;++M){const P=T+M*h.strides[2],B=M*s.strideHeight-u;for(let Y=0;Y<i;Y++){const q=B+Y*a;if(q<0||q>=s.inHeight)continue;const K=F+Y*t.strides[1],H=D+q*e.strides[2];for(let Q=0;Q<s.outWidth;++Q){const J=P+Q*s.outChannels,ie=Q*s.strideWidth-p;for(let ne=0;ne<r;ne++){const le=ie+ne*l;if(le<0||le>=s.inWidth)continue;const ue=K+ne*t.strides[2],oe=H+le*s.inChannels;let de=ue;for(let Ae=0;Ae<s.inChannels;++Ae){const Me=d[oe+Ae];for(let Qe=0;Qe<s.outChannels;++Qe)f[J+Qe]+=Me*m[de+Qe];de+=s.outChannels}}}}}}}}return h.toTensor()}conv2dDerInput(e,t,s){ee([e,t],"conv2dDerInput");const n=ge(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:g,outHeight:y,outWidth:w,strideHeight:x,strideWidth:T,dataFormat:A}=s,_=u-1-s.padInfo.top,E=h-1-s.padInfo.left,F=A==="channelsLast",D=n.strides[0],M=F?n.strides[1]:n.strides[2],P=F?n.strides[2]:1,B=F?1:n.strides[1],Y=e.strides[0],q=F?e.strides[1]:e.strides[2],K=F?e.strides[2]:1,H=F?1:e.strides[1];for(let Q=0;Q<p;++Q)for(let J=0;J<d;++J)for(let ie=0;ie<m;++ie){const ne=ie-_,le=Math.max(0,Math.ceil(ne/x)),ue=Math.min(y,(u+ne)/x);for(let oe=0;oe<f;++oe){const de=oe-E,Ae=Math.max(0,Math.ceil(de/T)),Me=Math.min(w,(h+de)/T);let Qe=0;for(let $t=le;$t<ue;++$t){const _s=$t*x-ne;for(let yt=Ae;yt<Me;++yt){const ps=yt*T-de,_n=Y*Q+q*$t+K*yt,Gs=a*(u-1-_s)+l*(h-1-ps)+c*J;for(let ks=0;ks<g;++ks){const an=r[_n+H*ks],dc=o[Gs+ks];Qe+=an*dc}}}const St=D*Q+M*ie+P*oe+B*J;i[St]=Qe}}return n.toTensor()}conv3dDerInput(e,t,s){const n=ge(s.inShape,"float32"),i=n.values,[r,o,a,l]=n.strides,c=this.readSync(e.dataId),[p,u,h,d]=e.strides,m=this.readSync(t.dataId),[f,g,y,w]=t.strides,{batchSize:x,filterDepth:T,filterHeight:A,filterWidth:_,inChannels:E,inDepth:F,inHeight:D,inWidth:M,outChannels:P,outDepth:B,outHeight:Y,outWidth:q,strideDepth:K,strideHeight:H,strideWidth:Q}=s,J=T-1-s.padInfo.front,ie=A-1-s.padInfo.top,ne=_-1-s.padInfo.left;for(let le=0;le<x;++le)for(let ue=0;ue<E;++ue)for(let oe=0;oe<F;++oe){const de=oe-J,Ae=Math.max(0,Math.ceil(de/K)),Me=Math.min(B,(T+de)/K);for(let Qe=0;Qe<D;++Qe){const St=Qe-ie,$t=Math.max(0,Math.ceil(St/H)),_s=Math.min(Y,(A+St)/H);for(let yt=0;yt<M;++yt){const ps=yt-ne,_n=Math.max(0,Math.ceil(ps/Q)),Gs=Math.min(q,(_+ps)/Q);let ks=0;for(let an=Ae;an<Me;++an){const dc=an*K-de;for(let Na=$t;Na<_s;++Na){const Ca=Na*H-St;for(let Ra=_n;Ra<Gs;++Ra){const ag=Ra*Q-ps,lg=p*le+u*an+h*Na+d*Ra,hE=f*(T-1-dc)+g*(A-1-Ca)+y*(_-1-ag)+w*ue;for(let Wu=0;Wu<P;++Wu){const dE=c[lg+Wu],mE=m[hE+Wu];ks+=dE*mE}}}}i[r*le+o*oe+a*Qe+l*yt+ue]=ks}}}return n.toTensor()}conv2dDerFilter(e,t,s){ee([e,t],"conv2dDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=s.dataFormat==="channelsLast",l=ge(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 g=0;g<o;++g){const y=Math.max(0,Math.ceil((c-g)/i)),w=Math.min(s.outWidth,(s.inWidth+c-g)/i);for(let x=0;x<s.inChannels;++x)for(let T=0;T<s.outChannels;++T){let A=0;for(let _=0;_<s.batchSize;++_)for(let E=m;E<f;++E){const F=d+E*n-p;for(let D=y;D<w;++D){const M=g+D*i-c;a?A+=u.get(_,F,M,x)*h.get(_,E,D,T):A+=u.get(_,x,F,M)*h.get(_,T,E,D)}}l.set(A,d,g,x,T)}}}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=ge(s.filterShape,"float32"),p=c.values,[u,h,d,m]=c.strides,f=this.readSync(t.dataId),[g,y,w,x]=t.strides,T=this.readSync(e.dataId),[A,_,E,F]=e.strides,D=s.padInfo.front,M=s.padInfo.left,P=s.padInfo.top;for(let B=0;B<o;++B){const Y=Math.max(0,Math.ceil((D-B)/n)),q=Math.min(s.outDepth,(s.inDepth+D-B)/n),K=B*u;for(let H=0;H<a;++H){const Q=Math.max(0,Math.ceil((P-H)/i)),J=Math.min(s.outHeight,(s.inHeight+P-H)/i),ie=H*h+K;for(let ne=0;ne<l;++ne){const le=Math.max(0,Math.ceil((M-ne)/r)),ue=Math.min(s.outWidth,(s.inWidth+M-ne)/r),oe=ne*d+ie;for(let de=0;de<s.inChannels;++de){const Ae=de*m+oe;for(let Me=0;Me<s.outChannels;++Me){let Qe=0;for(let St=0;St<s.batchSize;++St){const $t=St*A,_s=St*g;for(let yt=Y;yt<q;++yt){const ps=B+yt*n-D,_n=ps*_+$t,Gs=yt*y+_s;for(let ks=Q;ks<J;++ks){const an=H+ks*i-P,dc=an*E+_n,Na=ks*w+Gs;for(let Ca=le;Ca<ue;++Ca){const Ra=ne+Ca*r-M,ag=Ra*F+dc,lg=Ca*x+Na;Qe+=T[ag+de]*f[lg+Me]}}}}p[Ae+Me]=Qe}}}}}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=$(o,n)),i&&(o=mw(this,o,i,r)),o}depthwiseConv2D(e,t,s){ee([e,t],"depthwiseConv2D");const n=s.filterHeight,i=s.filterWidth,r=s.dilationHeight,o=s.dilationWidth,a=s.padInfo.left,l=s.padInfo.top,c=s.outChannels/s.inChannels,p=ge(s.outShape,e.dtype),u=this.readSync(e.dataId),h=this.readSync(t.dataId),d=p.values;for(let m=0;m<s.batchSize;++m){const f=m*e.strides[0],g=m*p.strides[0];for(let y=0;y<s.outHeight;++y){const w=g+y*p.strides[1],x=y*s.strideHeight-a;for(let T=0;T<n;++T){const A=x+T*r;if(A<0||A>=s.inHeight)continue;const _=T*t.strides[0],E=f+A*e.strides[1];for(let F=0;F<s.outWidth;++F){const D=w+F*p.strides[2],M=F*s.strideWidth-l;for(let P=0;P<i;++P){const B=M+P*o;if(B<0||B>=s.inWidth)continue;const Y=_+P*t.strides[1],q=E+B*s.inChannels;let K=D,H=Y;for(let Q=0;Q<s.inChannels;++Q){const J=u[q+Q];for(let ie=0;ie<c;++ie)d[K+ie]+=J*h[H+ie];K+=c,H+=c}}}}}}return p.toTensor()}depthwiseConv2DDerInput(e,t,s){ee([e,t],"depthwiseConv2DDerInput");const n=ge(s.inShape,"float32"),i=n.values,[r,o,a]=n.strides,l=this.readSync(e.dataId),[c,p,u]=e.strides,h=this.readSync(t.dataId),[d,m,f]=t.strides,{batchSize:g,filterHeight:y,filterWidth:w,inChannels:x,inHeight:T,inWidth:A,outChannels:_,outHeight:E,outWidth:F,strideHeight:D,strideWidth:M}=s,P=y-1-s.padInfo.top,B=w-1-s.padInfo.left,Y=_/x;for(let q=0;q<g;++q)for(let K=0;K<x;++K)for(let H=0;H<T;++H){const Q=H-P,J=Math.max(0,Math.ceil(Q/D)),ie=Math.min(E,(y+Q)/D);for(let ne=0;ne<A;++ne){const le=ne-B,ue=Math.max(0,Math.ceil(le/M)),oe=Math.min(F,(w+le)/M);let de=0;for(let Ae=J;Ae<ie;++Ae){const Me=Ae*D-Q;for(let Qe=ue;Qe<oe;++Qe){const St=Qe*M-le,$t=c*q+p*Ae+u*Qe,_s=d*(y-1-Me)+m*(w-1-St)+f*K;for(let yt=0;yt<Y;++yt){const ps=K*Y+yt,_n=l[$t+ps],Gs=h[_s+yt];de+=_n*Gs}}}i[r*q+o*H+a*ne+K]=de}}return n.toTensor()}depthwiseConv2DDerFilter(e,t,s){ee([e,t],"depthwiseConv2DDerFilter");const n=s.strideHeight,i=s.strideWidth,r=s.filterHeight,o=s.filterWidth,a=ge(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 g=0;g<o;++g){const y=Math.max(0,Math.ceil((l-g)/i)),w=Math.min(s.outWidth,(s.inWidth+l-g)/i);for(let x=0;x<s.outChannels;++x){const T=Math.trunc(x/p),A=x%p;let _=0;for(let E=0;E<s.batchSize;++E)for(let F=m;F<f;++F){const D=d+F*n-c;for(let M=y;M<w;++M){const P=g+M*i-l;_+=u.get(E,D,P,T)*h.get(E,F,M,x)}}a.set(_,d,g,T,A)}}}return a.toTensor()}tile(e,t){return ee(e,"tile"),kB(this.bufferSync(e),t)}gather(e,t,s){ee([e,t],"gather");const n=e.shape.slice(),i=this.readSync(t.dataId);n[s]=i.length;const r=ge(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){ee([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)}pool3d(e,t,s){ee(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,g=this.readSync(e.dataId),y=ge(t.outShape,e.dtype),w=y.values,x=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],T=t.outShape[2]*t.outShape[3]*t.outShape[4],A=t.outShape[3]*t.outShape[4],_=t.outShape[4];for(let E=0;E<t.batchSize;++E){const F=E*x,D=E*e.strides[0];for(let M=0;M<t.inChannels;++M)for(let P=0;P<t.outDepth;++P){const B=P*n-h;let Y=B;for(;Y<0;)Y+=o;const q=Math.min(t.inDepth,c+B),K=F+P*T;for(let H=0;H<t.outHeight;++H){const Q=H*i-d;let J=Q;for(;J<0;)J+=a;const ie=Math.min(t.inHeight,p+Q),ne=K+H*A;for(let le=0;le<t.outWidth;++le){const ue=le*r-m;let oe=ue;for(;oe<0;)oe+=l;const de=Math.min(t.inWidth,u+ue),Ae=ne+le*_;let Me=f,Qe=0,St=0;for(let _s=Y;_s<q;_s+=o){const yt=D+_s*e.strides[1];for(let ps=J;ps<ie;ps+=a){const _n=yt+ps*e.strides[2];for(let Gs=oe;Gs<de;Gs+=l){const ks=_n+Gs*e.strides[3],an=g[ks+M];if(s==="max"&&an>Me?Me=an:s==="avg"&&(Qe+=an,St++),isNaN(Me))break}if(isNaN(Me))break}if(isNaN(Me))break}const $t=Ae+M;w[$t]=s==="avg"?Qe/St:Me}}}}return y.toTensor()}avgPool3d(e,t){return ee(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,s){ee([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,g=m-1-s.padInfo.left,y=d-1-s.padInfo.top,w=ge(t.shape,"float32"),x=1/(o*a*l),T=this.bufferSync(e);for(let A=0;A<s.batchSize;++A)for(let _=0;_<s.inChannels;++_)for(let E=0;E<s.inDepth;++E)for(let F=0;F<s.inHeight;++F)for(let D=0;D<s.inWidth;++D){const M=E-f,P=F-y,B=D-g;let Y=0;for(let q=0;q<h;q+=c){const K=(M+q)/n;if(K<0||K>=s.outDepth||Math.floor(K)!==K)continue;for(let H=0;H<d;H+=p){const Q=(P+H)/i;if(Q<0||Q>=s.outHeight||Math.floor(Q)!==Q)continue;for(let J=0;J<m;J+=u){const ie=(B+J)/r;if(ie<0||ie>=s.outWidth||Math.floor(ie)!==ie)continue;const ne=T.get(A,K,Q,ie,_);Y+=ne}}}w.set(Y*x,A,E,F,D,_)}return w.toTensor()}maxPool3d(e,t){return ee(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const s=ge(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 g=0;g<t.batchSize;++g)for(let y=0;y<t.inChannels;++y)for(let w=0;w<t.outDepth;++w){const x=w*n-h;let T=x;for(;T<0;)T+=o;const A=Math.min(t.inDepth,c+x);for(let _=0;_<t.outHeight;++_){const E=_*i-d;let F=E;for(;F<0;)F+=a;const D=Math.min(t.inHeight,p+E);for(let M=0;M<t.outWidth;++M){const P=M*r-m;let B=P;for(;B<0;)B+=l;const Y=Math.min(t.inWidth,u+P);let q=Number.NEGATIVE_INFINITY,K=-1;for(let H=T;H<A;H+=o){const Q=H-x;for(let J=F;J<D;J+=a){const ie=J-E;for(let ne=B;ne<Y;ne+=l){const le=ne-P,ue=f.get(g,H,J,ne,y);ue>=q&&(q=ue,K=Q*p*u+ie*p+le)}}}s.set(K,g,w,_,M,y)}}}return s.toTensor()}maxPool3dBackprop(e,t,s,n){ee([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,g=h-1-n.padInfo.top,y=ge(t.shape,"float32"),w=this.bufferSync(i),x=this.bufferSync(e);for(let T=0;T<n.batchSize;++T)for(let A=0;A<n.inChannels;++A)for(let _=0;_<n.inDepth;++_)for(let E=0;E<n.inHeight;++E)for(let F=0;F<n.inWidth;++F){const D=_-m,M=E-g,P=F-f;let B=0;for(let Y=0;Y<u;Y+=l){const q=(D+Y)/r;if(q<0||q>=n.outDepth||Math.floor(q)!==q)continue;for(let K=0;K<h;K+=c){const H=(M+K)/o;if(H<0||H>=n.outHeight||Math.floor(H)!==H)continue;for(let Q=0;Q<d;Q+=p){const J=(P+Q)/a;if(J<0||J>=n.outWidth||Math.floor(J)!==J)continue;const ie=u*h*d-1-w.get(T,q,H,J,A),ne=Y*h*d+K*d+Q,le=ie===ne?1:0;if(le===0)continue;const ue=x.get(T,q,H,J,A);B+=ue*le}}}y.set(B,T,_,E,F,A)}return y.toTensor()}resizeBilinear(e,t,s,n){ee(e,"resizeBilinear");const[i,r,o,a]=e.shape,l=this.readSync(e.dataId),c=new Float32Array(N.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 g=0;g<t;g++){const y=d*g,w=Math.floor(y),x=y-w,T=Math.min(r-1,Math.ceil(y)),A=f*e.strides[0]+w*e.strides[1],_=f*e.strides[0]+T*e.strides[1];for(let E=0;E<s;E++){const F=m*E,D=Math.floor(F),M=F-D,P=Math.min(o-1,Math.ceil(F)),B=A+D*e.strides[2],Y=_+D*e.strides[2],q=A+P*e.strides[2],K=_+P*e.strides[2];for(let H=0;H<a;H++){const Q=l[B+H],J=l[Y+H],ie=l[q+H],ne=l[K+H],le=Q+(ie-Q)*M,ue=J+(ne-J)*M,oe=le+(ue-le)*x;c[h++]=oe}}}return ze(c,[i,t,s,a])}resizeBilinearBackprop(e,t,s){ee([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 g=0;g<n;g++){const y=g*t.strides[0];for(let w=0;w<a;w++){const x=w*h,T=Math.floor(x),A=Math.min(Math.ceil(x),i-1),_=y+T*t.strides[1],E=y+A*t.strides[1],F=x-T,D=1-F;for(let M=0;M<l;M++){const P=M*d,B=Math.floor(P),Y=Math.min(Math.ceil(P),r-1),q=P-B,K=1-q,H=_+B*t.strides[2],Q=_+Y*t.strides[2],J=E+B*t.strides[2],ie=E+Y*t.strides[2],ne=D*K,le=D*q,ue=F*K,oe=F*q;for(let de=0;de<o;de++){const Ae=m[f++];c[H+de]+=Ae*ne,c[Q+de]+=Ae*le,c[J+de]+=Ae*ue,c[ie+de]+=Ae*oe}}}}return ts(c,[n,r,i,o],t.dtype)}resizeNearestNeighbor(e,t,s,n){ee(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 g=f*e.strides[0];for(let y=0;y<t;y++){const w=h*y,x=Math.min(r-1,n?Math.round(w):Math.floor(w)),T=g+x*e.strides[1];for(let A=0;A<s;A++){const _=d*A,E=Math.min(o-1,n?Math.round(_):Math.floor(_)),F=T+E*e.strides[2];for(let D=0;D<a;D++){const M=l[F+D];c[m++]=M}}}}return ze(c,[i,t,s,a],e.dtype)}resizeNearestNeighborBackprop(e,t,s){ee([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,g=1/m,y=Math.ceil(f)*2+2,w=Math.ceil(g)*2+2;for(let x=0;x<n;x++){const T=x*t.strides[0];for(let A=0;A<i;A++){const _=T+A*t.strides[1],E=Math.floor(A*f),F=Math.floor(E-y/2);for(let D=0;D<r;D++){const M=_+D*t.strides[2],P=Math.floor(D*g),B=Math.floor(P-w/2);for(let Y=0;Y<o;Y++){let q=0;for(let K=0;K<y;K++){const H=K+F;if(H<0||H>=a)continue;const Q=T+H*e.strides[1],J=H*d,ie=Math.min(i-1,s?Math.round(J):Math.floor(J));if(A!==ie)continue;for(let ne=0;ne<w;ne++){const le=ne+B;if(le<0||le>=l)continue;const ue=Q+le*e.strides[2],oe=le*m,de=Math.min(r-1,s?Math.round(oe):Math.floor(oe));D===de&&(q+=p[ue+Y])}}c[M+Y]=q}}}}return ts(c,t.shape,t.dtype)}localResponseNormalization4D(e,t,s,n,i){ee(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 g=a[d];f+=g*g}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 ts(c,e.shape)}LRNGrad(e,t,s,n,i,r,o){ee(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),g=d-m+Math.min(a,m+n+1);let y=0;for(let w=f;w<g;w++)y+=Math.pow(c[w],2);y=r*y+i;for(let w=f;w<g;w++){let x=-2*r*o*c[w]*p[d]/y;d===w&&(x+=Math.pow(y,-o)),x*=l[d],u[w]+=x}}return ts(u,e.shape)}multinomial(e,t,s,n){ee(e,"multinomial");const i=t?e:es(e),r=i.shape[0],o=i.shape[1],a=ye([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=XN.alea(n.toString()),m=p*s;for(let f=0;f<s;++f){const g=d();l[m+f]=h.length;for(let y=0;y<h.length;y++)if(g<h[y]){l[m+f]=y;break}}}return a}oneHot(e,t,s,n){ee(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 as(i,[e.size,t],"int32")}nonMaxSuppression(e,t,s,n,i){ee(e,"nonMaxSuppression");const r=this.readSync(e.dataId),o=this.readSync(t.dataId);return EB(r,o,s,n,i)}depthToSpace(e,t,s){N.assert(s==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${s}`),N.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),g=m%t;for(let y=0;y<l;++y){const w=Math.floor(y/t),x=y%t,T=(g*t+x)*c;for(let A=0;A<c;++A){const _=A+T,E=_+o*(w+r*(f+i*d));u[h++]=p[E]}}}return ts(u,[n,a,l,c])}broadcastedBinaryOp(e,t,s,n){const i=U.assertAndGetBroadcastShape(e.shape,t.shape),r=ge(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 g=u.locToIndex(f),y=m.slice(-t.rank);c.forEach(x=>y[x]=0);const w=h.locToIndex(y);p[d]=n(o[g],a[w])}}return r.toTensor()}split(e,t,s){return _B(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=ge([p,u,h,c],"float32"),m=this.readSync(t.dataId),f=this.readSync(s.dataId),g=this.readSync(e.dataId),y=e.strides,w=d.strides;for(let x=0;x<p;x++){const T=x*4,A=m[T],_=m[T+1],E=m[T+2],F=m[T+3],D=f[x];if(D>=o)continue;const M=u>1?(E-A)*(a-1)/(u-1):0,P=h>1?(F-_)*(l-1)/(h-1):0;for(let B=0;B<u;B++){const Y=u>1?A*(a-1)+B*M:.5*(A+E)*(a-1);if(Y<0||Y>a-1){for(let q=0;q<h;q++)for(let K=0;K<c;K++){const H=K+q*w[2]+B*w[1]+x*w[0];d.values[H]=r}continue}if(i==="bilinear"){const q=Math.floor(Y),K=Math.ceil(Y),H=Y-q;for(let Q=0;Q<h;Q++){const J=h>1?_*(l-1)+Q*P:.5*(_+F)*(l-1);if(J<0||J>l-1){for(let ue=0;ue<c;ue++){const oe=ue+Q*w[2]+B*w[1]+x*w[0];d.values[oe]=r}continue}const ie=Math.floor(J),ne=Math.ceil(J),le=J-ie;for(let ue=0;ue<c;ue++){let oe=ue+ie*y[2]+q*y[1]+D*y[0];const de=g[oe];oe=ue+ne*y[2]+q*y[1]+D*y[0];const Ae=g[oe];oe=ue+ie*y[2]+K*y[1]+D*y[0];const Me=g[oe];oe=ue+ne*y[2]+K*y[1]+D*y[0];const Qe=g[oe],St=de+(Ae-de)*le,$t=Me+(Qe-Me)*le;oe=ue+Q*w[2]+B*w[1]+x*w[0],d.values[oe]=St+($t-St)*H}}}else for(let q=0;q<h;++q){const K=h>1?_*(l-1)+q*P:.5*(_+F)*(l-1);if(K<0||K>l-1){for(let J=0;J<c;J++){const ie=J+q*w[2]+B*w[1]+x*w[0];d.values[ie]=r}continue}const H=Math.round(K),Q=Math.round(Y);for(let J=0;J<c;J++){const ie=J+H*y[2]+Q*y[1]+D*y[0],ne=J+q*w[2]+B*w[1]+x*w[0];d.values[ne]=g[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 cn([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||N.inferDtype(t);const n=N.getArrayFromDType(s,N.sizeFromShape(e));return n.fill(t),Ms().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=N.getArrayFromDType(e.dtype,N.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 cn(p,t.dtype);d.values.fill(this.readSync(l.dataId)[0]);for(let m=0;m<r;m++){const f=[];let g=0;for(let y=0;y<o;y++){const w=u[m*o+y];f.push(w),g+=w*a[y]}if(g<0||g>=n/i)throw new Error(`Invalid indices: ${f} does not index into ${s}`);for(let y=0;y<i;y++)c?d.values[g*i+y]+=h[m*i+y]:d.values[g*i+y]=t.rank===0?h[0]:h[m*i+y]}return d.toTensor().reshape(s)}}function gw(e){const t=new Float32Array(e.length);for(let s=0;s<e.length;++s)t[s]=Math.abs(e[s]);return t}const MB=e=>{const{x:t}=e.inputs,s=e.backend;let n=new Float32Array(N.sizeFromShape(t.shape));if(t.dtype!=="complex64"){const i=s.data.get(t.dataId).values;n=gw(i)}else{const i=s.data.get(t.dataId),r=i.complexTensorInfos.real,o=i.complexTensorInfos.imag,a=s.data.get(r.dataId).values,l=s.data.get(o.dataId).values;for(let c=0;c<a.length;c++){const p=a[c],u=l[c];n[c]=Math.hypot(p,u)}}return s.makeOutput(n,t.shape,"float32")},JN={kernelName:yo,backendName:"cpu",kernelFunc:MB};function Bs(e){return(t,s,n,i,r)=>{const o=U.assertAndGetBroadcastShape(t,s),a=o.length,l=N.computeStrides(o),c=N.sizeFromShape(o),p=N.getTypedArrayFromDType(r,c),u=t.length,h=s.length,d=N.computeStrides(t),m=N.computeStrides(s),f=U.getBroadcastDims(t,o),g=U.getBroadcastDims(s,o);if(f.length+g.length===0)for(let y=0;y<p.length;++y)p[y]=e(n[y%n.length],i[y%i.length]);else for(let y=0;y<p.length;++y){const w=N.indexToLoc(y,a,l),x=w.slice(-u);f.forEach(E=>x[E]=0);const T=N.locToIndex(x,u,d),A=w.slice(-h);g.forEach(E=>A[E]=0);const _=N.locToIndex(A,h,m);p[y]=e(n[T],i[_])}return[p,o]}}function Rs(e){const{inputs:t,backend:s}=e,{real:n,imag:i}=t,r=s.data.get(n.dataId).values,o=s.data.get(i.dataId).values,a=s.makeTensorInfo(n.shape,"complex64"),l=s.data.get(a.dataId);return l.complexTensorInfos={real:s.makeTensorInfo(n.shape,"float32",r),imag:s.makeTensorInfo(i.shape,"float32",o)},a}const ZN={kernelName:vc,backendName:"cpu",kernelFunc:Rs};function fi(e){const{inputs:t,backend:s}=e,{x:n}=t;return 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r=0;r<t.length;++r)i[r]=e(t[r],n);return i}}function fe(e,t,s){return({inputs:n,attrs:i,backend:r})=>{const{x:o}=n;if(ee(o,e),o.dtype==="string"||s==="string")throw new Error("unaryKernelFunc does not support string input/output");const a=r,l=a.data.get(o.dataId).values,c=N.sizeFromShape(o.shape),p=s||o.dtype,u=N.getArrayFromDType(p,c);for(let h=0;h<c;++h)u[h]=t(l[h],i);return a.makeTensorInfo(o.shape,p,u)}}function Cn(e,t,s){return({inputs:n,attrs:i,backend:r})=>{const{x:o}=n;if(ee(o,e),o.dtype==="string"||s==="string")throw new Error("unaryKernelFunc does not support string input/output");const a=r,l=a.data.get(o.dataId).values,c=s||o.dtype,p=t(l,c,i);return a.makeTensorInfo(o.shape,c,p)}}const ww=Nn(e=>Math.ceil(e)),$B=Cn(cr,ww),nC={kernelName:cr,backendName:"cpu",kernelFunc:$B};const xw=Nn(e=>Math.exp(e)),WB=Cn(mr,xw),iC={kernelName:mr,backendName:"cpu",kernelFunc:WB};const Lw=Nn(e=>Math.expm1(e)),zB=Cn(fr,Lw),rC={kernelName:fr,backendName:"cpu",kernelFunc:zB};const 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hf(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t,{begin:r,size:o}=n;ee(i,"slice");const[a,l]=Fs.parseSliceParams(i,r,o);Fs.assertParamsValid(i,a,l);const c=s.data.get(i.dataId).values,p=Nw(c,a,l,i.shape,i.dtype);return s.makeTensorInfo(l,i.dtype,p)}const pC={kernelName:Co,backendName:"cpu",kernelFunc:hf};const Cw=Bs((e,t)=>e-t),GB=Fl((e,t,s,n)=>({real:e-s,imag:t-n})),Rw=An(_r,Cw,GB),uC={kernelName:_r,backendName:"cpu",kernelFunc:Rw};function Ml(e,t,s,n,i){const r=t.length,o=N.sizeFromShape(t),a=N.computeStrides(t),l=N.computeStrides(i),c=N.getTypedArrayFromDType(s,N.sizeFromShape(i));for(let p=0;p<o;++p){const u=N.indexToLoc(p,r,a),h=new Array(u.length);for(let m=0;m<h.length;m++)h[m]=u[n[m]];const d=N.locToIndex(h,r,l);c[d]=e[p]}return c}function df(e,t,s,n){const i=N.parseAxisParam(t,s)[0],r=[1,s[0],1];for(let m=0;m<i;m++)r[0]*=s[m];r[1]=s[i];for(let m=i+1;m<s.length;m++)r[2]*=s[m];const o={},a=new Int32Array(s[i]),l=new cn(r,n,e),c=[],p=r[0]===1&&r[2]===1;for(let m=0;m<s[i];m++){let f;if(p)f=e[m].toString();else{const g=[];for(let y=0;y<r[0];y++)for(let w=0;w<r[2];w++)g.push(l.get(y,m,w));f=g.join(",")}if(o[f]!==void 0)a[m]=o[f];else{const g=Object.keys(o).length;o[f]=g,a[m]=g,c.push(m)}}const u=r.slice();u[1]=Object.keys(o).length;const h=new cn(u,n);c.forEach((m,f)=>{for(let g=0;g<r[0];g++)for(let y=0;y<r[2];y++)h.set(l.get(g,m,y),g,f,y)});const d=s.slice();return d[i]=u[1],{outputValues:h.values,outputShape:d,indices:a}}const Ow={};Ee(Ow,{addImpl:()=>yw,ceilImpl:()=>ww,expImpl:()=>xw,expm1Impl:()=>Lw,floorImpl:()=>Sw,logImpl:()=>Iw,maxImpl:()=>uf,multiplyImpl:()=>vw,rsqrtImpl:()=>Aw,simpleAbsImpl:()=>gw,sliceImpl:()=>Nw,subImpl:()=>Cw,transposeImpl:()=>Ml,uniqueImpl:()=>df});const Ew="2.6.0";Tp("cpu",()=>new fw,1);const qB=fe(nr,e=>Math.acos(e)),hC={kernelName:nr,backendName:"cpu",kernelFunc:qB};const HB=fe(ir,e=>Math.acosh(e)),dC={kernelName:ir,backendName:"cpu",kernelFunc:HB};const 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le=M;le<P;le+=l){const ue=_+le*n[1];for(let oe=K;oe<H;oe+=c){const de=ue+oe*n[2],Ae=e[de+E];r==="max"&&Ae>Q?Q=Ae:r==="avg"&&(J+=Ae,ie++)}if(isNaN(Q))break}const ne=B+Y*x+E;g[ne]=r==="avg"?J/ie:Q}}}return f}function mf(e,t,s,n,i=!1,r=!1){const o=ge(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=ge(t,s,e);for(let g=0;g<n.batchSize;++g)for(let y=0;y<n.inChannels;++y)for(let w=0;w<n.outHeight;++w){const x=w*a-d;let T=x;for(;T<0;)T+=c;const A=Math.min(n.inHeight,u+x);for(let _=0;_<n.outWidth;++_){const E=_*l-m;let F=E;for(;F<0;)F+=p;const D=Math.min(n.inWidth,h+E);let M=Number.NEGATIVE_INFINITY,P=-1;for(let B=T;B<A;B+=c){const Y=B-x;for(let q=F;q<D;q+=p){const K=q-E,H=f.get(g,B,q,y);H>M&&(M=H,i?P=r?((g*n.inHeight+B)*n.inWidth+q)*n.inChannels+y:(B*n.inWidth+q)*n.inChannels+y:P=Y*h+K)}}o.set(P,g,w,_,y)}}return o}function ZB(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;ee(i,"avgPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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The new shape and old shape must have the same number of elements.`),s.incRef(i.dataId);const c=s.data.get(i.dataId);if(c.complexTensorInfos!=null){const p=c.complexTensorInfos.real,u=c.complexTensorInfos.imag;p.shape=a,u.shape=a}return{dataId:i.dataId,shape:a,dtype:i.dtype}}const IC={kernelName:Ci,backendName:"cpu",kernelFunc:Rn};function Wl(e){const{inputs:t,backend:s,attrs:n}=e,{axis:i}=n,r=N.parseAxisParam(i,t[0].shape)[0];let o=U.computeOutShape(t.map(d=>d.shape),r);if(N.sizeFromShape(o)===0)return s.makeTensorInfo(o,t[0].dtype,[]);const a=t.filter(d=>N.sizeFromShape(d.shape)>0);if(a.length===1)return a[0];const l=a.map(d=>d.shape);if(U.assertParamsConsistent(l,r),a[0].dtype==="complex64"){const d=a.map(w=>ao({inputs:{input:w},backend:s})),m=a.map(w=>$l({inputs:{input:w},backend:s})),f=Wl({inputs:d,backend:s,attrs:{axis:i}}),g=Wl({inputs:m,backend:s,attrs:{axis:i}}),y=Rs({inputs:{real:f,imag:g},backend:s});return d.forEach(w=>s.disposeIntermediateTensorInfo(w)),m.forEach(w=>s.disposeIntermediateTensorInfo(w)),s.disposeIntermediateTensorInfo(f),s.disposeIntermediateTensorInfo(g),y}const c=a.map(d=>{const m=N.sizeFromShape(d.shape.slice(r)),f=[-1,m];return Rn({inputs:{x:d},backend:s,attrs:{shape:f}})});o=U.computeOutShape(c.map(d=>d.shape),1);const p=N.getTypedArrayFromDType(a[0].dtype,N.sizeFromShape(o));if(c[0].shape[0]===1){let d=0;c.forEach(m=>{const f=s.data.get(m.dataId).values,g=N.sizeFromShape(m.shape);p.set(f,d),d+=g})}else{let d=0;c.forEach(m=>{const f=s.data.get(m.dataId).values;let g=0;for(let y=0;y<m.shape[0];++y){const w=y*o[1]+d;for(let x=0;x<m.shape[1];++x)p[w+x]=f[g++]}d+=m.shape[1]})}const u=U.computeOutShape(a.map(d=>d.shape),r),h=s.makeTensorInfo(u,t[0].dtype,p);return c.forEach(d=>s.disposeIntermediateTensorInfo(d)),h}const vC={kernelName:xo,backendName:"cpu",kernelFunc:Wl};const sj=fe(Xn,e=>Math.cos(e)),TC={kernelName:Xn,backendName:"cpu",kernelFunc:sj};const nj=fe(ur,e=>Math.cosh(e)),AC={kernelName:ur,backendName:"cpu",kernelFunc:nj};const NC={kernelName:Lo,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=l.data.get(n.dataId).values,p=n.shape.length,u=l.data.get(i.dataId).values,h=i.shape.length,{batchSize:d,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:w,padInfo:x,strideHeight:T,strideWidth:A,filterHeight:_,filterWidth:E,dilationHeight:F,dilationWidth:D,outShape:M}=U.computeDilation2DInfo(n.shape,i.shape,r,o,"NHWC",a),P=N.sizeFromShape(M),B=M.length,Y=N.getArrayFromDType(n.dtype,P);for(let K=0;K<d;++K)for(let H=0;H<y;++H){const Q=H*T-x.top;for(let J=0;J<w;++J){const ie=J*A-x.left;for(let ne=0;ne<g;++ne){let le=Number.MIN_SAFE_INTEGER;for(let oe=0;oe<_;++oe){const de=Q+oe*F;if(de>=0&&de<m)for(let Ae=0;Ae<E;++Ae){const Me=ie+Ae*D;if(Me>=0&&Me<f){const Qe=N.locToIndex([K,de,Me,ne],p,N.computeStrides(n.shape)),St=N.locToIndex([oe,Ae,ne],h,N.computeStrides(i.shape)),$t=c[Qe]+u[St];$t>le&&(le=$t)}}}const ue=N.locToIndex([K,H,J,ne],B,N.computeStrides(M));Y[ue]=le}}}const q=l.write(N.toTypedArray(Y,n.dtype),M,n.dtype);return{dataId:q,shape:M,dtype:n.dtype}}};const CC={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=N.toNestedArray(n.shape,c.data.get(n.dataId).values),u=N.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:w,strideHeight:x,strideWidth:T,filterHeight:A,filterWidth:_,dilationHeight:E,dilationWidth:F,outShape:D}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);N.assert(r.rank===D.length,()=>`Error in ${Ea}, dy must have the same rank as output ${D.length}, but got ${r.rank}`);const M=N.toNestedArray(D,c.data.get(r.dataId).values),P=N.makeZerosNestedTypedArray(i.shape,i.dtype);for(let Y=0;Y<h;++Y)for(let q=0;q<g;++q){const K=q*x-w.top;for(let H=0;H<y;++H){const Q=H*T-w.left;for(let J=0;J<f;++J){let ie=Number.MIN_SAFE_INTEGER,ne=0,le=0;for(let ue=0;ue<A;++ue){const oe=K+ue*E;if(oe>=0&&oe<d)for(let de=0;de<_;++de){const Ae=Q+de*F;if(Ae>=0&&Ae<m){const Me=p[Y][oe][Ae][J]+u[ue][de][J];Me>ie&&(ie=Me,ne=ue,le=de)}}}P[ne][le][J]+=M[Y][q][H][J]}}}const B=c.write(N.toTypedArray(P,n.dtype),i.shape,i.dtype);return{dataId:B,shape:i.shape,dtype:i.dtype}}};const RC={kernelName:Oa,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=N.toNestedArray(n.shape,c.data.get(n.dataId).values),u=N.toNestedArray(i.shape,c.data.get(i.dataId).values),{batchSize:h,inHeight:d,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:w,strideHeight:x,strideWidth:T,filterHeight:A,filterWidth:_,dilationHeight:E,dilationWidth:F,outShape:D}=U.computeDilation2DInfo(n.shape,i.shape,o,a,"NHWC",l);N.assert(r.rank===D.length,()=>`Error in ${Oa}, dy must have the same rank as output ${D.length}, but got ${r.rank}`);const M=N.toNestedArray(D,c.data.get(r.dataId).values),P=N.makeZerosNestedTypedArray(n.shape,n.dtype);for(let Y=0;Y<h;++Y)for(let q=0;q<g;++q){const K=q*x-w.top;for(let H=0;H<y;++H){const Q=H*T-w.left;for(let J=0;J<f;++J){let ie=Number.MIN_SAFE_INTEGER,ne=K<0?0:K,le=Q<0?0:Q;for(let ue=0;ue<A;++ue){const oe=K+ue*E;if(oe>=0&&oe<d)for(let de=0;de<_;++de){const Ae=Q+de*F;if(Ae>=0&&Ae<m){const Me=p[Y][oe][Ae][J]+u[ue][de][J];Me>ie&&(ie=Me,ne=oe,le=Ae)}}}P[Y][ne][le][J]+=M[Y][q][H][J]}}}const B=c.write(N.toTypedArray(P,n.dtype),n.shape,n.dtype);return{dataId:B,shape:n.shape,dtype:n.dtype}}};const ij=Bs((e,t)=>e/t),rj=An(Jn,ij),fu={kernelName:Jn,backendName:"cpu",kernelFunc:rj};const oj=fe(hr,e=>e>=0?e:Math.exp(e)-1),OC={kernelName:hr,backendName:"cpu",kernelFunc:oj};const aj=U.ERF_P,lj=U.ERF_A1,cj=U.ERF_A2,pj=U.ERF_A3,uj=U.ERF_A4,hj=U.ERF_A5,dj=fe(dr,e=>{const t=Math.sign(e),s=Math.abs(e),n=1/(1+aj*s);return t*(1-((((hj*n+uj)*n+pj)*n+cj)*n+lj)*n*Math.exp(-s*s))}),EC={kernelName:dr,backendName:"cpu",kernelFunc:dj};function ff(e,t,s){const n=e.shape,i=n[0],r=n[1],o=s.data.get(e.dataId),a=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,c=[i,r],p=N.sizeFromShape(c),u=N.getTypedArrayFromDType("float32",p),h=N.getTypedArrayFromDType("float32",p);for(let g=0;g<i;g++){const y=hf({inputs:{x:a},backend:s,attrs:{begin:[g,0],size:[1,r]}}),w=hf({inputs:{x:l},backend:s,attrs:{begin:[g,0],size:[1,r]}}),x=Rs({inputs:{real:y,imag:w},backend:s}),{real:T,imag:A}=mj(x,t,s),_=U.mergeRealAndImagArrays(T,A);for(let E=0;E<r;E++){const F=U.getComplexWithIndex(_,E);u[g*r+E]=F.real,h[g*r+E]=F.imag}s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(w),s.disposeIntermediateTensorInfo(x)}const d=s.makeTensorInfo(c,"float32",u),m=s.makeTensorInfo(c,"float32",h),f=Rs({inputs:{real:d,imag:m},backend:s});return s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(m),f}function mj(e,t,s){const n=N.sizeFromShape(e.shape),i=s.data.get(e.dataId),r=s.data.get(i.complexTensorInfos.real.dataId).values,o=s.data.get(i.complexTensorInfos.imag.dataId).values;if(fj(n)){const a=_w(r,o,n,t,s),l=[e.shape[0],e.shape[1]];if(t){const c=s.makeTensorInfo(l,"float32",a.real),p=s.makeTensorInfo(l,"float32",a.imag),u=s.makeTensorInfo([],"float32",N.createScalarValue(n,"float32")),h=fi({inputs:{x:u},backend:s}),d=fu.kernelFunc({inputs:{a:c,b:u},backend:s}),m=fu.kernelFunc({inputs:{a:p,b:h},backend:s}),f=s.data.get(d.dataId).values,g=s.data.get(m.dataId).values;return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(p),s.disposeIntermediateTensorInfo(u),s.disposeIntermediateTensorInfo(h),s.disposeIntermediateTensorInfo(d),s.disposeIntermediateTensorInfo(m),{real:f,imag:g}}return a}else{const a=U.mergeRealAndImagArrays(r,o),l=gj(a,n,t);return U.splitRealAndImagArrays(l)}}function fj(e){return(e&e-1)===0}function _w(e,t,s,n,i){if(s===1)return{real:e,imag:t};const r=U.mergeRealAndImagArrays(e,t),o=s/2,a=U.complexWithEvenIndex(r),l=a.real,c=a.imag,p=[l.length],u=i.makeTensorInfo(p,"float32",l),h=i.makeTensorInfo(p,"float32",c),d=Rs({inputs:{real:u,imag:h},backend:i}),m=U.complexWithOddIndex(r),f=m.real,g=m.imag,y=[f.length],w=i.makeTensorInfo(y,"float32",f),x=i.makeTensorInfo(y,"float32",g),T=Rs({inputs:{real:w,imag:x},backend:i}),A=_w(l,c,o,n,i),_=A.real,E=A.imag,F=[_.length],D=i.makeTensorInfo(F,"float32",_),M=i.makeTensorInfo(F,"float32",E),P=Rs({inputs:{real:D,imag:M},backend:i}),B=_w(f,g,o,n,i),Y=B.real,q=B.imag,K=[Y.length],H=i.makeTensorInfo(K,"float32",Y),Q=i.makeTensorInfo(K,"float32",q),J=Rs({inputs:{real:H,imag:Q},backend:i}),ie=U.exponents(s,n),ne=[ie.real.length],le=i.makeTensorInfo(ne,"float32",ie.real),ue=i.makeTensorInfo(ne,"float32",ie.imag),oe=Rs({inputs:{real:le,imag:ue},backend:i}),de=Tw({inputs:{a:oe,b:J},backend:i}),Ae=bw({inputs:{a:P,b:de},backend:i}),Me=Rw({inputs:{a:P,b:de},backend:i}),Qe=ao({inputs:{input:Ae},backend:i}),St=ao({inputs:{input:Me},backend:i}),$t=$l({inputs:{input:Ae},backend:i}),_s=$l({inputs:{input:Me},backend:i}),yt=Wl({inputs:[Qe,St],backend:i,attrs:{axis:0}}),ps=Wl({inputs:[$t,_s],backend:i,attrs:{axis:0}}),_n=i.data.get(yt.dataId).values,Gs=i.data.get(ps.dataId).values;return i.disposeIntermediateTensorInfo(u),i.disposeIntermediateTensorInfo(h),i.disposeIntermediateTensorInfo(d),i.disposeIntermediateTensorInfo(w),i.disposeIntermediateTensorInfo(x),i.disposeIntermediateTensorInfo(T),i.disposeIntermediateTensorInfo(D),i.disposeIntermediateTensorInfo(M),i.disposeIntermediateTensorInfo(P),i.disposeIntermediateTensorInfo(H),i.disposeIntermediateTensorInfo(Q),i.disposeIntermediateTensorInfo(J),i.disposeIntermediateTensorInfo(le),i.disposeIntermediateTensorInfo(ue),i.disposeIntermediateTensorInfo(oe),i.disposeIntermediateTensorInfo(de),i.disposeIntermediateTensorInfo(Ae),i.disposeIntermediateTensorInfo(Me),i.disposeIntermediateTensorInfo(Qe),i.disposeIntermediateTensorInfo($t),i.disposeIntermediateTensorInfo(St),i.disposeIntermediateTensorInfo(_s),i.disposeIntermediateTensorInfo(yt),i.disposeIntermediateTensorInfo(ps),{real:_n,imag:Gs}}function gj(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}function yj(e){const{inputs:t,backend:s}=e,{input:n}=t,i=N.sizeFromShape(n.shape),r=n.shape[n.shape.length-1],o=i/r,a=Rn({inputs:{x:n},backend:s,attrs:{shape:[o,r]}}),l=ff(a,!1,s),c=Rn({inputs:{x:l},backend:s,attrs:{shape:n.shape}});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(l),c}const _C={kernelName:Oc,backendName:"cpu",kernelFunc:yj};const kC={kernelName:So,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,i=s,r=N.getTypedArrayFromDType(n.dtype,N.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 g=0;g<l;g++){const y=g*c;for(let w=0;w<c;w++){const x=[o,m,g,w],T=x[2],A=Math.round(l-T),_=d+f+y+w;let E=p[_];if(A>=0&&A<l){const F=A*c,D=d+f+F+w;E=p[D]}r[_]=E}}}}const u=i.write(r,n.shape,n.dtype);return{dataId:u,shape:n.shape,dtype:n.dtype}}};function bj(e){const{inputs:t,backend:s}=e,{input:n}=t,i=N.sizeFromShape(n.shape),r=n.shape[n.shape.length-1],o=i/r,a=Rn({inputs:{x:n},backend:s,attrs:{shape:[o,r]}}),l=ff(a,!0,s),c=Rn({inputs:{x:l},backend:s,attrs:{shape:n.shape}});return s.disposeIntermediateTensorInfo(a),s.disposeIntermediateTensorInfo(l),c}const DC={kernelName:Dc,backendName:"cpu",kernelFunc:bj};const wj=fe(yr,e=>Number.isFinite(e)?1:0,"bool"),FC={kernelName:yr,backendName:"cpu",kernelFunc:wj};const xj=fe(br,e=>Math.abs(e)===Infinity?1:0,"bool"),MC={kernelName:br,backendName:"cpu",kernelFunc:xj};const Lj=fe(wr,e=>Number.isNaN(e)?1:0,"bool"),UC={kernelName:wr,backendName:"cpu",kernelFunc:Lj};const Sj=fe(Lr,e=>Math.log1p(e)),$C={kernelName:Lr,backendName:"cpu",kernelFunc:Sj};const Ij=fe(_a,e=>e?0:1,"bool"),WC={kernelName:_a,backendName:"cpu",kernelFunc:Ij};const zC={kernelName:Ai,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{reductionIndices:i,keepDims:r}=t,o=s;let a=n.shape;const l=a.length,c=N.parseAxisParam(i,a);let p=c;const u=U.getAxesPermutation(p,l);let h=o.data.get(n.dataId).values;if(u!=null){const x=new Array(l);for(let T=0;T<x.length;T++)x[T]=a[u[T]];h=Ml(h,a,n.dtype,u,x),p=U.getInnerMostAxes(p.length,l),a=x}ee(n,"max"),U.assertAxesAreInnerMostDims("max",p,l);const[d,m]=U.computeOutAndReduceShapes(a,p),f=N.sizeFromShape(m),g=uf(h,f,d,n.dtype),y=o.write(g,d,n.dtype);let w=d;if(r){const x=U.expandShapeToKeepDim(d,c);w=x}return{dataId:y,shape:w,dtype:n.dtype}}};function vj(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;ee(i,"maxPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);let u;if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))u=fi({inputs:{x:i},backend:s});else{const h=s.data.get(i.dataId).values,d=N.computeStrides(i.shape),m=Ul(h,i.shape,i.dtype,d,p,"max");u=s.makeTensorInfo(p.outShape,i.dtype,m.values)}return u}const PC={kernelName:Ni,backendName:"cpu",kernelFunc:vj};function Tj(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r,output:o}=t,a=r;ee([r,o],"maxPoolBackprop");const{filterSize:l,strides:c,pad:p,dimRoundingMode:u}=n,h=U.computePool2DInfo(a.shape,l,c,1,p,u),d=s.data.get(a.dataId).values,m=ge(h.outShape,a.dtype,mf(d,a.shape,a.dtype,h).values),f=h.strideHeight,g=h.strideWidth,y=h.dilationHeight,w=h.dilationWidth,x=h.effectiveFilterHeight,T=h.effectiveFilterWidth,A=T-1-h.padInfo.left,_=x-1-h.padInfo.top,E=ge(a.shape,"float32"),F=s.data.get(i.dataId).values,D=ge(i.shape,"float32",F);for(let M=0;M<h.batchSize;++M)for(let P=0;P<h.inChannels;++P)for(let B=0;B<h.inHeight;++B)for(let Y=0;Y<h.inWidth;++Y){const q=B-_,K=Y-A;let H=0;for(let Q=0;Q<x;Q+=y){const J=(q+Q)/f;if(J<0||J>=h.outHeight||Math.floor(J)!==J)continue;for(let ie=0;ie<T;ie+=w){const ne=(K+ie)/g;if(ne<0||ne>=h.outWidth||Math.floor(ne)!==ne)continue;const le=x*T-1-m.get(M,J,ne,P),ue=Q*T+ie,oe=le===ue?1:0;if(oe===0)continue;const de=D.get(M,J,ne,P);H+=de*oe}}E.set(H,M,B,Y,P)}return s.makeTensorInfo(E.shape,E.dtype,E.values)}const BC={kernelName:Io,backendName:"cpu",kernelFunc:Tj};function jC(e,t,s,n,i){const r=N.computeStrides(t),o=Ul(e,t,s,r,i,"max"),a=mf(e,t,s,i,!0,n);return[o.values,a.values]}const VC={kernelName:vo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{filterSize:i,strides:r,pad:o,includeBatchInIndex:a}=t,l=s;ee(n,"MaxPoolWithArgmax");const 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ve=W();ve.registerFlag("HAS_WEBGL",()=>ve.getNumber("WEBGL_VERSION")>0);ve.registerFlag("WEBGL_VERSION",()=>zw(2)?2:zw(1)?1:0);ve.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ve.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ve.get("WEBGL_VERSION")===2);ve.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ve.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ve.registerFlag("WEBGL_PACK",()=>ve.getBool("HAS_WEBGL"));ve.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_CLIP",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ve.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_REDUCE",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_LAZILY_UNPACK",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_CONV_IM2COL",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>R0(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>O0(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const e=ve.getNumber("WEBGL_VERSION");return e===0?0:E0(e)});ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ve.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!ja.isMobile());ve.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>_0(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ve.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ve.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ve.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>k0(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_FENCE_API_ENABLED",()=>D0(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const e=ve.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return e?4:0});ve.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});const{simpleAbsImpl:F0,addImpl:M0,ceilImpl:U0,expImpl:$0,expm1Impl:W0,floorImpl:z0,logImpl:P0,maxImpl:B0,multiplyImpl:j0,rsqrtImpl:V0,sliceImpl:G0,subImpl:q0,transposeImpl:Lf,uniqueImpl:H0}=Ow;class Y0{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,r)=>`T${r}`);const s=[];this.variableNames.forEach(i=>{s.push(`float v${i} = get${i}AtOutCoords();`)});const n=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
      void main() {
        ${s.join(`
        `)}

        float result = ${n};
        setOutput(result);
      }
    `}}class K0{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((i,r)=>`T${r}`);const s=[];this.variableNames.forEach(i=>{s.push(`vec4 v${i} = get${i}AtOutCoords();`)});const n=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
      void main() {
        ${s.join(`
        `)}

        vec4 result = ${n};
        setOutput(result);
      }
    `}}class X0{constructor(e,t,s){this.variableNames=["A"];const{windowSize:n,batchSize:i,outSize:r}=e;s||this.variableNames.push("bestIndicesA"),this.outputShape=[i,r];const o=t==="max"?">":"<",a=s?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${n}; i++) {
          int inIdx = ${a};
          float candidate = getA(batch, inIdx);
          if (candidate ${o} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}}function Bw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(s=>`${e}.${s}`)}function _t(e,t){return t===1?[e]:Bw(e,t)}function J0(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 at(){let e,t,s,n,i,r,o,a,l,c;return W().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 jn(e,t,s="index"){const n=N.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 Pl(e){const t=N.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:Z0}=U;function Q0(e,t,s,n){const i=[];e.forEach(m=>{const f=N.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=>c3(m,t,n)).join(`
`),a=t.texShape,l=at(),c=h3(l);let p,u,h=f3(l);t.isPacked?(p=p3(t.logicalShape,a),u=m3(l)):(p=u3(t.logicalShape,a),u=d3(l)),n&&(h+=g3);const d=[h,c,u,r,p,o,s].join(`
`);return d}function Bl(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return y3(e);case 1:return b3(e);case 2:return w3(e);case 3:return x3(e);case 4:return L3(e);case 5:return S3(e);case 6:return I3(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function eR(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return v3(e);case 1:return T3(e);case 2:return A3(e);case 3:return N3(e);default:return C3(e)}}function c3(e,t,s=!1){let n="";s?n+=eR(e):n+=Bl(e);const i=e.shapeInfo.logicalShape,r=t.logicalShape;return i.length<=r.length&&(s?n+=R3(e,t):n+=O3(e,t)),n}function p3(e,t){switch(e.length){case 0:return tR();case 1:return E3(e,t);case 2:return D3(e,t);case 3:return _3(e,t);default:return k3(e,t)}}function u3(e,t){switch(e.length){case 0:return tR();case 1:return F3(e,t);case 2:return z3(e,t);case 3:return M3(e,t);case 4:return U3(e,t);case 5:return $3(e,t);case 6:return W3(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function h3(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function d3(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function m3(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function f3(e){const t=`${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);
    }

    ${P3}
    ${B3}
    ${j3}
  `;return t}const P3=`
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);
}
`,B3=`
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);
}
`,j3=`
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);
}
`,g3=`
  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 tR(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function E3(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 F3(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 * ${t[1]} + resTexRC.y;
    }
  `}function _3(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[2]/2),i=n*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${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 M3(e,t){const s=jn(["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;
      ${s}
      return ivec3(r, c, d);
    }
  `}function k3(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],n=Math.ceil(e[e.length-1]/2),i=n*Math.ceil(e[e.length-2]/2);let r=i,o="",a="b, r, c";for(let l=2;l<e.length-1;l++)r*=e[e.length-l-1],o=`
      int b${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 U3(e,t){const s=jn(["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 $3(e,t){const s=jn(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${s}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function W3(e,t){const s=jn(["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 D3(e,t){const s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(N.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 z3(e,t){return N.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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function ua(e){return`offset${e}`}function v3(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1),n=at();return`
    vec4 ${s}() {
      return ${n.texture2D}(${t}, halfCR);
    }
  `}function y3(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=ua(t);return`
    float ${s}() {
      vec2 uv = uvFromFlat(${r}, ${o}, ${a});
      return sampleTexture(${t}, uv);
    }
  `}function T3(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=at();return`
    vec4 ${s}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${r.texture2D}(${t}, uv);
    }
  `}function b3(e){const t=e.name,s="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${s}(int index) {
        ${jl(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=ua(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 A3(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=at();if(i!=null&&N.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 w3(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&&N.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}=N.squeezeShape(t),a=r;if(a.length<t.length){const u=Vl(e,a),h=["row","col"];return`
      ${Bl(u)}
      float ${n}(int row, int col) {
        return ${n}(${Gl(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${jl(e)}
      }
    `;const l=i[0],c=i[1],p=ua(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 N3(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=Vl(e,u),m=["b","row","col"];return`
        ${eR(d)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${Gl(m,h)});
        }
      `}const o=r[0],a=r[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),p=at();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 x3(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}=N.squeezeShape(t),l=o;if(l.length<t.length){const m=Vl(e,l),f=["row","col","depth"];return`
        ${Bl(m)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${Gl(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)));
        ${jl(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=ua(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 C3(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=at();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 L3(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}=N.squeezeShape(t);if(a.length<t.length){const m=Vl(e,a),f=["row","col","depth","depth2"];return`
      ${Bl(m)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${Gl(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)));
        ${jl(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=ua(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 S3(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}=N.squeezeShape(t);if(l.length<t.length){const f=Vl(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${Bl(f)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${Gl(g,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;
        ${jl(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=ua(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 I3(e){const t=e.shapeInfo.logicalShape,s=e.name,n="get"+s.charAt(0).toUpperCase()+s.slice(1),{newShape:i,keptDims:r}=N.squeezeShape(t);if(i.length<t.length){const g=Vl(e,i),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${Bl(g)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${Gl(y,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)));
        ${jl(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=ua(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 jl(e){const t=e.name,s=N.sizeFromShape(e.shapeInfo.logicalShape);return s<2?`return ${t};`:`
    for (int i = 0; i < ${s}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function R3(e,t){const s=e.name,n=s.charAt(0).toUpperCase()+s.slice(1),i="get"+n+"AtOutCoords",r=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,a=Z0(e.shapeInfo.logicalShape,t.logicalShape),l=Re(o),c=o-r;let p;const u=["x","y","z","w","u","v"];r===0?p="":o<2&&a.length>=1?p="coords = 0;":p=a.map(w=>`coords.${u[w+c]} = 0;`).join(`
`);let h="";o<2&&r>0?h="coords":h=e.shapeInfo.logicalShape.map((w,x)=>`coords.${u[x+c]}`).join(", ");let d="return outputValue;";const m=N.sizeFromShape(e.shapeInfo.logicalShape),f=m===1,g=N.sizeFromShape(t.logicalShape),y=g===1;if(r===1&&!f&&!y)d=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(f&&!y)o===1?d=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:d=`
        return vec4(outputValue.x);
      `;else if(a.length){const w=r-2,x=r-1;a.indexOf(w)>-1&&a.indexOf(x)>-1?d="return vec4(outputValue.x);":a.indexOf(w)>-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 O3(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&&N.arraysEqual(o,r))return`
      float ${i}() {
        return sampleTexture(${s}, resultUV);
      }
    `;const c=Re(l),p=Z0(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.${d[f+u]} = 0;`).join(`
`);let m="";return l<2&&a>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${d[g+u]}`).join(", "),`
    float ${i}() {
      ${c} coords = getOutputCoords();
      ${h}
      return get${n}(${m});
    }
  `}function Re(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 ${e} is not yet supported`)}function Vl(e,t){const s=JSON.parse(JSON.stringify(e));return s.shapeInfo.logicalShape=t,s}function Gl(e,t){return t.map(s=>e[s]).join(", ")}class sR{constructor(e,t,s,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,N.assert(e.length>2,()=>`Packed arg${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=Re(a),c=_t("coords",a);let p,u;if(r===1){u=a+1;const E=Re(u);p=`
        ${E} sourceLocR = ${E}(${c.join()}, 0);
        ++${c[a-1]};
        ${E} sourceLocG = ${E}(${c.join()}, 0);
        ++${c[a-2]};
        ${E} sourceLocA = ${E}(${c.join()}, 0);
        --${c[a-1]};
        ${E} sourceLocB = ${E}(${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(E=>"int "+E),f=_t("sourceLocR",u-1).concat("inIdx.r"),g=_t("sourceLocG",u-1).concat("inIdx.g"),y=_t("sourceLocB",u-1).concat("inIdx.b"),w=_t("sourceLocA",u-1).concat("inIdx.a"),x=s==="max"?"greaterThan":"lessThan",T=n?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${w.join()})));`,A=`vec4(
            getAChannel(${f.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${y.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,_=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()}));
      }
      ${_}
      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;
          ${T}
          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 nR{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 iR{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,g=1/(t*s*n);this.userCode=`
      const ivec3 pads = ivec3(${d}, ${m}, ${f});
      const float avgMultiplier = float(${g});

      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);
      }
    `}}const jw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class Vw{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 rR=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,If="return a + b;",vf="return a - b;",Gw="return a * b;",oR=`
  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;
  }
`,aR=`
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);
`,lR="return float(a == b);",cR="return float(a != b);",pR="return float(a < b);",uR="return float(a <= b);",hR="return float(a > b);",dR="return float(a >= b);",mR="return float(a >= 1.0 && b >= 1.0);",fR="return float(a >= 1.0 || b >= 1.0);",gR=rR+`
  return max(a, b);
`,yR=rR+`
  return min(a, b);
`,bR=`if (b == 0.0) return NAN;
  return mod(a, b);`,wR="return (b >= 1.0) ? a : a * (b + 1.0);",qw="return (a < 0.) ? b * a : a;";class kt{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 Tf=`
  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;
`,xR=`
  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);
`,LR=`
  // 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));
  `+Tf+`
  return result;
`,Hw=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,SR=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,IR=`
  return vec4(equal(a, b));
`,vR=`
  return vec4(notEqual(a, b));
`,TR=`
  return vec4(lessThan(a, b));
`,AR=`
  return vec4(lessThanEqual(a, b));
`,NR=`
  return vec4(greaterThan(a, b));
`,CR=`
  return vec4(greaterThanEqual(a, b));
`,RR=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,OR=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,ER=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Tf+`
  return result;
`,_R=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Tf+`
  return result;
`,kR=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Tf+`
  return result;
`;class bi{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||N.sizeFromShape(this.outputShape)===1)r=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else{const o=Re(i);if(r=`
          ${o} coords = getOutputCoords();
        `,i===1)r+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{const a=_t("coords",i);r+=`
            bool nextRowOutOfBounds =
              (${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 DR{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 FR{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 MR{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 UR{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 $R{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=U.computeOutShape(e,t);const s=this.outputShape,n=s.length,i=Re(n),r=_t("coords",n),o=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((m,f)=>`T${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}(${Af(o,l,f)}),
            vec2(${Af(c,l,f)}));
        }`}const h=a.length,d=a[a.length-1];u+=`
        return getChannel(
          getT${h}(${Af(o,l,d)}),
          vec2(${Af(c,l,d)}));`,this.userCode=`
      float getValue(${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 Af(e,t,s){const n=e.indexOf(t),i=e.map((r,o)=>o===n?`${r} - ${s}`:r);return i.join()}class WR{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 zR{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 PR{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 BR{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 jR{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 VR{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 Yw{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,g=m?2:3,y=m?3:1;let w="",x="";s&&(n?w=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:w=`
          float activation(float x) {
            ${s}
          }
        `,x="result = activation(result);");const T=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${w}

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

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

        ivec2 xRCCorner =
            ivec2(coords[${f}], coords[${g}]) * 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;
        ${T}
        ${x}
        setOutput(result);
      }
    `}}class GR{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,s=e.padInfo.top,n=e.padInfo.left,i=e.strideDepth,r=e.strideHeight,o=e.strideWidth,a=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,u=e.filterHeight,h=e.filterWidth,d=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${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 Kw{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="",g="";s&&(n?f=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:f=`
          float activation(float x) {
            ${s}
          }
        `,g="result = activation(result);");const y=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;
        ${y}
        ${g}
        setOutput(result);
      }
    `}}class Xw{constructor(e,t=!1,s=null,n=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,r=e.inWidth,o=e.padInfo.top,a=e.padInfo.left,l=e.strideHeight,c=e.strideWidth,p=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,m=d;let f="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let T=0;T<d;T++)f+=`
          vec4 xTexelR${x}C${T*2} = vec4(0.);
          vec4 wR${x}C${T} = vec4(0.);
          vec4 xR${x}C${T} = vec4(0.);`;for(let x=0;x<h;x++)for(let T=0;T<m;T++){const A=T*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 _=a%2===0?N.nearestLargerEven(u):u;u%2===0&&a%2===1||u%2!==0&&a%2!==1?(f+=`
                  xCOffset = xC + ${a%2} + ${_};

                  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 + ${_};

                  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${A+1}.xz);`))}for(let x=0;x<h;x++)for(let T=0;T<d;T++)f+=`dotProd += xR${x}C${T} * wR${x}C${T};`;let g="",y="";s&&(n?g=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${s}
        }`:g=`vec4 activation(vec4 x) {
          ${s}
        }`,y="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${g}

      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;
        ${w}
        ${y}
        setOutput(result);
      }
    `}}class qR{constructor(e,t,s,n,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[r,o,a,l]=e,[c]=t,[p,u]=s;this.outputShape=[c,p,u,l];const h=n==="bilinear"?1:0,[d,m]=[`${o-1}.0`,`${a-1}.0`],[f,g,y]=p>1?[`${(o-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,x,T]=u>1?[`${(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(${w});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${r}) {
          return;
        }

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

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${i}));
          return;
        }
        float in_x = ${T};
        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 Jw{constructor(e,t,s){this.variableNames=["x"],this.outputShape=e;const n=e.length,i=t?"0.0":`getX(${HR(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() {
        ${Re(n)} coords = getOutputCoords();
        int end = ${YR(n,"coords")};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${o}) {
          int idx = ${a};
          ${YR(n,"coords")} = idx;
          val += getX(${HR(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 HR(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 ${e} is not yet supported`)}function YR(e,t){if(e===1)return`${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 KR{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=lo.DENSE;const t=co(e),s=at();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${jn(["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 XR{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=lo.DENSE;const t=co(e),s=at();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${jn(["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 JR{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 ZR{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 QR{constructor(e){this.variableNames=["A"],this.outTexUsage=ns.DOWNLOAD;const t=at();this.outputShape=e,this.userCode=`
      ${Sf}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}}class e2{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ns.DOWNLOAD;const t=at();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 t2{constructor(e,t,s=!1){this.variableNames=["A"];const n=at(),[i,r]=t;this.outputShape=e;let o="result";s&&(o="floor(result * 255. + 0.5)"),this.userCode=`
      ${Pl(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 s2{constructor(e,t,s=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const n=at(),[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=`
      ${Pl(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 Zw={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class Qw{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 n2{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 i2{constructor(e,t,s){this.variableNames=["A","indices"];const n=e.slice();n[s]=t,this.outputShape=n,this.rank=n.length;const i=Re(this.rank),r=V3(e,s);this.userCode=`
      void main() {
        ${i} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}}function V3(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 r2{constructor(e,t,s){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=s;const n=Re(t.length),i=Re(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 * ${r};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}}function o2(e){const t=at(),s=`${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 g0(e,s)}function a2(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 x0(e,t)}function l2(e){const t=new Uint16Array([0,1,2,2,1,3]);return L0(e,t)}function wu(e,t,s,n,i,r){I0(t,s);const o=S0(e),a=e.TEXTURE_2D;return pe(e,()=>e.bindTexture(a,o)),pe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(a,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),pe(e,()=>e.texParameteri(a,e.TEXTURE_MIN_FILTER,e.NEAREST)),pe(e,()=>e.texParameteri(a,e.TEXTURE_MAG_FILTER,e.NEAREST)),pe(e,()=>e.texImage2D(a,0,n,t,s,0,i,r,null)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function ex(e){return e.internalFormatFloat}function c2(e,t,s,n){const[i,r]=pa(t,s);return wu(e,i,r,ex(n),n.textureFormatFloat,e.FLOAT)}function tx(e){return e.internalFormatHalfFloat}function p2(e,t,s,n){const[i,r]=pa(t,s);return wu(e,i,r,tx(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function sx(e){return e.downloadTextureFormat}function u2(e,t,s,n){const[i,r]=pa(t,s);return wu(e,i,r,sx(n),e.RGBA,e.UNSIGNED_BYTE)}function nx(e){return e.internalFormatPackedFloat}function h2(e,t,s,n){const[i,r]=gi(t,s);return wu(e,i,r,nx(n),e.RGBA,e.FLOAT)}function ix(e){return e.internalFormatPackedHalfFloat}function d2(e,t,s,n){const[i,r]=gi(t,s);return wu(e,i,r,ix(n),e.RGBA,n.textureTypeHalfFloat)}function m2(e,t,s){const n=0,i=3*4,r=3*4+2*4;pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s));const o=Mw(e,t,"clipSpacePos",s,3,r,n);return o&&Mw(e,t,"uv",s,2,r,i)}function f2(e,t,s,n,i,r){pe(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),pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,s,n,0,e.RGBA,a,o)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function g2(e,t,s){pe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),s.data instanceof Uint8Array?pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,s.width,s.height,0,e.RGBA,e.UNSIGNED_BYTE,s.data)):pe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,s)),pe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function y2(e,t,s,n){const i=e.createBuffer();pe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const r=4,o=4,a=r*o*t*s;return pe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ)),pe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,0)),pe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function b2(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 w2(e,t,s,n){const[i,r]=pa(t,s),o=4,a=new Uint8Array(d0(t*s,o));return pe(e,()=>e.readPixels(0,0,i,r,n.downloadTextureFormat,e.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function x2(e,t,s,n,i,r,o,a){const l=e,c=new Float32Array(m0(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 L2(e,t,s){const n=new Float32Array(t*s*4);return pe(e,()=>e.readPixels(0,0,s,t,e.RGBA,e.FLOAT,n)),n}class rx{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Fw(t,e)):this.gl=nn(t);let s="WEBGL_color_buffer_float";const n="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",r="OES_texture_half_float";if(this.textureFloatExtension=yu(this.gl,i),rn(this.gl,r))this.textureHalfFloatExtension=yu(this.gl,r);else if(W().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),rn(this.gl,n))this.colorBufferHalfFloatExtension=yu(this.gl,n);else if(W().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",rn(this.gl,s))this.colorBufferFloatExtension=this.gl.getExtension(s);else if(rn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=a2(this.gl),this.indexBuffer=l2(this.gl),this.framebuffer=v0(this.gl),this.textureConfig=gu(this.gl,this.textureHalfFloatExtension)}get debug(){return W().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;pe(e,()=>e.finish()),pe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),pe(e,()=>e.deleteFramebuffer(this.framebuffer)),pe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),pe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),pe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),c2(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p2(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),u2(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),g2(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,s,n){this.throwIfDisposed(),f2(this.gl,e,t,s,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),d2(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),h2(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Uw(this.gl,this.framebuffer),this.outputTexture=null),pe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,s){return this.downloadMatrixDriver(e,()=>w2(this.gl,t,s,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,s,n,i,r){return x2(this.gl,e,t,s,n,i,r,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return b2(this.gl,e,t)}createBufferFromTexture(e,t,s){this.bindTextureToFrameBuffer(e);const n=y2(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(W().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 W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),s=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):s=()=>!0;return{query:t,isFencePassed:s}}downloadMatrixFromPackedTexture(e,t,s){return this.downloadMatrixDriver(e,()=>L2(this.gl,t,s))}createProgram(e){this.throwIfDisposed();const t=this.gl,s=y0(t,e),n=o2(t),i=b0(t);return pe(t,()=>t.attachShader(i,n)),pe(t,()=>t.attachShader(i,s)),w0(t,i),this.debug&&yf(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=m2(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&pe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&yf(this.gl,this.program),pe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,s=!0){return this.throwIfDisposed(),s?T0(this.gl,e,t):A0(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),pe(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(),N0(this.gl,e,t,s)}setOutputMatrixTexture(e,t,s){this.setOutputMatrixTextureDriver(e,s,t)}setOutputPackedMatrixTexture(e,t,s){this.throwIfDisposed();const[n,i]=gi(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&&yf(this.gl,this.program),bu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),pe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),pe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yu(this.gl,W().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(W().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(W().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 N.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().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=G3(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;N.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bf(this.gl,e,this.framebuffer),this.debug&&bu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bf(this.gl,this.outputTexture,this.framebuffer),this.debug&&bu(this.gl)):Uw(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;bf(n,e,this.framebuffer),this.debug&&bu(n),this.outputTexture=e,pe(n,()=>n.viewport(0,0,t,s)),pe(n,()=>n.scissor(0,0,t,s))}setOutputMatrixWriteRegionDriver(e,t,s,n){this.throwIfDisposed(),pe(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 G3(e){let t=0;for(;t<e.length;++t){const s=e[t]();if(!s)break}return t-1}function S2(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=Q0(r,a,i,t.packedInputs),c=e.createProgram(l);let p=null;const u=e.getUniformLocation(c,"NAN",!1);W().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 I2(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(!N.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(!N.arraysEqual(a,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${l} must match`)})}function v2(e,t,s,n,i){I2(t.inShapeInfos,s),I2([t.outShapeInfo],[n]);const r=n.texData.texture,o=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(r,o[0],o[1]):e.setOutputMatrixTexture(r,o[0],o[1]),e.setProgram(t.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),s.forEach((a,l)=>{const c=t.program.variableNames[l],p=t.uniformLocations[c],u=t.uniformLocations[`offset${c}`];if(p==null)return;if(a.isUniform){if(N.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 T2(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 A2{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=at(),g=u==="channelsLast",y=g?0:1,w=g?1:2;let x="";for(let T=0;T<=1;T++)for(let A=0;A<=1;A++)x+=`
          blockIndex = rc.y + ${A};
          pos = rc.x + ${T};

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

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

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

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

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

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

        ${f.output} = result;
      }
    `}}class N2{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[];const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${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 C2{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 * ${i});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}}class R2{constructor(e,t,s,n,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const r=t,o=e[3]-1;this.outputShape=e;let a;const l=`float(${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 * ${a};
        setOutput(result);
      }
    `}}class O2{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,s=e.strideWidth,n=e.dilationHeight,i=e.effectiveFilterHeight,r=e.effectiveFilterWidth,o=i-1-e.padInfo.top,a=r-1-e.padInfo.left,l=i*r-1;this.userCode=`
      const ivec2 pads = ivec2(${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 E2{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 Nf{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 _2{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(${t-1}));
      }
    `}getCustomSetupFunc(e){return(t,s)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(s,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}class k2{constructor(e,t,s,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${s}),
                      float(index == coords.y)));
      }
    `}}class D2{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=_t("rc",t),n=Re(t),i=q3(t,e,s),r=H3(t,e[e.length-1],e[e.length-2],s),o=Y3(e,s);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

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

            setOutput(vec4(${o}));
          }
        }
      `}}}function K3(e,t){const s=[];for(let n=0;n<=1;n++)for(let i=0;i<=1;i++){let r=`${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 q3(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 H3(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 Y3(e,t){const s=e.length,n=K3(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 F2{constructor(e,t,s){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);const n=e.length,i=Re(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 M2{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=Re(n),r=t.map(m=>m[0]).join(","),o=t.map((m,f)=>m[0]+e[f]).join(","),a=_t("rc",n),l=_t("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 Ki{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`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(m||(y="-1.0 / 1e-20"),s){const E=">=";this.userCode=`
        const ivec2 strides = ivec2(${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 ${E} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${n?i?f:g:`wR * ${u} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const w="max";let x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");const T=Math.floor(r/4)*4,A=r%4,_=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${w}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${o}, ${a});
      const ivec2 pads = ivec2(${h}, ${d});
      const float initializationValue = ${y};
      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(${y});
        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 < ${T}; wC += 4) {
            int xC = xCCorner + wC * ${c};

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

            ${_}
          }

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

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

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

            ${_}
          }
        }
        setOutput(${x});
      }
    `}}class Cf{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,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const w=t==="avg";let x="0.0";if(w||(x="-1.0 / 1e-20"),s){const D=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${o}, ${a}, ${l});
        const ivec3 pads = ivec3(${f}, ${g}, ${y});

        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 ${D} 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 * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const T="max";let A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");const _=Math.floor(r/4)*4,E=r%4,F=`
      if (${w}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${T}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${a}, ${l});
      const ivec3 pads = ivec3(${f}, ${g}, ${y});
      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 < ${_}; 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)
              );

              ${F}
            }

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

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

              ${F}
            } else if (${E===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
              );

              ${F}
            }
          }
          setOutput(${A});
        }
      }
    `}}class Rf{constructor(e,t){this.variableNames=["x"];const{windowSize:s,batchSize:n,inSize:i,outSize:r}=e;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 Of{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=`
      ${X3(t)}
      ${Pl(e)}

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

        vec4 result = vec4(0.);

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

        ${s}

        setOutput(result);
      }
    `}}function X3(e){const t=jn(["r","c","d"],e);return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t}
      return ivec3(r, c, d);
    }
  `}class U2{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 $2{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 W2{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 z2{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 P2{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 B2{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=Re(s);this.userCode=`
      void main() {
        ${r} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}}class j2{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-${s} tensor is not yet supported`);this.outputShape=e;const n=_t("rc",s),i=`${n[s-1]} + 1 < ${this.outputShape[s-1]}`,r=`${n[s-2]} + 1 < ${this.outputShape[s-2]}`,o=Re(s);s===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${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 = ${p(n.slice())};
            }
          }
          setOutput(result);
        }
    `;function a(d){return u(d)}function l(d){return d[s-1]="("+d[s-1]+" + 1)",u(d)}function c(d){return d[s-2]="("+d[s-2]+" + 1)",u(d)}function p(d){return d[s-1]="("+d[s-1]+" + 1)",d[s-2]="("+d[s-2]+" + 1)",u(d)}function u(d){const m=e.map((y,w)=>h(w,d)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function h(d,m){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${m[d]} - 1`:`${m[d]}`}}}class ox{constructor(e,t,s,n,i,r,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=r;const a=Re(i.length),l=Re(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 V2{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 G2{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=Re(s);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        float cVal = getC(${n});
        if (cVal >= 1.0) {
          setOutput(getA(${i}));
        } else {
          setOutput(getB(${i}));
        }
      }
    `}}class q2{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Re(this.rank),s=`uniform int start[${this.rank}];`,n=J3(this.rank);let i;const r=e.map((o,a)=>`sourceLoc.${ax[a]} = start[${a}] + coords.${ax[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 ax=["x","y","z","w","u","v"];function J3(e){if(e===1)return"sourceLoc";if(e<=6)return ax.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class H2{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Re(this.rank),s=_t("coords",this.rank),n=_t("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${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 (${e.length})`);return(t,s)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(s,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class Y2{constructor(e,t,s){this.variableNames=["x"],this.outputShape=s;const n=s.length,i=Re(s.length),r=Re(s.length);let o="";if(n===1)o="coords * strides + begin";else{let a=0;o=s.map((l,c)=>(a++,s.length===1?`coords * strides[${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 Z2{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=X2(t,s),i=J2(e,n,s);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const r=K2(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===Xt.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Xt.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Xt.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Xt.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Xt.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=X2(s,n),r=J2(t,i,n);r in this.freeTextures||(this.freeTextures[r]=[]);const o=K2(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,n),a=W().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 Z3(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 K2(e,t,s,n,i){const r=Q3(t,n);let o;if(i){const[l,c]=gi(e[0],e[1]);o=l*c}else{const[l,c]=pa(e[0],e[1]);o=l*c}const a=Z3(s,r);return o*a}function Q3(e,t){switch(e){case Xt.PACKED_2X2_FLOAT32:return nx(t);case Xt.PACKED_2X2_FLOAT16:return ix(t);case Xt.UNPACKED_FLOAT32:return ex(t);case Xt.UNPACKED_FLOAT16:return tx(t);case Xt.PACKED_4X1_UNSIGNED_BYTE:return sx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eV(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Xt.PACKED_2X2_FLOAT32:Xt.UNPACKED_FLOAT32:e?Xt.PACKED_2X2_FLOAT16:Xt.UNPACKED_FLOAT16}function X2(e,t){if(e===ns.UPLOAD)return Xt.PACKED_2X2_FLOAT32;if(e===ns.RENDER||e==null)return eV(t);if(e===ns.DOWNLOAD||e===ns.PIXELS)return Xt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function J2(e,t,s){return`${e[0]}_${e[1]}_${t}_${s}`}class Q2{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=Re(this.rank),i=tV(e);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${i}));
      }
    `}}function tV(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 Xi="if (isnan(x)) return x;",eO="return x;",lx="return abs(x);",cx=Xi+`
  return (x < 0.0) ? 0.0 : x;
`,px=Xi+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,ux="return (x >= 0.0) ? x : (exp(x) - 1.0);",tO=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${U.SELU_SCALEALPHA};
  float scale = ${U.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function sO(e=0){return Xi+`
    return x > 0.0 ? 1.0 : float(${e});
  `}const hx="return -x;",dx="return ceil(x);",mx="return floor(x);",nO=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,iO="return float(isnan(x));",rO="return float(isinf(x));",oO="return float(!isnan(x) && !isinf(x));",aO=`
  // 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;
    }
  }
`,fx="return exp(x);",gx="return exp(x) - 1.0;",lO=`if (x < 0.0) return NAN;
  return log(x);`,cO="return log(1.0 + x);",pO="return sqrt(x);",uO="return inversesqrt(x);",hO="return 1.0 / (1.0 + exp(-1.0 * x));",dO=`
  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;
`,mO=Xi+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,fO=Xi+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,gO=Xi+`
  return atan(x);
`,yO=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,bO=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,wO=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,xO=Xi+"return log(x + sqrt(x * x + 1.0));",LO=Xi+`
  if (x < 1.0) return NAN;
  return log(x + sqrt(x * x - 1.0));`,SO=Xi+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IO=`
  // 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));
`,vO="return 1.0 / x;",TO="return float(!(x >= 1.0));",AO="return float(int(x));",xu="return x;";const NO="return x;",CO=`
  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;
`,yx=`
  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;
`,bx=`
  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;
`,wx=`
  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 ql{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 RO{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,s=_t("rc",t),n=Re(t),i=J0(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:OO}=U,sV=vt.split,nV=vt.tile,iV=vt.topkImpl,rV=vt.whereImpl,oV=1e-7,aV=1e-4,Ef={};function lV(e){return e in Ef||(Ef[e]={}),Ef[e]}function _f(e,t=!1){if(e==="linear")return t?NO:eO;if(e==="relu")return t?yx:cx;if(e==="elu")return t?wx:ux;if(e==="relu6")return t?bx:px;if(e==="prelu")return t?Hw:qw;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const cV=128,pV=600;function uV(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*pV/1024/1024}const EO=1e3;class xx extends go{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,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=nn(W().getNumber("WEBGL_VERSION"));this.binaryCache=lV(W().getNumber("WEBGL_VERSION")),this.gpgpu=new rx(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 Z2(this.gpgpu),this.numMBBeforeWarning=uV(),this.texData=new gc(this,Ms())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,s){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().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,refCount:1}),n}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,s,n){if(W().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,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const s=this.texData.get(t);s.refCount--,s.refCount<1&&this.disposeData(t)}}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 ql(o,xu):u=new $e(o,xu);const h=this.runWebGLProgram(u,[{dataId:e,shape:o,dtype:n}],n),d=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),d}if(s!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return s;const l=this.activeTimers!=null;let c;l&&(c=N.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+=N.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 ql(n,xu):d=new $e(n,xu);const m=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:r}],r),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(s!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().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"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);const d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture,...co(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=N.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}c!=null&&this.disposeIntermediateTensorInfo(c);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(!f0(s))throw W().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 ${s} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:s,isPacked:n}=this.texData.get(e),i=N.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const u=this.decode(e),h=this.texData.get(u.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(h.texture,...co(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(u),d}const r=W().getBool("WEBGL_PACK")&&n===!0,o=r?wf(t):t,a=r?new e2(o):new QR(o),l=this.runWebGLProgram(a,[{shape:o,dtype:s,dataId:e}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(l),p}async time(e){const t=this.activeTimers,s=[];let n=!1;this.programTimersStack==null?(this.programTimersStack=s,n=!0):this.activeTimers.push(s),this.activeTimers=s,e();const i=N.flatten(this.activeTimers.map(a=>a.query)).filter(a=>a!=null),r=N.flatten(this.activeTimers.map(a=>a.name)).filter(a=>a!=null);this.activeTimers=t,n&&(this.programTimersStack=null);const o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const a=await Promise.all(i);o.kernelMs=N.sum(a),o.getExtraProfileInfo=()=>a.map((l,c)=>({name:r[c],ms:l})).map(l=>`${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 W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:N.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=N.now(),e)}async getQueryTime(e){if(W().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 W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ms().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=cV){const s=this.getCPUBackend();return!this.warnedAboutCPUBackend&&s==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),s!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&N.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:Ms().keep(e.clone()),imag:Ms().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])){const r=G0(this.texData.get(e.dataId).values,t,s,e.shape,e.dtype);return this.makeOutput(s,e.dtype,r)}if(N.sizeFromShape(s)===0)return ze([],s,e.dtype);const{isPacked:n}=this.texData.get(e.dataId),i=Fs.isSliceContinous(e.shape,t,s);if(n||!i){const r=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new H2(s):new q2(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=Fs.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){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,s,n));if(i)return i;const r=Fs.computeOutShape(t,s,n);if(r.some(a=>a===0))return ze([],r);const o=new Y2(t,n,r);return this.compileAndRun(o,[e])}reverse(e,t){const s=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new j2(e.shape,t):new B2(e.shape,t);return this.compileAndRun(s,[e])}concat(e,t){if(e[0].dtype==="complex64"){const o=e.map(l=>Xs(l)),a=e.map(l=>dn(l));return Gt(this.concat(o,t),this.concat(a,t))}if(e.length===1)return e[0];if(e.length>W().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(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const o=new $R(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,N.sizeFromShape(o.shape.slice(t)))),i=new UR(n.map(o=>o.shape)),r=this.compileAndRun(i,n);return r.reshape(s)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,hx,e.dtype);const s=new $e(e.shape,hx);return this.compileAndRun(s,[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>EO){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=Ft(e.dtype,t.dtype),c=new Nf(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=Ft(e.dtype,t.dtype),u=i!=null,h=o!=null,d=r?_f(r,!0):null,m=new Nf(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 i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new Vw(jw.REAL,e.shape,t.shape),a=new Vw(jw.IMAG,e.shape,t.shape),l=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,r.complexTensors.real),this.makeComplexComponentTensorInfo(t,r.complexTensors.imag)],c=this.compileAndRun(o,l),p=this.compileAndRun(a,l),u=this.complex(c,p);return c.dispose(),p.dispose(),u}const s=Ft(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),[o,a]=j0(e.shape,t.shape,i.values,r.values,s);return this.makeOutput(a,s,o)}if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,Gw,e.dtype);const n=new kt(Gw,e.shape,t.shape);return this.compileAndRun(n,[e,t],e.dtype)}localResponseNormalization4D(e,t,s,n,i){const r=W().getBool("WEBGL_PACK_NORMALIZATION")?new R2(e.shape,t,s,n,i):new N2(e.shape,t,s,n,i);return this.compileAndRun(r,[e])}LRNGrad(e,t,s,n,i,r,o){const a=new C2(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=>N.decodeString(o)),r=ge(e.shape,e.dtype,i);return nV(r,t)}const s=new Q2(e.shape,t);return this.compileAndRun(s,[e])}pad(e,t,s){const n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new M2(e.shape,t,s):new F2(e.shape,t,s);return this.compileAndRun(n,[e])}gather(e,t,s){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,s));if(n)return n;const i=new i2(e.shape,t.size,s);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,s){N.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){N.assert(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented 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this.compileAndRun(n,[e,t])}all(e,t){U.assertAxesAreInnerMostDims("all",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=N.sizeFromShape(n),r=e.as2D(-1,i);return this.reduce(r,"all",r.dtype).reshape(s)}any(e,t){U.assertAxesAreInnerMostDims("any",t,e.rank);const[s,n]=U.computeOutAndReduceShapes(e.shape,t),i=N.sizeFromShape(n),r=e.as2D(-1,i);return this.reduce(r,"any",r.dtype).reshape(s)}floorDiv(e,t){const s=oR,n="int32";if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,xR,n);const i=new kt(s,e.shape,t.shape);return this.compileAndRun(i,[e,t],n)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,If);const s=Ft(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),[o,a]=M0(e.shape,t.shape,i.values,r.values,s);return this.makeOutput(a,s,o)}if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,If,s);const n=new 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t=e.map(r=>r.dtype).reduce((r,o)=>Ft(r,o)),s=e.map(r=>r.shape),n=W().getBool("WEBGL_PACK"),i=n?new K0(e[0].shape,s):new Y0(e[0].shape,s);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,vf);const s=Ft(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),r=this.texData.get(t.dataId),[o,a]=q0(e.shape,t.shape,i.values,r.values,s);return this.makeOutput(a,s,o)}if(W().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,vf,e.dtype);const n=new kt(vf,e.shape,t.shape);return this.compileAndRun(n,[e,t],s)}pow(e,t){const s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS"),n=s?new bi(LR,e.shape,t.shape):new kt(aR,e.shape,t.shape),i=Ft(e.dtype,t.dtype);return this.compileAndRun(n,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const s=U0(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}sqrt(e){const t=new $e(e.shape,pO);return this.compileAndRun(t,[e])}rsqrt(e){if(this.shouldExecuteOnCPU([e])){const s=V0(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,s)}const t=new $e(e.shape,uO);return this.compileAndRun(t,[e])}reciprocal(e){const t=new $e(e.shape,vO);return this.compileAndRun(t,[e])}relu(e){let t;return W().getBool("WEBGL_PACK")?t=new ql(e.shape,yx):t=new $e(e.shape,cx),this.compileAndRun(t,[e])}relu6(e){let t;return W().getBool("WEBGL_PACK")?t=new ql(e.shape,bx):t=new $e(e.shape,px),this.compileAndRun(t,[e])}prelu(e,t){const s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bi(Hw,e.shape,t.shape):new kt(qw,e.shape,t.shape);return this.compileAndRun(s,[e,t])}elu(e){if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,wx,e.dtype);const t=new $e(e.shape,ux);return this.compileAndRun(t,[e])}eluDer(e,t){const s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bi(SR,e.shape,t.shape):new 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this.compileAndRun(t,[e])}asin(e){const t=new $e(e.shape,mO);return this.compileAndRun(t,[e])}acos(e){const t=new $e(e.shape,fO);return this.compileAndRun(t,[e])}atan(e){const t=new $e(e.shape,gO);return this.compileAndRun(t,[e])}sinh(e){const t=new $e(e.shape,yO);return this.compileAndRun(t,[e])}cosh(e){const t=new $e(e.shape,bO);return this.compileAndRun(t,[e])}tanh(e){const t=new $e(e.shape,wO);return this.compileAndRun(t,[e])}asinh(e){const t=new $e(e.shape,xO);return this.compileAndRun(t,[e])}acosh(e){const t=new $e(e.shape,LO);return this.compileAndRun(t,[e])}atanh(e){const t=new $e(e.shape,SO);return this.compileAndRun(t,[e])}erf(e){const t=new $e(e.shape,IO);return this.compileAndRun(t,[e])}step(e,t){const s=new $e(e.shape,sO(t));return this.compileAndRun(s,[e])}conv2dByMatMul(e,t,s,n,i,r){const o=e.shape,a=this.texData.get(e.dataId),l=s.inChannels,c=o[0]*o[1]*o[2],p=s.outChannels,u=s.dataFormat==="channelsLast",h=!1,d=!1,m=(c===1||p===1)&&l>EO,f=o[2]%2!==0&&!!a.isPacked;if(m||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!f){const _=u?o[0]*o[1]*o[2]:o[0]*o[2]*o[3],E=O(e,[1,_,s.inChannels]),F=O(t,[1,s.inChannels,s.outChannels]),D=this.fusedBatchMatMul({a:E,b:F,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r});return O(D,s.outShape)}const g=u?o[0]*o[1]*(o[2]+1):o[0]*o[2]*(o[3]+1),y={dataId:e.dataId,shape:[1,g,s.inChannels],dtype:e.dtype},w=a.shape;a.shape=a.shape.slice(),a.shape[a.shape.length-2]++,N.assert(zl(a.shape,y.shape),()=>`packed reshape ${a.shape} to ${y.shape} isn't free`);const x=O(t,[1,s.inChannels,s.outChannels]),T=this.fusedBatchMatMul({a:y,b:x,transposeA:h,transposeB:d,bias:n,activation:i,preluActivationWeights:r}),A=this.texData.get(T.dataId);return N.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),a.shape=w,A.shape=s.outShape,Ms().makeTensorFromDataId(T.dataId,s.outShape,T.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],g=!0,y=!1,w=e.squeeze([0]),x=t.reshape([1,d,-1]),T=new A2(f,w.shape,s),A=this.compileAndRun(T,[w]).reshape([1,f[0],f[1]]),_=n!=null,E=r!=null,F=i?_f(i,!0):null,D=new Nf(A.shape,[1,m,s.outChannels],g,y,_,F,E),M=[A,x];n&&M.push(n),E&&M.push(r);const P=this.compileAndRun(D,M);return h?P.reshape([1,p,c,s.outChannels]):P.reshape([1,s.outChannels,p,c])}fusedConv2d({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){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,n,i,r);if(W().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,s,n,i,r);const o=n!=null,a=r!=null,l=i?_f(i,!1):null,c=new Yw(s,o,l,a),p=[e,t];return 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(W().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,s);const n=new Yw(s);return this.compileAndRun(n,[e,t])}conv2dDerInput(e,t,s){const n=new zR(s);return this.compileAndRun(n,[e,t])}conv2dDerFilter(e,t,s){const n=new WR(s);return this.compileAndRun(n,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:s,bias:n,activation:i,preluActivationWeights:r}){const o=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&s.strideWidth<=2&&s.outChannels/s.inChannels===1,a=i?_f(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 Xw(s,c,a,p),this.compileAndRun(u,l)):(u=new Kw(s,c,a,p),this.compileAndRun(u,l))}depthwiseConv2D(e,t,s){let n;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&s.strideWidth<=2&&s.outChannels/s.inChannels===1?(n=new Xw(s),this.compileAndRun(n,[e,t])):(n=new Kw(s),this.compileAndRun(n,[e,t]))}depthwiseConv2DDerInput(e,t,s){const n=new VR(s);return this.compileAndRun(n,[e,t])}depthwiseConv2DDerFilter(e,t,s){const n=new jR(s);return this.compileAndRun(n,[e,t])}conv3d(e,t,s){const n=new GR(s);return this.compileAndRun(n,[e,t])}conv3dDerInput(e,t,s){const n=new BR(s);return this.compileAndRun(n,[e,t])}conv3dDerFilter(e,t,s){const n=new PR(s);return this.compileAndRun(n,[e,t])}cast(e,t){return U.castTensor(e,t,this)}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}avgPool3d(e,t){const s=new Cf(t,"avg",!1);return this.compileAndRun(s,[e],"float32")}avgPool3dBackprop(e,t,s){const n=new iR(s);return this.compileAndRun(n,[e],t.dtype)}maxPool3d(e,t){const s=new Cf(t,"max",!1);return this.compileAndRun(s,[e],"float32")}maxPool3dBackprop(e,t,s,n){const i=!0,r=new Cf(n,"max",i),o=this.compileAndRun(r,[t]),a=new E2(n),l=this.compileAndRun(a,[e,o],t.dtype);return o.dispose(),l}resizeBilinear(e,t,s,n){const i=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new W2(e.shape,t,s,n):new $2(e.shape,t,s,n);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,s){const n=new U2(e,t,s);return this.compileAndRun(n,[e])}resizeNearestNeighbor(e,t,s,n){const i=new P2(e.shape,t,s,n);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,s){const n=new z2(e,t,s);return this.compileAndRun(n,[e])}multinomial(e,t,s,n){const i=t?e:es(e),r=i.shape[0],o=i.shape[1],a=new _2(r,o,s),l=a.getCustomSetupFunc(n);return this.compileAndRun(a,[i],"int32",l)}oneHot(e,t,s,n){const i=new k2(e.size,t,s,n);return this.compileAndRun(i,[e])}diag(e){const t=new ZR(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,s,n,i,r){const o=new qR(e.shape,t.shape,n,i,r);return this.compileAndRun(o,[e,t,s],"float32")}depthToSpace(e,t,s){N.assert(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const 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 JR(p,t,s);return this.compileAndRun(u,[e])}split(e,t,s){return sV(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 ox(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 ox(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 Qw(Zw.REAL,e.shape,t),i=new Qw(Zw.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 r2(n,a,[r,o]),u=this.compileAndRun(p,[c,l]);return u.reshape(i)}fill(e,t,s){if(s=s||N.inferDtype(t),s==="string"){const n=N.getArrayFromDType(s,N.sizeFromShape(e));return n.fill(t),Ms().makeTensor(n,e,s,this)}else{const n=new n2(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,s){const n=this.write(s,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,s){const{dataId:n}=this.makeTensorInfo(e,t,s);return Ms().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){const t=new RO(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new D2(e.shape),s=!0;return this.runWebGLProgram(t,[e],e.dtype,null,s)}packedReshape(e,t){const s=[po(e.shape),...uo(e.shape)],n={dtype:e.dtype,shape:s,dataId:e.dataId},i=[po(t),...uo(t)],r=new Of(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=wf(n);let o;s?o=new XR(r):o=new KR(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===lo.DENSE){const m=co(e.outputShape);o.texShape=m.map(f=>f*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),N.sizeFromShape(r.shape)===0)return o.values=N.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&&N.sizeFromShape(m.shape)<=W().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&&!zl(f.shape,m.shape)){const g=m,y=m.shape;m.shape=f.shape,m=this.packedReshape(m,y),a.push(m),f=this.texData.get(m.dataId),g.shape=y}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=T2(e,l,c),u=this.getAndSaveBinary(p,()=>S2(this.gpgpu,e,l,c)),h=this.activeTimers!=null;let d;if(h&&(d=this.startTimer()),v2(this.gpgpu,u,l,c,n),a.forEach(m=>this.disposeIntermediateTensorInfo(m)),h&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!W().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&i===!1){const m=this.unpackTensor(r);return this.disposeIntermediateTensorInfo(r),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 Ms().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(!W().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=C(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=W().getBool("DEBUG");W().set("DEBUG",!1);const t=this.abs(j(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oV:aV}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=N.now());let p=t.texShape;if(p==null&&(p=C0(s,a),t.texShape=p),i!=null){const u=wf(s);let h,d=p[1],m=p[0];const f=i instanceof Uint8Array;a?([d,m]=gi(p[0],p[1]),h=new s2(u,[m,d],f)):h=new t2(u,[m,d],f);const g=this.makeTensorInfo([m,d],n);f?this.texData.get(g.dataId).usage=ns.PIXELS:this.texData.get(g.dataId).usage=ns.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),d,m,i);const y=!0,w=this.runWebGLProgram(h,[g],n,null,y),x=this.texData.get(w.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(w.dataId),t.values=null,l&&(this.uploadWaitMs+=N.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=hV(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]*N.bytesPerElement(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(s){if(W().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function hV(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 Lx="2.6.0";function _O(){W().set("WEBGL_FORCE_F16_TEXTURES",!0)}ja.isBrowser()&&Tp("webgl",()=>new xx,2);const H8e={forceHalfFloat:_O};const kf="if (isnan(x)) return x;",kO=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,DO=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function ho(e){return({inputs:t,backend:s})=>{const{x:n}=t,i=s,r=new $e(n.shape,e);return i.runWebGLProgram(r,[n],n.dtype)}}function Hl(e,t,s,n){return({inputs:i,backend:r})=>{const{a:o,b:a}=i,l=r,c=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new bi(t,o.shape,a.shape,!!s):new kt(e,o.shape,a.shape),p=n||o.dtype,u=l.runWebGLProgram(c,[o,a],p);return u}}const dV=kO+`
  return atan(a, b);
`,mV=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+DO+`
  return result;
`,fV=Hl(dV,mV),FO={kernelName:bo,backendName:"webgl",kernelFunc:fV};function Lu(e){const{inputs:t,backend:s}=e,{x:n}=t;return s.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}const MO={kernelName:Ti,backendName:"webgl",kernelFunc:Lu};function gV(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;yi(i,"avgPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))return Lu({inputs:{x:i},backend:s});const u=new Ki(p,"avg",!1);return s.runWebGLProgram(u,[i],"float32")}const UO={kernelName:Si,backendName:"webgl",kernelFunc:gV};function yV(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r}=t,o=r;yi([i,r],"avgPoolBackprop");const{filterSize:a,strides:l,pad:c}=n,p=U.computePool2DInfo(o.shape,a,l,1,c),u=new nR(p);return s.runWebGLProgram(u,[i],o.dtype)}const $O={kernelName:wo,backendName:"webgl",kernelFunc:yV};class WO{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 zO{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 bV=({inputs:e,backend:t,attrs:s})=>{const{x:n,mean:i,variance:r,offset:o,scale:a}=e;N.assert(i.shape.length===r.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),N.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),N.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=s;l==null&&(l=.001);const c=[n,i,r];let p=null;o!=null&&(p=o.shape,c.push(o));let u=null;a!=null&&(u=a.shape,c.push(a));const h=W().getBool("WEBGL_PACK_NORMALIZATION")?new zO(n.shape,i.shape,r.shape,p,u,l):new WO(n.shape,i.shape,r.shape,p,u,l),d=t.runWebGLProgram(h,c,c[0].dtype);return d},PO={kernelName:vi,backendName:"webgl",kernelFunc:bV};const wV=kf+`
  return cos(x);
`,xV=ho(wV),BO={kernelName:Xn,backendName:"webgl",kernelFunc:xV};const LV=`
if (a == b) {
  return 1.0;
};
return a / b;`,SV=`
  // 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;
`,IV=Hl(LV,SV,!0),jO={kernelName:Jn,backendName:"webgl",kernelFunc:IV};class VO{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 < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const GO={kernelName:So,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:s}=e,n=t,i=new VO(s.shape),r=n.runWebGLProgram(i,[s],s.dtype);return r}};class qO{constructor(e){this.variableNames=["A"];const t=at(),[s,n]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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 HO{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=at(),[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);
          }
        }

        ${t.output} = result;
      }
    `}}const YO={kernelName:Da,backendName:"webgl",kernelFunc:vV};let Yl;function vV(e){const{inputs:t,backend:s,attrs:n}=e;let{pixels:i}=t;const{numChannels:r}=n,o=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[l,c]=o?[i.videoWidth,i.videoHeight]:[i.width,i.height],p=[c,l],u=[c,l,r];(a||o)&&(Yl==null&&(Yl=document.createElement("canvas").getContext("2d")),Yl.canvas.width=l,Yl.canvas.height=c,Yl.drawImage(i,0,0,l,c),i=Yl.canvas);const h=s.makeTensorInfo(p,"int32");s.texData.get(h.dataId).usage=ns.PIXELS,s.gpgpu.uploadPixelDataToTexture(s.getTexture(h.dataId),i);const d=W().getBool("WEBGL_PACK")?new HO(u):new qO(u),m=s.runWebGLProgram(d,[h],"int32");return s.disposeData(h.dataId),m}function TV(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const s=t.length?t[t.length-1].outSize:e[1],n=U.computeOptimalWindowSize(s);t.push({inSize:s,windowSize:n,outSize:Math.ceil(s/n)})}return t}function KO(e,t,s,n){const i=TV(e.shape);let r=e;for(let o=0;o<i.length;o++){const{inSize:a,windowSize:l,outSize:c}=i[o],p=new Rf({windowSize:l,inSize:a,batchSize:e.shape[0],outSize:c},s),u=r;r=n.runWebGLProgram(p,[r],t),u.dataId!==e.dataId&&n.disposeData(u.dataId)}return r}function XO(e,t,s){const n=[po(e.shape),...uo(e.shape)],i={dtype:e.dtype,shape:n,dataId:e.dataId},r=[po(t),...uo(t)],o=new Of(r,n),a=!0,l=s.runWebGLProgram(o,[i],e.dtype,null,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Df(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t,{shape:r}=n,o=s,a=N.sizeFromShape(i.shape),l=N.inferFromImplicitShape(r,a),c=N.sizeFromShape(l);N.assert(a===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${i.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);const p=o.texData.get(i.dataId);return p.isPacked&&!zl(i.shape,l)&&!(p.texture!==null&&zl(p.shape,l))?XO(i,l,o):(o.incRef(i.dataId),{dataId:i.dataId,shape:l,dtype:i.dtype})}const JO={kernelName:Ci,backendName:"webgl",kernelFunc:Df};function ZO(e,t,s,n){const i=N.sizeFromShape(t),r=N.sizeFromShape(e.shape),o=r/i,a=Df({inputs:{x:e},attrs:{shape:[o,i]},backend:n}),l=KO(a,e.dtype,"max",n),c=Df({inputs:{x:l},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(l),c}class QO{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=Re(this.rank),i=AV(t);this.userCode=`
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${i}));
    }
    `}}function AV(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 e1{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 ${this.rank} is not yet supported.`);const n=Re(this.rank),i=Bw("rc",this.rank),r=new Array(this.rank);for(let c=0;c<t.length;c++)r[t[c]]=i[c];const o=`vec2(${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 Ff(e,t,s){const n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new e1(e.shape,t):new QO(e.shape,t);return s.runWebGLProgram(n,[e],e.dtype)}const t1={kernelName:Ai,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=N.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 w=o.texData.get(d.dataId),x=w.values,T=new Array(a);for(let E=0;E<T.length;E++)T[E]=n.shape[p[E]];const A=Lf(x,n.shape,n.dtype,p,T);d=o.makeTensorInfo(T,n.dtype);const _=o.texData.get(d.dataId);_.values=A}else d=Ff(n,p,o);c=U.getInnerMostAxes(c.length,a)}U.assertAxesAreInnerMostDims("max",c,a);const[m,f]=U.computeOutAndReduceShapes(d.shape,c);let g=m;r&&(g=U.expandShapeToKeepDim(m,l));let y;if(h){const w=o.texData.get(d.dataId),x=w.values,T=B0(x,N.sizeFromShape(f),g,n.dtype);y=o.makeTensorInfo(g,n.dtype);const A=o.texData.get(y.dataId);A.values=T}else y=ZO(d,f,g,o);return u&&o.disposeIntermediateTensorInfo(d),y}};function NV(e){const{inputs:t,backend:s,attrs:n}=e,{x:i}=t;yi(i,"maxPool");const{filterSize:r,strides:o,pad:a,dimRoundingMode:l}=n,c=1;N.assert(U.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);const p=U.computePool2DInfo(i.shape,r,o,c,a,l);if(p.filterWidth===1&&p.filterHeight===1&&N.arraysEqual(p.inShape,p.outShape))return Lu({inputs:{x:i},backend:s});const u=new Ki(p,"max",!1);return s.runWebGLProgram(u,[i],i.dtype)}const s1={kernelName:Ni,backendName:"webgl",kernelFunc:NV};function CV(e){const{inputs:t,backend:s,attrs:n}=e,{dy:i,input:r,output:o}=t,a=r;yi([r,o],"maxPoolBackprop");const{filterSize:l,strides:c,pad:p,dimRoundingMode:u}=n,h=U.computePool2DInfo(a.shape,l,c,1,p,u),d=!0,m=new Ki(h,"max",d),f=s.runWebGLProgram(m,[a],a.dtype),g=new O2(h),y=s.runWebGLProgram(g,[i,f],a.dtype);return s.disposeIntermediateTensorInfo(f),y}const n1={kernelName:Io,backendName:"webgl",kernelFunc:CV};function i1(e,t,s,n){let i=new Ki(s,"max",!1);const r=n.runWebGLProgram(i,[e],"float32");i=new Ki(s,"max",!0,!0,t);const o=n.runWebGLProgram(i,[e],"float32");return[r,o]}const r1={kernelName:vo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{x:n}=e,{filterSize:i,strides:r,pad:o,includeBatchInIndex:a}=t,l=s;N.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);const c=[1,1];N.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]=i1(n,a,p,l);return[u,h]}};const o1={kernelName:Vc,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 vt.nonMaxSuppressionV3Impl(c,p,u,h,d)}};const RV=vt.nonMaxSuppressionV4Impl,a1={kernelName:To,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}=RV(p,u,r,o,a,l);return[h,d]}};const OV=vt.nonMaxSuppressionV5Impl,l1={kernelName:Ao,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:g,selectedScores:y}=OV(p,u,h,d,m,f);return[g,y]}};class c1{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 p1={kernelName:_o,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:s})=>{const{image:n}=e,{radians:i,fillValue:r,center:o}=t,a=s,l=new c1(n.shape,i,r,o),c=a.runWebGLProgram(l,[n],n.dtype);return c}};const EV=kf+`
  return sin(x);
`,_V=ho(EV),u1={kernelName:Zn,backendName:"webgl",kernelFunc:_V};const kV="return x * x;",DV=ho(kV),h1={kernelName:Oo,backendName:"webgl",kernelFunc:DV};const d1="return (a - b) * (a - b);",FV=Hl(d1,d1),m1={kernelName:Qn,backendName:"webgl",kernelFunc:FV};const MV="return tan(x);",UV=ho(MV),f1={kernelName:ei,backendName:"webgl",kernelFunc:UV};const g1={kernelName:Ri,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=Lf(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=Ff(n,i,r);return l}};function $V(e){const{inputs:t,attrs:s,backend:n}=e,{axis:i}=s,{x:r}=t;yi(r,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const 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Fe(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 Fe(e,t))}shiftByPoint(e){return this.shiftBy(e.x,e.y)}align(e,t={}){if(e){const i=e instanceof Lt?e.box.floor():new lt(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/qV),a=ma(e),l=Math.floor(Math.max(0,a.x-VV*o)),c=Math.floor(Math.max(0,a.y-GV*o));return new vu(l,c,Math.min(o,this.imageWidth+l),Math.min(o,this.imageHeight+c))}alignMinBbox(e){const t=Cx(this.positions);return t.pad(t.width*e,t.height*e)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class HV extends On{getRefPointsForAlignment(){const e=this.positions;return[e[0],e[1],ma([e[3],e[4]])]}}class Tu extends On{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(ma)}}class $f{constructor(e,t){this._label=e,this._distance=t}get label(){return this._label}get distance(){return this._distance}toString(e=!0){return`${this.label}${e?` (${da(this.distance)})`:""}`}}class Wf extends lt{constructor(e,t){super(e);this._label=t}static assertIsValidLabeledBox(e,t){if(lt.assertIsValidBox(e,t),!Vn(e.label))throw new Error(`${t} - expected property label (${e.label}) to be a number`)}get label(){return this._label}}class fa{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 fa(e.label,t)}}class YV extends Wf{constructor(e,t,s,n){super(e,t);this._score=s,this._classScore=n}static assertIsValidPredictedBox(e,t){if(Wf.assertIsValidLabeledBox(e,t),!Kl(e.score)||!Kl(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 xi(e){return e.detection instanceof Lt}function ga(e,t){const s={detection:t};return Object.assign({},e,s)}function Ex(){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 zf(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 _x(){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=zf();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 kx(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const Dx=mc(RL());let Jt;function KV(){if(!Jt)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return Jt}function Fx(e){Jt=e}function Mx(){if(kx())return Fx(Ex());if(Dx.isNodejs())return Fx(_x())}function XV(e){if(Jt||Mx(),!Jt)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:t=Jt.Canvas,Image:s=Jt.Image}=e;Jt.Canvas=t,Jt.Image=s,Jt.createCanvasElement=e.createCanvasElement||(()=>new t),Jt.createImageElement=e.createImageElement||(()=>new s),Jt.ImageData=e.ImageData||Jt.ImageData,Jt.Video=e.Video||Jt.Video,Jt.fetch=e.fetch||Jt.fetch,Jt.readFile=e.readFile||Jt.readFile}const Ze={getEnv:KV,setEnv:Fx,initialize:Mx,createBrowserEnv:Ex,createFileSystem:zf,createNodejsEnv:_x,monkeyPatch:XV,isBrowser:kx,isNodejs:Dx.isNodejs};Mx();function ya(e){return!Ze.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Os(e){const{Canvas:t,CanvasRenderingContext2D:s}=Ze.getEnv();if(e instanceof s)return e;const n=ya(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 Pf{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 Jl{constructor(e,t,s={}){this.text=typeof e=="string"?[e]:e instanceof Jl?e.text:e,this.anchor=t,this.options=new Pf(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=ya(e),s=Os(t),{backgroundColor:n,fontColor:i,fontSize:r,fontStyle:o,padding:a}=this.options;s.font=`${r}px ${o}`;const l=this.measureWidth(s),c=this.measureHeight();s.fillStyle=n;const p=this.getUpperLeft(s,t);s.fillRect(p.x,p.y,l,c),s.fillStyle=i,this.text.forEach((u,h)=>{const d=a+p.x,m=a+p.y+(h+1)*r;s.fillText(u,d,m)})}}class w1{constructor(e={}){const{boxColor:t,lineWidth:s,label:n,drawLabelOptions:i}=e;this.boxColor=t||"rgba(0, 0, 255, 1)",this.lineWidth=s||2,this.label=n;const r={anchorPosition:Li.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Pf(Object.assign({},r,i))}}class Ux{constructor(e,t={}){this.box=new lt(e),this.options=new w1(t)}draw(e){const t=Os(e),{boxColor:s,lineWidth:n}=this.options,{x:i,y:r,width:o,height:a}=this.box;t.strokeStyle=s,t.lineWidth=n,t.strokeRect(i,r,o,a);const{label:l}=this.options;l&&new Jl([l],{x:i-n/2,y:r},this.options.drawLabelOptions).draw(e)}}function JV(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof Lt?n.score:xi(n)?n.detection.score:void 0,r=n instanceof Lt?n.box:xi(n)?n.detection.box:new lt(n),o=i?`${da(i)}`:void 0;new Ux(r,{label:o}).draw(e)})}function Au(e){const{Image:t,Video:s}=Ze.getEnv();return e instanceof t&&e.complete||e instanceof s&&e.readyState>=3}function $x(e){return new Promise((t,s)=>{if(e instanceof Ze.getEnv().Canvas||Au(e))return t(null);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 Wx(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=Ze.getEnv().createImageElement();i.onload=()=>t(i),i.onerror=s,i.src=n.result},n.onerror=s,n.readAsDataURL(e)})}function ba(e){const{Image:t,Video:s}=Ze.getEnv();return e instanceof t?new js(e.naturalWidth,e.naturalHeight):e instanceof s?new js(e.videoWidth,e.videoHeight):new js(e.width,e.height)}function Zl({width:e,height:t}){const{createCanvasElement:s}=Ze.getEnv(),n=s();return n.width=e,n.height=t,n}function Nu(e,t){const{ImageData:s}=Ze.getEnv();if(!(e instanceof s)&&!Au(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:n,height:i}=t||ba(e),r=Zl({width:n,height:i});return e instanceof s?Os(r).putImageData(e,0,0):Os(r).drawImage(e,0,0,n,i),r}async function zx(e,t){const s=t||Ze.getEnv().createCanvasElement(),[n,i,r]=e.shape.slice(on(e)?1:0),o=C(()=>e.as3D(n,i,r).toInt());return await Fr.toPixels(o,s),o.dispose(),s}function Bf(e){const{Image:t,Canvas:s,Video:n}=Ze.getEnv();return e instanceof t||e instanceof s||e instanceof n}function Px(e,t,s=!1){const{Image:n,Canvas:i}=Ze.getEnv();if(!(e instanceof n||e instanceof i))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const r=ba(e),o=t/Math.max(r.height,r.width),a=o*r.width,l=o*r.height,c=Zl({width:t,height:t}),p=e instanceof i?e:Nu(e),u=Math.abs(a-l)/2,h=s&&a<l?u:0,d=s&&l<a?u:0;return Os(c).drawImage(p,h,d,a,l),c}class mo{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(Zi(s)){this._imageTensors[n]=s,this._inputDimensions[n]=s.shape;return}if(on(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 Ze.getEnv().Canvas?s:Nu(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 wi(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 Ax({width:t,height:s},this.inputSize)}toBatchTensor(e,t=!0){return this._inputSize=e,C(()=>{const s=wi(this.batchSize,0,1).map(i=>{const r=this.getInput(i);if(r instanceof me){let o=on(r)?r:r.expandDims();return o=Ox(o,t),(o.shape[1]!==e||o.shape[2]!==e)&&(o=Zs.resizeBilinear(o,[e,e])),o.as3D(e,e,3)}if(r instanceof Ze.getEnv().Canvas)return Fr.fromPixels(Px(r,e,t));throw new Error(`toBatchTensor - at batchIdx ${i}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${r}`)}),n=Ve(s.map(i=>G(i,"float32"))).as4D(this.batchSize,e,e,3);return n})}}async function ct(e){if(e instanceof mo)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 ${i}:`:"",n=t.map(ya);return n.forEach((i,r)=>{if(!Bf(i)&&!Zi(i)&&!on(i))throw typeof t[r]=="string"?new Error(`toNetInput -${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(on(i)){const o=i.shape[0];if(o!==1)throw new Error(`toNetInput -${s(r)} tf.Tensor4D with batchSize ${o} passed, but not supported in input array`)}}),await Promise.all(n.map(i=>Bf(i)&&$x(i))),new mo(n,Array.isArray(e))}async function Ql(e,t){const{Canvas:s}=Ze.getEnv();let n=e;if(!(e instanceof s)){const o=await ct(e);if(o.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const a=o.getInput(0);n=a instanceof s?a:await zx(a)}const i=Os(n),r=t.map(o=>o instanceof Lt?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=Zl({width:l,height:c});return Os(p).putImageData(i.getImageData(o,a,l,c),0,0),p})}async function ec(e,t){if(!Zi(e)&&!on(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(on(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return C(()=>{const[s,n,i]=e.shape.slice(on(e)?1:0),r=t.map(a=>a instanceof Lt?a.forSize(n,s).box:a).map(a=>a.clipAtImageBorders(n,s)),o=r.map(({x:a,y:l,width:c,height:p})=>Gr(e.as3D(s,n,i),[l,a,0],[p,c,i]));return o})}async function wa(e,t){const s=Ze.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 ZV(e){const t=await wa(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: ${t.url}`);return Wx(s)}async function Bx(e){return(await wa(e)).json()}async function QV(e){return new Float32Array(await(await wa(e)).arrayBuffer())}function jf(e,t){const s=`${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 jx(e,t){const{manifestUri:s,modelBaseUri:n}=jf(e,t);let i=await Bx(s);return Rt.loadWeights(i,n)}function eG(e,t,s=!1){const{width:n,height:i}=s?ba(t):t;return e.width=n,e.height=i,{width:n,height:i}}class cs{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 oi)}getFrozenParams(){return this.getParamList().filter(e=>!(e.tensor instanceof oi))}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 jx(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}=Ze.getEnv(),{manifestUri:s,modelBaseUri:n}=jf(e,this.getDefaultModelName()),i=l=>Promise.all(l.map(c=>t(c).then(p=>p.buffer))),r=Rt.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 me))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${e}`);return{obj:s,objProp:n}}}function Es(e,t,s){return C(()=>{let n=Vr(e,t.depthwise_filter,t.pointwise_filter,s,"same");return n=$(n,t.bias),n})}function Vf(e,t,s=!1){return C(()=>{const n=De(s?$(nt(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):Es(e,t.conv0,[2,2])),i=Es(n,t.conv1,[1,1]),r=De($(n,i)),o=Es(r,t.conv2,[1,1]);return De($(n,$(i,o)))})}function Cu(e,t,s=!1,n=!0){return C(()=>{const i=De(s?$(nt(e,t.conv0.filters,n?[2,2]:[1,1],"same"),t.conv0.bias):Es(e,t.conv0,n?[2,2]:[1,1])),r=Es(i,t.conv1,[1,1]),o=De($(i,r)),a=Es(o,t.conv2,[1,1]),l=De($(i,$(r,a))),c=Es(l,t.conv3,[1,1]);return De($(i,$(r,$(a,c))))})}function xa(e,t,s="same",n=!1){return C(()=>{const i=$(nt(e,t.filters,[1,1],s),t.bias);return n?De(i):i})}function Ss(e,t){Object.keys(e).forEach(s=>{t.some(n=>n.originalPath===s)||e[s].dispose()})}function tc(e,t){return function(s,n,i,r){const o=ts(e(s*n*i*i),[i,i,s,n]),a=Oe(e(n));return t.push({paramPath:`${r}/filters`},{paramPath:`${r}/bias`}),{filters:o,bias:a}}}function Gf(e,t){return function(s,n,i){const r=as(e(s*n),[s,n]),o=Oe(e(n));return t.push({paramPath:`${i}/weights`},{paramPath:`${i}/bias`}),{weights:r,bias:o}}}class Vx{constructor(e,t,s){this.depthwise_filter=e,this.pointwise_filter=t,this.bias=s}}function sc(e,t){return function(s,n,i){const r=ts(e(3*3*s),[3,3,s,1]),o=ts(e(s*n),[1,1,s,n]),a=Oe(e(n));return t.push({paramPath:`${i}/depthwise_filter`},{paramPath:`${i}/pointwise_filter`},{paramPath:`${i}/bias`}),new Vx(r,o,a)}}function nc(e){return function(t){const s=e(`${t}/depthwise_filter`,4),n=e(`${t}/pointwise_filter`,4),i=e(`${t}/bias`,1);return new Vx(s,n,i)}}function Vs(e,t){return function(s,n,i){const r=e[s];if(!ha(r,n))throw new Error(`expected weightMap[${s}] to be a Tensor${n}D, instead have ${r}`);return t.push({originalPath:s,paramPath:i||s}),r}}function Is(e){let t=e;function s(i){const r=t.slice(0,i);return t=t.slice(i),r}function n(){return t}return{extractWeights:s,getRemainingWeights:n}}function qf(e,t){const s=tc(e,t),n=sc(e,t);function i(o,a,l,c=!1){const p=c?s(o,a,3,`${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 x1(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Is(e),{extractDenseBlock4Params:i}=qf(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: ${n().length}`);return{paramMappings:t,params:{dense0:r,dense1:o,dense2:a,dense3:l}}}function Hf(e){return function(t){const s=e(`${t}/filters`,4),n=e(`${t}/bias`,1);return{filters:s,bias:n}}}function Yf(e,t){const s=Vs(e,t),n=Hf(s),i=nc(s);function r(a,l=!1){const c=l?n(`${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 L1(e){const t=[],{extractDenseBlock4Params:s}=Yf(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2"),dense3:s("dense3")};return Ss(e,t),{params:n,paramMappings:t}}class Kf extends cs{constructor(){super("FaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceFeatureExtractor - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=Gn(s,n).div(j(255));let r=Cu(i,t.dense0,!0);return r=Cu(r,t.dense1),r=Cu(r,t.dense2),r=Cu(r,t.dense3),r=hs(r,[7,7],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await ct(e))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(e){return L1(e)}extractParams(e){return x1(e)}}function Ru(e,t){return C(()=>$(Te(e,t.weights),t.bias))}function S1(e,t,s){const n=[],{extractWeights:i,getRemainingWeights:r}=Is(e),o=Gf(i,n),a=o(t,s,"fc");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{fc:a}}}function I1(e){const t=[],s=Vs(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:n("fc")};return Ss(e,t),{params:i,paramMappings:t}}function Xf(e){const t={},s={};return Object.keys(e).forEach(n=>{const i=n.startsWith("fc")?s:t;i[n]=e[n]}),{featureExtractorMap:t,classifierMap:s}}class Jf extends cs{constructor(e,t){super(e);this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return C(()=>{const s=e instanceof mo?this.faceFeatureExtractor.forwardInput(e):e;return Ru(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 S1(e,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Xf(e);return this.faceFeatureExtractor.loadFromWeightMap(t),I1(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 Gx=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class La{constructor(e){if(e.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${e.length}`);Gx.forEach((t,s)=>{this[t]=e[s]})}asSortedArray(){return Gx.map(e=>({expression:e,probability:this[e]})).sort((e,t)=>t.probability-e.probability)}}class qx extends Jf{constructor(e=new Kf){super("FaceExpressionNet",e)}forwardInput(e){return C(()=>es(this.runNet(e)))}async forward(e){return this.forwardInput(await ct(e))}async predictExpressions(e){const t=await ct(e),s=await this.forwardInput(t),n=await Promise.all(Ge(s).map(async r=>{const o=await r.data();return r.dispose(),o}));s.dispose();const i=n.map(r=>new La(r));return t.isBatchInput?i:i[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function Hx(e){return e.expressions instanceof La}function Zf(e,t){const s={expressions:t};return Object.assign({},e,s)}function tG(e,t,s=.1,n){const i=Array.isArray(t)?t:[t];i.forEach(r=>{const o=r instanceof La?r:Hx(r)?r.expressions:void 0;if(!o)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const a=o.asSortedArray(),l=a.filter(u=>u.probability>s),c=xi(r)?r.detection.box.bottomLeft:n||new Fe(0,0),p=new Jl(l.map(u=>`${u.expression} (${da(u.probability)})`),c);p.draw(e)})}function Sa(e){return xi(e)&&e.landmarks instanceof On&&e.unshiftedLandmarks instanceof On&&e.alignedRect instanceof Lt}function ic(e,t){const{box:s}=e.detection,n=t.shiftBy(s.x,s.y),i=n.align(),{imageDims:r}=e.detection,o=new Lt(e.detection.score,i.rescale(r.reverse()),r),a={landmarks:n,unshiftedLandmarks:t,alignedRect:o};return Object.assign({},e,a)}class v1{constructor(e={}){const{drawLines:t=!0,drawPoints:s=!0,lineWidth:n,lineColor:i,pointSize:r,pointColor:o}=e;this.drawLines=t,this.drawPoints=s,this.lineWidth=n||1,this.pointSize=r||2,this.lineColor=i||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}}class T1{constructor(e,t={}){this.faceLandmarks=e,this.options=new v1(t)}draw(e){const t=Os(e),{drawLines:s,drawPoints:n,lineWidth:i,lineColor:r,pointSize:o,pointColor:a}=this.options;if(s&&this.faceLandmarks instanceof Tu&&(t.strokeStyle=r,t.lineWidth=i,Ji(t,this.faceLandmarks.getJawOutline()),Ji(t,this.faceLandmarks.getLeftEyeBrow()),Ji(t,this.faceLandmarks.getRightEyeBrow()),Ji(t,this.faceLandmarks.getNose()),Ji(t,this.faceLandmarks.getLeftEye(),!0),Ji(t,this.faceLandmarks.getRightEye(),!0),Ji(t,this.faceLandmarks.getMouth(),!0)),n){t.strokeStyle=a,t.fillStyle=a;const l=c=>{t.beginPath(),t.arc(c.x,c.y,o,0,2*Math.PI),t.fill()};this.faceLandmarks.positions.forEach(l)}}}function sG(e,t){const s=Array.isArray(t)?t:[t];s.forEach(n=>{const i=n instanceof On?n:Sa(n)?n.landmarks:void 0;if(!i)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new T1(i).draw(e)})}const Yx={};Ee(Yx,{AnchorPosition:()=>Li,DrawBox:()=>Ux,DrawBoxOptions:()=>w1,DrawFaceLandmarks:()=>T1,DrawFaceLandmarksOptions:()=>v1,DrawTextField:()=>Jl,DrawTextFieldOptions:()=>Pf,drawContour:()=>Ji,drawDetections:()=>JV,drawFaceExpressions:()=>tG,drawFaceLandmarks:()=>sG});function nG(e,t){const s=tc(e,t),n=sc(e,t);function i(o,a,l){const c=n(o,a,`${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,`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:s,extractSeparableConvParams:n,extractReductionBlockParams:i,extractMainBlockParams:r}}function A1(e,t){const s=[],{extractWeights:n,getRemainingWeights:i}=Is(e),{extractConvParams:r,extractSeparableConvParams:o,extractReductionBlockParams:a,extractMainBlockParams:l}=nG(n,s),c=r(3,32,3,"entry_flow/conv_in"),p=a(32,64,"entry_flow/reduction_block_0"),u=a(64,128,"entry_flow/reduction_block_1"),h={conv_in:c,reduction_block_0:p,reduction_block_1:u},d={};wi(t,0,1).forEach(y=>{d[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});const m=a(128,256,"exit_flow/reduction_block"),f=o(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{paramMappings:s,params:{entry_flow:h,middle_flow:d,exit_flow:g}}}function iG(e,t){const s=Vs(e,t),n=Hf(s),i=nc(s);function r(a){const l=i(`${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(`${a}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}}function N1(e,t){const s=[],{extractConvParams:n,extractSeparableConvParams:i,extractReductionBlockParams:r,extractMainBlockParams:o}=iG(e,s),a=n("entry_flow/conv_in"),l=r("entry_flow/reduction_block_0"),c=r("entry_flow/reduction_block_1"),p={conv_in:a,reduction_block_0:l,reduction_block_1:c},u={};wi(t,0,1).forEach(f=>{u[`main_block_${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 Ss(e,s),{params:{entry_flow:p,middle_flow:u,exit_flow:m},paramMappings:s}}function C1(e,t,s){return $(nt(e,t.filters,s,"same"),t.bias)}function Kx(e,t,s=!0){let n=s?De(e):e;return n=Es(n,t.separable_conv0,[1,1]),n=Es(De(n),t.separable_conv1,[1,1]),n=mt(n,[3,3],[2,2],"same"),n=$(n,C1(e,t.expansion_conv,[2,2])),n}function rG(e,t){let s=Es(De(e),t.separable_conv0,[1,1]);return s=Es(De(s),t.separable_conv1,[1,1]),s=Es(De(s),t.separable_conv2,[1,1]),s=$(s,e),s}class R1 extends cs{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyXception - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=Gn(s,n).div(j(256));let r=De(C1(i,t.entry_flow.conv_in,[2,2]));return r=Kx(r,t.entry_flow.reduction_block_0,!1),r=Kx(r,t.entry_flow.reduction_block_1),wi(this._numMainBlocks,0,1).forEach(o=>{r=rG(r,t.middle_flow[`main_block_${o}`])}),r=Kx(r,t.exit_flow.reduction_block),r=De(Es(r,t.exit_flow.separable_conv,[1,1])),r})}async forward(e){return this.forwardInput(await ct(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(e){return N1(e,this._numMainBlocks)}extractParams(e){return A1(e,this._numMainBlocks)}}function O1(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Is(e),i=Gf(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: ${n().length}`);return{paramMappings:t,params:{fc:{age:r,gender:o}}}}function E1(e){const t=[],s=Vs(e,t);function n(r){const o=s(`${r}/weights`,2),a=s(`${r}/bias`,1);return{weights:o,bias:a}}const i={fc:{age:n("fc/age"),gender:n("fc/gender")}};return Ss(e,t),{params:i,paramMappings:t}}var Qi;(function(e){e.FEMALE="female",e.MALE="male"})(Qi||(Qi={}));class Xx extends cs{constructor(e=new R1(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){const{params:t}=this;if(!t)throw new Error(`${this._name} - load model before inference`);return C(()=>{const s=e instanceof mo?this.faceFeatureExtractor.forwardInput(e):e,n=hs(s,[7,7],[2,2],"valid").as2D(s.shape[0],-1),i=Ru(n,t.fc.age).as1D(),r=Ru(n,t.fc.gender);return{age:i,gender:r}})}forwardInput(e){return C(()=>{const{age:t,gender:s}=this.runNet(e);return{age:t,gender:es(s)}})}async forward(e){return this.forwardInput(await ct(e))}async predictAgeAndGender(e){const t=await ct(e),s=await this.forwardInput(t),n=Ge(s.age),i=Ge(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?Qi.MALE:Qi.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 O1(e)}extractParamsFromWeigthMap(e){const{featureExtractorMap:t,classifierMap:s}=Xf(e);return this.faceFeatureExtractor.loadFromWeightMap(t),E1(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 Qf extends Jf{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 C(()=>{const r=(p,u)=>Ve([Wt([68],p),Wt([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(Wt([i,136],t)).sub(Ve(Array.from(Array(i),(p,u)=>r(a(u),l(u))))).div(Ve(Array.from(Array(i),(p,u)=>r(n[u].width,n[u].height))));return c})}forwardInput(e){return C(()=>{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 ct(e))}async detectLandmarks(e){const t=await ct(e),s=C(()=>Ge(this.forwardInput(t))),n=await Promise.all(s.map(async(i,r)=>{const o=Array.from(await i.data()),a=o.filter((c,p)=>Mf(p)),l=o.filter((c,p)=>!Mf(p));return new Tu(Array(68).fill(0).map((c,p)=>new Fe(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 Ou extends Qf{constructor(e=new Kf){super("FaceLandmark68Net",e)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function _1(e){const t=[],{extractDenseBlock3Params:s}=Yf(e,t),n={dense0:s("dense0",!0),dense1:s("dense1"),dense2:s("dense2")};return Ss(e,t),{params:n,paramMappings:t}}function k1(e){const t=[],{extractWeights:s,getRemainingWeights:n}=Is(e),{extractDenseBlock3Params:i}=qf(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 D1 extends cs{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("TinyFaceFeatureExtractor - load model before inference");return C(()=>{const s=e.toBatchTensor(112,!0),n=[122.782,117.001,104.298],i=Gn(s,n).div(j(255));let r=Vf(i,t.dense0,!0);return r=Vf(r,t.dense1),r=Vf(r,t.dense2),r=hs(r,[14,14],[2,2],"valid"),r})}async forward(e){return this.forwardInput(await ct(e))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(e){return _1(e)}extractParams(e){return k1(e)}}class Jx extends Qf{constructor(e=new D1){super("FaceLandmark68TinyNet",e)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class oG extends Ou{}function F1(e,t){return $(R(e,t.weights),t.biases)}function Zx(e,t,s,n,i="same"){const{filters:r,bias:o}=t.conv;let a=nt(e,r,s,i);return a=$(a,o),a=F1(a,t.scale),n?De(a):a}function M1(e,t){return Zx(e,t,[1,1],!0)}function Qx(e,t){return Zx(e,t,[1,1],!1)}function eg(e,t){return Zx(e,t,[2,2],!0,"valid")}function aG(e,t){function s(a,l,c){const p=e(a),u=p.length/(l*c*c);if(Tx(u))throw new Error(`depth has to be an integer: ${u}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${c}`);return C(()=>se(ts(p,[l,u,c,c]),[2,3,1,0]))}function n(a,l,c,p){const u=s(a,l,c),h=Oe(e(l));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/bias`}),{filters:u,bias:h}}function i(a,l){const c=Oe(e(a)),p=Oe(e(a));return t.push({paramPath:`${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,`${p}/conv2`);return{conv1:h,conv2:d}}return{extractConvLayerParams:r,extractResidualLayerParams:o}}function 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T={conv32_down:i,conv32_1:r,conv32_2:o,conv32_3:a,conv64_down:l,conv64_1:c,conv64_2:p,conv64_3:u,conv128_down:h,conv128_1:d,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:w,fc:x};return Ss(e,t),{params:T,paramMappings:t}}function qn(e,t){let s=M1(e,t.conv1);return s=Qx(s,t.conv2),s=$(s,e),s=De(s),s}function Eu(e,t){let s=eg(e,t.conv1);s=Qx(s,t.conv2);let n=hs(e,2,2,"valid");const i=ye(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=ye(a);s=be([s,l],1);const c=[...s.shape];c[2]=1;const p=ye(c);s=be([s,p],2)}return n=r?be([n,i],3):n,s=$(n,s),s=De(s),s}class _u extends cs{constructor(){super("FaceRecognitionNet")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("FaceRecognitionNet - load model before inference");return C(()=>{const s=G(e.toBatchTensor(150,!0),"float32"),n=[122.782,117.001,104.298],i=Gn(s,n).div(j(256));let 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l=i(3,32,3,"mobilenetv1/conv_0"),c=r(32,64,"mobilenetv1/conv_1"),p=r(64,128,"mobilenetv1/conv_2"),u=r(128,128,"mobilenetv1/conv_3"),h=r(128,256,"mobilenetv1/conv_4"),d=r(256,256,"mobilenetv1/conv_5"),m=r(256,512,"mobilenetv1/conv_6"),f=r(512,512,"mobilenetv1/conv_7"),g=r(512,512,"mobilenetv1/conv_8"),y=r(512,512,"mobilenetv1/conv_9"),w=r(512,512,"mobilenetv1/conv_10"),x=r(512,512,"mobilenetv1/conv_11"),T=r(512,1024,"mobilenetv1/conv_12"),A=r(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:p,conv_3:u,conv_4:h,conv_5:d,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:w,conv_11:x,conv_12:T,conv_13:A}}function a(){const l=i(1024,256,1,"prediction_layer/conv_0"),c=i(256,512,3,"prediction_layer/conv_1"),p=i(512,128,1,"prediction_layer/conv_2"),u=i(128,256,3,"prediction_layer/conv_3"),h=i(256,128,1,"prediction_layer/conv_4"),d=i(128,256,3,"prediction_layer/conv_5"),m=i(256,64,1,"prediction_layer/conv_6"),f=i(64,128,3,"prediction_layer/conv_7"),g=n(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=n(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),w=n(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=n(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),T=n(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),A=n(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),_=n(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),E=n(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),F=n(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),D=n(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),M=n(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),P=n(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),B={box_encoding_predictor:g,class_predictor:y},Y={box_encoding_predictor:w,class_predictor:x},q={box_encoding_predictor:T,class_predictor:A},K={box_encoding_predictor:_,class_predictor:E},H={box_encoding_predictor:F,class_predictor:D},Q={box_encoding_predictor:M,class_predictor:P};return{conv_0:l,conv_1:c,conv_2:p,conv_3:u,conv_4:h,conv_5:d,conv_6:m,conv_7:f,box_predictor_0:B,box_predictor_1:Y,box_predictor_2:q,box_predictor_3:K,box_predictor_4:H,box_predictor_5:Q}}return{extractMobilenetV1Params:o,extractPredictionLayerParams:a}}function 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n=En(e,s.conv_0,[1,1]),i=En(n,s.conv_1,[2,2]),r=En(i,s.conv_2,[1,1]),o=En(r,s.conv_3,[2,2]),a=En(o,s.conv_4,[1,1]),l=En(a,s.conv_5,[2,2]),c=En(l,s.conv_6,[1,1]),p=En(c,s.conv_7,[2,2]),u=Ia(t,s.box_predictor_0),h=Ia(e,s.box_predictor_1),d=Ia(i,s.box_predictor_2),m=Ia(o,s.box_predictor_3),f=Ia(l,s.box_predictor_4),g=Ia(p,s.box_predictor_5),y=be([u.boxPredictionEncoding,h.boxPredictionEncoding,d.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),w=be([u.classPrediction,h.classPrediction,d.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:w}})}class Hn{constructor({minConfidence:e,maxResults:t}={}){if(this._name="SsdMobilenetv1Options",this._minConfidence=e||.5,this._maxResults=t||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}class rc extends cs{constructor(){super("SsdMobilenetv1")}forwardInput(e){const{params:t}=this;if(!t)throw new Error("SsdMobilenetv1 - load model before inference");return C(()=>{const s=G(e.toBatchTensor(512,!1),"float32"),n=X(R(s,j(.007843137718737125)),j(1)),i=P1(n,t.mobilenetv1),{boxPredictions:r,classPredictions:o}=V1(i.out,i.conv11,t.prediction_layer);return j1(r,o,t.output_layer)})}async forward(e){return this.forwardInput(await ct(e))}async locateFaces(e,t={}){const{maxResults:s,minConfidence:n}=new Hn(t),i=await ct(e),{boxes:r,scores:o}=this.forwardInput(i),a=r[0],l=o[0];for(let w=1;w<r.length;w++)r[w].dispose(),o[w].dispose();const c=Array.from(await l.data()),p=.5,u=B1(a,c,s,p,n),h=i.getReshapedInputDimensions(0),d=i.inputSize,m=d/h.width,f=d/h.height,g=a.arraySync(),y=u.map(w=>{const[x,T]=[Math.max(0,g[w][0]),Math.min(1,g[w][2])].map(E=>E*f),[A,_]=[Math.max(0,g[w][1]),Math.min(1,g[w][3])].map(E=>E*m);return new Lt(c[w],new vu(A,x,_-A,T-x),{height:i.getInputHeight(0),width:i.getInputWidth(0)})});return a.dispose(),l.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(e){return z1(e)}extractParams(e){return W1(e)}}function G1(e){const t=new rc;return t.extractWeights(e),t}function xG(e){return G1(e)}class LG extends rc{}const q1=.4,H1=[new Fe(.738768,.874946),new Fe(2.42204,2.65704),new Fe(4.30971,7.04493),new Fe(10.246,4.59428),new Fe(12.6868,11.8741)],Y1=[new Fe(1.603231,2.094468),new Fe(6.041143,7.080126),new Fe(2.882459,3.518061),new Fe(4.266906,5.178857),new Fe(9.041765,10.66308)],K1=[117.001,114.697,97.404],X1="tiny_yolov2_model",J1="tiny_yolov2_separable_conv_model";const ig=e=>typeof e=="number";function eL(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!ig(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>ig(t.x)&&ig(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(ig)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function oc(e){return C(()=>{const t=R(e,j(.10000000149011612));return $(De(X(e,t)),t)})}function er(e,t){return C(()=>{let s=Pt(e,[[0,0],[1,1],[1,1],[0,0]]);return s=nt(s,t.conv.filters,[1,1],"valid"),s=X(s,t.bn.sub),s=R(s,t.bn.truediv),s=$(s,t.conv.bias),oc(s)})}function tr(e,t){return C(()=>{let s=Pt(e,[[0,0],[1,1],[1,1],[0,0]]);return s=Vr(s,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),s=$(s,t.bias),oc(s)})}function SG(e,t){const s=tc(e,t);function n(o,a){const l=Oe(e(o)),c=Oe(e(o));return t.push({paramPath:`${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=sc(e,t);return{extractConvParams:s,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}}function Z1(e,t,s,n){const{extractWeights:i,getRemainingWeights:r}=Is(e),o=[],{extractConvParams:a,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=SG(i,o);let p;if(t.withSeparableConvs){const[u,h,d,m,f,g,y,w,x]=n,T=t.isFirstLayerConv2d?a(u,h,3,"conv0"):c(u,h,"conv0"),A=c(h,d,"conv1"),_=c(d,m,"conv2"),E=c(m,f,"conv3"),F=c(f,g,"conv4"),D=c(g,y,"conv5"),M=w?c(y,w,"conv6"):void 0,P=x?c(w,x,"conv7"):void 0,B=a(x||w||y,5*s,1,"conv8");p={conv0:T,conv1:A,conv2:_,conv3:E,conv4:F,conv5:D,conv6:M,conv7:P,conv8:B}}else{const[u,h,d,m,f,g,y,w,x]=n,T=l(u,h,"conv0"),A=l(h,d,"conv1"),_=l(d,m,"conv2"),E=l(m,f,"conv3"),F=l(f,g,"conv4"),D=l(g,y,"conv5"),M=l(y,w,"conv6"),P=l(w,x,"conv7"),B=a(x,5*s,1,"conv8");p={conv0:T,conv1:A,conv2:_,conv3:E,conv4:F,conv5:D,conv6:M,conv7:P,conv8:B}}if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{params:p,paramMappings:o}}function IG(e,t){const s=Vs(e,t);function n(a){const l=s(`${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(`${a}/bn`);return{conv:l,bn:c}}const o=nc(s);return{extractConvParams:i,extractConvWithBatchNormParams:r,extractSeparableConvParams:o}}function Q1(e,t){const s=[],{extractConvParams:n,extractConvWithBatchNormParams:i,extractSeparableConvParams:r}=IG(e,s);let o;if(t.withSeparableConvs){const a=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?n("conv0"):r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:a>7?r("conv6"):void 0,conv7:a>8?r("conv7"):void 0,conv8:n("conv8")}}else o={conv0:i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:i("conv6"),conv7:i("conv7"),conv8:n("conv8")};return Ss(e,s),{params:o,paramMappings:s}}var tL;(function(e){e[e.XS=224]="XS",e[e.SM=320]="SM",e[e.MD=416]="MD",e[e.LG=608]="LG"})(tL||(tL={}));class sr{constructor({inputSize:e,scoreThreshold:t}={}){if(this._name="TinyYolov2Options",this._inputSize=e||416,this._scoreThreshold=t||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 ac extends cs{constructor(e){super("TinyYolov2");eL(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=er(e,t.conv0);return s=mt(s,[2,2],[2,2],"same"),s=er(s,t.conv1),s=mt(s,[2,2],[2,2],"same"),s=er(s,t.conv2),s=mt(s,[2,2],[2,2],"same"),s=er(s,t.conv3),s=mt(s,[2,2],[2,2],"same"),s=er(s,t.conv4),s=mt(s,[2,2],[2,2],"same"),s=er(s,t.conv5),s=mt(s,[2,2],[1,1],"same"),s=er(s,t.conv6),s=er(s,t.conv7),xa(s,t.conv8,"valid",!1)}runMobilenet(e,t){let s=this.config.isFirstLayerConv2d?oc(xa(e,t.conv0,"valid",!1)):tr(e,t.conv0);return s=mt(s,[2,2],[2,2],"same"),s=tr(s,t.conv1),s=mt(s,[2,2],[2,2],"same"),s=tr(s,t.conv2),s=mt(s,[2,2],[2,2],"same"),s=tr(s,t.conv3),s=mt(s,[2,2],[2,2],"same"),s=tr(s,t.conv4),s=mt(s,[2,2],[2,2],"same"),s=tr(s,t.conv5),s=mt(s,[2,2],[1,1],"same"),s=t.conv6?tr(s,t.conv6):s,s=t.conv7?tr(s,t.conv7):s,xa(s,t.conv8,"valid",!1)}forwardInput(e,t){const{params:s}=this;if(!s)throw new Error("TinyYolov2 - load model before inference");return C(()=>{let n=G(e.toBatchTensor(t,!1),"float32");return n=this.config.meanRgb?Gn(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 ct(e),t)}async detect(e,t={}){const{inputSize:s,scoreThreshold:n}=new sr(t),i=await ct(e),r=await this.forwardInput(i,s),o=C(()=>Ge(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=Rx(c.map(f=>f.rescale(s)),p,this.config.iouThreshold,!0),m=d.map(f=>new Xl(p[f],u[f],h[f],c[f],a));return m}getDefaultModelName(){return""}extractParamsFromWeigthMap(e){return Q1(e,this.config)}extractParams(e){const t=this.config.filterSizes||ac.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 Z1(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]=C(()=>{const g=e.reshape([l,l,c,this.boxEncodingSize]),y=g.slice([0,0,0,0],[l,l,c,4]),w=g.slice([0,0,0,4],[l,l,c,1]),x=this.withClassScores?es(g.slice([0,0,0,5],[l,l,c,this.config.classes.length]),3):j(0);return[y,w,x]}),d=[],m=await u.array(),f=await p.array();for(let g=0;g<l;g++)for(let y=0;y<l;y++)for(let w=0;w<c;w++){const x=Iu(m[g][y][w][0]);if(!s||x>s){const T=(y+Iu(f[g][y][w][0]))/l*o,A=(g+Iu(f[g][y][w][1]))/l*a,_=Math.exp(f[g][y][w][2])*this.config.anchors[w].x/l*o,E=Math.exp(f[g][y][w][3])*this.config.anchors[w].y/l*a,F=T-_/2,D=A-E/2,M={row:g,col:y,anchor:w},{classScore:P,label:B}=this.withClassScores?await this.extractPredictedClass(h,M):{classScore:1,label:0};d.push({box:new Su(F,D,F+_,D+E),score:x,classScore:x*P,label:B,...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)}}ac.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class ku extends ac{constructor(e=!0){const t=Object.assign({},{withSeparableConvs:e,iouThreshold:q1,classes:["face"]},e?{anchors:Y1,meanRgb:K1}:{anchors:H1,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 Lt(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?J1:X1}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}function vG(e,t=!0){const s=new ku(t);return s.extractWeights(e),s}class sL extends sr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Yn{async then(e){return e(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}async function va(e,t,s,n,i=({alignedRect:r})=>r){const r=e.map(l=>Sa(l)?i(l):l.detection),o=n||(t instanceof me?await ec(t,r):await Ql(t,r)),a=await s(o);return o.forEach(l=>l instanceof me&&l.dispose()),a}async function lc(e,t,s,n,i){return va([e],t,async r=>s(r[0]),n,i)}const eE=.4,tE=[new Fe(1.603231,2.094468),new Fe(6.041143,7.080126),new Fe(2.882459,3.518061),new Fe(4.266906,5.178857),new Fe(9.041765,10.66308)],sE=[117.001,114.697,97.404];class Du extends ac{constructor(){const e={withSeparableConvs:!0,iouThreshold:eE,classes:["face"],anchors:tE,meanRgb:sE,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 Lt(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(e){return super.extractParamsFromWeigthMap(e)}}const je={ssdMobilenetv1:new rc,tinyFaceDetector:new Du,tinyYolov2:new ku,faceLandmark68Net:new Ou,faceLandmark68TinyNet:new Jx,faceRecognitionNet:new _u,faceExpressionNet:new qx,ageGenderNet:new Xx},nE=(e,t)=>je.ssdMobilenetv1.locateFaces(e,t),TG=(e,t)=>je.tinyFaceDetector.locateFaces(e,t),AG=(e,t)=>je.tinyYolov2.locateFaces(e,t),iE=e=>je.faceLandmark68Net.detectLandmarks(e),NG=e=>je.faceLandmark68TinyNet.detectLandmarks(e),CG=e=>je.faceRecognitionNet.computeFaceDescriptor(e),RG=e=>je.faceExpressionNet.predictExpressions(e),OG=e=>je.ageGenderNet.predictAgeAndGender(e),rE=e=>je.ssdMobilenetv1.load(e),EG=e=>je.tinyFaceDetector.load(e),_G=e=>je.tinyYolov2.load(e),kG=e=>je.faceLandmark68Net.load(e),DG=e=>je.faceLandmark68TinyNet.load(e),FG=e=>je.faceRecognitionNet.load(e),MG=e=>je.faceExpressionNet.load(e),UG=e=>je.ageGenderNet.load(e),$G=rE,WG=nE,zG=iE;class oE extends Yn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class Uu extends oE{async run(){const e=await this.parentTask,t=await va(e,this.input,async s=>await Promise.all(s.map(n=>je.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return e.map((s,n)=>Zf(s,t[n]))}withAgeAndGender(){return new Fu(this,this.input)}}class $u extends oE{async run(){const e=await this.parentTask;if(!e)return;const t=await lc(e,this.input,s=>je.faceExpressionNet.predictExpressions(s),this.extractedFaces);return Zf(e,t)}withAgeAndGender(){return new Mu(this,this.input)}}class uc extends Uu{withAgeAndGender(){return new cc(this,this.input)}withFaceDescriptors(){return new Ta(this,this.input)}}class hc extends $u{withAgeAndGender(){return new pc(this,this.input)}withFaceDescriptor(){return new Aa(this,this.input)}}class aE extends Yn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.extractedFaces=s}}class Fu extends aE{async run(){const e=await this.parentTask,t=await va(e,this.input,async s=>await Promise.all(s.map(n=>je.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return e.map((s,n)=>{const{age:i,gender:r,genderProbability:o}=t[n];return sg(ng(s,r,o),i)})}withFaceExpressions(){return new Uu(this,this.input)}}class Mu extends aE{async run(){const e=await this.parentTask;if(!e)return;const{age:t,gender:s,genderProbability:n}=await lc(e,this.input,i=>je.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sg(ng(e,s,n),t)}withFaceExpressions(){return new $u(this,this.input)}}class cc extends Fu{withFaceExpressions(){return new uc(this,this.input)}withFaceDescriptors(){return new Ta(this,this.input)}}class pc extends Mu{withFaceExpressions(){return new hc(this,this.input)}withFaceDescriptor(){return new Aa(this,this.input)}}class nL extends Yn{constructor(e,t){super();this.parentTask=e,this.input=t}}class Ta extends nL{async run(){const e=await this.parentTask,t=await va(e,this.input,s=>Promise.all(s.map(n=>je.faceRecognitionNet.computeFaceDescriptor(n))),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return t.map((s,n)=>tg(e[n],s))}withFaceExpressions(){return new uc(this,this.input)}withAgeAndGender(){return new cc(this,this.input)}}class Aa extends nL{async run(){const e=await this.parentTask;if(!e)return;const t=await lc(e,this.input,s=>je.faceRecognitionNet.computeFaceDescriptor(s),null,s=>s.landmarks.align(null,{useDlibAlignment:!0}));return tg(e,t)}withFaceExpressions(){return new hc(this,this.input)}withAgeAndGender(){return new pc(this,this.input)}}class iL extends Yn{constructor(e,t,s){super();this.parentTask=e,this.input=t,this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?je.faceLandmark68TinyNet:je.faceLandmark68Net}}class rL extends iL{async run(){const e=await this.parentTask,t=e.map(i=>i.detection),s=this.input instanceof me?await ec(this.input,t):await Ql(this.input,t),n=await Promise.all(s.map(i=>this.landmarkNet.detectLandmarks(i)));return s.forEach(i=>i instanceof me&&i.dispose()),e.map((i,r)=>ic(i,n[r]))}withFaceExpressions(){return new uc(this,this.input)}withAgeAndGender(){return new cc(this,this.input)}withFaceDescriptors(){return new Ta(this,this.input)}}class oL extends iL{async run(){const e=await this.parentTask;if(!e)return;const{detection:t}=e,s=this.input instanceof me?await ec(this.input,[t]):await Ql(this.input,[t]),n=await this.landmarkNet.detectLandmarks(s[0]);return s.forEach(i=>i instanceof me&&i.dispose()),ic(e,n)}withFaceExpressions(){return new hc(this,this.input)}withAgeAndGender(){return new pc(this,this.input)}withFaceDescriptor(){return new Aa(this,this.input)}}class aL extends Yn{constructor(e,t=new Hn){super();this.input=e,this.options=t}}class rg extends aL{async run(){const{input:e,options:t}=this,s=t instanceof sL?n=>je.tinyFaceDetector.locateFaces(n,t):t instanceof Hn?n=>je.ssdMobilenetv1.locateFaces(n,t):t instanceof sr?n=>je.tinyYolov2.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=>ga({},s)))})}withFaceLandmarks(e=!1){return new rL(this.runAndExtendWithFaceDetections(),this.input,e)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Fu(this.runAndExtendWithFaceDetections(),this.input)}}class lL extends aL{async run(){const e=await new rg(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?ga({},t):void 0)})}withFaceLandmarks(e=!1){return new oL(this.runAndExtendWithFaceDetection(),this.input,e)}withFaceExpressions(){return new $u(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Mu(this.runAndExtendWithFaceDetection(),this.input)}}function PG(e,t=new Hn){return new lL(e,t)}function og(e,t=new Hn){return new rg(e,t)}async function lE(e,t){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await og(e,new Hn(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function BG(e,t={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await og(e,new sr(t)).withFaceLandmarks().withFaceDescriptors()}const jG=lE;function cL(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 cE{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 fa)return r;if(r instanceof Float32Array)return new fa(i(),[r]);if(r.descriptor&&r.descriptor instanceof Float32Array)return new fa(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=>cL(s,e)).reduce((s,n)=>s+n,0)/(t.length||1)}matchDescriptor(e){return this.labeledDescriptors.map(({descriptors:t,label:s})=>new $f(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 $f("unknown",t.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(e=>e.toJSON())}}static fromJSON(e){const t=e.labeledDescriptors.map(s=>fa.fromJSON(s));return new cE(t,e.distanceThreshold)}}function VG(e){const t=new Du;return t.extractWeights(e),t}function pE(e,t){const{width:s,height:n}=new js(t.width,t.height);if(s<=0||n<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:s,height:n})}`);if(Array.isArray(e))return e.map(i=>pE(i,{width:s,height:n}));if(Sa(e)){const i=e.detection.forSize(s,n),r=e.unshiftedLandmarks.forSize(i.box.width,i.box.height);return ic(ga(e,i),r)}return xi(e)?ga(e,e.detection.forSize(s,n)):e instanceof On||e instanceof Lt?e.forSize(s,n):e}var uE="0.6.2";const GG=typeof process!="undefined",qG=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",HG={faceapi:uE,node:GG,browser:qG};export{Xx as AgeGenderNet,Su as BoundingBox,lt as Box,Yn as ComposableTask,Ta as ComputeAllFaceDescriptorsTask,nL as ComputeFaceDescriptorsTaskBase,Aa as ComputeSingleFaceDescriptorTask,rL as DetectAllFaceLandmarksTask,rg as DetectAllFacesTask,iL as DetectFaceLandmarksTaskBase,aL as DetectFacesTaskBase,oL as DetectSingleFaceLandmarksTask,lL as DetectSingleFaceTask,js as Dimensions,Gx as FACE_EXPRESSION_LABELS,Lt as FaceDetection,LG as FaceDetectionNet,qx as FaceExpressionNet,La as FaceExpressions,Ou as FaceLandmark68Net,Jx as FaceLandmark68TinyNet,oG as FaceLandmarkNet,On as FaceLandmarks,HV as FaceLandmarks5,Tu as FaceLandmarks68,$f as FaceMatch,cE as FaceMatcher,_u as FaceRecognitionNet,Qi as Gender,Wf as LabeledBox,fa as LabeledFaceDescriptors,mo as NetInput,cs as NeuralNetwork,Xl as ObjectDetection,Fe as Point,YV as PredictedBox,vu as Rect,rc as SsdMobilenetv1,Hn as SsdMobilenetv1Options,Du as TinyFaceDetector,sL as TinyFaceDetectorOptions,ku as TinyYolov2,sr as TinyYolov2Options,tL as TinyYolov2SizeType,jG as allFaces,lE as allFacesSsdMobilenetv1,BG as allFacesTinyYolov2,$x as awaitMediaLoaded,Wx as bufferToImage,CG as computeFaceDescriptor,Zl as createCanvas,Nu as createCanvasFromMedia,xG as createFaceDetectionNet,cG as createFaceRecognitionNet,G1 as createSsdMobilenetv1,VG as createTinyFaceDetector,vG as createTinyYolov2,og as detectAllFaces,iE as detectFaceLandmarks,NG as detectFaceLandmarksTiny,zG as detectLandmarks,PG as detectSingleFace,Yx as draw,Ze as env,cL as euclideanDistance,sg as extendWithAge,tg as extendWithFaceDescriptor,ga as extendWithFaceDetection,Zf as extendWithFaceExpressions,ic as extendWithFaceLandmarks,ng as extendWithGender,ec as extractFaceTensors,Ql as extractFaces,ZV as fetchImage,Bx as fetchJson,QV as fetchNetWeights,wa as fetchOrThrow,Os as getContext2dOrThrow,ba as getMediaDimensions,zx as imageTensorToCanvas,Px as imageToSquare,jV as inverseSigmoid,Nx as iou,Bf as isMediaElement,Au as isMediaLoaded,pG as isWithAge,xi as isWithFaceDetection,Hx as isWithFaceExpressions,Sa as isWithFaceLandmarks,uG as isWithGender,UG as loadAgeGenderModel,$G as loadFaceDetectionModel,MG as loadFaceExpressionModel,kG as loadFaceLandmarkModel,DG as loadFaceLandmarkTinyModel,FG as loadFaceRecognitionModel,rE as loadSsdMobilenetv1Model,EG as loadTinyFaceDetectorModel,_G as loadTinyYolov2Model,jx as loadWeightMap,WG as locateFaces,eG as matchDimensions,Cx as minBbox,je as nets,Rx as nonMaxSuppression,Gn as normalize,Ox as padToSquare,OG as predictAgeAndGender,RG as recognizeFaceExpressions,pE as resizeResults,ya as resolveInput,BV as shuffleArray,Iu as sigmoid,nE as ssdMobilenetv1,Sx as tf,TG as tinyFaceDetector,AG as tinyYolov2,ct as toNetInput,Ix as utils,eL as validateConfig,HG as version};
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/** @license See the LICENSE file. */
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